Format conversions

When graph2mat is initialized, a global graph2mat.ConversionManager is created and made available at graph2mat.conversions. It can be imported like:

import graph2mat
# Use it like
graph2mat.conversions

# Or alternatively
from graph2mat import conversions

This conversion manager contains all the conversions between formats implemented in graph2mat.

Note

See the documentation of graph2mat.ConversionManager for some remarks on how to access/use the existing converters, as well as how to add new ones.

Summary of available conversions

The following table summarizes all the available conversions. Each row contains:

  • Source: The format to convert from.

  • Target: The format to convert to.

  • Function: The function that performs the conversion, click on it to go to its documentation.

See graph2mat.Formats for a list of all available formats, together with a description of what each format is.

Source

Target

Function

basisconfiguration

basismatrixdata

basisconfiguration_to_basismatrixdata

basisconfiguration

orbitalconfiguration

basisconfiguration_to_orbitalconfiguration

basisconfiguration

sisl_geometry

basisconfiguration_to_sisl_geometry

basisconfiguration

torch_basismatrixdata

basisconfiguration_to_torch_basismatrixdata

basismatrixdata

basismatrixdata

basismatrixdata_to_basismatrixdata

basismatrixdata

numpy

basismatrixdata_to_numpy

basismatrixdata

scipy_coo

basismatrixdata_to_scipy_coo

basismatrixdata

scipy_csr

basismatrixdata_to_scipy_csr

basismatrixdata

sisl

basismatrixdata_to_sisl

basismatrixdata

sisl_DM

basismatrixdata_to_sisl_DM

basismatrixdata

sisl_EDM

basismatrixdata_to_sisl_EDM

basismatrixdata

sisl_H

basismatrixdata_to_sisl_H

basismatrixdata

torch

basismatrixdata_to_torch

block_dict

numpy

block_dict_to_numpy

block_dict

scipy_coo

block_dict_to_scipy_coo

block_dict

scipy_csr

block_dict_to_scipy_csr

block_dict

torch

block_dict_to_torch

nodesedges

numpy

nodesedges_to_numpy

nodesedges

scipy_coo

nodesedges_to_scipy_coo

nodesedges

scipy_csr

nodesedges_to_scipy_csr

nodesedges

sisl

nodesedges_to_sisl

nodesedges

sisl_DM

nodesedges_to_sisl_DM

nodesedges

sisl_EDM

nodesedges_to_sisl_EDM

nodesedges

sisl_H

nodesedges_to_sisl_H

nodesedges

torch

nodesedges_to_torch

numpy

torch

numpy_to_torch

orbitalconfiguration

basismatrixdata

orbitalconfiguration_to_basismatrixdata

orbitalconfiguration

torch_basismatrixdata

orbitalconfiguration_to_torch_basismatrixdata

scipy_coo

basismatrix

scipy_coo_to_basismatrix

scipy_coo

numpy

scipy_coo_to_numpy

scipy_coo

scipy_csr

scipy_coo_to_scipy_csr

scipy_coo

sisl

scipy_coo_to_sisl

scipy_coo

sisl_DM

scipy_coo_to_sisl_DM

scipy_coo

sisl_EDM

scipy_coo_to_sisl_EDM

scipy_coo

sisl_H

scipy_coo_to_sisl_H

scipy_coo

torch

scipy_coo_to_torch

scipy_csr

basismatrix

scipy_csr_to_basismatrix

scipy_csr

numpy

scipy_csr_to_numpy

scipy_csr

scipy_coo

scipy_csr_to_scipy_coo

scipy_csr

sisl

scipy_csr_to_sisl

scipy_csr

sisl_DM

scipy_csr_to_sisl_DM

scipy_csr

sisl_EDM

scipy_csr_to_sisl_EDM

scipy_csr

sisl_H

scipy_csr_to_sisl_H

scipy_csr

torch

scipy_csr_to_torch

sisl

basismatrixdata

sisl_to_basismatrixdata

sisl

orbitalconfiguration

sisl_to_orbitalconfiguration

sisl

torch_basismatrixdata

sisl_to_torch_basismatrixdata

sisl_geometry

basismatrixdata

sisl_geometry_to_basismatrixdata

sisl_geometry

orbitalconfiguration

sisl_geometry_to_orbitalconfiguration

sisl_geometry

torch_basismatrixdata

sisl_geometry_to_torch_basismatrixdata

sisl_sile

basismatrixdata

sisl_sile_to_basismatrixdata

sisl_sile

orbitalconfiguration

sisl_sile_to_orbitalconfiguration

sisl_sile

torch_basismatrixdata

sisl_sile_to_torch_basismatrixdata

torch

numpy

torch_to_numpy

torch_basismatrixdata

basismatrixdata

torch_basismatrixdata_to_basismatrixdata

torch_basismatrixdata

numpy

torch_basismatrixdata_to_numpy

torch_basismatrixdata

scipy_coo

torch_basismatrixdata_to_scipy_coo

torch_basismatrixdata

scipy_csr

torch_basismatrixdata_to_scipy_csr

torch_basismatrixdata

sisl

torch_basismatrixdata_to_sisl

torch_basismatrixdata

sisl_DM

torch_basismatrixdata_to_sisl_DM

torch_basismatrixdata

sisl_EDM

torch_basismatrixdata_to_sisl_EDM

torch_basismatrixdata

sisl_H

torch_basismatrixdata_to_sisl_H

torch_basismatrixdata

torch

torch_basismatrixdata_to_torch

torch_basismatrixdata

torch_coo

torch_basismatrixdata_to_torch_coo

torch_basismatrixdata

torch_csr

torch_basismatrixdata_to_torch_csr

torch_coo

numpy

torch_coo_to_numpy

torch_coo

torch

torch_coo_to_torch

torch_coo

torch_csr

torch_coo_to_torch_csr

torch_csr

numpy

torch_csr_to_numpy

torch_csr

torch

torch_csr_to_torch

torch_csr

torch_coo

torch_csr_to_torch_coo

torch_nodesedges

torch

torch_nodesedges_to_torch

torch_nodesedges

torch_coo

torch_nodesedges_to_torch_coo

torch_nodesedges

torch_csr

torch_nodesedges_to_torch_csr

Documentation of conversion functions

Formats.BASISCONFIGURATION -> Formats.BASISMATRIXDATA

graph2mat.conversions.basisconfiguration_to_basismatrixdata(config: BasisConfiguration, data_processor: MatrixDataProcessor, nsc=None) → BasisMatrixData

Creates a basis matrix data object from a configuration.

