4.1. tnpy.tsdrg.Node

class tnpy.tsdrg.Node(node_id, left=None, right=None, *args, **kwargs)

Bases: quimb.tensor.tensor_core.Tensor

The node of binary tree, while data structure is inherited from quimb.Tensor.

Parameters

• node_id (int) – An integer ID of this node.

• left (Node) – The left child node. Default None.

• right (Node) – The right child node. Default None.

• *args – Arguments to initialize quimb.Tensor.

• **kwargs – Keywords to initialize quimb.Tensor.

__init__(node_id, left=None, right=None, *args, **kwargs)

The node of binary tree, while data structure is inherited from quimb.Tensor.

Parameters

• node_id (int) – An integer ID of this node.

• left (Optional[tnpy.tsdrg.Node]) – The left child node. Default None.

• right (Optional[tnpy.tsdrg.Node]) – The right child node. Default None.

• *args – Arguments to initialize quimb.Tensor.

• **kwargs – Keywords to initialize quimb.Tensor.

Methods

__init__(node_id[, left, right])	The node of binary tree, while data structure is inherited from quimb.Tensor.
add_owner(tn, tid)	Add tn as owner of this Tensor - it's tag and ind maps will be updated whenever this tensor is retagged or reindexed.
add_tag(tag)	Add a tag or multiple tags to this tensor.
almost_equals(other, **kwargs)	Check if this tensor is almost the same as another.
apply_to_arrays(fn)	Apply the function fn to the underlying data array(s).
as_network([virtual])	Return a TensorNetwork with only this tensor.
astype(dtype[, inplace])	Change the type of this tensor to dtype.
astype_(dtype, *[, inplace])
bonds(other)	Return a tuple of the shared indices between this tensor and other.
check()	Do some basic diagnostics on this tensor, raising errors if something is wrong.
check_owners()	Check if this tensor is 'owned' by any alive TensorNetworks.
collapse_repeated([inplace])	Take the diagonals of any repeated indices, such that each index only appears once.
collapse_repeated_(*[, inplace])
compute_reduced_factor(side, left_inds, ...)
conj([inplace])	Conjugate this tensors data (does nothing to indices).
conj_(*[, inplace])
contract(*[, output_inds, optimize, get, ...])	Contract a collection of tensors into a scalar or tensor, automatically aligning their indices and computing an optimized contraction path.
copy([deep, virtual])	Copy this tensor.
direct_product(T2[, sum_inds, inplace])	Direct product of two Tensors.
direct_product_(T2[, sum_inds, inplace])
distance(tnB[, xAA, xAB, xBB, method, ...])	Compute the Frobenius norm distance between two tensor networks:
distance_normalized(tnB[, xAA, xAB, xBB, ...])
draw([color, show_inds, show_tags, ...])	Plot this tensor network as a networkx graph using matplotlib, with edge width corresponding to bond dimension.
drop_tags([tags])	Drop certain tags, defaulting to all, from this tensor.
entropy(left_inds[, method])	Return the entropy associated with splitting this tensor according to left_inds.
expand_ind(ind, size[, mode, rand_strength, ...])	Inplace increase the size of the dimension of ind, the new array entries will be filled with zeros by default.
filter_bonds(other)	Sort this tensor's indices into a list of those that it shares and doesn't share with another tensor.
flip(ind[, inplace])	Reverse the axis on this tensor corresponding to ind.
flip_(ind, *[, inplace])
fuse(fuse_map[, inplace])	Combine groups of indices into single indices.
fuse_(fuse_map, *[, inplace])
gate(G, ind[, preserve_inds, inplace])	Gate this tensor - contract a matrix into one of its indices without changing its indices.
gate_(G, ind[, preserve_inds, inplace])
get_params()	A simple function that returns the 'parameters' of the underlying data array.
graph([color, show_inds, show_tags, ...])	Plot this tensor network as a networkx graph using matplotlib, with edge width corresponding to bond dimension.
idxmax([f])	Get the index configuration of the maximum element of this tensor, optionally applying f first.
idxmin([f])	Get the index configuration of the minimum element of this tensor, optionally applying f first.
ind_size(ind)	Return the size of dimension corresponding to ind.
inds_size(inds)	Return the total size of dimensions corresponding to inds.
inner_inds()	Get all indices that appear on two or more tensors.
iscomplex()
isel(selectors[, inplace])	Select specific values for some dimensions/indices of this tensor, thereby removing them.
isel_(selectors, *[, inplace])
isometrize([left_inds, method, inplace])	Make this tensor unitary (or isometric) with respect to left_inds.
