qml.data.DatasetList

class DatasetList(value=UnsetType.UNSET, info=None, *, bind=None, parent_and_key=None)

Bases: Generic[pennylane.data.base.typing_util.T], pennylane.data.base.attribute.DatasetAttribute[collections.abc.MutableMapping, collections.abc.Sequence[pennylane.data.base.typing_util.T], collections.abc.Iterable[pennylane.data.base.typing_util.T]], collections.abc.MutableSequence[pennylane.data.base.typing_util.T], pennylane.data.base.mapper.MapperMixin

Provides a list-like collection type for Dataset Attributes.

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 = 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'>, 'pytree': <class 'pennylane.data.attributes.pytree.DatasetPyTree'>, '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'>})

Maps type_ids to their DatasetAttribute classes.

type_consumer_registry = 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 = 'list'

Methods

append(value)	S.append(value) -- append value to the end of the sequence
clear()
consumes_types()	Returns an iterable of types for which this should be the default codec.
copy()	Returns a copy of this list as a builtin list, with all elements copied..
copy_value()	Deserializes the mapped value from bind, and also perform a 'deep-copy' of any nested values contained in bind.
count(value)
default_value()
extend(values)	S.extend(iterable) -- extend sequence by appending elements from the iterable
get_value()	Deserializes the mapped value from bind.
hdf5_to_value(bind)	Parses bind into Python object.
index(value, [start, [stop]])	Raises ValueError if the value is not present.
insert(index, value)	Implements the insert() method.
pop([index])	Raise IndexError if list is empty or index is out of range.
py_type(value_type)	Determines the py_type of an attribute during value initialization, if it was not provided in the info argument.
remove(value)	S.remove(value) -- remove first occurrence of value.
reverse()	S.reverse() -- reverse IN PLACE
value_to_hdf5(bind_parent, key, value)	Converts value into a HDF5 Array or Group under bind_parent[key].

append(value)

S.append(value) – append value to the end of the sequence

clear() → None -- remove all items from S

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()

Returns a copy of this list as a builtin list, with all elements copied..

copy_value()

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

count(value) → integer -- return number of occurrences of value

classmethod default_value()

extend(values)

S.extend(iterable) – extend sequence by appending elements from the iterable

get_value()

Deserializes the mapped value from bind.

hdf5_to_value(bind)

Parses bind into Python object.

index(value[, start[, stop]]) → integer -- return first index of value.

Raises ValueError if the value is not present.

Supporting start and stop arguments is optional, but recommended.

insert(index, value)

Implements the insert() method.

pop([index]) → item -- remove and return item at index (default last).

Raise IndexError if list is empty or index is out of range.

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__}.

remove(value)

S.remove(value) – remove first occurrence of value. Raise ValueError if the value is not present.

reverse()

S.reverse() – reverse IN PLACE

value_to_hdf5(bind_parent, key, value)

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

code/api/pennylane.data.DatasetList

 

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