Parameters:

  • config – The configuration from which to create the basis matrix data object.

  • data_processor – The data processor that contains all the information needed to convert the configuration into the basis matrix data object. E.g. it contains the basis table.

Formats.BASISCONFIGURATION -> Formats.ORBITALCONFIGURATION

graph2mat.conversions.basisconfiguration_to_orbitalconfiguration(config: BasisConfiguration, cls: type[OrbitalConfiguration] = graph2mat.core.data.configuration.OrbitalConfiguration, **kwargs) → OrbitalConfiguration

Conversion from ‘basisconfiguration’ to ‘orbitalconfiguration’.

This function has been defined automatically.

The full path of the conversion is (original converter in square brackets):

basisconfiguration -> [ sisl_geometry -> orbitalconfiguration ]

Parameters:

  • cls – Class to initialize, should be a subclass of OrbitalConfiguration.

  • **kwargs – Additional arguments to be passed to the OrbitalConfiguration constructor.

See also

sisl_geometry_to_orbitalconfiguration

The original converter that has been expanded to create this one.

Formats.BASISCONFIGURATION -> Formats.SISL_GEOMETRY

graph2mat.conversions.basisconfiguration_to_sisl_geometry(config: BasisConfiguration) → Geometry

Formats.BASISCONFIGURATION -> Formats.TORCH_BASISMATRIXDATA

graph2mat.conversions.basisconfiguration_to_torch_basismatrixdata(config: BasisConfiguration, data_processor: MatrixDataProcessor, nsc=None) → BasisMatrixData

Creates a basis matrix data object from a configuration.

Parameters:

  • config – The configuration from which to create the basis matrix data object.

  • data_processor – The data processor that contains all the information needed to convert the configuration into the basis matrix data object. E.g. it contains the basis table.

Formats.BASISMATRIXDATA -> Formats.BASISMATRIXDATA

graph2mat.conversions.basismatrixdata_to_basismatrixdata(data) → BasisMatrixData

Formats.BASISMATRIXDATA -> Formats.NUMPY

graph2mat.conversions.basismatrixdata_to_numpy(data, threshold: float | None = None, **kwargs) → ndarray

Conversion from ‘basismatrixdata’ to ‘numpy’.

This function has been defined automatically.

The full path of the conversion is (original converter in square brackets):

[ basismatrixdata -> scipy_csr ] -> numpy

See also

basismatrixdata_to_scipy_csr

The original converter that has been expanded to create this one.

Formats.BASISMATRIXDATA -> Formats.SCIPY_COO

graph2mat.conversions.basismatrixdata_to_scipy_coo(data, threshold: float | None = None, **kwargs) → coo_array

Conversion from ‘basismatrixdata’ to ‘scipy_coo’.

This function has been defined automatically.

The full path of the conversion is (original converter in square brackets):

[ basismatrixdata -> scipy_csr ] -> scipy_coo

See also

basismatrixdata_to_scipy_csr

The original converter that has been expanded to create this one.

Formats.BASISMATRIXDATA -> Formats.SCIPY_CSR

graph2mat.conversions.basismatrixdata_to_scipy_csr(data, threshold: float | None = None, **kwargs)

Formats.BASISMATRIXDATA -> Formats.SISL

graph2mat.conversions.basismatrixdata_to_sisl(data, threshold: float | None = None, **kwargs)

Formats.BASISMATRIXDATA -> Formats.SISL_DM

graph2mat.conversions.basismatrixdata_to_sisl_DM(data, threshold: float | None = None, **kwargs)

Formats.BASISMATRIXDATA -> Formats.SISL_EDM

graph2mat.conversions.basismatrixdata_to_sisl_EDM(data, threshold: float | None = None, **kwargs)

Formats.BASISMATRIXDATA -> Formats.SISL_H

graph2mat.conversions.basismatrixdata_to_sisl_H(data, threshold: float | None = None, **kwargs)

Formats.BASISMATRIXDATA -> Formats.TORCH

graph2mat.conversions.basismatrixdata_to_torch(data, threshold: float | None = None, **kwargs) → Tensor

Conversion from ‘basismatrixdata’ to ‘torch’.

This function has been defined automatically.

The full path of the conversion is (original converter in square brackets):

[ basismatrixdata -> scipy_csr ] -> torch

See also

basismatrixdata_to_scipy_csr

The original converter that has been expanded to create this one.

Formats.BLOCK_DICT -> Formats.NUMPY

graph2mat.conversions.block_dict_to_numpy(block_dict: Dict[Tuple[int, int, int], ndarray], first_orb: ndarray, n_supercells: int = 1, threshold: float = 1e-08) → ndarray

Conversion from ‘block_dict’ to ‘numpy’.

This function has been defined automatically.

The full path of the conversion is (original converter in square brackets):

[ block_dict -> scipy_coo ] -> numpy

See also

block_dict_to_scipy_coo

The original converter that has been expanded to create this one.

Formats.BLOCK_DICT -> Formats.SCIPY_COO

graph2mat.conversions.block_dict_to_scipy_coo(block_dict: Dict[Tuple[int, int, int], ndarray], first_orb: ndarray, n_supercells: int = 1, threshold: float = 1e-08) → coo_array

Converts a block dictionary into a coo array.

Conversions to any other sparse structure can be done once we’ve got the coo array.

Formats.BLOCK_DICT -> Formats.SCIPY_CSR

graph2mat.conversions.block_dict_to_scipy_csr(block_dict: Dict[Tuple[int, int, int], ndarray], first_orb: ndarray, n_supercells: int = 1, threshold: float = 1e-08) → csr_array

Conversion from ‘block_dict’ to ‘scipy_csr’.

