qml.data.DatasetArray

class DatasetArray(value=<UnsetType.UNSET: 'UNSET'>, info=None, *, bind=None, parent_and_key=None)

Bases: pennylane.data.base.attribute.DatasetAttribute[Union[numpy._typing._array_like._SupportsArray[numpy.dtype[Any]], numpy._typing._nested_sequence._NestedSequence[numpy._typing._array_like._SupportsArray[numpy.dtype[Any]]], bool, int, float, complex, str, bytes, numpy._typing._nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]], numpy.ndarray, pennylane.typing.TensorLike]

Attribute type for objects that implement the Array protocol, including numpy arrays and pennylane.math.tensor.

Attributes

bind	Returns the HDF5 object that contains this attribute’s data.
info	Returns the AttributeInfo for this attribute.
registry	Maps type_ids to their DatasetAttribute classes.
type_consumer_registry	Maps types to their default DatasetAttribute
type_id

bind

Returns the HDF5 object that contains this attribute’s data.

info

Returns the AttributeInfo for this attribute.

registry: Mapping[str, Type[DatasetAttribute]] = mappingproxy({'dataset': <class 'pennylane.data.base.dataset._DatasetAttributeType'>, 'array': <class 'pennylane.data.attributes.array.DatasetArray'>, 'dict': <class 'pennylane.data.attributes.dictionary.DatasetDict'>, 'json': <class 'pennylane.data.attributes.json.DatasetJSON'>, 'list': <class 'pennylane.data.attributes.list.DatasetList'>, 'molecule': <class 'pennylane.data.attributes.molecule.DatasetMolecule'>, 'none': <class 'pennylane.data.attributes.none.DatasetNone'>, 'operator': <class 'pennylane.data.attributes.operator.operator.DatasetOperator'>, 'scalar': <class 'pennylane.data.attributes.scalar.DatasetScalar'>, 'sparse_array': <class 'pennylane.data.attributes.sparse_array.DatasetSparseArray'>, 'string': <class 'pennylane.data.attributes.string.DatasetString'>, 'tuple': <class 'pennylane.data.attributes.tuple.DatasetTuple'>, 'pytree': <class 'pennylane.data.attributes.pytree.DatasetPyTree'>})

Maps type_ids to their DatasetAttribute classes.

type_consumer_registry: Mapping[type, Type[DatasetAttribute]] = mappingproxy({<class 'pennylane.qchem.molecule.Molecule'>: <class 'pennylane.data.attributes.molecule.DatasetMolecule'>, <class 'NoneType'>: <class 'pennylane.data.attributes.none.DatasetNone'>, <class 'scipy.sparse._bsr.bsr_array'>: <class 'pennylane.data.attributes.sparse_array.DatasetSparseArray'>, <class 'scipy.sparse._coo.coo_array'>: <class 'pennylane.data.attributes.sparse_array.DatasetSparseArray'>, <class 'scipy.sparse._csc.csc_array'>: <class 'pennylane.data.attributes.sparse_array.DatasetSparseArray'>, <class 'scipy.sparse._csr.csr_array'>: <class 'pennylane.data.attributes.sparse_array.DatasetSparseArray'>, <class 'scipy.sparse._dia.dia_array'>: <class 'pennylane.data.attributes.sparse_array.DatasetSparseArray'>, <class 'scipy.sparse._dok.dok_array'>: <class 'pennylane.data.attributes.sparse_array.DatasetSparseArray'>, <class 'scipy.sparse._lil.lil_array'>: <class 'pennylane.data.attributes.sparse_array.DatasetSparseArray'>, <class 'scipy.sparse._csc.csc_matrix'>: <class 'pennylane.data.attributes.sparse_array.DatasetSparseArray'>, <class 'scipy.sparse._csr.csr_matrix'>: <class 'pennylane.data.attributes.sparse_array.DatasetSparseArray'>, <class 'scipy.sparse._bsr.bsr_matrix'>: <class 'pennylane.data.attributes.sparse_array.DatasetSparseArray'>, <class 'scipy.sparse._coo.coo_matrix'>: <class 'pennylane.data.attributes.sparse_array.DatasetSparseArray'>, <class 'scipy.sparse._dia.dia_matrix'>: <class 'pennylane.data.attributes.sparse_array.DatasetSparseArray'>, <class 'scipy.sparse._dok.dok_matrix'>: <class 'pennylane.data.attributes.sparse_array.DatasetSparseArray'>, <class 'scipy.sparse._lil.lil_matrix'>: <class 'pennylane.data.attributes.sparse_array.DatasetSparseArray'>, <class 'str'>: <class 'pennylane.data.attributes.string.DatasetString'>, <class 'tuple'>: <class 'pennylane.data.attributes.tuple.DatasetTuple'>})

Maps types to their default DatasetAttribute

type_id: ClassVar[str] = 'array'

Methods

consumes_types()	Returns an iterable of types for which this should be the default codec.
copy_value()	Deserializes the mapped value from bind, and also perform a ‘deep-copy’ of any nested values contained in bind.
default_value()	Returns a valid default value for this type, or UNSET if this type must be initialized with a value.
get_value()	Deserializes the mapped value from bind.
hdf5_to_value(bind)	Parses bind into Python object.
py_type(value_type)	Determines the py_type of an attribute during value initialization, if it was not provided in the info argument.
value_to_hdf5(bind_parent, key, value)	Converts value into a HDF5 Array or Group under bind_parent[key].

classmethod consumes_types()

Returns an iterable of types for which this should be the default codec. If a value of one of these types is assigned to a Dataset without specifying a type_id, this type will be used.

copy_value()

Deserializes the mapped value from bind, and also perform a ‘deep-copy’ of any nested values contained in bind.

classmethod default_value()

Returns a valid default value for this type, or UNSET if this type must be initialized with a value.

get_value()

Deserializes the mapped value from bind.

hdf5_to_value(bind)

Parses bind into Python object.

classmethod py_type(value_type)

Determines the py_type of an attribute during value initialization, if it was not provided in the info argument. This method returns f"{value_type.__module__}.{value_type.__name__}.

value_to_hdf5(bind_parent, key, value)

Converts value into a HDF5 Array or Group under bind_parent[key].

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