# This Source Code Form is subject to the terms of the Mozilla Public
# License, v. 2.0. If a copy of the MPL was not distributed with this
# file, You can obtain one at https://mozilla.org/MPL/2.0/.
from __future__ import annotations
from collections import deque, namedtuple
from itertools import product
from numbers import Integral
from typing import Optional
import numpy as np
from sisl.physics.brillouinzone import MonkhorstPack
try:
from . import _siesta
has_fortran_module = True
except ImportError as _e:
has_fortran_module = False
import sisl._array as _a
from sisl import (
Atom,
AtomicOrbital,
Atoms,
AtomUnknown,
Geometry,
Grid,
Lattice,
SparseCSR,
)
from sisl._core.sparse import _ncol_to_indptr
from sisl._internal import set_module
from sisl.messages import SislError, deprecate_argument, info, warn
from sisl.physics import BrillouinZone, DensityMatrix, EnergyDensityMatrix, Hamiltonian
from sisl.physics.electron import EigenstateElectron
from sisl.physics.overlap import Overlap
from sisl.physics.sparse import SparseOrbitalBZ
from sisl.unit.siesta import unit_convert
from .._help import grid_reduce_indices
from ..sile import MissingFermiLevelWarning, SileError, SileWarning, add_sile
from ._help import *
from .sile import SileBinSiesta
__all__ = ["tshsSileSiesta", "onlysSileSiesta", "tsdeSileSiesta"]
__all__ += ["hsxSileSiesta", "dmSileSiesta"]
__all__ += ["wfsxSileSiesta"]
__all__ += ["gridSileSiesta"]
__all__ += ["tsgfSileSiesta"]
_Bohr2Ang = unit_convert("Bohr", "Ang")
_Ry2eV = unit_convert("Ry", "eV")
_eV2Ry = unit_convert("eV", "Ry")
def _toF(array, dtype, scale=None):
if scale is None:
# we won't force the fortran order, f2py,
# will handle this anyways. So we might as
# well let that machinery deal with the
# details.
return array.astype(dtype, copy=False)
# As we are writing to Fortran, we might as well
# do the proper copy to F-contiguous.
# This will save a 2nd copy!
out = np.empty_like(array, dtype, order="F")
np.multiply(array, scale, out=out)
return out
def _geometry_align(geom_b, geom_u, cls, method):
"""Routine used to align two geometries
There are a few twists in this since the fdf-reads will automatically
try and pass a geometry from the output files.
In cases where the *.ion* files are non-existing this will
result in a twist.
This routine will select and return a merged Geometry which
fulfills the correct number of atoms and orbitals.
However, if the input geometries have mis-matching number
of atoms a SislError will be raised.
Parameters
----------
geom_b : Geometry from binary file
geom_u : Geometry supplied by user
Raises
------
SislError
if the geometries have non-equal atom count
"""
if geom_b is None:
return geom_u
elif geom_u is None:
return geom_b
# Default to use the users geometry
geom = geom_u
is_copy = False
def get_copy(geom, is_copy):
if is_copy:
return geom, True
return geom.copy(), True
if geom_b.na != geom.na:
# we have no way of solving this issue...
raise SileError(
f"{cls.__name__}.{method} could not use the passed geometry as the "
f"of atoms is not consistent, user-atoms={geom_u.na}, file-atoms={geom_b.na}."
)
# Try and figure out what to do
if not np.allclose(geom_b.xyz, geom.xyz):
warn(
f"{cls.__name__}.{method} has mismatched atomic coordinates, will copy geometry and use file XYZ."
)
geom, is_copy = get_copy(geom, is_copy)
geom.xyz[:, :] = geom_b.xyz[:, :]
if not np.allclose(geom_b.lattice.cell, geom.lattice.cell):
warn(
f"{cls.__name__}.{method} has non-equal lattice vectors, will copy geometry and use file lattice."
)
geom, is_copy = get_copy(geom, is_copy)
geom.lattice.cell[:, :] = geom_b.lattice.cell[:, :]
if not np.array_equal(geom_b.nsc, geom.nsc):
warn(
f"{cls.__name__}.{method} has non-equal number of supercells, will copy geometry and use file supercell count."
)
geom, is_copy = get_copy(geom, is_copy)
geom.set_nsc(geom_b.nsc)
# Now for the difficult part.
# If there is a mismatch in the number of orbitals we will
# prefer to use the user-supplied atomic species, but fill with
# *random* orbitals
if not np.array_equal(geom_b.atoms.orbitals, geom.atoms.orbitals):
warn(
f"{cls.__name__}.{method} has non-equal number of orbitals per atom, will correct with *empty* orbitals."
)
geom, is_copy = get_copy(geom, is_copy)
# Now create a new atom specie with the correct number of orbitals
norbs = geom_b.atoms.orbitals[:]
atoms = Atoms(
[geom.atoms[i].copy(orbitals=[-1.0] * norbs[i]) for i in range(geom.na)]
)
geom._atoms = atoms
return geom
def _add_overlap(M, S, str_method):
"""Adds the overlap matrix to the matrix `M`
Handles different cases of `S`
"""
if S is None:
return
fail = True
if isinstance(S, np.ndarray):
if len(S) == len(M._csr._D):
fail = False
M._csr._D[:, M.S_idx] = S[:]
elif M.spsame(S):
if isinstance(S, Overlap):
fail = False
M._csr._D[:, M.S_idx] = S._csr._D[:, 0]
elif isinstance(S, SparseOrbitalBZ):
if not S.orthogonal:
fail = False
M._csr._D[:, M.S_idx] = S._csr._D[:, S.S_idx]
if fail:
raise NotImplementedError(
f"{str_method} could not paste overlap matrix into the "
"matrix due to non-conforming sparse elements."