This function has been defined automatically.

The full path of the conversion is (original converter in square brackets):

[ block_dict -> scipy_coo ] -> scipy_csr

See also

block_dict_to_scipy_coo

The original converter that has been expanded to create this one.

Formats.BLOCK_DICT -> Formats.TORCH

graph2mat.conversions.block_dict_to_torch(block_dict: Dict[Tuple[int, int, int], ndarray], first_orb: ndarray, n_supercells: int = 1, threshold: float = 1e-08) → Tensor

Conversion from ‘block_dict’ to ‘torch’.

This function has been defined automatically.

The full path of the conversion is (original converter in square brackets):

[ block_dict -> scipy_coo ] -> torch

See also

block_dict_to_scipy_coo

The original converter that has been expanded to create this one.

Formats.NODESEDGES -> Formats.NUMPY

graph2mat.conversions.nodesedges_to_numpy(node_vals: ndarray, edge_vals: ndarray, edge_index: ndarray, orbitals: ndarray, n_supercells: int = 1, edge_neigh_isc: ndarray | None = None, threshold: float | None = None, symmetrize_edges: bool = False) → ndarray

Conversion from ‘nodesedges’ to ‘numpy’.

This function has been defined automatically.

The full path of the conversion is (original converter in square brackets):

[ nodesedges -> scipy_coo ] -> numpy

Parameters:

  • node_vals – Flat array containing the values of the node blocks. The order of the values is first by node index, then row then column.

  • edge_vals – Flat array containing the values of the edge blocks. The order of the values is first by edge index, then row then column.

  • edge_index – Array of shape (2, n_edges) containing the indices of the atoms that participate in each edge.

  • orbitals – Array of shape (n_nodes, ) containing the number of orbitals for each atom.

  • n_supercells – Number of auxiliary supercells.

  • edge_neigh_isc – Array of shape (n_edges, ) containing the supercell index of the second atom in each edge with respect to the first atom. If not provided, all interactions are assumed to be in the unit cell.

  • threshold – Matrix elements with a value below this number are set to 0.

  • symmetrize_edges – whether for each edge only one direction is provided. The edge block for the opposite direction is then created as the transpose.

See also

nodesedges_to_scipy_coo

The original converter that has been expanded to create this one.

Formats.NODESEDGES -> Formats.SCIPY_COO

graph2mat.conversions.nodesedges_to_scipy_coo(node_vals: ndarray, edge_vals: ndarray, edge_index: ndarray, orbitals: ndarray, n_supercells: int = 1, edge_neigh_isc: ndarray | None = None, threshold: float | None = None, symmetrize_edges: bool = False) → coo_array

Converts an orbital matrix from node and edges array to scipy coo.

Conversions to any other sparse structure can be done once we’ve got the coo array.

Parameters:

  • node_vals – Flat array containing the values of the node blocks. The order of the values is first by node index, then row then column.

  • edge_vals – Flat array containing the values of the edge blocks. The order of the values is first by edge index, then row then column.

  • edge_index – Array of shape (2, n_edges) containing the indices of the atoms that participate in each edge.

  • orbitals – Array of shape (n_nodes, ) containing the number of orbitals for each atom.

  • n_supercells – Number of auxiliary supercells.

  • edge_neigh_isc – Array of shape (n_edges, ) containing the supercell index of the second atom in each edge with respect to the first atom. If not provided, all interactions are assumed to be in the unit cell.

  • threshold – Matrix elements with a value below this number are set to 0.

  • symmetrize_edges – whether for each edge only one direction is provided. The edge block for the opposite direction is then created as the transpose.

Formats.NODESEDGES -> Formats.SCIPY_CSR

graph2mat.conversions.nodesedges_to_scipy_csr(node_vals: ndarray, edge_vals: ndarray, edge_index: ndarray, orbitals: ndarray, n_supercells: int = 1, edge_neigh_isc: ndarray | None = None, threshold: float | None = None, symmetrize_edges: bool = False) → csr_array

Conversion from ‘nodesedges’ to ‘scipy_csr’.

This function has been defined automatically.

The full path of the conversion is (original converter in square brackets):

[ nodesedges -> scipy_coo ] -> scipy_csr

Parameters:

  • node_vals – Flat array containing the values of the node blocks. The order of the values is first by node index, then row then column.

  • edge_vals – Flat array containing the values of the edge blocks. The order of the values is first by edge index, then row then column.

  • edge_index – Array of shape (2, n_edges) containing the indices of the atoms that participate in each edge.

  • orbitals – Array of shape (n_nodes, ) containing the number of orbitals for each atom.

  • n_supercells – Number of auxiliary supercells.

  • edge_neigh_isc – Array of shape (n_edges, ) containing the supercell index of the second atom in each edge with respect to the first atom. If not provided, all interactions are assumed to be in the unit cell.

  • threshold – Matrix elements with a value below this number are set to 0.

  • symmetrize_edges – whether for each edge only one direction is provided. The edge block for the opposite direction is then created as the transpose.

See also

nodesedges_to_scipy_coo

The original converter that has been expanded to create this one.

