API reference

Note

Work in progress. Many function do not yet follow the numpy standard in their documentation!

This page contains a structured overview of the python API of adcc. See also the Index.

adcc module

Functions

`run_adc`(data_or_matrix[, n_states, kind, ...])	Run an ADC calculation.
`einsum`(subscripts, *operands[, optimise])	Evaluate Einstein summation convention for the operands similar to numpy's einsum function.
`contract`(subscripts, a, b)	Form a single, einsum-like contraction, that is contract tensor a and be to form out via a contraction defined by the first argument string, e.g.
`copy`(a)	Return a copy of the input tensor.
`dot`(a, b)	Form the scalar product between two tensors.
`empty_like`(a)	Return an empty tensor of the same shape and symmetry as the input tensor.
`evaluate`(a)	Force full evaluation of a tensor expression
`lincomb`(coefficients, tensors[, evaluate])	Form a linear combination from a list of tensors.
`nosym_like`(a)	Return tensor of the same shape, but without the symmetry setup of the input tensor.
`ones_like`(a)	Return tensor of the same shape and symmetry as the input tensor, but initialised to 1, that is the canonical blocks are 1 and the other ones are symmetry-equivalent (-1 or 0)
`transpose`(a[, axes])	Return the transpose of a tensor as a copy.
`linear_combination`(args, *kwargs)
`zeros_like`(a)	Return a zero tensor of the same shape and symmetry as the input tensor.
`direct_sum`(subscripts, *operands)
`set_n_threads`(arg0)	Set the number of running worker threads used by adcc
`get_n_threads`()	Get the number of running worker threads used by adcc.
`guesses_singlet`(matrix, n_guesses[, block])	Obtain guesses for computing singlet states by inspecting the passed ADC matrix.
`guesses_triplet`(matrix, n_guesses[, block])	Obtain guesses for computing triplet states by inspecting the passed ADC matrix.
`guesses_any`(matrix, n_guesses[, block, ...])	Obtain guesses by inspecting a block of the diagonal of the passed ADC matrix.
`guess_symmetries`(matrix[, spin_change, ...])	Return guess symmetry objects (one for each AmplitudeVector block) such that the specified requirements on the guesses are satisfied.
`guesses_spin_flip`(matrix, n_guesses[, block])	Obtain guesses for computing spin-flip states by inspecting the passed ADC matrix.
`guess_zero`(matrix[, spin_change, ...])	Return an AmplitudeVector object filled with zeros, but where the symmetry has been properly set up to meet the specified requirements on the guesses.
`adc0`(args, *kwargs)	Run an ADC calculation.
`cis`(args, *kwargs)	Run an ADC calculation.
`adc1`(args, *kwargs)	Run an ADC calculation.
`adc2`(args, *kwargs)	Run an ADC calculation.
`adc2x`(args, *kwargs)	Run an ADC calculation.
`adc3`(args, *kwargs)	Run an ADC calculation.
`cvs_adc0`(args, *kwargs)	Run an ADC calculation.
`cvs_adc1`(args, *kwargs)	Run an ADC calculation.
`cvs_adc2`(args, *kwargs)	Run an ADC calculation.
`cvs_adc2x`(args, *kwargs)	Run an ADC calculation.
`cvs_adc3`(args, *kwargs)	Run an ADC calculation.
`banner`([colour])	Return a nice banner describing adcc and its components

Classes

`InputError`	Exception thrown during the validation stage of the arguments passed to :py:`run_adc` to signal that an input is not valid.
`AdcMatrix`(method, hf_or_mp[, block_orders, ...])	Initialise an ADC matrix.
`AdcBlockView`(fullmatrix, block)	Initialise an ADC matrix.
`AdcMethod`(method)
`Symmetry`(args, *kwargs)	Overloaded function.
`ReferenceState`(hfdata[, core_orbitals, ...])	Construct a ReferenceState holding information about the employed SCF reference.
`MoSpaces`(hfdata[, core_orbitals, ...])	Construct an MoSpaces object, which holds information for translating between the adcc convention of arranging molecular orbitals and the convention of the host program.
`AmplitudeVector`(args, *kwargs)	Construct an AmplitudeVector.
`HartreeFockProvider`(self)
`ExcitedStates`(data[, method, property_method])	Construct an ExcitedStates class from some data obtained from an interative solver or another `ExcitedStates` object.
`State2States`(data[, method, ...])	Construct a State2States class from some data obtained from an interative solver or an `ExcitedStates` object.
`Excitation`(parent_state, index, method)	Construct an Excitation from an `adcc.ExcitedStates` parent object.
`ElectronicTransition`(data[, method, ...])	Construct an ElectronicTransition class from some data obtained from an interative solver or another `ElectronicTransition` object.
`Tensor`(sym_or_mo[, space, permutations, ...])	Construct an uninitialised Tensor from an `MoSpaces` or a `Symmetry` object.
`DictHfProvider`(args, *kwargs)	Initialise the DataHfProvider class with the data being a supported data container (currently python dictionary or HDF5 file).
`DataHfProvider`(data)	Initialise the DataHfProvider class with the data being a supported data container (currently python dictionary or HDF5 file).
`OneParticleOperator`(spaces[, is_symmetric])	Construct an OneParticleOperator object.
`LazyMp`(hf)	Initialise the class dealing with the Møller-Plesset ground state.

libadcc: Python bindings

The libadcc Python module contains python bindings of libadcc: C++ library. They are generated directly from the C++ source code using pybind11 and the sources contained in the extension subfolder of the adcc github repository.

It is not recommended to call these functions directly, but instead resort to the higher-level functionality from the adcc module.

Functions

`amplitude_vector_enforce_spin_kind`(arg0, ...)	Apply the spin symmetrisation required to make the doubles and higher parts of an amplitude vector consist of components for a particular spin kind only.
`direct_sum`(a, b)
`evaluate`(arg0)
`fill_pp_doubles_guesses`(guesses_d, mospaces, ...)	Fill the passed vector of doubles blocks with doubles guesses using the delta-Fock matrices df02 and df13, which are the two delta-Fock matrices involved in the doubles block.
`get_n_threads`()	Get the number of running worker threads used by adcc.
`get_n_threads_total`()	Get the total number of threads (running and sleeping) used by adcc.
`linear_combination_strict`(coefficients, tensors)
`make_symmetry_eri`(arg0, arg1)	Return the Symmetry object like it would be set up for the passed subspace of the electron-repulsion tensor.
`make_symmetry_operator`(arg0, arg1, arg2, arg3)	Return the Symmetry object for an orbital subspace block of a one-particle operator
`make_symmetry_operator_basis`(arg0, arg1, arg2)	Return the symmetry object for an operator in the AO basis. The object will represent a block-diagonal matrix of the form ( M 0 ) ( 0 M ). where M is an n_bas x n_bas block and is indentical in upper-left and lower-right.
`make_symmetry_orbital_coefficients`(arg0, ...)	Return the Symmetry object like it would be set up for the passed subspace of the orbital coefficients tensor.
`make_symmetry_orbital_energies`(arg0, arg1)	Return the Symmetry object like it would be set up for the passed subspace of the orbital energies tensor.
`set_n_threads`(arg0)	Set the number of running worker threads used by adcc
`set_n_threads_total`(arg0)	Set the total number of threads (running and sleeping) used by adcc.
`tensordot`(args, *kwargs)	Overloaded function.
`trace`(args, *kwargs)	Overloaded function.

Classes

`AdcMemory`(self)
`HartreeFockProvider`(self)
`HartreeFockSolution_i`	Interface class representing the data expected in adcc from an interfacing HF / SCF program.
`MoIndexTranslation`(args, *kwargs)	Overloaded function.
`MoSpaces`(self, arg0, arg1, arg2, arg3, arg4)	Construct an MoSpaces object from a HartreeFockSolution_i, a pointer to an AdcMemory object.
`ReferenceState`(self, arg0, arg1, arg2)	Setup a ReferenceStateject using an MoSpaces object.
`Symmetry`(args, *kwargs)	Overloaded function.
`Tensor`(self, arg0)	Construct a Tensor object using a Symmetry object describing its symmetry properties.

libadcc: C++ library

A reference of the C++ part of libadcc and its classes and functions can be found in the following. The functions and classes discussed here are exposed to Python as the libadcc: Python bindings python module.

Reference state

This category lists the libadcc functionality, which imports the data from the libadcc::HartreeFockSolution_i interface into the libadcc::ReferenceState for internal use by the library. See Connecting host programs to adcc for details how to connect host programs to adcc. Important classes in the process are libadcc::MoSpaces, which collects information about the occupied and virtual orbital spaces, and libadcc::MoIndexTranslation, which maps orbitals indices between the ordering used by libadcc and the one used by the SCF program.

std::vector<size_t> construct_blocks(std::vector<size_t> block_starts_crude, size_t length, size_t max_block_size)

Construct a list of Tensor blocks (by identifying their starting indices) from a list of indices that identify places where a block definitely has to start and a maximal block size.

