SparseAtom¶

class sisl.SparseAtom(geometry, dim=1, dtype=None, nnzpr=None, **kwargs)[source]¶

Sparse object with number of rows equal to the total number of atoms in the Geometry

Attributes

`dim`	Number of components per element
`dkind`	Data type of sparse elements (in str)
`dtype`	Data type of sparse elements
`finalized`	Whether the contained data is finalized and non-used elements have been removed
`geom`	Associated geometry
`geometry`	Associated geometry
`nnz`	Number of non-zero elements
`shape`	Shape of sparse matrix

Methods

`Rij`([dtype])	Create a sparse matrix with the vectors between atoms
`__init__`(geometry[, dim, dtype, nnzpr])	Create sparse object with element between orbitals
`construct`(func[, na_iR, method, eta])	Automatically construct the sparse model based on a function that does the setting up of the elements
`copy`([dtype])	A copy of this object
`create_construct`(R, param)	Create a simple function for passing to the `construct` function.
`cut`(seps, axis, args, *kwargs)	Cuts the sparse atom model into different parts.
`edges`(atom[, exclude])	Retrieve edges (connections) of a given `atom` or list of `atom`’s
`eliminate_zeros`([atol])	Removes all zero elements from the sparse matrix
`empty`([keep_nnz])	See `empty` for details
`finalize`()	Finalizes the model
`fromsp`(geom, *sp)	Returns a sparse model from a preset Geometry and a list of sparse matrices
`iter_nnz`([atom])	Iterations of the non-zero elements
`make_hermitian`()	Ensures the matrix is Hermitian by doing an in-place symmetrization
`nonzero`([atom, only_col])	Indices row and column indices where non-zero elements exists
`remove`(atom)	Create a subset of this sparse matrix by removing the atoms corresponding to `atom`
`repeat`(reps, axis)	Create a repeated sparse atom object, equivalent to `Geometry.repeat`
`reset`([dim, dtype, nnzpr])	The sparsity pattern has all elements removed and everything is reset.
`rij`([dtype])	Create a sparse matrix with the distance between atoms
`set_nsc`(args, *kwargs)	Reset the number of allowed supercells in the sparse atom
`spalign`(other)	See `align` for details
`spsame`(other)	Compare two sparse objects and check whether they have the same entries.
`sub`(atom)	Create a subset of this sparse matrix by only retaining the elements corresponding to the `atom`
`swap`(a, b)	Swaps atoms in the sparse geometry to obtain a new order of atoms
`tile`(reps, axis)	Create a tiled sparse atom object, equivalent to `Geometry.tile`
`tocsr`(index[, isc])	Return a `scipy.sparse.csr_matrix` of the specified index

Rij(dtype=<class 'numpy.float64'>)[source]¶

Create a sparse matrix with the vectors between atoms

Parameters:	dtype : numpy.dtype, optional the data-type of the sparse matrix.

Notes

The returned sparse matrix with vectors are taken from the current sparse pattern. I.e. a subsequent addition of sparse elements will make them inequivalent. It is thus important to only create the sparse vector matrix when the sparse structure is completed.

construct(func, na_iR=1000, method='rand', eta=False)¶

Automatically construct the sparse model based on a function that does the setting up of the elements

This may be called in two variants.

Pass a function (func), see e.g. create_construct which does the setting up.
Pass a tuple/list in func which consists of two elements, one is R the radii parameters for the corresponding parameters. The second is the parameters corresponding to the R[i] elements. In this second case all atoms must only have one orbital.

Parameters:

func: callable or array_like

this function must take 4 arguments. 1. Is this object (self) 2. Is the currently examined atom (ia) 3. Is the currently bounded indices (idxs) 4. Is the currently bounded indices atomic coordinates (idxs_xyz) An example func could be:

>>> def func(self, ia, idxs, idxs_xyz=None): 
...     idx = self.geometry.close(ia, R=[0.1, 1.44], idx=idxs, idx_xyz=idxs_xyz) 
...     self[ia, idx[0]] = 0 
...     self[ia, idx[1]] = -2.7 

na_iR : int, optional

number of atoms within the sphere for speeding up the iter_block loop.

method : {‘rand’, str}

method used in Geometry.iter_block, see there for details

eta: bool, optional

whether an ETA will be printed

See also

create_construct: a generic function used to create a generic function which this routine requires
tile: tiling after construct is much faster for very large systems
repeat: repeating after construct is much faster for very large systems

copy(dtype=None)¶

A copy of this object

Parameters:	dtype : numpy.dtype, optional it is possible to convert the data to a different data-type If not specified, it will use `self.dtype`

create_construct(R, param)¶

Create a simple function for passing to the construct function.

This is simply to leviate the creation of simplistic functions needed for setting up the sparse elements.

Basically this returns a function:

>>> def func(self, ia, idxs, idxs_xyz=None): 
...     idx = self.geometry.close(ia, R=R, idx=idxs) 
...     for ix, p in zip(idx, param): 
...         self[ia, ix] = p 

Parameters:	R : array_like radii parameters for different shells. Must have same length as `param` or one less. If one less it will be extended with `R[0]/100` param : array_like coupling constants corresponding to the `R` ranges. `param[0,:]` are the elements for the all atoms within `R[0]` of each atom.

See also

construct: routine to create the sparse matrix from a generic function (as returned from create_construct)

Notes

This function only works for geometry sparse matrices (i.e. one element per atom). If you have more than one element per atom you have to implement the function your-self.

cut(seps, axis, *args, **kwargs)[source]¶

Cuts the sparse atom model into different parts.

Recreates a new sparse atom object with only the cutted atoms in the structure.

Cutting is the opposite of tiling.