Formats.NODESEDGES -> Formats.SISL

graph2mat.conversions.nodesedges_to_sisl(node_vals: ndarray, edge_vals: ndarray, edge_index: ndarray, geometry: Geometry, sp_class: Type[SparseOrbital] = sisl.SparseOrbital, edge_neigh_isc: ndarray | None = None, threshold: float = 1e-08, symmetrize_edges: bool = False) → SparseOrbital

Formats.NODESEDGES -> Formats.SISL_DM

graph2mat.conversions.nodesedges_to_sisl_DM(node_vals: ndarray, edge_vals: ndarray, edge_index: ndarray, geometry: Geometry, *, sp_class: Type[SparseOrbital] = sisl.physics.DensityMatrix, edge_neigh_isc: ndarray | None = None, threshold: float = 1e-08, symmetrize_edges: bool = False) → SparseOrbital

Formats.NODESEDGES -> Formats.SISL_EDM

graph2mat.conversions.nodesedges_to_sisl_EDM(node_vals: ndarray, edge_vals: ndarray, edge_index: ndarray, geometry: Geometry, *, sp_class: Type[SparseOrbital] = sisl.physics.EnergyDensityMatrix, edge_neigh_isc: ndarray | None = None, threshold: float = 1e-08, symmetrize_edges: bool = False) → SparseOrbital

Formats.NODESEDGES -> Formats.SISL_H

graph2mat.conversions.nodesedges_to_sisl_H(node_vals: ndarray, edge_vals: ndarray, edge_index: ndarray, geometry: Geometry, *, sp_class: Type[SparseOrbital] = sisl.physics.Hamiltonian, edge_neigh_isc: ndarray | None = None, threshold: float = 1e-08, symmetrize_edges: bool = False) → SparseOrbital

Formats.NODESEDGES -> Formats.TORCH

graph2mat.conversions.nodesedges_to_torch(node_vals: ndarray, edge_vals: ndarray, edge_index: ndarray, orbitals: ndarray, n_supercells: int = 1, edge_neigh_isc: ndarray | None = None, threshold: float | None = None, symmetrize_edges: bool = False) → Tensor

Conversion from ‘nodesedges’ to ‘torch’.

This function has been defined automatically.

The full path of the conversion is (original converter in square brackets):

[ nodesedges -> scipy_coo ] -> torch

Parameters:

  • node_vals – Flat array containing the values of the node blocks. The order of the values is first by node index, then row then column.

  • edge_vals – Flat array containing the values of the edge blocks. The order of the values is first by edge index, then row then column.

  • edge_index – Array of shape (2, n_edges) containing the indices of the atoms that participate in each edge.

  • orbitals – Array of shape (n_nodes, ) containing the number of orbitals for each atom.

  • n_supercells – Number of auxiliary supercells.

  • edge_neigh_isc – Array of shape (n_edges, ) containing the supercell index of the second atom in each edge with respect to the first atom. If not provided, all interactions are assumed to be in the unit cell.

  • threshold – Matrix elements with a value below this number are set to 0.

  • symmetrize_edges – whether for each edge only one direction is provided. The edge block for the opposite direction is then created as the transpose.

See also

nodesedges_to_scipy_coo

The original converter that has been expanded to create this one.

Formats.NUMPY -> Formats.TORCH

graph2mat.conversions.numpy_to_torch(array: ndarray) → Tensor

Formats.ORBITALCONFIGURATION -> Formats.BASISMATRIXDATA

graph2mat.conversions.orbitalconfiguration_to_basismatrixdata(config: BasisConfiguration, data_processor: MatrixDataProcessor, nsc=None) → BasisMatrixData

Creates a basis matrix data object from a configuration.

Parameters:

  • config – The configuration from which to create the basis matrix data object.

  • data_processor – The data processor that contains all the information needed to convert the configuration into the basis matrix data object. E.g. it contains the basis table.

Formats.ORBITALCONFIGURATION -> Formats.TORCH_BASISMATRIXDATA

graph2mat.conversions.orbitalconfiguration_to_torch_basismatrixdata(config: BasisConfiguration, data_processor: MatrixDataProcessor, nsc=None) → BasisMatrixData

Creates a basis matrix data object from a configuration.

Parameters:

  • config – The configuration from which to create the basis matrix data object.

  • data_processor – The data processor that contains all the information needed to convert the configuration into the basis matrix data object. E.g. it contains the basis table.

Formats.SCIPY_COO -> Formats.BASISMATRIX

graph2mat.conversions.scipy_coo_to_basismatrix(coo: coo_array, atoms: Atoms, nsc: ndarray, geometry_atoms: Atoms | None = None, matrix_cls: Type[BasisMatrix] = graph2mat.core.data.matrices.physics.orbital_matrix.OrbitalMatrix) → BasisMatrix

Conversion from ‘scipy_coo’ to ‘basismatrix’.

This function has been defined automatically.

The full path of the conversion is (original converter in square brackets):

scipy_coo -> [ scipy_csr -> basismatrix ]

Parameters:

  • atoms – The atoms object for the matrix, containing orbital information.

  • nsc – The auxiliary supercell size.

  • matrix_cls – Matrix class to initialize.

  • geometry_atoms – The atoms object for the full geometry. This allows the matrix to contain atoms without any orbital. Geometry atoms should contain the matrix atoms first and then the orbital-less atoms.

See also

scipy_csr_to_basismatrix

The original converter that has been expanded to create this one.

Formats.SCIPY_COO -> Formats.NUMPY

graph2mat.conversions.scipy_coo_to_numpy(coo: coo_array) → ndarray

Formats.SCIPY_COO -> Formats.SCIPY_CSR

graph2mat.conversions.scipy_coo_to_scipy_csr(coo: coo_array) → csr_array

Formats.SCIPY_COO -> Formats.SISL

graph2mat.conversions.scipy_coo_to_sisl(coo: coo_array, geometry: Geometry, sp_class: Type[SparseOrbital] = sisl.SparseOrbital) → SparseOrbital

Conversion from ‘scipy_coo’ to ‘sisl’.

This function has been defined automatically.

The full path of the conversion is (original converter in square brackets):

scipy_coo -> [ scipy_csr -> sisl ]

See also

scipy_csr_to_sisl

The original converter that has been expanded to create this one.

Formats.SCIPY_COO -> Formats.SISL_DM

graph2mat.conversions.scipy_coo_to_sisl_DM(coo: coo_array, geometry: Geometry, *, sp_class: Type[SparseOrbital] = sisl.physics.DensityMatrix) → SparseOrbital

Conversion from ‘scipy_coo’ to ‘sisl_DM’.

This function has been defined automatically.

The full path of the conversion is (original converter in square brackets):

scipy_coo -> [ scipy_csr -> sisl_DM ]

See also

scipy_csr_to_sisl_DM

The original converter that has been expanded to create this one.