)
@set_module("sisl.io.siesta")
class onlysSileSiesta(SileBinSiesta):
"""Geometry and overlap matrix"""
@property
def version(self) -> int:
"""The version of the file"""
return _siesta.read_tshs_version(self.file)
[docs]
def read_lattice(self) -> Lattice:
"""Returns a Lattice object from a TranSiesta file"""
n_s = _siesta.read_tshs_sizes(self.file)[3]
self._fortran_check("read_lattice", "could not read sizes.")
arr = _siesta.read_tshs_cell(self.file, n_s)
self._fortran_check("read_lattice", "could not read cell.")
nsc = np.array(arr[0], np.int32)
# We have to transpose since the data is read *as-is*
# The cell in fortran files are (:, A1)
# after reading this is still obeyed (regardless of order)
# So we transpose to get it C-like
# Note that care must be taken for the different data-structures
# In particular not all data needs to be transposed (sparse H and S)
cell = arr[1].T * _Bohr2Ang
return Lattice(cell, nsc=nsc)
[docs]
@deprecate_argument(
"geometry",
"atoms",
"use atoms=geometry.atoms instead of geometry=",
"0.15",
"0.17",
)
def read_geometry(self, atoms: Optional[Union[Atoms, Geometry]] = None):
"""Returns Geometry object from a TranSiesta file"""
# Read supercell
lattice = self.read_lattice()
na = _siesta.read_tshs_sizes(self.file)[1]
self._fortran_check("read_geometry", "could not read sizes.")
arr = _siesta.read_tshs_geom(self.file, na)
self._fortran_check("read_geometry", "could not read geometry.")
# see onlysSileSiesta.read_lattice for .T
xyz = arr[0].T * _Bohr2Ang
lasto = arr[1]
# Since the TSHS file does not contain species information
# and/or other stuff we *can* reuse an existing
# geometry which contains the correct atomic numbers etc.
orbs = np.diff(lasto)
if isinstance(atoms, Geometry):
atoms = atoms.atoms
if isinstance(atoms, Atoms):
if len(atoms) != len(orbs):
raise ValueError(
f"{self.__class__.__name__}.read_geometry "
"got a basis set (Atoms object) with incorrect "
"number of atoms."
)
if atoms is None:
# Create all different atoms...
# The TSHS file does not contain the
# atomic numbers, so we will just
# create them individually
# Get unique orbitals
uorb = np.unique(orbs)
# Create atoms
atoms = []
for Z, orb in enumerate(uorb):
atoms.append(Atom(Z + 1, [-1] * orb))
def get_atom(atoms, orbs):
for atom in atoms:
if atom.no == orbs:
return atom
atom = []
for orb in orbs:
atom.append(get_atom(atoms, orb))
else:
# Create a new geometry with the correct atomic numbers
atom = []
for a, no in zip(atoms, orbs):
if a.no == no:
atom.append(a)
else:
# correct atom
atom.append(
a.__class__(
a.Z, [-1.0 for io in range(no)], mass=a.mass, tag=a.tag
)
)
# Create and return geometry object
return Geometry(xyz, atom, lattice=lattice)
[docs]
def read_overlap(self, **kwargs) -> Overlap:
"""Returns the overlap matrix from the TranSiesta file"""
tshs_g = self.read_geometry()
geom = _geometry_align(
tshs_g, kwargs.get("geometry", tshs_g), self.__class__, "read_overlap"
)
# read the sizes used...
sizes = _siesta.read_tshs_sizes(self.file)
self._fortran_check("read_overlap", "could not read sizes.")
# see onlysSileSiesta.read_lattice for .T
isc = _siesta.read_tshs_cell(self.file, sizes[3])[2].T
self._fortran_check("read_overlap", "could not read cell.")
no = sizes[2]
nnz = sizes[4]
ncol, col, dS = _siesta.read_tshs_s(self.file, no, nnz)
self._fortran_check("read_overlap", "could not read overlap matrix.")
# Create the Hamiltonian container
S = Overlap(geom, nnzpr=1)
# Create the new sparse matrix
S._csr.ncol = ncol.astype(np.int32, copy=False)
S._csr.ptr = _ncol_to_indptr(ncol)
# Correct fortran indices
S._csr.col = col.astype(np.int32, copy=False) - 1
S._csr._nnz = len(col)
S._csr._D = _a.emptyd([nnz, 1])
S._csr._D[:, 0] = dS[:]
# Convert to sisl supercell
# equivalent as _csr_from_siesta with explicit isc from file
_csr_from_sc_off(S.geometry, isc, S._csr)
# In siesta the matrix layout is written in CSC format
# due to fortran indexing, this means that we need to transpose
# to get it to correct layout.
return S.transpose(sort=kwargs.get("sort", True))
[docs]
def read_fermi_level(self) -> float:
r"""Query the Fermi-level contained in the file
Returns
-------
float
fermi-level of the system
"""
Ef = _siesta.read_tshs_ef(self.file) * _Ry2eV
self._fortran_check("read_fermi_level", "could not read fermi-level.")
return Ef
[docs]
def read_brillouinzone(self, trs: bool = True) -> MonkhorstPack:
"""Read the Brillouin zone object"""
kcell, kdispl = _siesta.read_tshs_k(self.file)
self._fortran_check("read_brillouinzone", "could not read the file.")
geom = self.read_geometry()
return MonkhorstPack(geom, kcell, displacement=kdispl, trs=trs)
@set_module("sisl.io.siesta")
class tshsSileSiesta(onlysSileSiesta):
"""Geometry, Hamiltonian and overlap matrix file"""
[docs]
def read_hamiltonian(self, geometry=None, **kwargs) -> Hamiltonian:
"""Electronic structure from the siesta.TSHS file
The TSHS file format does *not* contain exact orbital information.