isometrize_([left_inds, method, inplace])
item()	Return the scalar value of this tensor, if it has a single element.
largest_element()	Return the largest element, in terms of absolute magnitude, of this tensor.
max_dim()	Return the maximum size of any dimension, or 1 if scalar.
modify(**kwargs)	Overwrite the data of this tensor in place.
moveindex(ind, axis[, inplace])	Move the index ind to position axis.
moveindex_(ind, axis, *[, inplace])
multiply_index_diagonal(ind, x[, inplace])	Multiply this tensor by 1D array x as if it were a diagonal tensor being contracted into index ind.
multiply_index_diagonal_(ind, x, *[, inplace])
negate([inplace])	Negate this tensor.
negate_(*[, inplace])
new_bond(T2[, size, name, axis1, axis2])	Inplace addition of a new bond between tensors T1 and T2.
new_ind(name[, size, axis, mode, ...])	Inplace add a new index - a named dimension.
new_ind_pair_with_identity(new_left_ind, ...)	Expand this tensor with two new indices of size d, by taking an (outer) tensor product with the identity operator.
new_ind_pair_with_identity_(new_left_ind, ...)
new_ind_with_identity(name, left_inds, ...)	Inplace add a new index, where the newly stacked array entries form the identity from left_inds to right_inds.
norm()	Frobenius norm of this tensor:
normalize([inplace])
normalize_(*[, inplace])
randomize([dtype, inplace])	Randomize the entries of this tensor.
randomize_([dtype, inplace])
reindex(index_map[, inplace])	Rename the indices of this tensor, optionally in-place.
reindex_(index_map, *[, inplace])
remove_owner(tn)	Remove TensorNetwork tn as an owner of this Tensor.
retag(retag_map[, inplace])	Rename the tags of this tensor, optionally, in-place.
retag_(retag_map, *[, inplace])
set_params(params)	A simple function that sets the 'parameters' of the underlying data array.
shared_bond_size(other)	Get the total size of the shared index(es) with other.
singular_values(left_inds[, method])	Return the singular values associated with splitting this tensor according to left_inds.
split(left_inds[, method, get, absorb, ...])	Decompose this tensor into two tensors.
squeeze([include, exclude, inplace])	Drop any singlet dimensions from this tensor.
squeeze_([include, exclude, inplace])
sum_reduce(ind[, inplace])	Sum over index ind, removing it from this tensor.
sum_reduce_(ind, *[, inplace])
symmetrize(ind1, ind2[, inplace])	Hermitian symmetrize this tensor for indices ind1 and ind2.
symmetrize_(ind1, ind2, *[, inplace])
to_dense(*inds_seq[, to_qarray])	Convert this Tensor into an dense array, with a single dimension for each of inds in inds_seqs.
to_qarray(*inds_seq[, to_qarray])
trace(left_inds, right_inds[, ...])	Trace index or indices left_inds with right_inds, removing them.
transpose(*output_inds[, inplace])	Transpose this tensor - permuting the order of both the data and the indices.
transpose_(*output_inds[, inplace])
transpose_like(other[, inplace])	Transpose this tensor to match the indices of other, allowing for one index to be different.
transpose_like_(other, *[, inplace])
unfuse(unfuse_map, shape_map[, inplace])	Reshape single indices into groups of multiple indices
unfuse_(unfuse_map, shape_map, *[, inplace])
unitize([left_inds, method, inplace])	Make this tensor unitary (or isometric) with respect to left_inds.
unitize_	Method descriptor with partial application of the given arguments and keywords.
vector_reduce(ind, v[, inplace])	Contract the vector v with the index ind of this tensor, removing it.
vector_reduce_(ind, v, *[, inplace])
visualize(**kwargs)	Visualize all entries of a tensor, with indices mapped into the plane and values mapped into a color wheel.

Attributes

H	Conjugate this tensors data (does nothing to indices).
backend	The backend inferred from the data.
data
dtype	The data type of the array elements.
inds
is_leaf
left
left_inds
ndim	The number of dimensions.
node_id
owners
right
shape	The size of each dimension.
size	The total number of array elements.
tags

__init__(node_id, left=None, right=None, *args, **kwargs)

The node of binary tree, while data structure is inherited from quimb.Tensor.

Parameters

• node_id (int) – An integer ID of this node.

• left (Optional[tnpy.tsdrg.Node]) – The left child node. Default None.

• right (Optional[tnpy.tsdrg.Node]) – The right child node. Default None.

• *args – Arguments to initialize quimb.Tensor.

• **kwargs – Keywords to initialize quimb.Tensor.

property node_id: int

property left: tnpy.tsdrg.Node | None

property right: tnpy.tsdrg.Node | None

property is_leaf: bool