qml.data.DatasetDict

class DatasetDict(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.Mapping[str, pennylane.data.base.typing_util.T], collections.abc.Mapping[str, pennylane.data.base.typing_util.T]], collections.abc.MutableMapping[str, pennylane.data.base.typing_util.T], pennylane.data.base.mapper.MapperMixin

Provides a dict-like collection for Dataset attribute types. Keys must be strings.

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 = 'dict'

Methods

clear()
consumes_types()	Returns an iterable of types for which this should be the default codec.
copy()	Returns a copy of this mapping as a builtin dict, 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.
default_value()
get(key[, default])	Retrieve the corresponding layout by the string key.
get_value()	Deserializes the mapped value from bind.
hdf5_to_value(bind)	Parses bind into Python object.
items()
keys()
pop(k[,d])	If key is not found, d is returned if given, otherwise KeyError is raised.
popitem()	as a 2-tuple; but raise KeyError if D is empty.
py_type(value_type)	Determines the py_type of an attribute during value initialization, if it was not provided in the info argument.
setdefault(k[,d])
update([E, ]**F)	If E present and has a .keys() method, does: for k in E: D[k] = E[k] If E present and lacks .keys() method, does: for (k, v) in E: D[k] = v In either case, this is followed by: for k, v in F.items(): D[k] = v
value_to_hdf5(bind_parent, key, value)	Converts value into a HDF5 Array or Group under bind_parent[key].
values()

clear() → None.  Remove all items from D.

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 mapping as a builtin dict, 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.

classmethod default_value()

get(key, default=None)

Retrieve the corresponding layout by the string key.

When there isn’t an exact match, all the existing keys in the layout map will be treated as a regex and map against the input key again. The first match will be returned, based on the key insertion order. Return None if there isn’t any match found.

Parameters

key – the string key as the query for the layout.

Returns

Corresponding layout based on the query.

get_value()

Deserializes the mapped value from bind.

hdf5_to_value(bind)

Parses bind into Python object.

items() → a set-like object providing a view on D's items

keys() → a set-like object providing a view on D's keys

pop(k[, d]) → v, remove specified key and return the corresponding value.

If key is not found, d is returned if given, otherwise KeyError is raised.

popitem() → (k, v), remove and return some (key, value) pair

as a 2-tuple; but raise KeyError if D is empty.

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__}.

setdefault(k[, d]) → D.get(k,d), also set D[k]=d if k not in D

update([E, ]**F) → None.  Update D from mapping/iterable E and F.

If E present and has a .keys() method, does: for k in E: D[k] = E[k] If E present and lacks .keys() method, does: for (k, v) in E: D[k] = v In either case, this is followed by: for k, v in F.items(): D[k] = v

value_to_hdf5(bind_parent, key, value)

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

values() → an object providing a view on D's values

code/api/pennylane.data.DatasetDict

 

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