Parameters:	seps : int number of times the structure will be cut axis : int the axis that will be cut

dim¶: Number of components per element

dkind¶: Data type of sparse elements (in str)

dtype¶: Data type of sparse elements

edges(atom, exclude=None)¶

Retrieve edges (connections) of a given atom or list of atom’s

The returned edges are unique and sorted (see numpy.unique) and are returned in supercell indices (i.e. 0 <= edge < self.geometry.na_s).

Parameters:	atom : int or list of int the edges are returned only for the given atom exclude : int or list of int, optional remove edges which are in the exclude list. Default to `atom`.

See also

SparseCSR.edges: the underlying routine used for extracting the edges

eliminate_zeros(atol=0.0)¶

Removes all zero elements from the sparse matrix

This is an in-place operation.

Parameters:	atol : float, optional absolute tolerance below this value will be considered 0.

empty(keep_nnz=False)¶: See empty for details

finalize()¶

Finalizes the model

Finalizes the model so that all non-used elements are removed. I.e. this simply reduces the memory requirement for the sparse matrix.

Note that adding more elements to the sparse matrix is more time-consuming than for a non-finalized sparse matrix due to the internal data-representation.

finalized¶: Whether the contained data is finalized and non-used elements have been removed

classmethod fromsp(geom, *sp)¶: Returns a sparse model from a preset Geometry and a list of sparse matrices

geom¶: Associated geometry

geometry¶: Associated geometry

iter_nnz(atom=None)[source]¶

Iterations of the non-zero elements

An iterator on the sparse matrix with, row and column

Parameters:	atom : int or array_like only loop on the non-zero elements coinciding with the atoms

Examples

>>> for i, j in self.iter_nnz(): 
...    self[i, j] # is then the non-zero value 

make_hermitian()¶: Ensures the matrix is Hermitian by doing an in-place symmetrization

nnz¶: Number of non-zero elements

nonzero(atom=None, only_col=False)[source]¶

Indices row and column indices where non-zero elements exists

Parameters:	atom : int or array_like of int, optional only return the tuples for the requested atoms, default is all atoms only_col : bool, optional only return then non-zero columns

See also

SparseCSR.nonzero: the equivalent function call

remove(atom)¶

Create a subset of this sparse matrix by removing the atoms corresponding to atom

Negative indices are wrapped and thus works.

Parameters:	atom : array_like of int indices of removed atoms

See also

Geometry.remove: equivalent to the resulting Geometry from this routine
Geometry.sub: the negative of Geometry.remove
sub: the opposite of remove, i.e. retain a subset of atoms

repeat(reps, axis)[source]¶

Create a repeated sparse atom object, equivalent to Geometry.repeat

The already existing sparse elements are extrapolated to the new supercell by repeating them in blocks like the coordinates.

Parameters:	reps : int number of repetitions along cell-vector axis axis : int 0, 1, 2 according to the cell-direction

See also

Geometry.repeat: the same ordering as the final geometry
Geometry.tile: a different ordering of the final geometry
tile: a different ordering of the final geometry

reset(dim=None, dtype=<class 'numpy.float64'>, nnzpr=None)¶

The sparsity pattern has all elements removed and everything is reset.

The object will be the same as if it had been initialized with the same geometry as it were created with.

Parameters:	dim: int, optional number of dimensions per element, default to the current number of elements per matrix element. dtype: numpy.dtype, optional the datatype of the sparse elements nnzpr: int, optional number of non-zero elements per row

rij(dtype=<class 'numpy.float64'>)[source]¶

Create a sparse matrix with the distance between atoms

Parameters:	dtype : numpy.dtype, optional the data-type of the sparse matrix.

Notes

The returned sparse matrix with distances are taken from the current sparse pattern. I.e. a subsequent addition of sparse elements will make them inequivalent. It is thus important to only create the sparse distance when the sparse structure is completed.

set_nsc(*args, **kwargs)[source]¶

Reset the number of allowed supercells in the sparse atom

If one reduces the number of supercells any sparse element that references the supercell will be deleted.

See SuperCell.set_nsc for allowed parameters.

See also

SuperCell.set_nsc: the underlying called method

shape¶: Shape of sparse matrix

spalign(other)¶: See align for details

spsame(other)¶

Compare two sparse objects and check whether they have the same entries.

This does not necessarily mean that the elements are the same

sub(atom)[source]¶

Create a subset of this sparse matrix by only retaining the elements corresponding to the atom

Indices passed MUST be unique.

Negative indices are wrapped and thus works.

Parameters:	atom : array_like of int indices of retained atoms

See also

Geometry.remove: the negative of Geometry.sub
Geometry.sub: equivalent to the resulting Geometry from this routine
remove: the negative of sub, i.e. remove a subset of atoms

swap(a, b)¶

Swaps atoms in the sparse geometry to obtain a new order of atoms

This can be used to reorder elements of a geometry.

Parameters:	a : array_like the first list of atomic coordinates b : array_like the second list of atomic coordinates

tile(reps, axis)[source]¶

Create a tiled sparse atom object, equivalent to Geometry.tile

The already existing sparse elements are extrapolated to the new supercell by repeating them in blocks like the coordinates.

Parameters:	reps : int number of repetitions along cell-vector axis axis : int 0, 1, 2 according to the cell-direction

See also

Geometry.tile: the same ordering as the final geometry
Geometry.repeat: a different ordering of the final geometry
repeat: a different ordering of the final geometry

Notes

Calling this routine will automatically finalize the SparseAtom. This is required to greatly increase performance.

tocsr(index, isc=None, **kwargs)¶

Return a scipy.sparse.csr_matrix of the specified index

Parameters:	index : int the index in the sparse matrix (for non-orthogonal cases the last dimension is the overlap matrix) isc : int, optional the supercell index, or all (if `isc=None`)