When reading the Hamiltonian directly using this class one will find
wrong orbital information. In such cases it may be beneficial to pass
the `geometry` argument to override the contained geometry.
The orbital order is unaltered but the atomic and orbital information
will be somewhat arbitrary.
Parameters
----------
geometry : Geometry, optional
override the contained geometry in the returned Hamiltonian. Useful
when reading files directly using this class.
Examples
--------
Reading the Hamiltonian using the `tshsSileSiesta` backend through
the fdf sile. Since the fdf file will try and read the basis sets
using other files than the TSHS.
>>> H = sisl.get_sile("RUN.fdf").read_hamiltonian(order="TSHS")
An equivalent, but different way would be:
>>> geom = sisl.get_sile("RUN.fdf").read_geometry()
>>> H = sisl.get_sile("siesta.TSHS").read_hamiltonian(geometry=geom)
"""
tshs_g = self.read_geometry()
if geometry is None:
geometry = tshs_g
geom = _geometry_align(tshs_g, geometry, self.__class__, "read_hamiltonian")
# read the sizes used...
sizes = _siesta.read_tshs_sizes(self.file)
self._fortran_check("read_hamiltonian", "could not read sizes.")
# see onlysSileSiesta.read_lattice for .T
isc = _siesta.read_tshs_cell(self.file, sizes[3])[2].T
self._fortran_check("read_hamiltonian", "could not read cell.")
spin = sizes[0]
no = sizes[2]
nnz = sizes[4]
ncol, col, dH, dS = _siesta.read_tshs_hs(self.file, spin, no, nnz)
self._fortran_check(
"read_hamiltonian", "could not read Hamiltonian and overlap matrix."
)
# Check whether it is an orthogonal basis set
# TODO, this is not an exhaustive test, but is *fine* for most
# cases
orthogonal = np.abs(dS).sum() == geom.no
# Create the Hamiltonian container
H = Hamiltonian(geom, spin, nnzpr=1, orthogonal=orthogonal)
# Create the new sparse matrix
H._csr.ncol = ncol.astype(np.int32, copy=False)
H._csr.ptr = _ncol_to_indptr(ncol)
# Correct fortran indices
H._csr.col = col.astype(np.int32, copy=False) - 1
H._csr._nnz = len(col)
if orthogonal:
self._log("read orthogonal hamiltonian")
H._csr._D = _a.emptyd([nnz, spin])
H._csr._D[:, :] = dH[:, :] * _Ry2eV
else:
self._log("read non-orthogonal hamiltonian")
H._csr._D = _a.emptyd([nnz, spin + 1])
H._csr._D[:, :spin] = dH[:, :] * _Ry2eV
H._csr._D[:, spin] = dS[:]
_mat_siesta2sisl(H)
H = H.astype(dtype=kwargs.get("dtype"), copy=False)
# Convert to sisl supercell
# equivalent as _csr_from_siesta with explicit isc from file
_csr_from_sc_off(H.geometry, isc, H._csr)
# Find all indices where dS == 1 (remember col is in fortran indices)
idx = col[np.isclose(dS, 1.0).nonzero()[0]]
if np.any(idx > no):
print(f"Number of orbitals: {no}")
print(idx)
raise SileError(
f"{self!r}.read_hamiltonian could not assert "
"the supercell connections in the primary unit-cell."
)
# see onlysSileSiesta.read_overlap for .transpose()
# For H, DM and EDM we also need to Hermitian conjugate it.
return H.transpose(spin=False, sort=kwargs.get("sort", True))
[docs]
def write_hamiltonian(self, H, **kwargs):
"""Writes the Hamiltonian to a siesta.TSHS file"""
# we sort below, so no need to do it here
# see onlysSileSiesta.read_overlap for .transpose()
H = H.transpose(spin=False, sort=False)
if H._csr.nnz == 0:
raise SileError(
f"{self!r}.write_hamiltonian cannot write "
"a zero element sparse matrix!"
)
sort = kwargs.get("sort", True)
# Convert to siesta CSR
_csr_to_siesta(H.geometry, H._csr, diag=True)
H.finalize(sort=sort)
H = H.astype(dtype=np.float64, copy=False)
_mat_sisl2siesta(H)
csr = H._csr
# Extract the data to pass to the fortran routine
cell = H.geometry.cell
xyz = H.geometry.xyz
# Get H and S
if H.orthogonal:
self._log("writing orthogonal hamiltonian")
s = csr.copy(dims=0)
s.empty(keep_nnz=True)
s += s.diags(1)
missing_diags = s.nnz - csr.nnz
if missing_diags:
# This should never happen as _csr_to_siesta should ensure
# that the diagonal entries always exists.
# Hence it gets changed before writing.
# Not compeletely optimal, but for now this is OK
raise SislError(
f"{self.__class__.__name__}.write_hamiltonian "
"The diagonal elements of your orthogonal Hamiltonian "
f"have not been defined. Got {len(csr) - missing_diags} elements, expected {len(csr)}."
)
h = csr._D
s = s._D
else:
self._log("writing non-orthogonal hamiltonian")
h = csr._D[:, : H.S_idx]
s = csr._D[:, H.S_idx]
# Get shorter variants
nsc = H.geometry.nsc[:].astype(np.int32)
isc = _siesta.siesta_sc_off(*nsc)
# see onlysSileSiesta.read_lattice for .T
_siesta.write_tshs_hs(
self.file,
nsc[0],
nsc[1],
nsc[2],
_toF(cell.T, np.float64, 1 / _Bohr2Ang),
_toF(xyz.T, np.float64, 1 / _Bohr2Ang),
H.geometry.firsto,
csr.ncol,
csr.col + 1,
_toF(h, np.float64, _eV2Ry),
_toF(s, np.float64),
isc,
)
self._fortran_check(
"write_hamiltonian", "could not write Hamiltonian and overlap matrix."
