Tensor

class libadcc.Tensor(self: libadcc.Tensor, symmetry: libadcc.Symmetry)

Bases: pybind11_object

Construct a Tensor object using a Symmetry object describing its symmetry properties. The returned object is not guaranteed to contain initialised memory. Python binding to libadcc::Tensor

Attributes Summary

T
flags
mutable
ndim
needs_evaluation	Does the tensor need evaluation or is it fully evaluated and resilient in memory.
shape
size
space
subspaces

Methods Summary

antisymmetrise(*args, **kwargs)	Overloaded function.
copy(self)	Returns a deep copy of the tensor.
describe_expression(*args, **kwargs)	Overloaded function.
describe_symmetry(self)	Return a string providing a hopefully descriptive representation of the symmetry information stored inside the tensor.
diagonal(self, *args)
dot(*args, **kwargs)	Overloaded function.
empty_like(self)
evaluate(self)	Ensure the tensor to be fully evaluated and resilient in memory.
is_allowed(self, idcs)	Is a particular index allowed by symmetry
nosym_like(self)
ones_like(self)
select_n_absmax(self, n)	Select the n absolute maximal elements.
select_n_absmin(self, n)	Select the n absolute minimal elements.
select_n_max(self, n)	Select the n maximal elements.
select_n_min(self, n)	Select the n minimal elements.
set_from_ndarray(*args, **kwargs)	Overloaded function.
set_immutable(self)	Set the tensor as immutable, allowing some optimisations to be performed.
set_mask(self, mask, value)	Set all elements corresponding to an index mask, which is given by a string eg.
set_random(self)	Set all tensor elements to random data, adhering to the internal symmetry.
symmetrise(*args, **kwargs)	Overloaded function.
to_ndarray(self)	Export the tensor data to a standard np::ndarray by making a copy.
transpose(*args, **kwargs)	Overloaded function.
zeros_like(self)

Attributes Documentation

T

flags

mutable

ndim

needs_evaluation

Does the tensor need evaluation or is it fully evaluated and resilient in memory.

shape

size

space

subspaces

Methods Documentation

antisymmetrise(*args, **kwargs)

Overloaded function.

1. antisymmetrise(self: libadcc.Tensor, permutations: collections.abc.Iterable[int] | collections.abc.Iterable[collections.abc.Iterable[int]]) -> libadcc.Tensor

2. antisymmetrise(self: libadcc.Tensor, *args: int) -> libadcc.Tensor

copy(self: libadcc.Tensor) → libadcc.Tensor

Returns a deep copy of the tensor.

describe_expression(*args, **kwargs)

Overloaded function.

1. describe_expression(self: libadcc.Tensor, stage: str) -> str

Return a string providing a hopefully descriptive representation of the tensor expression stored inside the object.

2. describe_expression(self: libadcc.Tensor) -> str

describe_symmetry(self: libadcc.Tensor) → str

Return a string providing a hopefully descriptive representation of the symmetry information stored inside the tensor.

diagonal(self: libadcc.Tensor, *args: int) → libadcc.Tensor

dot(*args, **kwargs)

Overloaded function.

1. dot(self: libadcc.Tensor, other: libadcc.Tensor) -> float

2. dot(self: libadcc.Tensor, tensors: list[libadcc.Tensor]) -> numpy.ndarray[tuple[int], numpy.dtype[numpy.float64]]

empty_like(self: libadcc.Tensor) → libadcc.Tensor

evaluate(self: libadcc.Tensor) → libadcc.Tensor

Ensure the tensor to be fully evaluated and resilient in memory. Usually happens automatically when needed. Might be useful for fine-tuning, however.

is_allowed(self: libadcc.Tensor, idcs: tuple[SupportsInt | SupportsIndex, ...]) → bool

Is a particular index allowed by symmetry

nosym_like(self: libadcc.Tensor) → libadcc.Tensor

ones_like(self: libadcc.Tensor) → libadcc.Tensor

select_n_absmax(self: libadcc.Tensor, n: SupportsInt | SupportsIndex) → list[tuple[list[int], float]]

Select the n absolute maximal elements.

select_n_absmin(self: libadcc.Tensor, n: SupportsInt | SupportsIndex) → list[tuple[list[int], float]]

Select the n absolute minimal elements.

select_n_max(self: libadcc.Tensor, n: SupportsInt | SupportsIndex) → list[tuple[list[int], float]]

Select the n maximal elements.

select_n_min(self: libadcc.Tensor, n: SupportsInt | SupportsIndex) → list[tuple[list[int], float]]

Select the n minimal elements.

set_from_ndarray(*args, **kwargs)

Overloaded function.

1. set_from_ndarray(self: libadcc.Tensor, in_array: typing.Annotated[numpy.typing.ArrayLike, numpy.float64]) -> libadcc.Tensor

Set all tensor elements from a standard np::ndarray by making a copy.

2. set_from_ndarray(self: libadcc.Tensor, in_array: typing.Annotated[numpy.typing.ArrayLike, numpy.float64], symmetry_tolerance: typing.SupportsFloat | typing.SupportsIndex) -> libadcc.Tensor

Set all tensor elements from a standard np::ndarray by making a copy. Provide an optional tolerance argument to increase the tolerance for the check for symmetry consistency.

set_immutable(self: libadcc.Tensor) → None

Set the tensor as immutable, allowing some optimisations to be performed.

set_mask(self: libadcc.Tensor, mask: str, value: SupportsFloat | SupportsIndex) → None

Set all elements corresponding to an index mask, which is given by a string eg. ‘iijkli’ sets elements T_{iijkli}

set_random(self: libadcc.Tensor) → libadcc.Tensor

Set all tensor elements to random data, adhering to the internal symmetry.

symmetrise(*args, **kwargs)

Overloaded function.

1. symmetrise(self: libadcc.Tensor, permutations: collections.abc.Iterable[int] | collections.abc.Iterable[collections.abc.Iterable[int]]) -> libadcc.Tensor

2. symmetrise(self: libadcc.Tensor, *args: int) -> libadcc.Tensor

to_ndarray(self: libadcc.Tensor) → numpy.typing.NDArray[numpy.float64]

Export the tensor data to a standard np::ndarray by making a copy.

transpose(*args, **kwargs)

Overloaded function.

1. transpose(self: libadcc.Tensor) -> libadcc.Tensor

2. transpose(self: libadcc.Tensor, axes: tuple[typing.SupportsInt | typing.SupportsIndex, …]) -> libadcc.Tensor

zeros_like(self: libadcc.Tensor) → libadcc.Tensor