The function checks the block sizes resulting from block_starts_crude and if they are beyond max_block_size tries to split them evenly.

Parameters

block_starts_crude – list of crude block starts
length – Total number of indices
max_block_size – Maximal size of a block

struct libadcc::AxisRange : public std::pair<size_t, size_t>

#include <MoIndexTranslation.hh>

Very simple structure holding the range of indices for a single axis

Note

This structure is not intended for wide use throughout the adcc code, much rather it should help to make the MoIndexTranslation code more clear. Outside this class it can be well thought of as a std::pair<size_t, size_t>.

Public Functions

inline size_t start() const: Start of the range, i.e. the first index

inline size_t end() const: End of the range, i.e. the index past the last index

inline size_t length() const: Length of the range, i.e. number of indices contained

struct libadcc::SimpleRange : public std::vector<std::pair<size_t, size_t>>

#include <MoIndexTranslation.hh>

Very simple structure holding an index range.

The ranges are given as lists of ranges in each axis, where for each axis the first number is the first index of the range to be taken and the second number is the first index past the range, i.e. the index range in each axis are given as half-open intervals [start, end).

Note

This structure is not intended for wide use throughout the adcc code, much rather it should help to make the MoIndexTranslation code more clear. Outside this class it can be well thought of as a std::vector<std::pair<size_t, size_t>>.

Public Functions

SimpleRange(std::vector<size_t> starts, std::vector<size_t> ends): Build a Range object by specifying all starts and all ends

inline SimpleRange(std::vector<std::pair<size_t, size_t>> in): Build a Range object by providing a vector of [start, end) pairs

inline SimpleRange(): Build an empty SimpleRange object

inline size_t ndim() const: Return the dimensionality, i.e. the size of the vector

inline AxisRange axis(size_t i) const: Return the range along an axis

inline void push_axis(std::pair<size_t, size_t> new_axis): Add an axis by specifying its range

std::vector<size_t> starts() const: Get the vector of all range starts

std::vector<size_t> lasts() const: Get the vectors of the last indices in all axes

std::vector<size_t> ends() const: Get the vector of all range ends

struct libadcc::RangeMapping : public std::pair<SimpleRange, SimpleRange>

#include <MoIndexTranslation.hh>

Datastructure to describe a mapping of one range of indices to another range of indices.

Note

This structure is not intended for wide use throughout the adcc code, much rather it should help to make the MoIndexTranslation code more clear. Outside this class it can be well thought of as a pair of above range objects, which are itself effectively std::vector<std::pair<size_t, size_t>>.

Public Functions

inline SimpleRange &from(): The range from which we map

inline SimpleRange &to(): The range to which we map

class libadcc::MoIndexTranslation

#include <MoIndexTranslation.hh>

Translation object, which helps to translate various representations of a tensorial index over orbitals subspaces. E.g. it helps to identify the spin block, reduce indices to spatial indices, translate indices or index ranges to the original ordering used in the SCF program and similar tasks.

Public Functions

MoIndexTranslation(std::shared_ptr<const MoSpaces> mospaces_ptr, const std::string &space): Create an MoIndexTranslation object for the provided space string.

MoIndexTranslation(std::shared_ptr<const MoSpaces> mospaces_ptr, const std::vector<std::string> &subspaces): Create an MoIndexTranslation object for a list of subspace identifiers

inline std::shared_ptr<const MoSpaces> mospaces_ptr() const: Return the MoSpaces to which this MoIndexTranslation is set up

std::string space() const: The space used to initialise the object

inline const std::vector<std::string> &subspaces() const: The space splitup into subspaces along each dimension

inline size_t ndim() const: Number of dimensions

inline const std::vector<size_t> &shape() const: Shape of each dimension

std::vector<size_t> full_index_of(const std::vector<size_t> &index) const: Map an index given in the space passed upon construction to the corresponding index in the range of all MO indices (the ffff space)

std::vector<size_t> block_index_of(const std::vector<size_t> &index) const: Get the block index of an index

std::vector<size_t> block_index_spatial_of(const std::vector<size_t> &index) const

Get the spatial block index of an index

The spatial block index is the result of block_index_of modulo the spin blocks, i.e. it maps an index onto the index of the spatial blocks only, such that the resulting value is idential for two index where the MOs only differ by spin. For example the 1st core alpha and the 1st core beta orbital will map to the same value upon a call of this function.

inline std::vector<size_t> inblock_index_of(const std::vector<size_t> &index) const: Get the in-block index, i.e. the index within the tensor block

std::string spin_of(const std::vector<size_t> &index) const: Get the spin block of each of the index components

std::pair<std::vector<size_t>, std::vector<size_t>> split(const std::vector<size_t> &index) const: Split an index into block index and in-block index and return the result

std::tuple<std::string, std::vector<size_t>, std::vector<size_t>> split_spin(const std::vector<size_t> &index) const: Split an index into a spin block descriptor, a spatial block index and an in-block index.

std::vector<size_t> combine(const std::vector<size_t> &block_index, const std::vector<size_t> &inblock_index) const: Combine a block index and an in-block index into a subspace index

std::vector<size_t> combine(const std::string &spin_block, const std::vector<size_t> &block_index_spatial, const std::vector<size_t> &inblock_index) const: Combine a descriptor for the spin block, a spatial-only block index and an in-block index into a subspace index.

std::vector<size_t> hf_provider_index_of(const std::vector<size_t> &index) const: Map an index given in the space passed upon construction to the corresponding index in the HF provider, which was used to start off the adcc calculation

std::vector<RangeMapping> map_range_to_hf_provider(const SimpleRange &range) const

Map a range of indices to host program indices, i.e. the indexing convention used in the HfProvider / HartreeFockSolution_i classes.

Since the mapping between subspace and host program indices might not be contiguous, a list of range pairs is returned. In each pair the first entry represents a range of indices (indexed in the MO subspace) and the second entry represents the equivalent range of indices in the Hartree-Fock provider these are mapped to.

struct libadcc::MoSpaces

#include <MoSpaces.hh>

Information about the molecular orbitals spaces and subspaces, i.e. the occupied versus virtual orbitals, active versus frozen orbitals, core-occupied versus valence-occupied orbitals.

Public Functions

inline size_t n_orbs(const std::string &space = "f") const: The number of orbitals inside a particular orbital space

inline size_t n_orbs_alpha(const std::string &space = "f") const: The number of alpha orbitals inside a particular orbital space

inline size_t n_orbs_beta(const std::string &space = "f") const: The number of beta orbitals inside a particular orbital space

inline std::string subspace_name(const std::string &space = "f") const: Obtain a pretty name for the provided subspace

inline bool has_core_occupied_space() const: Does this object have a core-occupied space (i.e. is ready for core-valence separation)

inline bool has_subspace(const std::string &space) const: Does this object have a particular subspace

inline std::string irrep_totsym() const: Return the totally symmetric irreducible representation.

MoSpaces(const HartreeFockSolution_i &hf, std::shared_ptr<const AdcMemory> adcmem_ptr, std::vector<size_t> core_orbitals, std::vector<size_t> frozen_core_orbitals, std::vector<size_t> frozen_virtuals)

Construct an MoSpaces object from a HartreeFockSolution_i, a pointer to an AdcMemory object.

Parameters

hf – HartreeFockSolution_i object to use
adcmem_ptr – ADC memory keep-alive object to be used in all Tensors constructed using this MoSpaces object.
core_orbitals – List of orbitals indices (in the full fock space, original ordering of the hf object), which defines the orbitals to be put into the core space, if any. The same number of alpha and beta orbitals should be selected. These will be forcibly occupied.
frozen_core_orbitals – List of orbital indices, which define the frozen core, i.e. those occupied orbitals, which do not take part in the ADC calculation. The same number of alpha and beta orbitals has to be selected.
frozen_virtuals – List of orbital indices, which the frozen virtuals, i.e. those virtual orbitals, which do not take part in the ADC calculation. The same number of alpha and beta orbitals has to be selected.

inline std::shared_ptr<const AdcMemory> adcmem_ptr() const: Return a pointer to the memory keep-alive object

size_t libtensor_irrep_index(std::string irrep) const: Return the libtensor index used for the irrep string

Note

this function is for internal usage only.