)
@set_module("sisl.io.siesta")
class dmSileSiesta(SileBinSiesta):
"""Density matrix file"""
[docs]
def read_density_matrix(self, **kwargs) -> DensityMatrix:
"""Returns the density matrix from the siesta.DM file
Parameters
----------
geometry : Geometry, optional
attach a geometry object to the sparse matrix
overlap : SparseMatrix, optional
attach the overlap matrix to the sparse matrix
"""
# Now read the sizes used...
spin, no, nsc, nnz = _siesta.read_dm_sizes(self.file)
self._fortran_check(
"read_density_matrix", "could not read density matrix sizes."
)
ncol, col, dDM = _siesta.read_dm(self.file, spin, no, nsc, nnz)
self._fortran_check("read_density_matrix", "could not read density matrix.")
# Try and immediately attach a geometry
geom = kwargs.get("geometry", kwargs.get("geom", None))
if geom is None:
# We truly, have no clue,
# Just generate a boxed system
xyz = [[x, 0, 0] for x in range(no)]
lattice = Lattice([no, 1, 1], nsc=nsc)
geom = Geometry(xyz, Atom(1), lattice=lattice)
if nsc[0] != 0 and np.any(geom.nsc != nsc):
# We have to update the number of supercells!
geom.set_nsc(nsc)
if geom.no != no:
raise SileError(
f"{self!r}.read_density_matrix could not use the "
"passed geometry as the number of atoms or orbitals is "
"inconsistent with DM file."
)
# Create the density matrix container
DM = DensityMatrix(geom, spin, nnzpr=1, dtype=np.float64, orthogonal=False)
# Create the new sparse matrix
DM._csr.ncol = ncol.astype(np.int32, copy=False)
DM._csr.ptr = _ncol_to_indptr(ncol)
# Correct fortran indices
DM._csr.col = col.astype(np.int32, copy=False) - 1
DM._csr._nnz = len(col)
DM._csr._D = _a.emptyd([nnz, spin + 1])
DM._csr._D[:, :spin] = dDM[:, :]
# DM file does not contain overlap matrix... so neglect it for now.
DM._csr._D[:, spin] = 0.0
_mat_siesta2sisl(DM)
DM = DM.astype(dtype=kwargs.get("dtype"), copy=False)
# Convert the supercells to sisl supercells
if nsc[0] != 0 or geom.no_s >= col.max():
_csr_from_siesta(geom, DM._csr)
else:
warn(f"{self!r}.read_density_matrix may result in a wrong sparse pattern!")
DM = DM.transpose(spin=False, sort=kwargs.get("sort", True))
_add_overlap(
DM,
kwargs.get("overlap", None),
f"{self.__class__.__name__}.read_density_matrix",
)
return DM
[docs]
def write_density_matrix(self, DM, **kwargs):
"""Writes the density matrix to a siesta.DM file"""
DM = DM.transpose(spin=False, sort=False)
# This ensures that we don"t have any *empty* elements
if DM._csr.nnz == 0:
raise SileError(
f"{self!r}.write_density_matrix cannot write "
"a zero element sparse matrix!"
)
_csr_to_siesta(DM.geometry, DM._csr)
# We do not really need to sort this one, but we do for consistency
# of the interface.
DM.finalize(sort=kwargs.get("sort", True))
DM = DM.astype(dtype=np.float64, copy=False)
_mat_sisl2siesta(DM)
csr = DM._csr
# Get DM
if DM.orthogonal:
dm = csr._D
else:
dm = csr._D[:, : DM.S_idx]
# Ensure shapes (say if only 1 spin)
dm.shape = (-1, DM.spin.size(DM.dtype))
nsc = DM.geometry.lattice.nsc.astype(np.int32)
_siesta.write_dm(self.file, nsc, csr.ncol, csr.col + 1, _toF(dm, np.float64))
self._fortran_check("write_density_matrix", "could not write density matrix.")
@set_module("sisl.io.siesta")
class tsdeSileSiesta(dmSileSiesta):
"""Non-equilibrium density matrix and energy density matrix file"""
[docs]
def read_energy_density_matrix(self, **kwargs) -> EnergyDensityMatrix:
"""Returns the energy density matrix from the siesta.TSDE file
Parameters
----------
geometry : Geometry, optional
attach a geometry object to the sparse matrix
overlap : SparseMatrix, optional
attach the overlap matrix to the sparse matrix
"""
# Now read the sizes used...
spin, no, nsc, nnz = _siesta.read_tsde_sizes(self.file)
self._fortran_check(
"read_energy_density_matrix", "could not read energy density matrix sizes."
)
ncol, col, dEDM = _siesta.read_tsde_edm(self.file, spin, no, nsc, nnz)
self._fortran_check(
"read_energy_density_matrix", "could not read energy density matrix."
)
# Try and immediately attach a geometry
geom = kwargs.get("geometry", kwargs.get("geom", None))
if geom is None:
# We truly, have no clue,
# Just generate a boxed system
xyz = [[x, 0, 0] for x in range(no)]
lattice = Lattice([no, 1, 1], nsc=nsc)
geom = Geometry(xyz, Atom(1), lattice=lattice)
if nsc[0] != 0 and np.any(geom.nsc != nsc):
# We have to update the number of supercells!
geom.set_nsc(nsc)
if geom.no != no:
raise SileError(
f"{self!r}.read_energy_density_matrix could "
"not use the passed geometry as the number of atoms or orbitals "
"is inconsistent with DM file."