Formats.SCIPY_COO -> Formats.SISL_EDM

graph2mat.conversions.scipy_coo_to_sisl_EDM(coo: coo_array, geometry: Geometry, *, sp_class: Type[SparseOrbital] = sisl.physics.EnergyDensityMatrix) → SparseOrbital

Conversion from ‘scipy_coo’ to ‘sisl_EDM’.

This function has been defined automatically.

The full path of the conversion is (original converter in square brackets):

scipy_coo -> [ scipy_csr -> sisl_EDM ]

See also

scipy_csr_to_sisl_EDM

The original converter that has been expanded to create this one.

Formats.SCIPY_COO -> Formats.SISL_H

graph2mat.conversions.scipy_coo_to_sisl_H(coo: coo_array, geometry: Geometry, *, sp_class: Type[SparseOrbital] = sisl.physics.Hamiltonian) → SparseOrbital

Conversion from ‘scipy_coo’ to ‘sisl_H’.

This function has been defined automatically.

The full path of the conversion is (original converter in square brackets):

scipy_coo -> [ scipy_csr -> sisl_H ]

See also

scipy_csr_to_sisl_H

The original converter that has been expanded to create this one.

Formats.SCIPY_COO -> Formats.TORCH

graph2mat.conversions.scipy_coo_to_torch(coo: coo_array) → Tensor

Conversion from ‘scipy_coo’ to ‘torch’.

This function has been defined automatically.

The full path of the conversion is (original converter in square brackets):

[ scipy_coo -> numpy ] -> torch

See also

scipy_coo_to_numpy

The original converter that has been expanded to create this one.

Formats.SCIPY_CSR -> Formats.BASISMATRIX

graph2mat.conversions.scipy_csr_to_basismatrix(spmat: SparseCSR, atoms: Atoms, nsc: ndarray, geometry_atoms: Atoms | None = None, matrix_cls: Type[BasisMatrix] = graph2mat.core.data.matrices.physics.orbital_matrix.OrbitalMatrix) → BasisMatrix

Creates a BasisMatrix object from a SparseCSR matrix

Parameters:

  • spmat – The sparse matrix to convert to a block dictionary.

  • atoms – The atoms object for the matrix, containing orbital information.

  • nsc – The auxiliary supercell size.

  • matrix_cls – Matrix class to initialize.

  • geometry_atoms – The atoms object for the full geometry. This allows the matrix to contain atoms without any orbital. Geometry atoms should contain the matrix atoms first and then the orbital-less atoms.

Formats.SCIPY_CSR -> Formats.NUMPY

graph2mat.conversions.scipy_csr_to_numpy(csr: csr_array) → ndarray

Formats.SCIPY_CSR -> Formats.SCIPY_COO

graph2mat.conversions.scipy_csr_to_scipy_coo(csr: csr_array) → coo_array

Formats.SCIPY_CSR -> Formats.SISL

graph2mat.conversions.scipy_csr_to_sisl(csr: csr_array, geometry: Geometry, sp_class: Type[SparseOrbital] = sisl.SparseOrbital) → SparseOrbital

Converts a scipy CSR array to a sisl sparse orbital matrix.

Formats.SCIPY_CSR -> Formats.SISL_DM

graph2mat.conversions.scipy_csr_to_sisl_DM(csr: csr_array, geometry: Geometry, *, sp_class: Type[SparseOrbital] = sisl.physics.DensityMatrix) → SparseOrbital

Converts a scipy CSR array to a sisl sparse orbital matrix.

Formats.SCIPY_CSR -> Formats.SISL_EDM

graph2mat.conversions.scipy_csr_to_sisl_EDM(csr: csr_array, geometry: Geometry, *, sp_class: Type[SparseOrbital] = sisl.physics.EnergyDensityMatrix) → SparseOrbital

Converts a scipy CSR array to a sisl sparse orbital matrix.

Formats.SCIPY_CSR -> Formats.SISL_H

graph2mat.conversions.scipy_csr_to_sisl_H(csr: csr_array, geometry: Geometry, *, sp_class: Type[SparseOrbital] = sisl.physics.Hamiltonian) → SparseOrbital

Converts a scipy CSR array to a sisl sparse orbital matrix.

Formats.SCIPY_CSR -> Formats.TORCH

graph2mat.conversions.scipy_csr_to_torch(csr: csr_array) → Tensor

Conversion from ‘scipy_csr’ to ‘torch’.

This function has been defined automatically.

The full path of the conversion is (original converter in square brackets):

[ scipy_csr -> numpy ] -> torch

See also

scipy_csr_to_numpy

The original converter that has been expanded to create this one.

Formats.SISL -> Formats.BASISMATRIXDATA

graph2mat.conversions.sisl_to_basismatrixdata(obj: BasisConfiguration | Geometry | SparseOrbital | str | Path, data_processor: MatrixDataProcessor, labels: bool = True, **kwargs) → BasisMatrixData

Creates a new basis matrix data object.

If obj is a configuration, the from_config method is called. Otherwise, we try to first create a configuration from the provided arguments and then call the from_config method.

Parameters:

  • obj – The object to convert into this class.

  • data_processor – If obj is not a configuration, the data processor is needed to understand how to create the basis matrix data object. In any case, the data processor is needed to convert from configuration to basis matrix data ready for models to use (e.g. because it contains the basis table).

See also

OrbitalConfiguration.new

The method called to initialize a configuration if obj is not a configuration.

from_config

The method called to initialize the basis matrix data object.

Formats.SISL -> Formats.ORBITALCONFIGURATION

graph2mat.conversions.sisl_to_orbitalconfiguration(matrix: SparseOrbital, geometry: Geometry | None = None, labels: bool = True, cls: type[OrbitalConfiguration] = graph2mat.core.data.configuration.OrbitalConfiguration, **kwargs) → OrbitalConfiguration

Initializes an OrbitalConfiguration object from a sisl matrix.

Parameters:

  • matrix – The matrix to associate to the OrbitalConfiguration. This matrix should have an associated geometry, which will be used.

  • geometry – The geometry to associate to the OrbitalConfiguration. If None, the geometry of the matrix will be used.

