Provides access to the low-level HDF5 “H5D” dataset interface.
Represents an HDF5 dataset identifier.
Objects of this class may be used in any HDF5 function which expects a dataset identifier. Also, all H5D* functions which take a dataset instance as their first argument are presented as methods of this class.
Properties: dtype: Numpy dtype representing the dataset type shape: Numpy-style shape tuple representing the dataspace rank: Integer giving dataset rank
Numpy dtype object representing the dataset type
Extend the given dataset so it’s at least as big as “shape”. Note that a dataset may only be extended up to the maximum dimensions of its dataspace, which are fixed when the dataset is created.
Create an return a new copy of the dataset creation property list used when this dataset was created.
Get the offset of this dataset in the file, in bytes, or None if it doesn’t have one. This is always the case for datasets which use chunked storage, compact datasets, and datasets for which space has not yet been allocated in the file.
Create and return a new copy of the dataspace for this dataset.
Determine if space has been allocated for a dataset. Return value is one of:
Determine the amount of file space required for a dataset. Note this only counts the space which has actually been allocated; it may even be zero.
Create and return a new copy of the datatype for this dataset.
Integer giving the dataset rank (0 = scalar)
Read data from an HDF5 dataset into a Numpy array.
It is your responsibility to ensure that the memory dataspace provided is compatible with the shape of the Numpy array. Since a wide variety of dataspace configurations are possible, this is not checked. You can easily crash Python by reading in data from too large a dataspace.
If a memory datatype is not specified, one will be auto-created based on the array’s dtype.
The provided Numpy array must be writable and C-contiguous. If this is not the case, ValueError will be raised and the read will fail. Keyword dxpl may be a dataset transfer property list.
Set the size of the dataspace to match the given shape. If the new size is larger in any dimension, it must be compatible with the maximum dataspace size.
Numpy-style shape tuple representing the dataspace
Write data from a Numpy array to an HDF5 dataset. Keyword dxpl may be a dataset transfer property list.
It is your responsibility to ensure that the memory dataspace provided is compatible with the shape of the Numpy array. Since a wide variety of dataspace configurations are possible, this is not checked. You can easily crash Python by writing data from too large a dataspace.
If a memory datatype is not specified, one will be auto-created based on the array’s dtype.
The provided Numpy array must be C-contiguous. If this is not the case, ValueError will be raised and the read will fail.
Create a new dataset. If “name” is None, the dataset will be anonymous.
Open an existing dataset attached to a group or file object, by name.