)
# Create the energy density matrix container
EDM = EnergyDensityMatrix(
geom, spin, nnzpr=1, dtype=np.float64, orthogonal=False
)
# Create the new sparse matrix
EDM._csr.ncol = ncol.astype(np.int32, copy=False)
EDM._csr.ptr = _ncol_to_indptr(ncol)
# Correct fortran indices
EDM._csr.col = col.astype(np.int32, copy=False) - 1
EDM._csr._nnz = len(col)
EDM._csr._D = _a.emptyd([nnz, spin + 1])
EDM._csr._D[:, :spin] = dEDM[:, :] * _Ry2eV
# EDM file does not contain overlap matrix... so neglect it for now.
EDM._csr._D[:, spin] = 0.0
_mat_siesta2sisl(EDM)
EDM = EDM.astype(dtype=kwargs.get("dtype"), copy=False)
# Convert the supercells to sisl supercells
if nsc[0] != 0 or geom.no_s >= col.max():
_csr_from_siesta(geom, EDM._csr)
else:
warn(
f"{self!r}.read_energy_density_matrix may result in a wrong sparse pattern!"
)
EDM = EDM.transpose(spin=False, sort=kwargs.get("sort", True))
_add_overlap(
EDM,
kwargs.get("overlap", None),
f"{self.__class__.__name__}.read_energy_density_matrix",
)
return EDM
[docs]
def read_fermi_level(self) -> float:
r"""Query the Fermi-level contained in the file
Returns
-------
float :
fermi-level of the system
"""
Ef = _siesta.read_tsde_ef(self.file) * _Ry2eV
self._fortran_check("read_fermi_level", "could not read fermi-level.")
return Ef
[docs]
def write_density_matrices(self, DM, EDM, Ef: float = 0.0, **kwargs):
r"""Writes the density matrix to a siesta.DM file
Parameters
----------
DM : DensityMatrix
density matrix to write to the file
EDM : EnergyDensityMatrix
energy density matrix to write to the file
Ef :
fermi-level to be contained
"""
sort = kwargs.get("sort", True)
DM = DM.transpose(spin=False, sort=sort)
EDM = EDM.transpose(spin=False, sort=sort)
DM._csr.align(EDM._csr)
EDM._csr.align(DM._csr)
if DM._csr.nnz == 0:
raise SileError(
f"{self!r}.write_density_matrices cannot write "
"a zero element sparse matrix!"
)
_csr_to_siesta(DM.geometry, DM._csr)
_csr_to_siesta(DM.geometry, EDM._csr)
DM._csr.finalize(sort=sort)
EDM._csr.finalize(sort=sort)
DM = DM.astype(dtype=np.float64, copy=False)
_mat_sisl2siesta(DM)
EDM = EDM.astype(dtype=np.float64, copy=False)
_mat_sisl2siesta(EDM)
# Ensure everything is correct
if not (
np.allclose(DM._csr.ncol, EDM._csr.ncol)
and np.allclose(DM._csr.col, EDM._csr.col)
):
raise ValueError(
f"{self!r}.write_density_matrices got non compatible "
"DM and EDM matrices."
)
if DM.orthogonal:
dm = DM._csr._D
else:
dm = DM._csr._D[:, : DM.S_idx]
if EDM.orthogonal:
edm = EDM._csr._D
else:
edm = EDM._csr._D[:, : EDM.S_idx]
nsc = DM.geometry.lattice.nsc.astype(np.int32)
_siesta.write_tsde_dm_edm(
self.file,
nsc,
DM._csr.ncol,
DM._csr.col + 1,
_toF(dm, np.float64),
_toF(edm, np.float64, _eV2Ry),
Ef * _eV2Ry,
)
self._fortran_check(
"write_density_matrices", "could not write DM + EDM matrices."
)
@set_module("sisl.io.siesta")
class hsxSileSiesta(SileBinSiesta):
"""Hamiltonian and overlap matrix file
Historically there are several HSX file versions output from Siesta.
Since Siesta 5.3, the HSX file format is containing all the information present in
the TranSiesta specific TSHS file-format, plus more information.
As such, the TSHS file format is deprecated and will not be used anymore.
Therefore users should adapt their workflows to be done with HSX
files.
Basically, one should never work with HSX files from Siesta <= 4, since they are
not information complete.
Notes
-----
At some point we will deprecate the older HSX file versions (<= 4).
"""
@property
def version(self) -> int:
"""The version of the file"""
return int(_siesta.read_hsx_version(self.file))
@property
def _stored_dtype(self):
is_dp = _siesta.read_hsx_is_dp(self.file)
return {True: np.float64, False: np.float32}[is_dp]
def _xij2system(self, xij, geometry=None, **kwargs):
"""Create a new geometry with *correct* nsc and somewhat correct xyz
Parameters
----------
xij : SparseCSR
orbital distances
geometry : Geometry, optional
passed geometry
"""
def get_geom_handle(xij):
atoms = self.read_basis(geometry=geometry, **kwargs)
if not atoms is None:
return Geometry(np.zeros([len(atoms), 3]), atoms)
N = len(xij)
# convert csr to dok format
row = (xij.ncol > 0).nonzero()[0]
# Now we have [0 0 0 0 1 1 1 1 2 2 ... no-1 no-1]
row = np.repeat(row, xij.ncol[row])
col = xij.col
# Parse xij to correct geometry
# first figure out all zeros (i.e. self-atom-orbitals)
idx0 = np.isclose(np.fabs(xij._D).sum(axis=1), 0.0).nonzero()[0]
row0 = row[idx0]
# convert row0 and col0 to a first attempt of "atomization"
atoms = []
for r in range(N):
idx0r = (row0 == r).nonzero()[0]
# row0r = row0[idx0r]
# although xij == 0, we just do % to ensure unit-cell orbs
col0r = col[idx0[idx0r]] % N
if np.all(col0r >= r):
# we have a new atom
atoms.append(set(col0r))
else:
atoms[-1].update(set(col0r))
# convert list of orbitals to lists
atoms = [list(a) for a in atoms]
if sum(map(len, atoms)) != len(xij):
raise ValueError(
f"{self.__class__.__name__} could not determine correct "
"number of orbitals."