Public Members

std::string point_group: The name of the point group for which the data in this class has been set up.

std::vector<std::string> irreps: The list of all irreducible representations in this point group. The first entry will always be the totally symmetric irreducible representation.

bool restricted: Are the orbitals resulting from a restricted calculation, such that alpha and beta electron share the same spatial part

std::vector<std::string> subspaces_occupied: The list of occupied subspaces known to this object

std::vector<std::string> subspaces_virtual: List of virtual subspaces known to this object

std::vector<std::string> subspaces: List of all subspaces known to this object

std::map<std::string, std::vector<size_t>> map_index_hf_provider: Contains for each orbital space (e.g. f, o1) a mapping from the indices used inside the respective space to the molecular orbital index convention used in the host program, i.e. to the ordering in which the molecular orbitals have been exposed in the HartreeFockSolution_i or HfProvider object.

std::map<std::string, std::vector<size_t>> map_block_start: Contains for each orbital space the indices at which a new block starts. Thus this list contains at least on index, namely 0.

std::map<std::string, std::vector<std::string>> map_block_irrep: Contains for each orbital space the mapping from each block used inside the space to the irreducible representation it correspond to.

std::map<std::string, std::vector<char>> map_block_spin: Contains for each orbital space the mapping from each block used inside the space to the spin it correspond to (‘a’ is alpha and ‘b’ is beta)

class libadcc::ReferenceState

#include <ReferenceState.hh>

Struct containing data about the reference state on top of which the ADC calculation is performed

Public Functions

inline bool restricted() const: Return whether the reference is restricted or not

size_t spin_multiplicity() const: Return the spin multiplicity of the reference state. 0 indicates that the spin cannot be determined or is not integer (e.g. UHF)

inline bool has_core_occupied_space() const: Flag to indicate whether this is a cvs reference state

std::string irreducible_representation() const: Ground state irreducible representation

inline std::shared_ptr<const MoSpaces> mospaces_ptr() const: Return the MoSpaces to which this ReferenceState is set up

inline size_t n_orbs() const: Return the number of orbitals

inline size_t n_orbs_alpha() const: Return the number of alpha orbitals

inline size_t n_orbs_beta() const: Return the number of beta orbitals

inline size_t n_alpha() const: Return the number of alpha electrons

inline size_t n_beta() const: Return the number of beta electrons

std::vector<scalar_type> nuclear_multipole(size_t order) const: Return the nuclear contribution to the cartesian multipole moment (in standard ordering, i.e. xx, xy, xz, yy, yz, zz) of the given order.

inline double conv_tol() const: Return the SCF convergence tolerance

inline double energy_scf() const: Final total SCF energy

inline std::string backend() const: String identifying the backend used for the computation

std::shared_ptr<Tensor> orbital_energies(const std::string &space) const: Return the orbital energies corresponding to the provided space

std::shared_ptr<Tensor> orbital_coefficients(const std::string &space) const: Return the molecular orbital coefficients corresponding to the provided space (alpha and beta coefficients are returned)

std::shared_ptr<Tensor> orbital_coefficients_alpha(const std::string &space) const: Return the alpha molecular orbital coefficients corresponding to the provided space

std::shared_ptr<Tensor> orbital_coefficients_beta(const std::string &space) const: Return the beta molecular orbital coefficients corresponding to the provided space

std::shared_ptr<Tensor> fock(const std::string &space) const: Return the fock object of the context corresponding to the provided space.

std::shared_ptr<Tensor> eri(const std::string &space) const: Return the ERI (electron-repulsion integrals) contained in the context corresponding to the provided space.

void import_all() const

Normally the class only imports the Fock matrix blocks and electron-repulsion integrals of a particular space combination when this is requested by a call to above fock() or eri() functions.

This function call, however, instructs the class to immediately import all ERI tensors.

Note

This typically does not need to be called explicitly.

std::vector<std::string> cached_fock_blocks() const: Return the list of momentarily cached fock matrix blocks

void set_cached_fock_blocks(std::vector<std::string> newlist): Set the list of momentarily cached fock matrix blocks

Note

This function is both capable to drop blocks (to save memory) and to request extra caching.

std::vector<std::string> cached_eri_blocks() const: Return the list of momentarily cached eri tensor blocks

void set_cached_eri_blocks(std::vector<std::string> newlist): Set the list of momentarily cached ERI tensor blocks

Note

This function is both capable to drop blocks (to save memory) and to request extra caching.

inline void flush_hf_cache() const: Tell the contained HartreeFockSolution_i object, that a larger amount of ERI data has just been computed and that the next request to further ERI tensor objects will potentially take some time, such that intermediate caches can now be flushed to save memory or other resources.

inline const Timer &timer() const: Obtain the timing info contained in this object

ADC guess setup

void amplitude_vector_enforce_spin_kind(std::shared_ptr<Tensor> tensor, std::string block, std::string spin_kind)

Apply the spin projection required to make the doubles part of an amplitude vector consist of components for a singlet state only.

Note

This function assumes a restricted reference with a closed-shell ground state (e.g. RHF).

Parameters

tensor – The tensor to apply the symmetrisation to
block – The block of an amplitude this tensor represents
spin_kind – The kind of spin to enforce

One-particle operators

Warning

doxygengroup: Cannot find group “Properties” in doxygen xml output for project “libadcc” from directory: /home/mfh/Dokumente/Forschung_Wissenschaft/ADC/adcc/docs/../build/libadcc_docs/xml

Tensor interface

The generalised libadcc::Tensor interface used by adcc and libadcc to perform tensor operations.

TensorBackend tensor_backend(): Get some info about libtensor

std::shared_ptr<Symmetry> make_symmetry_orbital_energies(std::shared_ptr<const MoSpaces> mospaces_ptr, const std::string &space): Return the Symmetry object for the orbital energies tensor for the passed orbital subspace.

std::shared_ptr<Symmetry> make_symmetry_orbital_coefficients(std::shared_ptr<const MoSpaces> mospaces_ptr, const std::string &space, size_t n_bas, const std::string &blocks = "ab")

Return the Symmetry object for the orbital coefficient tensor for the passed orbital subspace.

Parameters

mospaces_ptr – MoSpaces pointer
space – Space to use (e.g. fb or o1b)
n_bas – Number of AO basis functions
blocks – Which blocks of the coefficients to return. Valid values are “ab” to return a tensor for both alpha and beta block as a block-diagonal tensor, “a” to only return a tensor for only alpha block and “b” for only the beta block. The fourth option is “abstack”, which returns the symmetry for a alpha block stacked on top of a beta block (This is the convention used by adcman).

std::shared_ptr<Symmetry> make_symmetry_eri(std::shared_ptr<const MoSpaces> mospaces_ptr, const std::string &space): Return the Symmetry object for the (antisymmetrised) electron-repulsion integral tensor in the physicists’ indexing convention for the passed orbital subspace.

std::shared_ptr<Symmetry> make_symmetry_eri_symm(std::shared_ptr<const MoSpaces> mospaces_ptr, const std::string &space): Return the Symmetry object for the (non-antisymmetrised) electron-repulsion integral tensor in the physicists’ indexing convention for the passed orbital subspace.

Note

This is not the symmetry of the eri objects in adcc. This is only needed for some special routines importing the ERI tensor into adcc. Use it only if you know you need it.

std::shared_ptr<Symmetry> make_symmetry_operator(std::shared_ptr<const MoSpaces> mospaces_ptr, const std::string &space, bool symmetric, const std::string &cartesian_transformation)

Return the Symmetry object for a MO spaces block of a one-particle operator

Note: This can be used for the Fock matrix with operator_class = “1”

Parameters

mospaces_ptr – MoSpaces pointer
space – Space to use (e.g. o1o1 or o1v1)
symmetric – Is the tensor symmetric (only in effect if both space axes identical). false disables a setup of permutational symmetry.
cartesian_transformation – The cartesian function accordung to which the operator transforms. Valid values are: “1” Totally symmetric (default) “x”, “y”, “z” Coordinate axis “xx”, “xy”, “yz” … Products of two coordinate axis “Rx”, “Ry”, “Rz” Rotations about the coordinate axis

std::shared_ptr<Symmetry> make_symmetry_operator_basis(std::shared_ptr<const MoSpaces> mospaces_ptr, size_t n_bas, bool symmetric)

Return the symmetry object for an operator in the AO basis. The object will represent a block-diagonal matrix of the form ( M 0 ) ( 0 M ). where M is an n_bas x n_bas block and is indentical in upper-left and lower-right.