  • labels – Whether to process the labels from the matrix. If False, the only thing to read will be the atomic structure, which is likely the input of your model.

  • cls – Class to initialize, should be a subclass of OrbitalConfiguration.

  • **kwargs – Additional arguments to be passed to the OrbitalConfiguration constructor.

Formats.SISL -> Formats.TORCH_BASISMATRIXDATA

graph2mat.conversions.sisl_to_torch_basismatrixdata(obj: BasisConfiguration | Geometry | SparseOrbital | str | Path, data_processor: MatrixDataProcessor, labels: bool = True, **kwargs) → BasisMatrixData

Creates a new basis matrix data object.

If obj is a configuration, the from_config method is called. Otherwise, we try to first create a configuration from the provided arguments and then call the from_config method.

Parameters:

  • obj – The object to convert into this class.

  • data_processor – If obj is not a configuration, the data processor is needed to understand how to create the basis matrix data object. In any case, the data processor is needed to convert from configuration to basis matrix data ready for models to use (e.g. because it contains the basis table).

See also

OrbitalConfiguration.new

The method called to initialize a configuration if obj is not a configuration.

from_config

The method called to initialize the basis matrix data object.

Formats.SISL_GEOMETRY -> Formats.BASISMATRIXDATA

graph2mat.conversions.sisl_geometry_to_basismatrixdata(obj: BasisConfiguration | Geometry | SparseOrbital | str | Path, data_processor: MatrixDataProcessor, labels: bool = True, **kwargs) → BasisMatrixData

Creates a new basis matrix data object.

If obj is a configuration, the from_config method is called. Otherwise, we try to first create a configuration from the provided arguments and then call the from_config method.

Parameters:

  • obj – The object to convert into this class.

  • data_processor – If obj is not a configuration, the data processor is needed to understand how to create the basis matrix data object. In any case, the data processor is needed to convert from configuration to basis matrix data ready for models to use (e.g. because it contains the basis table).

See also

OrbitalConfiguration.new

The method called to initialize a configuration if obj is not a configuration.

from_config

The method called to initialize the basis matrix data object.

Formats.SISL_GEOMETRY -> Formats.ORBITALCONFIGURATION

graph2mat.conversions.sisl_geometry_to_orbitalconfiguration(geometry: Geometry, cls: type[OrbitalConfiguration] = graph2mat.core.data.configuration.OrbitalConfiguration, **kwargs) → OrbitalConfiguration

Initializes an OrbitalConfiguration object from a sisl geometry.

Note that the created object will not have an associated matrix, unless it is passed explicitly as a keyword argument.

Parameters:

  • geometry – The geometry to associate to the OrbitalConfiguration.

  • cls – Class to initialize, should be a subclass of OrbitalConfiguration.

  • **kwargs – Additional arguments to be passed to the OrbitalConfiguration constructor.

Formats.SISL_GEOMETRY -> Formats.TORCH_BASISMATRIXDATA

graph2mat.conversions.sisl_geometry_to_torch_basismatrixdata(obj: BasisConfiguration | Geometry | SparseOrbital | str | Path, data_processor: MatrixDataProcessor, labels: bool = True, **kwargs) → BasisMatrixData

Creates a new basis matrix data object.

If obj is a configuration, the from_config method is called. Otherwise, we try to first create a configuration from the provided arguments and then call the from_config method.

Parameters:

  • obj – The object to convert into this class.

  • data_processor – If obj is not a configuration, the data processor is needed to understand how to create the basis matrix data object. In any case, the data processor is needed to convert from configuration to basis matrix data ready for models to use (e.g. because it contains the basis table).

See also

OrbitalConfiguration.new

The method called to initialize a configuration if obj is not a configuration.

from_config

The method called to initialize the basis matrix data object.

Formats.SISL_SILE -> Formats.BASISMATRIXDATA

graph2mat.conversions.sisl_sile_to_basismatrixdata(obj: BasisConfiguration | Geometry | SparseOrbital | str | Path, data_processor: MatrixDataProcessor, labels: bool = True, **kwargs) → BasisMatrixData

Creates a new basis matrix data object.

If obj is a configuration, the from_config method is called. Otherwise, we try to first create a configuration from the provided arguments and then call the from_config method.

Parameters:

  • obj – The object to convert into this class.

  • data_processor – If obj is not a configuration, the data processor is needed to understand how to create the basis matrix data object. In any case, the data processor is needed to convert from configuration to basis matrix data ready for models to use (e.g. because it contains the basis table).

See also

OrbitalConfiguration.new

The method called to initialize a configuration if obj is not a configuration.

from_config

The method called to initialize the basis matrix data object.

Formats.SISL_SILE -> Formats.ORBITALCONFIGURATION

graph2mat.conversions.sisl_sile_to_orbitalconfiguration(runfilepath: str | Path, geometry_path: str | Path | None = None, out_matrix: Literal['density_matrix', 'hamiltonian', 'energy_density_matrix', 'dynamical_matrix'] | None = None, cls: type[OrbitalConfiguration] = graph2mat.core.data.configuration.OrbitalConfiguration, basis: Atoms | None = None) → OrbitalConfiguration

Initializes an OrbitalConfiguration object from the main input file of a run.

Parameters:

  • runfilepath – The path of the main input file. E.g. in SIESTA this is the path to the “.fdf” file

  • geometry_path – The path to the geometry file. If None, the geometry will be read from the run file.

  • out_matrix – The matrix to be read from the output of the run. The configuration object will contain the matrix. If it is None, then no matrices are read from the output. This is the case when trying to predict matrices, since you don’t have the output yet.

  • cls – Class to initialize, should be a subclass of OrbitalConfiguration.

  • basis – The basis to use for the configuration. If None, the basis of the read geometry will be used.

Formats.SISL_SILE -> Formats.TORCH_BASISMATRIXDATA

graph2mat.conversions.sisl_sile_to_torch_basismatrixdata(obj: BasisConfiguration | Geometry | SparseOrbital | str | Path, data_processor: MatrixDataProcessor, labels: bool = True, **kwargs) → BasisMatrixData

Creates a new basis matrix data object.