)
atms = Atoms(Atom("H", [-1.0 for _ in atoms[0]]))
for orbs in atoms[1:]:
atms.append(Atom("H", [-1.0 for _ in orbs]))
return Geometry(np.zeros([len(atoms), 3]), atms)
geom_handle = get_geom_handle(xij)
def convert_to_atom(geom, xij):
# o2a does not check for correct super-cell index
n_s = xij.shape[1] // xij.shape[0]
atm_s = geom.o2a(np.arange(xij.shape[1]))
# convert csr to dok format
row = (xij.ncol > 0).nonzero()[0]
row = np.repeat(row, xij.ncol[row])
col = xij.col
arow = atm_s[row]
acol = atm_s[col]
del atm_s, row, col
idx = np.lexsort((acol, arow))
arow = arow[idx]
acol = acol[idx]
xij = xij._D[idx]
del idx
# Now figure out if xij is consistent
duplicates = np.logical_and(
np.diff(acol) == 0, np.diff(arow) == 0
).nonzero()[0]
if duplicates.size > 0:
if not np.allclose(xij[duplicates + 1] - xij[duplicates], 0.0):
raise ValueError(
f"{self.__class__.__name__} converting xij(orb) -> xij(atom) went wrong. "
"This may happen if your coordinates are not inside the unitcell, please pass "
"a usable geometry."
)
# remove duplicates to create new matrix
arow = np.delete(arow, duplicates)
acol = np.delete(acol, duplicates)
xij = np.delete(xij, duplicates, axis=0)
# Create a new sparse matrix
# Create the new index pointer
indptr = np.insert(
np.array([0, len(xij)], np.int32),
1,
(np.diff(arow) != 0).nonzero()[0] + 1,
)
assert len(indptr) == geom.na + 1
return SparseCSR((xij, acol, indptr), shape=(geom.na, geom.na * n_s))
def coord_from_xij(xij):
# first atom is at 0, 0, 0
na = len(xij)
xyz = _a.zerosd([na, 3])
xyz[0] = [0, 0, 0]
mark = _a.zerosi(na)
mark[0] = 1
run_atoms = deque([0])
while len(run_atoms) > 0:
atm = run_atoms.popleft()
xyz_atm = xyz[atm].reshape(1, 3)
neighbors = xij.edges(atm, exclude=atm)
neighbors = neighbors[neighbors < na]
# update those that haven't been calculated
idx = mark[neighbors] == 0
neigh_idx = neighbors[idx]
if len(neigh_idx) == 0:
continue
xyz[neigh_idx, :] = xij[atm, neigh_idx] - xyz_atm
mark[neigh_idx] = 1
# add more atoms to be processed, since we have *mark*
# we will only run every atom once
run_atoms.extend(neigh_idx.tolist())
# check that everything is correct
if (~idx).sum() > 0:
neg_neighbors = neighbors[~idx]
if not np.allclose(
xyz[neg_neighbors, :], xij[atm, neg_neighbors] - xyz_atm
):
raise ValueError(
f"{self.__class__.__name__} xij(orb) -> xyz did not "
f"find same coordinates for different connections"
)
if mark.sum() != na:
raise ValueError(
f"{self.__class__.__name__} xij(orb) -> Geometry does not "
f"have a fully connected geometry. It is impossible to create relative coordinates"
)
return xyz
def sc_from_xij(xij, xyz):
na = xij.shape[0]
n_s = xij.shape[1] // xij.shape[0]
if n_s == 1:
# easy!!
return Lattice(xyz.max(axis=0) - xyz.min(axis=0) + 10.0, nsc=[1] * 3)
sc_off = _a.zerosd([n_s, 3])
mark = _a.zerosi(n_s)
mark[0] = 1
for atm in range(na):
neighbors = xij.edges(atm, exclude=atm)
uneighbors = neighbors % na
neighbor_isc = neighbors // na
# get offset in terms of unit-cell
off = xij[atm, neighbors] - (xyz[uneighbors] - xyz[atm].reshape(1, 3))
idx = mark[neighbor_isc] == 0
if not np.allclose(off[~idx], sc_off[neighbor_isc[~idx]]):
raise ValueError(
f"{self.__class__.__name__} xij(orb) -> xyz did not "
f"find same supercell offsets for different connections"
)
if idx.sum() == 0:
continue
for idx in idx.nonzero()[0]:
nidx = neighbor_isc[idx]
if mark[nidx] == 0:
mark[nidx] = 1
sc_off[nidx] = off[idx]
elif not np.allclose(sc_off[nidx], off[idx]):
raise ValueError(
f"{self.__class__.__name__} xij(orb) -> xyz did not "
f"find same supercell offsets for different connections"
)
# We know that siesta returns isc
# for iz in [0, 1, 2, 3, -3, -2, -1]:
# for iy in [0, 1, 2, -2, -1]:
# for ix in [0, 1, -1]:
# every block we find a half monotonically increasing vector additions
# Note the first is always [0, 0, 0]
# So our best chance is to *guess* the first nsc
# then reshape, then guess, then reshape, then guess :)
# sc_diff = np.diff(sc_off, axis=0)
def get_nsc(sc_off):
"""Determine nsc depending on the axis"""
# correct the offsets
ndim = sc_off.ndim
if sc_off.shape[0] == 1:
return 1
# always select the 2nd one since that contains the offset
# for the first isc [1, 0, 0] or [0, 1, 0] or [0, 0, 1]
sc_dir = sc_off[(1,) + np.index_exp[0] * (ndim - 2)]
norm2_sc_dir = sc_dir @ sc_dir