Parameters

mospaces_ptr – MoSpaces pointer
n_bas – Number of AO basis functions
symmetric – Is the tensor symmetric (only in effect if both space axes identical). false disables a setup of permutational symmetry.

inline std::shared_ptr<Tensor> evaluate(std::shared_ptr<Tensor> in)

std::shared_ptr<Tensor> make_tensor(std::shared_ptr<Symmetry> symmetry): Construct an uninitialised tensor using a Symmetry object

std::shared_ptr<Tensor> make_tensor(std::shared_ptr<const AdcMemory> adcmem_ptr, std::vector<AxisInfo> axes): Construct an uninitialised tensor using no symmetry at all

std::shared_ptr<Tensor> make_tensor_zero(std::shared_ptr<Symmetry> symmetry): Construct a tensor initialised to zero using a Symmetry object

struct TensorBackend: #include <backend.hh>

Structure to hold information about the tensor backends

class libadcc::Symmetry

#include <Symmetry.hh>

Container for Tensor symmetry

Public Functions

inline Symmetry(std::shared_ptr<const MoSpaces> mospaces_ptr, const std::string &space): Create an empty symmetry object for a Tensor with the provided space string

Symmetry(std::shared_ptr<const MoSpaces> mospaces_ptr, const std::string &space, std::map<std::string, std::pair<size_t, size_t>> extra_axes_orbs)

Create an empty symmetry object for a Tensor with the provided space string, optionally using a map to supply the number of orbitals for some additional axes.

For the additional axis the pair contains either two numbers (for the number of alpha and beta orbitals in this axis) or only one number and a zero (for an axis, which as only one spin kind, alpha or beta).

inline std::shared_ptr<const MoSpaces> mospaces_ptr() const: Return the MoSpaces to which this Symmetry is set up

inline std::shared_ptr<const AdcMemory> adcmem_ptr() const: Return the adc memory keepalive object

inline const std::vector<AxisInfo> axes() const: Return the info about each of the contained tensor axes in order

std::string space() const: The space used to initialise the object

inline const std::vector<std::string> &subspaces() const: The space splitup into subspaces along each dimension

inline size_t ndim() const: Number of dimensions

std::vector<size_t> shape() const: Shape of each dimension

void clear(): Clear all symmetry elements stored in the datastructure

bool empty() const: Is the datastructure empty, i.e. are there no symmetry elements stored in here

void set_irreps_allowed(std::vector<std::string> irreps): Set the list of irreducible representations, for which the symmetry shall be non-zero. If this is not set all irreps will be allowed

inline std::vector<std::string> irreps_allowed() const: Get the list of allowed irreducible representations. If this empty, all irreps are allowed.

inline bool has_irreps_allowed() const: Is a restriction to irreps set

inline void clear_irreps_allowed(): Clear the restriction to particular irreps, i.e. allow all irreps

void set_permutations(std::vector<std::string> permutations)

Set a list of index permutations, which do not change the tensor. A minus may be used to indicate anti-symmetric permutations with respect to the first (reference) permutation.

For example the vector {“ij”, “ji”} defines a symmetric matrix and {“ijkl”, “-jikl”, “-ijlk”, “klij”} the symmetry of the ERI tensor. Not all permutations need to be given to fully describe the symmetry. The check for errors and conflicts is only rudimentary at the moment, however.

std::vector<std::string> permutations() const: Return the list of equivalent permutations

inline bool has_permutations() const: Are there equivalent permutations set up

inline void clear_permutations(): Clear the equivalent permutations

std::vector<std::pair<std::vector<size_t>, scalar_type>> permutations_parsed() const: Return the list of equivalent permutations in a parsed internal format.

Note

Internal function. May go or change at any time

void set_spin_block_maps(std::vector<std::tuple<std::string, std::string, double>> spin_maps): Set lists of tuples of the form {“aaaa”, “bbbb”, -1.0}, i.e. two spin blocks followed by a factor. This maps the second onto the first with a factor of -1.0 between them.

inline std::vector<std::tuple<std::string, std::string, double>> spin_block_maps() const: Return the list of equivalent spin blocks

inline bool has_spin_block_maps() const: Are there equivalent spin blocks set up?

inline void clear_spin_block_maps(): CLear the equivalent spin blocks

void set_spin_blocks_forbidden(std::vector<std::string> forbidden): Mark spin-blocks as forbidden (i.e. enforce them to stay zero). Blocks are give as a list in the letters ‘a’ and ‘b’, e.g. {“aaaa”, “abba”}

inline std::vector<std::string> spin_blocks_forbidden() const: Return the list of currently forbidden spin blocks

inline bool has_spin_blocks_forbidden() const: Are there forbidden spin blocks set up?

inline void clear_spin_blocks_forbidden(): Clear the forbidden spin blocks, i.e. allow all blocks

struct TensorOrScalar: #include <Tensor.hh>

Poor-mans variant for a Tensor and a scalar

class libadcc::Tensor

#include <Tensor.hh>

The tensor interface used by libadcc

Subclassed by libadcc::TensorImpl< N >

Interface of a tensor, which is exposed to python.

inline size_t ndim() const: Number of dimensions

inline const std::vector<size_t> &shape() const: Shape of each dimension

inline size_t size() const: Number of elements

std::vector<std::string> subspaces() const: Subspace labels identifying each axis

std::string space() const: Space to which the tensor is initialised

inline const std::vector<std::string> &flags() const: Set some flags for playing around

virtual std::shared_ptr<Tensor> empty_like() const = 0: Return a new tensor with the same dimensionality and symmetry as the passed tensor. The elements have undefined values.

virtual std::shared_ptr<Tensor> nosym_like() const = 0: Return a new tensor with same dimensionality and shape (and bispace) but without any symmetry copied over

std::shared_ptr<Tensor> zeros_like() const: Return a new tensor with the same dimensionality and symmetry as the passed tensor and all elements set to zero.

std::shared_ptr<Tensor> ones_like() const: Return a new tensor with the same dimensionality and symmetry as the passed tensor and all elements set to one.

virtual void set_mask(std::string mask, scalar_type value) = 0: Set a mask into a tensor to a specific value. The mask to set is defined by the mask string. Repetitive indices define the values to be set. E.g. for a 6D tensor the mask string “iijkli” would set all elements T_{iijkli} for all ijkl to the given value.

void fill(scalar_type value): Shift the elements matched by a mask into a tensor by a specific value. The mask to set is defined by the mask string. Repetitive indices define the values to be shifted. E.g. for a 6D tensor the mask string “iijkli” would shift all elements T_{iijkli} for all ijkl by the given value. Fill the tensor with a particular value, adhereing to symmetry.

virtual std::shared_ptr<Tensor> diagonal(std::vector<size_t> axes) = 0: Extract a generalised diagonal from this Tensor. axes defines the axes over which to extract. The resulting new axis is appended to the remaining axis. For example if we have a Tensor fourth order t then diagonal(1, 2) returns d_{iax} = t_{ixxa} and diagonal(0, 1, 2) returns d_{ax} = t_{xxxa}.

virtual void set_random() = 0: Set the tensor to random data preserving symmetry

virtual std::shared_ptr<Tensor> scale(scalar_type c) const = 0: Scale tensor by a scalar value and return the result

virtual std::shared_ptr<Tensor> add(std::shared_ptr<Tensor> other) const = 0: Add another tensor and return the result

virtual void add_linear_combination(std::vector<scalar_type> scalars, std::vector<std::shared_ptr<Tensor>> tensors) const = 0: Add a linear combination to the represented tensor and store the result. Note that unlike add and scale this operation is not lazy and is provided for optimising on cases where a lazy linear combination introduces a too large overhead.

virtual std::shared_ptr<Tensor> multiply(std::shared_ptr<Tensor> other) const = 0: Multiply another tensor elementwise and return the result

virtual std::shared_ptr<Tensor> divide(std::shared_ptr<Tensor> other) const = 0: Divide another tensor elementwise and return the result

virtual std::shared_ptr<Tensor> copy() const = 0: Return a deep copy of this tensor.

virtual std::shared_ptr<Tensor> transpose(std::vector<size_t> axes) const = 0

Return a transpose form of this tensor

Parameters: axes – New permutation of the dimensions of this tensor (e.g. (1,0,2,3) will permute dimension 0 and 1 and keep the others) If missing, just reverse the dimension order.

virtual TensorOrScalar tensordot(std::shared_ptr<Tensor> other, std::pair<std::vector<size_t>, std::vector<size_t>> axes) const = 0

Return the tensordot of this tensor with another

Parameters: axes – Axes to contract over. The first vector refers to the axes of this tensor, the second vector to the axes of the other tensor.

virtual std::vector<scalar_type> dot(std::vector<std::shared_ptr<Tensor>> tensors) const = 0: Compute the Frobenius or l2 inner product with a list of other tensors, returning the results as a scalar array

virtual double trace(std::string contraction) const = 0

Return the full trace of a tensor

Parameters: contraction – Indices to contract over using Einstein summation convention, e.g. “abab” traces over the 1st and 3rd and 2nd and 4th axis.

virtual std::shared_ptr<Tensor> direct_sum(std::shared_ptr<Tensor> other) const = 0: Compute the direct sum of this tensor with another tensor. All axes are used in the order of appearance (this and then other).

virtual std::shared_ptr<Tensor> symmetrise(const std::vector<std::vector<size_t>> &permutations) const = 0

Symmetrise with respect to the given index permutations by adding the elements resulting from appropriate index permutations.