If obj is a configuration, the from_config method is called. Otherwise, we try to first create a configuration from the provided arguments and then call the from_config method.

Parameters:

  • obj – The object to convert into this class.

  • data_processor – If obj is not a configuration, the data processor is needed to understand how to create the basis matrix data object. In any case, the data processor is needed to convert from configuration to basis matrix data ready for models to use (e.g. because it contains the basis table).

See also

OrbitalConfiguration.new

The method called to initialize a configuration if obj is not a configuration.

from_config

The method called to initialize the basis matrix data object.

Formats.TORCH -> Formats.NUMPY

graph2mat.conversions.torch_to_numpy(tensor: Tensor) → ndarray

Formats.TORCH_BASISMATRIXDATA -> Formats.BASISMATRIXDATA

graph2mat.conversions.torch_basismatrixdata_to_basismatrixdata(data) → BasisMatrixData

Formats.TORCH_BASISMATRIXDATA -> Formats.NUMPY

graph2mat.conversions.torch_basismatrixdata_to_numpy(first_arg: None, threshold: float | None = None, **kwargs) → ndarray

Conversion from ‘torch_basismatrixdata’ to ‘numpy’.

This function has been defined automatically.

The full path of the conversion is (original converter in square brackets):

torch_basismatrixdata -> [ basismatrixdata -> scipy_csr ] -> numpy

See also

basismatrixdata_to_scipy_csr

The original converter that has been expanded to create this one.

Formats.TORCH_BASISMATRIXDATA -> Formats.SCIPY_COO

graph2mat.conversions.torch_basismatrixdata_to_scipy_coo(first_arg: None, threshold: float | None = None, **kwargs) → coo_array

Conversion from ‘torch_basismatrixdata’ to ‘scipy_coo’.

This function has been defined automatically.

The full path of the conversion is (original converter in square brackets):

torch_basismatrixdata -> [ basismatrixdata -> scipy_csr ] -> scipy_coo

See also

basismatrixdata_to_scipy_csr

The original converter that has been expanded to create this one.

Formats.TORCH_BASISMATRIXDATA -> Formats.SCIPY_CSR

graph2mat.conversions.torch_basismatrixdata_to_scipy_csr(first_arg: None, threshold: float | None = None, **kwargs)

Conversion from ‘torch_basismatrixdata’ to ‘scipy_csr’.

This function has been defined automatically.

The full path of the conversion is (original converter in square brackets):

torch_basismatrixdata -> [ basismatrixdata -> scipy_csr ]

See also

basismatrixdata_to_scipy_csr

The original converter that has been expanded to create this one.

Formats.TORCH_BASISMATRIXDATA -> Formats.SISL

graph2mat.conversions.torch_basismatrixdata_to_sisl(first_arg: None, threshold: float | None = None, **kwargs)

Conversion from ‘torch_basismatrixdata’ to ‘sisl’.

This function has been defined automatically.

The full path of the conversion is (original converter in square brackets):

torch_basismatrixdata -> [ basismatrixdata -> sisl ]

See also

basismatrixdata_to_sisl

The original converter that has been expanded to create this one.

Formats.TORCH_BASISMATRIXDATA -> Formats.SISL_DM

graph2mat.conversions.torch_basismatrixdata_to_sisl_DM(first_arg: None, threshold: float | None = None, **kwargs)

Conversion from ‘torch_basismatrixdata’ to ‘sisl_DM’.

This function has been defined automatically.

The full path of the conversion is (original converter in square brackets):

torch_basismatrixdata -> [ basismatrixdata -> sisl_DM ]

See also

basismatrixdata_to_sisl_DM

The original converter that has been expanded to create this one.

Formats.TORCH_BASISMATRIXDATA -> Formats.SISL_EDM

graph2mat.conversions.torch_basismatrixdata_to_sisl_EDM(first_arg: None, threshold: float | None = None, **kwargs)

Conversion from ‘torch_basismatrixdata’ to ‘sisl_EDM’.

This function has been defined automatically.

The full path of the conversion is (original converter in square brackets):

torch_basismatrixdata -> [ basismatrixdata -> sisl_EDM ]

See also

basismatrixdata_to_sisl_EDM

The original converter that has been expanded to create this one.

Formats.TORCH_BASISMATRIXDATA -> Formats.SISL_H

graph2mat.conversions.torch_basismatrixdata_to_sisl_H(first_arg: None, threshold: float | None = None, **kwargs)

Conversion from ‘torch_basismatrixdata’ to ‘sisl_H’.

This function has been defined automatically.

The full path of the conversion is (original converter in square brackets):

torch_basismatrixdata -> [ basismatrixdata -> sisl_H ]

See also

basismatrixdata_to_sisl_H

The original converter that has been expanded to create this one.

Formats.TORCH_BASISMATRIXDATA -> Formats.TORCH

graph2mat.conversions.torch_basismatrixdata_to_torch(first_arg: None, threshold: float | None = None, **kwargs) → Tensor

Conversion from ‘torch_basismatrixdata’ to ‘torch’.

This function has been defined automatically.

The full path of the conversion is (original converter in square brackets):

torch_basismatrixdata -> [ basismatrixdata -> scipy_csr ] -> torch

See also

basismatrixdata_to_scipy_csr

The original converter that has been expanded to create this one.

Formats.TORCH_BASISMATRIXDATA -> Formats.TORCH_COO

graph2mat.conversions.torch_basismatrixdata_to_torch_coo(data, threshold: float | None = None, **kwargs)

Formats.TORCH_BASISMATRIXDATA -> Formats.TORCH_CSR

graph2mat.conversions.torch_basismatrixdata_to_torch_csr(data, threshold: float | None = None, **kwargs) → sparse_csr_tensor

Conversion from ‘torch_basismatrixdata’ to ‘torch_csr’.

This function has been defined automatically.

The full path of the conversion is (original converter in square brackets):

[ torch_basismatrixdata -> torch_coo ] -> torch_csr

See also

torch_basismatrixdata_to_torch_coo

The original converter that has been expanded to create this one.