# figure out the maximum integer part
# we select 0 indices for all already determined lattice
# vectors since we know the first one is [0, 0, 0]
idx = np.index_exp[:] + np.index_exp[0] * (ndim - 2)
projection = (sc_off[idx] * sc_dir).sum(-1) / norm2_sc_dir
iprojection = np.rint(projection)
# reduce, find 0
idx_zero = np.isclose(iprojection, 0, atol=1e-5).nonzero()[0]
if idx_zero.size <= 1:
return 1
# only take those values that are continuous
# we *must* have some supercell connections
idx_max = idx_zero[1]
# find where they are close
# since there may be *many* zeros (non-coupling elements)
# we first have to cut off anything that is not integer
if np.allclose(projection[:idx_max], iprojection[:idx_max], atol=1e-5):
return idx_max
raise ValueError(f"Could not determine nsc from coordinates")
nsc = _a.onesi(3)
nsc[0] = get_nsc(sc_off)
sc_off = sc_off.reshape(-1, nsc[0], 3)
nsc[1] = get_nsc(sc_off)
sc_off = sc_off.reshape(-1, nsc[1], nsc[0], 3)
nsc[2] = sc_off.shape[0]
# now determine cell parameters
if all(nsc > 1):
cell = _a.arrayd([sc_off[0, 0, 1], sc_off[0, 1, 0], sc_off[1, 0, 0]])
else:
# we will never have all(nsc == 1) since that is
# taken care of at the start
# this gets a bit tricky, since we don't know one of the
# lattice vectors
cell = _a.zerosd([3, 3])
i = 0
for idx, isc in enumerate(nsc):
if isc > 1:
sl = [0, 0, 0]
sl[2 - idx] = 1
cell[i] = sc_off[tuple(sl)]
i += 1
# figure out the last vectors
# We will just use Cartesian coordinates
while i < 3:
# this means we don't have any supercell connections
# along at least 1 other lattice vector.
lcell = np.fabs(cell).sum(0)
# figure out which Cartesian direction we are *missing*
cart_dir = np.argmin(lcell)
cell[i, cart_dir] = (
xyz[:, cart_dir].max() - xyz[:, cart_dir].min() + 10.0
)
i += 1
return Lattice(cell, nsc)
# now we have all orbitals, ensure compatibility with passed geometry
if geometry is None:
atm_xij = convert_to_atom(geom_handle, xij)
xyz = coord_from_xij(atm_xij)
lattice = sc_from_xij(atm_xij, xyz)
geometry = Geometry(xyz, geom_handle.atoms, lattice)
# Move coordinates into unit-cell
geometry.xyz[:, :] = (geometry.fxyz % 1.0) @ geometry.cell
else:
if geometry.n_s != xij.shape[1] // xij.shape[0]:
atm_xij = convert_to_atom(geom_handle, xij)
lattice = sc_from_xij(atm_xij, coord_from_xij(atm_xij))
geometry.set_nsc(lattice.nsc)
def conv(orbs, atm):
if len(orbs) == len(atm):
return atm
return atm.copy(orbitals=[-1.0 for _ in orbs])
atms = Atoms(list(map(conv, geom_handle.atoms, geometry.atoms)))
if len(atms) != len(geometry):
raise ValueError(
f"{self.__class__.__name__} passed geometry for reading "
"sparse matrix does not contain same number of atoms!"
)
geometry = geometry.copy()
# TODO check that geometry and xyz are the same!
geometry._atoms = atms
geometry = _geometry_align(
geometry, kwargs.get("geometry", geometry), self.__class__, "read_geometry"
)
return geometry
[docs]
def read_basis(self, **kwargs) -> Atoms:
"""Reads basis set and geometry information from the HSX file"""
self._log(
"basis %r %r", kwargs.get("atoms", None), kwargs.get("geometry", None)
)
# Now read the sizes used...
no, na, nspecies = _siesta.read_hsx_species_sizes(self.file)
self._fortran_check("read_basis", "could not read specie sizes.")
# Read specie information
labels, val_q, norbs, isa = _siesta.read_hsx_species_info(
self.file, nspecies, no, na
)
# convert to proper string
labels = labels.T.reshape(nspecies, -1)
labels = labels.view(f"S{labels.shape[1]}")
labels = list(
map(lambda s: b"".join(s).decode("utf-8").strip(), labels.tolist())
)
self._fortran_check("read_basis", "could not read species.")
# to python index
isa -= 1
from sisl._core.atom import _ptbl
# try and convert labels into symbols
# We do this by:
# 1. label -> symbol
# 2. label[:2] -> symbol
# 3. label[:1] -> symbol
symbols = []
lbls = []
for i, label in enumerate(labels):
lbls.append(label)
# 1.
symbol = _ptbl.Z_label(label)
if symbol != "fa":
symbols.append(symbol)
continue
# 2.
symbol = _ptbl.Z_label(label[:2])
if symbol != "fa":
symbols.append(symbol)
continue
# 3.
symbol = _ptbl.Z_label(label[:1])
if symbol != "fa":
symbols.append(symbol)
continue
# 4. we have no clue, assign -1
symbols.append(-200 - i)
# Read in orbital information
atoms = []
for ispecie in range(nspecies):
n_l_zeta = _siesta.read_hsx_species(self.file, ispecie + 1, norbs[ispecie])
self._fortran_check("read_basis", f"could not read specie {ispecie}.")