Examples for permutations. Take a rank-4 tensor T_{ijkl} {{0,1}} Permute the first two indices, i.e. form 0.5 * (T_{ijkl} + T_{jikl}) {{0,1}, {2,3}} Form 0.5 * (T_{ijkl} + T_{jilk})

Note

Unlike the implementation in libtensor, this function includes the prefactor 0.5

Parameters: permutations – The list of permutations to be applied simultaneously

virtual std::shared_ptr<Tensor> antisymmetrise(const std::vector<std::vector<size_t>> &permutations) const = 0: Antisymmetrise with respect to the given index permutations and return the result. For details with respect to the format of the permutations, see symmetrise. In this case the output tensor will be antisymmetric with respect to these permutations.

Note

Unlike the implementation in libtensor, this function includes the prefactor 0.5

virtual void evaluate() const = 0: Make sure to evaluate this tensor in case this has not yet happened

virtual bool needs_evaluation() const = 0: Check weather the object represents an evaluated expression or not.

virtual void set_immutable() = 0: Flag the tensor as immutable, allows some optimisations to be performed

virtual bool is_mutable() const = 0: Is the tensor mutable

virtual void set_element(const std::vector<size_t> &idx, scalar_type value) = 0: Set the value of a single tensor element

Note

This is a slow function and should be avoided.

virtual scalar_type get_element(const std::vector<size_t> &tidx) const = 0: Get the value of a single tensor element

Note

This is a slow function and should be avoided.

virtual bool is_element_allowed(const std::vector<size_t> &tidx) const = 0: Return whether the element referenced by tidx is allowed (non-zero) by the symmetry of the Tensor or not.

virtual std::vector<std::pair<std::vector<size_t>, scalar_type>> select_n_absmax(size_t n, bool unique_by_symmetry = true) const = 0

Get the n absolute largest elements along with their values

Parameters

n – Number of elements to select
unique_by_symmetry – By default the returned elements are made unique by symmetry of the tensor. Setting this to false disables this feature.

virtual std::vector<std::pair<std::vector<size_t>, scalar_type>> select_n_absmin(size_t n, bool unique_by_symmetry = true) const = 0

Get the n absolute smallest elements along with their values

Parameters

n – Number of elements to select
unique_by_symmetry – By default the returned elements are made unique by symmetry of the tensor. Setting this to false disables this feature.

virtual std::vector<std::pair<std::vector<size_t>, scalar_type>> select_n_max(size_t n, bool unique_by_symmetry = true) const = 0

Get the n largest elements along with their values

Parameters

n – Number of elements to select
unique_by_symmetry – By default the returned elements are made unique by symmetry of the tensor. Setting this to false disables this feature.

virtual std::vector<std::pair<std::vector<size_t>, scalar_type>> select_n_min(size_t n, bool unique_by_symmetry = true) const = 0

Get the n smallest elements along with their values

Parameters

n – Number of elements to select
unique_by_symmetry – By default the returned elements are made unique by symmetry of the tensor. Setting this to false disables this feature.

virtual void export_to(scalar_type *memptr, size_t size) const = 0: Extract the tensor to plain memory provided by the given pointer.

Note

This will return a full, dense tensor. At least size elements of space are assumed at the provided memory location. The data is stored in row-major (C-like) format

inline virtual void export_to(std::vector<scalar_type> &output) const: Extract the tensor into a std::vector, which will be resized to fit the data.

Note

All data is stored in row-major (C-like) format

virtual void import_from(const scalar_type *memptr, size_t size, scalar_type tolerance = 0, bool symmetry_check = true) = 0

Import the tensor from plain memory provided by the given pointer. The memory will be copied and all existing data overwritten. If symmetry_check is true, the process will check that the data has the required symmetry to fit into the tensor. This requires a slower algorithm to be chosen.

Note

This function requires a full, dense tensor with the data stored in row-major (C-like) format.

Parameters

memptr – Full, dense memory pointer to the tensor data to be imported.
size – Size of the dense memory.
tolerance – Threshold to account for numerical inconsistencies when checking the symmetry or for determining zero blocks.
symmetry_check – Should symmetry be explicitly checked during the import.

inline virtual void import_from(const std::vector<scalar_type> &input, scalar_type tolerance = 0, bool symmetry_check = true)

Import the tensor from plain memory provided by the given vector. The memory will be copied and all existing data overwritten. If symmetry_check is true, the process will check that the data has the required symmetry to fit into the tensor. This requires a slower algorithm to be chosen.

Note

This function requires a full, dense tensor with the data stored in row-major (C-like) format.

Parameters

input – Input data in a linearised vector
tolerance – Threshold to account for numerical inconsistencies when checking the symmetry or for determining zero blocks.
symmetry_check – Should symmetry be explicitly checked during the import.

virtual void import_from(std::function<void(const std::vector<std::pair<size_t, size_t>>&, scalar_type*)> generator, scalar_type tolerance = 0, bool symmetry_check = true) = 0

Import the tensor from a generator functor. All existing data will be overwritten. If symmetry_check is true, the process will check that the data has the required symmetry to fit into the tensor. This requires a slower algorithm to be chosen.

Note

The generator is required to produce data in row-major (C-like) format at a designated memory location.

Parameters

generator – Generator functor. The functor is called with a list of ranges for each dimension. The ranges are half-open, left-inclusive and right-exclusive. The corresponding data should be written to the passed pointer into raw memory. This is an advanced functionality. Use only if you know what you are doing.
tolerance – Threshold to account for numerical inconsistencies when checking the symmetry or for determining zero blocks.
symmetry_check – Should symmetry be explicitly checked during the import.

virtual std::string describe_symmetry() const = 0: Return a std::string providing hopefully helpful information about the symmetries stored inside the tensor object.

virtual std::string describe_expression(std::string stage = "unoptimised") const = 0: Return a std::string providing hopefully helpful information about the expression tree stored inside the tensor object. Valid values for stage are “unoptimised”, “optimised”, “evaluation” returning the unoptimised expression tree as stored, an optimised form of it or the tree as used actually for evaluating the expression.

inline std::shared_ptr<const AdcMemory> adcmem_ptr() const: Return a pointer to the memory keep-alive object

inline const std::vector<AxisInfo> &axes() const: Return the axes info for this tensor

Public Functions

Tensor(std::shared_ptr<const AdcMemory> adcmem_ptr, std::vector<AxisInfo> axes)

Construct a tensor interface class.

Parameters

adcmem_ptr – Memory to use for the tensor.
axes – Information about the tensor axes.

Utilities

Some random things to set up shop.

typedef double real_type: The real type to use

typedef double scalar_type: The scalar type to use

std::string shape_to_string(const std::vector<size_t> &shape): Convert a std::vector<size_t> representing a tensor shape to a string

class libadcc::AdcMemory

#include <AdcMemory.hh>

Manages memory for adc calculations.

This class has to remain in scope for as long as the allocated memory is needed. Normally this is achieved by keeping some sort of keep-alive shared pointer in the private context of all classes which require access to this memory. All changes to the environment are undone when the class goes out of scope.

Note

Undefined behaviour may result if this class is ever initialised twice.

Public Functions

inline AdcMemory(std::string pagefile_directory, size_t max_block_size = 16): Setup the class and directly call initialise with the standard allocator

AdcMemory(): Setup the AdcMemory class using the std::allocator for memory management

inline ~AdcMemory(): Destroy the AdcMemory class and free all the allocated memory

inline std::string allocator() const: Return the allocator to which the class is initialised.

inline std::string pagefile_directory() const: Return the pagefileprefix value

Note

This value is only meaningful if allocator() != “standard”

size_t contraction_batch_size() const: Get the contraction batch size, this is the number of tensor elements, which are processed in a batch in a tensor contraction.

void set_contraction_batch_size(size_t bsize): Set the contraction batch size

inline size_t max_block_size() const: Get the maximal block size a tensor may have along each axis

void initialise(std::string pagefile_directory, size_t max_block_size = 16, std::string allocator = "standard")

Setup the environment for the memory management.

Note

For some allocators pagefile_directory is not supported and thus ignored.

Note

“libxm” is an extra features, which might not be available in a default setup.