Formats.TORCH_COO -> Formats.NUMPY

graph2mat.conversions.torch_coo_to_numpy(coo: sparse_coo_tensor) → ndarray

Conversion from ‘torch_coo’ to ‘numpy’.

This function has been defined automatically.

The full path of the conversion is (original converter in square brackets):

[ torch_coo -> torch ] -> numpy

See also

torch_coo_to_torch

The original converter that has been expanded to create this one.

Formats.TORCH_COO -> Formats.TORCH

graph2mat.conversions.torch_coo_to_torch(coo: sparse_coo_tensor) → Tensor

Formats.TORCH_COO -> Formats.TORCH_CSR

graph2mat.conversions.torch_coo_to_torch_csr(coo: sparse_coo_tensor) → sparse_csr_tensor

Formats.TORCH_CSR -> Formats.NUMPY

graph2mat.conversions.torch_csr_to_numpy(csr: sparse_csr_tensor) → ndarray

Conversion from ‘torch_csr’ to ‘numpy’.

This function has been defined automatically.

The full path of the conversion is (original converter in square brackets):

[ torch_csr -> torch ] -> numpy

See also

torch_csr_to_torch

The original converter that has been expanded to create this one.

Formats.TORCH_CSR -> Formats.TORCH

graph2mat.conversions.torch_csr_to_torch(csr: sparse_csr_tensor) → Tensor

Formats.TORCH_CSR -> Formats.TORCH_COO

graph2mat.conversions.torch_csr_to_torch_coo(csr: sparse_csr_tensor) → sparse_coo_tensor

Formats.TORCH_NODESEDGES -> Formats.TORCH

graph2mat.conversions.torch_nodesedges_to_torch(node_vals: Tensor, edge_vals: Tensor, edge_index: Tensor, orbitals: Tensor, n_supercells: int = 1, edge_neigh_isc: Tensor | None = None, threshold: float | None = None, symmetrize_edges: bool = False) → Tensor

Conversion from ‘torch_nodesedges’ to ‘torch’.

This function has been defined automatically.

The full path of the conversion is (original converter in square brackets):

[ torch_nodesedges -> torch_coo ] -> torch

Parameters:

  • node_vals – Flat array containing the values of the node blocks. The order of the values is first by node index, then row then column.

  • edge_vals – Flat array containing the values of the edge blocks. The order of the values is first by edge index, then row then column.

  • edge_index – Array of shape (2, n_edges) containing the indices of the atoms that participate in each edge.

  • orbitals – Array of shape (n_nodes, ) containing the number of orbitals for each atom.

  • n_supercells – Number of auxiliary supercells.

  • edge_neigh_isc – Array of shape (n_edges, ) containing the supercell index of the second atom in each edge with respect to the first atom. If not provided, all interactions are assumed to be in the unit cell.

  • threshold – Matrix elements with a value below this number are set to 0.

  • symmetrize_edges – whether for each edge only one direction is provided. The edge block for the opposite direction is then created as the transpose.

See also

torch_nodesedges_to_torch_coo

The original converter that has been expanded to create this one.

Formats.TORCH_NODESEDGES -> Formats.TORCH_COO

graph2mat.conversions.torch_nodesedges_to_torch_coo(node_vals: Tensor, edge_vals: Tensor, edge_index: Tensor, orbitals: Tensor, n_supercells: int = 1, edge_neigh_isc: Tensor | None = None, threshold: float | None = None, symmetrize_edges: bool = False) → sparse_coo_tensor

Converts an orbital matrix from node and edges array to torch coo.

Conversions to any other sparse structure can be done once we’ve got the coo array.

Parameters:

  • node_vals – Flat array containing the values of the node blocks. The order of the values is first by node index, then row then column.

  • edge_vals – Flat array containing the values of the edge blocks. The order of the values is first by edge index, then row then column.

  • edge_index – Array of shape (2, n_edges) containing the indices of the atoms that participate in each edge.

  • orbitals – Array of shape (n_nodes, ) containing the number of orbitals for each atom.

  • n_supercells – Number of auxiliary supercells.

  • edge_neigh_isc – Array of shape (n_edges, ) containing the supercell index of the second atom in each edge with respect to the first atom. If not provided, all interactions are assumed to be in the unit cell.

  • threshold – Matrix elements with a value below this number are set to 0.

  • symmetrize_edges – whether for each edge only one direction is provided. The edge block for the opposite direction is then created as the transpose.

Formats.TORCH_NODESEDGES -> Formats.TORCH_CSR

graph2mat.conversions.torch_nodesedges_to_torch_csr(node_vals: Tensor, edge_vals: Tensor, edge_index: Tensor, orbitals: Tensor, n_supercells: int = 1, edge_neigh_isc: Tensor | None = None, threshold: float | None = None, symmetrize_edges: bool = False) → sparse_csr_tensor

Conversion from ‘torch_nodesedges’ to ‘torch_csr’.

This function has been defined automatically.

The full path of the conversion is (original converter in square brackets):

[ torch_nodesedges -> torch_coo ] -> torch_csr

Parameters:

  • node_vals – Flat array containing the values of the node blocks. The order of the values is first by node index, then row then column.

  • edge_vals – Flat array containing the values of the edge blocks. The order of the values is first by edge index, then row then column.

  • edge_index – Array of shape (2, n_edges) containing the indices of the atoms that participate in each edge.

  • orbitals – Array of shape (n_nodes, ) containing the number of orbitals for each atom.

  • n_supercells – Number of auxiliary supercells.

  • edge_neigh_isc – Array of shape (n_edges, ) containing the supercell index of the second atom in each edge with respect to the first atom. If not provided, all interactions are assumed to be in the unit cell.

  • threshold – Matrix elements with a value below this number are set to 0.

  • symmetrize_edges – whether for each edge only one direction is provided. The edge block for the opposite direction is then created as the transpose.

See also

torch_nodesedges_to_torch_coo

The original converter that has been expanded to create this one.