Parameters

pagefile_directory – File prefix for page files
max_block_size – Maximal block size a tensor may have along each axis.
allocator – The allocator to be used. Valid values are “libxm” or “standard” where “standard” is just the standard C++ allocator.

struct dimension_mismatch : public invalid_argument

struct not_implemented_error : public runtime_error

class libadcc::ThreadPool

#include <ThreadPool.hh>

Pool managing how many threads may be used for calculations.

Public Functions

inline ThreadPool(size_t n_running, size_t n_total)

Initialise the thread pool.

Parameters

n_running – The total number of running threads to employ
n_total – The total number of worker threads to use (Either running or ready)

void reinit(size_t n_running, size_t n_total)

Reinitialise the thread pool.

Parameters

n_running – The total number of running threads to employ
n_total – The total number of worker threads to use (Either running or idle)

inline ThreadPool(): Initialise a thread pool without parallelisation

inline size_t n_running() const: Return the number of running threads.

inline size_t n_total() const: Return the total number of worker threads (running or ready)

class libadcc::Timer

#include <Timer.hh>

Minimalistic timer class: Just records the start and stop times for tasks, i.e. the intervals they ran

Public Functions

inline Timer(): Construct an empty timer object

void start(const std::string &task): Start a particular task

double stop(const std::string &task): Stop the task again

Public Members

double time_construction: Time when this class was constructed

std::map<std::string, std::vector<std::pair<double, double>>> intervals: The intervals stored for a particular key

std::map<std::string, double> start_times: The start time stored for each key

Public Static Functions

static double now(): A float representing the current time on the clock used for measurement. For consistency, this function should always be used to obtain a representation of “now”. The unit is seconds.

class libadcc::RecordTime

#include <Timer.hh>

RAI-like class for timing tasks. Construction starts the timer, destruction ends it automatically. It is assumed that the passed task has a longer lifetime than the RecordTime object

Public Functions

inline RecordTime(Timer &timer_, const std::string &task_): Construct the class and start the timer

Public Members

Timer &timer: Timer object, which is managed.

std::string task: String describing the task.

Tensor implementation using libtensor

This section describes the implementation of the Tensor functionality of libadcc::Tensor using the libtensor tensor library.

inline libtensor::btensor<1, scalar_type> &asbt1(const std::shared_ptr<Tensor> &tensor)

inline libtensor::btensor<2, scalar_type> &asbt2(const std::shared_ptr<Tensor> &tensor)

inline libtensor::btensor<3, scalar_type> &asbt3(const std::shared_ptr<Tensor> &tensor)

inline libtensor::btensor<4, scalar_type> &asbt4(const std::shared_ptr<Tensor> &tensor)

std::map<size_t, std::string> setup_point_group_table(libtensor::product_table_container &ptc, const std::string &point_group): Setup point group symmetry table inside libtensor and return the irrep mapping as libtensor needs it.

template<size_t N> libtensor::bispace<N> as_bispace(const std::vector<AxisInfo> &axes): Form a bispace from a list of AxisInfo representing the axes of a tensor

template<size_t N> std::shared_ptr<libtensor::symmetry<N, scalar_type>> as_lt_symmetry(const Symmetry &sym): Translate a adcc::Symmetry object into a shared pointer of the libtensor symmetry object

TensorImpl(std::shared_ptr<const AdcMemory> adcmem_ptr, std::vector<AxisInfo> axes, std::shared_ptr<libtensor::btensor<N, scalar_type>> libtensor_ptr = nullptr, std::shared_ptr<ExpressionTree> expr_ptr = nullptr): Construction of the tensor implementation class

Note

This constructor is for experts only. Non-experts should use the make_tensor method instead.

inline TensorImpl(std::shared_ptr<const AdcMemory> adcmem_ptr, std::vector<AxisInfo> axes, std::shared_ptr<ExpressionTree> expr_ptr)

virtual std::shared_ptr<Tensor> empty_like() const override: Return a new tensor with the same dimensionality and symmetry as the passed tensor. The elements have undefined values.

virtual std::shared_ptr<Tensor> nosym_like() const override: Return a new tensor with same dimensionality and shape (and bispace) but without any symmetry copied over

virtual void set_mask(std::string mask, scalar_type value) override: Set a mask into a tensor to a specific value. The mask to set is defined by the mask string. Repetitive indices define the values to be set. E.g. for a 6D tensor the mask string “iijkli” would set all elements T_{iijkli} for all ijkl to the given value.

virtual std::shared_ptr<Tensor> diagonal(std::vector<size_t> axes) override: Extract a generalised diagonal from this Tensor. axes defines the axes over which to extract. The resulting new axis is appended to the remaining axis. For example if we have a Tensor fourth order t then diagonal(1, 2) returns d_{iax} = t_{ixxa} and diagonal(0, 1, 2) returns d_{ax} = t_{xxxa}.

virtual void set_random() override: Set the tensor to random data preserving symmetry

virtual std::shared_ptr<Tensor> scale(scalar_type c) const override: Scale tensor by a scalar value and return the result

virtual std::shared_ptr<Tensor> add(std::shared_ptr<Tensor> other) const override: Add another tensor and return the result

virtual std::shared_ptr<Tensor> multiply(std::shared_ptr<Tensor> other) const override: Multiply another tensor elementwise and return the result

virtual std::shared_ptr<Tensor> divide(std::shared_ptr<Tensor> other) const override: Divide another tensor elementwise and return the result

virtual void add_linear_combination(std::vector<scalar_type> scalars, std::vector<std::shared_ptr<Tensor>> tensors) const override: Add a linear combination to the represented tensor and store the result. Note that unlike add and scale this operation is not lazy and is provided for optimising on cases where a lazy linear combination introduces a too large overhead.

virtual std::shared_ptr<Tensor> copy() const override: Return a deep copy of this tensor.

virtual std::shared_ptr<Tensor> transpose(std::vector<size_t> axes) const override

Return a transpose form of this tensor

Parameters: axes – New permutation of the dimensions of this tensor (e.g. (1,0,2,3) will permute dimension 0 and 1 and keep the others) If missing, just reverse the dimension order.

virtual std::shared_ptr<Tensor> direct_sum(std::shared_ptr<Tensor> other) const override: Compute the direct sum of this tensor with another tensor. All axes are used in the order of appearance (this and then other).

virtual TensorOrScalar tensordot(std::shared_ptr<Tensor> other, std::pair<std::vector<size_t>, std::vector<size_t>> axes) const override

Return the tensordot of this tensor with another

Parameters: axes – Axes to contract over. The first vector refers to the axes of this tensor, the second vector to the axes of the other tensor.

virtual std::vector<scalar_type> dot(std::vector<std::shared_ptr<Tensor>> tensors) const override: Compute the Frobenius or l2 inner product with a list of other tensors, returning the results as a scalar array

virtual std::shared_ptr<Tensor> symmetrise(const std::vector<std::vector<size_t>> &permutations) const override

Symmetrise with respect to the given index permutations by adding the elements resulting from appropriate index permutations.

Examples for permutations. Take a rank-4 tensor T_{ijkl} {{0,1}} Permute the first two indices, i.e. form 0.5 * (T_{ijkl} + T_{jikl}) {{0,1}, {2,3}} Form 0.5 * (T_{ijkl} + T_{jilk})

Note

Unlike the implementation in libtensor, this function includes the prefactor 0.5

Parameters: permutations – The list of permutations to be applied simultaneously

virtual std::shared_ptr<Tensor> antisymmetrise(const std::vector<std::vector<size_t>> &permutations) const override: Antisymmetrise with respect to the given index permutations and return the result. For details with respect to the format of the permutations, see symmetrise. In this case the output tensor will be antisymmetric with respect to these permutations.

Note

Unlike the implementation in libtensor, this function includes the prefactor 0.5

virtual double trace(std::string contraction) const override

Return the full trace of a tensor

Parameters: contraction – Indices to contract over using Einstein summation convention, e.g. “abab” traces over the 1st and 3rd and 2nd and 4th axis.

virtual void evaluate() const override: Make sure to evaluate this tensor in case this has not yet happened

inline virtual bool needs_evaluation() const override: Check weather the object represents an evaluated expression or not.

inline virtual void set_immutable() override: Flag the tensor as immutable, allows some optimisations to be performed

inline virtual bool is_mutable() const override: Is the tensor mutable

virtual void set_element(const std::vector<size_t> &tidx, scalar_type value) override: Set the value of a single tensor element

Note

This is a slow function and should be avoided.

virtual scalar_type get_element(const std::vector<size_t> &tidx) const override: Get the value of a single tensor element

Note

This is a slow function and should be avoided.

virtual bool is_element_allowed(const std::vector<size_t> &tidx) const override: Return whether the element referenced by tidx is allowed (non-zero) by the symmetry of the Tensor or not.

virtual std::vector<std::pair<std::vector<size_t>, scalar_type>> select_n_absmax(size_t n, bool unique_by_symmetry) const override

Get the n absolute largest elements along with their values

Parameters

n – Number of elements to select
unique_by_symmetry – By default the returned elements are made unique by symmetry of the tensor. Setting this to false disables this feature.

virtual std::vector<std::pair<std::vector<size_t>, scalar_type>> select_n_absmin(size_t n, bool unique_by_symmetry) const override

Get the n absolute smallest elements along with their values

Parameters

n – Number of elements to select
unique_by_symmetry – By default the returned elements are made unique by symmetry of the tensor. Setting this to false disables this feature.

virtual std::vector<std::pair<std::vector<size_t>, scalar_type>> select_n_max(size_t n, bool unique_by_symmetry) const override

Get the n largest elements along with their values

Parameters

n – Number of elements to select
unique_by_symmetry – By default the returned elements are made unique by symmetry of the tensor. Setting this to false disables this feature.

virtual std::vector<std::pair<std::vector<size_t>, scalar_type>> select_n_min(size_t n, bool unique_by_symmetry) const override

Get the n smallest elements along with their values

Parameters

n – Number of elements to select
unique_by_symmetry – By default the returned elements are made unique by symmetry of the tensor. Setting this to false disables this feature.

virtual void export_to(scalar_type *memptr, size_t size) const override: Extract the tensor to plain memory provided by the given pointer.

Note

This will return a full, dense tensor. At least size elements of space are assumed at the provided memory location. The data is stored in row-major (C-like) format

virtual void import_from(const scalar_type *memptr, size_t size, scalar_type tolerance, bool symmetry_check) override

Import the tensor from plain memory provided by the given pointer. The memory will be copied and all existing data overwritten. If symmetry_check is true, the process will check that the data has the required symmetry to fit into the tensor. This requires a slower algorithm to be chosen.

Note

This function requires a full, dense tensor with the data stored in row-major (C-like) format.

Parameters

memptr – Full, dense memory pointer to the tensor data to be imported.
size – Size of the dense memory.
tolerance – Threshold to account for numerical inconsistencies when checking the symmetry or for determining zero blocks.
symmetry_check – Should symmetry be explicitly checked during the import.

virtual void import_from(std::function<void(const std::vector<std::pair<size_t, size_t>>&, scalar_type*)> generator, scalar_type tolerance, bool symmetry_check) override

Import the tensor from a generator functor. All existing data will be overwritten. If symmetry_check is true, the process will check that the data has the required symmetry to fit into the tensor. This requires a slower algorithm to be chosen.

Note

The generator is required to produce data in row-major (C-like) format at a designated memory location.

Parameters

generator – Generator functor. The functor is called with a list of ranges for each dimension. The ranges are half-open, left-inclusive and right-exclusive. The corresponding data should be written to the passed pointer into raw memory. This is an advanced functionality. Use only if you know what you are doing.
tolerance – Threshold to account for numerical inconsistencies when checking the symmetry or for determining zero blocks.
symmetry_check – Should symmetry be explicitly checked during the import.

virtual std::string describe_symmetry() const override: Return a std::string providing hopefully helpful information about the symmetries stored inside the tensor object.

virtual std::string describe_expression(std::string stage = "unoptimised") const override: Return a std::string providing hopefully helpful information about the expression tree stored inside the tensor object. Valid values for stage are “unoptimised”, “optimised”, “evaluation” returning the unoptimised expression tree as stored, an optimised form of it or the tree as used actually for evaluating the expression.

inline explicit operator libtensor::btensor<N, scalar_type>&(): Convert object to btensor for use in libtensor functions.

inline std::shared_ptr<libtensor::btensor<N, scalar_type>> libtensor_ptr(): Return inner btensor object

Note

This is an advanced function. Use with care, since this can corrupt the internal state of the Tensor object.

std::shared_ptr<ExpressionTree> expression_ptr() const: Return permutation, expression object and required keep-alives to this tensor

Note

This is an advanced function. Use with care, since this can corrupt the internal state of the Tensor object.

std::shared_ptr<ExpressionTree> as_expression(const std::shared_ptr<Tensor> &tensor): Extractor function to convert the contained tensor from adcc::Tensor to an expression.

Note

This is an internal function. Use only if you know what you are doing.

template<size_t N> libtensor::btensor<N, scalar_type> &as_btensor(const std::shared_ptr<Tensor> &tensor): Extractor functions to convert the contained tensor of an adcc::Tensor object to an appropriate libtensor::btensor, The dimensionality N has to match

template<size_t N> std::shared_ptr<libtensor::btensor<N, scalar_type>> as_btensor_ptr(const std::shared_ptr<Tensor> &tensor): Extractor functions to convert the contained tensor of an adcc::Tensor object to an appropriate libtensor::btensor, The dimensionality N has to match Extractor function to convert the contained tensor of an adcc::Tensor object to an appropriate pointer to libtensor::btensor. The dimensionality N has to match.

template<size_t N> class libadcc::TensorImpl : public libadcc::Tensor 

#include <TensorImpl.hh>

Actual implementation of the Tensor class. For details about the functions see Tensor.hh

Public Functions

TensorImpl(std::shared_ptr<const AdcMemory> adcmem_ptr, std::vector<AxisInfo> axes, std::shared_ptr<libtensor::btensor<N, scalar_type>> libtensor_ptr = nullptr, std::shared_ptr<ExpressionTree> expr_ptr = nullptr): Construction of the tensor implementation class

Note

This constructor is for experts only. Non-experts should use the make_tensor method instead.

virtual std::shared_ptr<Tensor> empty_like() const override: Return a new tensor with the same dimensionality and symmetry as the passed tensor. The elements have undefined values.

virtual std::shared_ptr<Tensor> nosym_like() const override: Return a new tensor with same dimensionality and shape (and bispace) but without any symmetry copied over

virtual void set_mask(std::string mask, scalar_type value) override: Set a mask into a tensor to a specific value. The mask to set is defined by the mask string. Repetitive indices define the values to be set. E.g. for a 6D tensor the mask string “iijkli” would set all elements T_{iijkli} for all ijkl to the given value.

virtual std::shared_ptr<Tensor> diagonal(std::vector<size_t> axes) override: Extract a generalised diagonal from this Tensor. axes defines the axes over which to extract. The resulting new axis is appended to the remaining axis. For example if we have a Tensor fourth order t then diagonal(1, 2) returns d_{iax} = t_{ixxa} and diagonal(0, 1, 2) returns d_{ax} = t_{xxxa}.

virtual void set_random() override: Set the tensor to random data preserving symmetry

virtual std::shared_ptr<Tensor> scale(scalar_type c) const override: Scale tensor by a scalar value and return the result

virtual std::shared_ptr<Tensor> add(std::shared_ptr<Tensor> other) const override: Add another tensor and return the result

virtual std::shared_ptr<Tensor> multiply(std::shared_ptr<Tensor> other) const override: Multiply another tensor elementwise and return the result

virtual std::shared_ptr<Tensor> divide(std::shared_ptr<Tensor> other) const override: Divide another tensor elementwise and return the result

virtual void add_linear_combination(std::vector<scalar_type> scalars, std::vector<std::shared_ptr<Tensor>> tensors) const override: Add a linear combination to the represented tensor and store the result. Note that unlike add and scale this operation is not lazy and is provided for optimising on cases where a lazy linear combination introduces a too large overhead.

virtual std::shared_ptr<Tensor> copy() const override: Return a deep copy of this tensor.

virtual std::shared_ptr<Tensor> transpose(std::vector<size_t> axes) const override

Return a transpose form of this tensor

Parameters: axes – New permutation of the dimensions of this tensor (e.g. (1,0,2,3) will permute dimension 0 and 1 and keep the others) If missing, just reverse the dimension order.

virtual std::shared_ptr<Tensor> direct_sum(std::shared_ptr<Tensor> other) const override: Compute the direct sum of this tensor with another tensor. All axes are used in the order of appearance (this and then other).

virtual TensorOrScalar tensordot(std::shared_ptr<Tensor> other, std::pair<std::vector<size_t>, std::vector<size_t>> axes) const override

Return the tensordot of this tensor with another

Parameters: axes – Axes to contract over. The first vector refers to the axes of this tensor, the second vector to the axes of the other tensor.

virtual std::vector<scalar_type> dot(std::vector<std::shared_ptr<Tensor>> tensors) const override: Compute the Frobenius or l2 inner product with a list of other tensors, returning the results as a scalar array

virtual std::shared_ptr<Tensor> symmetrise(const std::vector<std::vector<size_t>> &permutations) const override

Symmetrise with respect to the given index permutations by adding the elements resulting from appropriate index permutations.

Examples for permutations. Take a rank-4 tensor T_{ijkl} {{0,1}} Permute the first two indices, i.e. form 0.5 * (T_{ijkl} + T_{jikl}) {{0,1}, {2,3}} Form 0.5 * (T_{ijkl} + T_{jilk})

Note

Unlike the implementation in libtensor, this function includes the prefactor 0.5

Parameters: permutations – The list of permutations to be applied simultaneously

virtual std::shared_ptr<Tensor> antisymmetrise(const std::vector<std::vector<size_t>> &permutations) const override: Antisymmetrise with respect to the given index permutations and return the result. For details with respect to the format of the permutations, see symmetrise. In this case the output tensor will be antisymmetric with respect to these permutations.

Note

Unlike the implementation in libtensor, this function includes the prefactor 0.5

virtual double trace(std::string contraction) const override

Return the full trace of a tensor

Parameters: contraction – Indices to contract over using Einstein summation convention, e.g. “abab” traces over the 1st and 3rd and 2nd and 4th axis.

virtual void evaluate() const override: Make sure to evaluate this tensor in case this has not yet happened

inline virtual bool needs_evaluation() const override: Check weather the object represents an evaluated expression or not.

inline virtual void set_immutable() override: Flag the tensor as immutable, allows some optimisations to be performed

inline virtual bool is_mutable() const override: Is the tensor mutable

virtual void set_element(const std::vector<size_t> &tidx, scalar_type value) override: Set the value of a single tensor element

Note

This is a slow function and should be avoided.

virtual scalar_type get_element(const std::vector<size_t> &tidx) const override: Get the value of a single tensor element

Note

This is a slow function and should be avoided.

virtual bool is_element_allowed(const std::vector<size_t> &tidx) const override: Return whether the element referenced by tidx is allowed (non-zero) by the symmetry of the Tensor or not.

virtual std::vector<std::pair<std::vector<size_t>, scalar_type>> select_n_absmax(size_t n, bool unique_by_symmetry) const override

Get the n absolute largest elements along with their values

Parameters

n – Number of elements to select
unique_by_symmetry – By default the returned elements are made unique by symmetry of the tensor. Setting this to false disables this feature.

virtual std::vector<std::pair<std::vector<size_t>, scalar_type>> select_n_absmin(size_t n, bool unique_by_symmetry) const override

Get the n absolute smallest elements along with their values

Parameters

n – Number of elements to select
unique_by_symmetry – By default the returned elements are made unique by symmetry of the tensor. Setting this to false disables this feature.

virtual std::vector<std::pair<std::vector<size_t>, scalar_type>> select_n_max(size_t n, bool unique_by_symmetry) const override

Get the n largest elements along with their values

Parameters

n – Number of elements to select
unique_by_symmetry – By default the returned elements are made unique by symmetry of the tensor. Setting this to false disables this feature.

virtual std::vector<std::pair<std::vector<size_t>, scalar_type>> select_n_min(size_t n, bool unique_by_symmetry) const override

Get the n smallest elements along with their values

Parameters

n – Number of elements to select
unique_by_symmetry – By default the returned elements are made unique by symmetry of the tensor. Setting this to false disables this feature.

virtual void export_to(scalar_type *memptr, size_t size) const override: Extract the tensor to plain memory provided by the given pointer.

Note

This will return a full, dense tensor. At least size elements of space are assumed at the provided memory location. The data is stored in row-major (C-like) format

virtual void import_from(const scalar_type *memptr, size_t size, scalar_type tolerance, bool symmetry_check) override

Import the tensor from plain memory provided by the given pointer. The memory will be copied and all existing data overwritten. If symmetry_check is true, the process will check that the data has the required symmetry to fit into the tensor. This requires a slower algorithm to be chosen.

Note

This function requires a full, dense tensor with the data stored in row-major (C-like) format.

Parameters

memptr – Full, dense memory pointer to the tensor data to be imported.
size – Size of the dense memory.
tolerance – Threshold to account for numerical inconsistencies when checking the symmetry or for determining zero blocks.
symmetry_check – Should symmetry be explicitly checked during the import.

virtual void import_from(std::function<void(const std::vector<std::pair<size_t, size_t>>&, scalar_type*)> generator, scalar_type tolerance, bool symmetry_check) override

Import the tensor from a generator functor. All existing data will be overwritten. If symmetry_check is true, the process will check that the data has the required symmetry to fit into the tensor. This requires a slower algorithm to be chosen.

Note

The generator is required to produce data in row-major (C-like) format at a designated memory location.

Parameters

generator – Generator functor. The functor is called with a list of ranges for each dimension. The ranges are half-open, left-inclusive and right-exclusive. The corresponding data should be written to the passed pointer into raw memory. This is an advanced functionality. Use only if you know what you are doing.
tolerance – Threshold to account for numerical inconsistencies when checking the symmetry or for determining zero blocks.
symmetry_check – Should symmetry be explicitly checked during the import.

virtual std::string describe_symmetry() const override: Return a std::string providing hopefully helpful information about the symmetries stored inside the tensor object.

virtual std::string describe_expression(std::string stage = "unoptimised") const override: Return a std::string providing hopefully helpful information about the expression tree stored inside the tensor object. Valid values for stage are “unoptimised”, “optimised”, “evaluation” returning the unoptimised expression tree as stored, an optimised form of it or the tree as used actually for evaluating the expression.

inline explicit operator libtensor::btensor<N, scalar_type>&(): Convert object to btensor for use in libtensor functions.

inline std::shared_ptr<libtensor::btensor<N, scalar_type>> libtensor_ptr(): Return inner btensor object

Note

This is an advanced function. Use with care, since this can corrupt the internal state of the Tensor object.

std::shared_ptr<ExpressionTree> expression_ptr() const: Return permutation, expression object and required keep-alives to this tensor

Note

This is an advanced function. Use with care, since this can corrupt the internal state of the Tensor object.

std::vector<scalar_type> dot(std::vector<std::shared_ptr<Tensor>> tensors) const = 0: Compute the Frobenius or l2 inner product with a list of other tensors, returning the results as a scalar array

void export_to(scalar_type *memptr, size_t size) const = 0: Extract the tensor to plain memory provided by the given pointer.

Note

This will return a full, dense tensor. At least size elements of space are assumed at the provided memory location. The data is stored in row-major (C-like) format

inline void export_to(std::vector<scalar_type> &output) const: Extract the tensor into a std::vector, which will be resized to fit the data.

Note

All data is stored in row-major (C-like) format

void import_from(const scalar_type *memptr, size_t size, scalar_type tolerance = 0, bool symmetry_check = true) = 0

Import the tensor from plain memory provided by the given pointer. The memory will be copied and all existing data overwritten. If symmetry_check is true, the process will check that the data has the required symmetry to fit into the tensor. This requires a slower algorithm to be chosen.

Note

This function requires a full, dense tensor with the data stored in row-major (C-like) format.

Parameters

memptr – Full, dense memory pointer to the tensor data to be imported.
size – Size of the dense memory.
tolerance – Threshold to account for numerical inconsistencies when checking the symmetry or for determining zero blocks.
symmetry_check – Should symmetry be explicitly checked during the import.

inline void import_from(const std::vector<scalar_type> &input, scalar_type tolerance = 0, bool symmetry_check = true)

Import the tensor from plain memory provided by the given vector. The memory will be copied and all existing data overwritten. If symmetry_check is true, the process will check that the data has the required symmetry to fit into the tensor. This requires a slower algorithm to be chosen.

Note

This function requires a full, dense tensor with the data stored in row-major (C-like) format.

Parameters

input – Input data in a linearised vector
tolerance – Threshold to account for numerical inconsistencies when checking the symmetry or for determining zero blocks.
symmetry_check – Should symmetry be explicitly checked during the import.

void import_from(std::function<void(const std::vector<std::pair<size_t, size_t>>&, scalar_type*)> generator, scalar_type tolerance = 0, bool symmetry_check = true) = 0

Import the tensor from a generator functor. All existing data will be overwritten. If symmetry_check is true, the process will check that the data has the required symmetry to fit into the tensor. This requires a slower algorithm to be chosen.

Note

The generator is required to produce data in row-major (C-like) format at a designated memory location.

Parameters

generator – Generator functor. The functor is called with a list of ranges for each dimension. The ranges are half-open, left-inclusive and right-exclusive. The corresponding data should be written to the passed pointer into raw memory. This is an advanced functionality. Use only if you know what you are doing.
tolerance – Threshold to account for numerical inconsistencies when checking the symmetry or for determining zero blocks.
symmetry_check – Should symmetry be explicitly checked during the import.