# create orbital
# since the n, l, zeta is unique per atomic orbital (before expanding to
# m shells), we will figure this out manually.
old_values = (-1, -1, -1)
m = 0
orbs = []
for n, l, zeta in zip(*n_l_zeta):
if old_values != (n, l, zeta) or m > l:
old_values = (n, l, zeta)
m = -l
orbs.append(AtomicOrbital(n=n, l=l, m=m, zeta=zeta, R=-1.0))
m += 1
# now create atom
atoms.append(Atom(symbols[ispecie], orbs, tag=lbls[ispecie]))
# now create atoms object
atoms = Atoms([atoms[ia] for ia in isa])
base = kwargs.get("geometry", kwargs.get("geom", None))
base = kwargs.get("atoms", base)
if base is None:
return atoms
if isinstance(base, Geometry):
base = base.atoms
# Now compare the atoms such that we select the best one
def get_best(aF, aI):
if len(aF) != len(aI):
# the file has the correct number of orbitals
self._log("basis file atom=%r better than input %r", aF, aI)
return aF
if not isinstance(aF, AtomUnknown):
if aF.Z != aI.Z:
self._log(
"basis file atom=%r has different Z than input %r", aF, aI
)
return aF
# check for orbitals being atomicorbital
for orb in aI:
if not isinstance(orb, AtomicOrbital):
self._log(
"basis file, input atom=%r does not use AtomicOrbital", aI
)
return aF
for oF, oI in zip(aF, aI):
# we can't check polarization, not stored in the HSX file format
for prop in ("n", "l", "m", "zeta"):
if getattr(oF, prop) != getattr(oI, prop):
self._log(
"basis file atom, orbital %r,%r property %s cannot be matched with input atom, orbital %r,%r",
aF,
oF,
prop,
aI,
oI,
)
return aF
# the atoms are the same, so we select the input atom
# since it likely contains the spherical functions
# and the charge
self._log("basis file, choose input atom=%r over file atom %r", aI, aF)
return aI
atoms = Atoms(map(get_best, atoms, base))
return atoms
def _r_lattice_v0(self, **kwargs):
"""Read the Lattice from the old file version"""
spin, _, no, no_s, nnz = _siesta.read_hsx_sizes(self.file)
self._fortran_check("read_geometry", "could not read geometry sizes.")
ncol, col, _, _, dxij = _siesta.read_hsx_hsx0(self.file, spin, no, no_s, nnz)
dxij = dxij.T * _Bohr2Ang
col -= 1
self._fortran_check("read_geometry", "could not read Hamiltonian.")
ptr = _ncol_to_indptr(ncol)
xij = SparseCSR((dxij, col, ptr), shape=(no, no_s))
geom = self._xij2system(xij, kwargs.get("geometry", kwargs.get("geom", None)))
lattice = geom.lattice
del geom
return lattice
def _r_lattice_v1(self, **kwargs):
_, na, _, _, _ = _siesta.read_hsx_sizes(self.file)
cell, nsc, _, _ = _siesta.read_hsx_geom1_2(self.file, na)
self._log("lattice v1_2")
return Lattice(cell.T * _Bohr2Ang, nsc=nsc)
# Same
_r_lattice_v2 = _r_lattice_v1
[docs]
def read_lattice(self, **kwargs) -> Lattice:
"""Read the lattice from the file
This will always work on new files Siesta >=5, but only sometimes on older
versions of the HSX file format.
"""
return getattr(self, f"_r_lattice_v{self.version}")(**kwargs)
def _r_geometry_v0(self, **kwargs):
"""Read the geometry from the old file version"""
self._log(
"geometry v0 %r, %r",
kwargs.get("atoms", None),
kwargs.get("geometry", None),
)
spin, _, no, no_s, nnz = _siesta.read_hsx_sizes(self.file)
self._fortran_check("read_geometry", "could not read geometry sizes.")
ncol, col, _, _, dxij = _siesta.read_hsx_hsx0(self.file, spin, no, no_s, nnz)
dxij = dxij.T * _Bohr2Ang
col -= 1
self._fortran_check("read_geometry", "could not read Hamiltonian.")
ptr = _ncol_to_indptr(ncol)
xij = SparseCSR((dxij, col, ptr), shape=(no, no_s))
geom = self._xij2system(xij, **kwargs)
return geom
def _r_geometry_v1(self, **kwargs):
self._log(
f"geometry v{self.version} %r, %r",
kwargs.get("atoms", None),
kwargs.get("geometry", None),
)
# first read the atoms object
atoms = self.read_basis(**kwargs)
# now read coordinates and cell sizes
_, na, _, _, _ = _siesta.read_hsx_sizes(self.file)
cell, nsc, xa, _ = _siesta.read_hsx_geom1_2(self.file, na)
lattice = Lattice(cell.T * _Bohr2Ang, nsc=nsc)
return Geometry(xa.T * _Bohr2Ang, atoms, lattice=lattice)
_r_geometry_v2 = _r_geometry_v1
[docs]
def read_geometry(self, **kwargs) -> Geometry:
"""Read the geometry from the file
This will always work on new files Siesta >=5, but only sometimes on older
versions of the HSX file format.
"""
return getattr(self, f"_r_geometry_v{self.version}")(**kwargs)
[docs]
def read_fermi_level(self, **kwargs) -> float:
"""Reads the fermi level in the file
Only valid for files created by Siesta >=5.
"""
if self.version == 0:
warn(
MissingFermiLevelWarning(
f"{self.base_file!s} (base={self.base_directory()!s}) "
"does not contain Ef, too old version file."
)
)
return None
Ef = _siesta.read_hsx_ef(self.file)
self._fortran_check("read_fermi_level", "could not read Fermi-level")
if Ef >= np.finfo(np.float32).max:
# we can't compare with math.isinf, as it doesn't work.
# fortran huge returns the maximum represented value that is not
# inf.
# So we will just compare against the max of the float.
# This should then also work for HSX files in single-precision.
warn(
MissingFermiLevelWarning(
f"{self.base_file!s} (base={self.base_directory()!s}) "
"does not contain a usable Ef, likely the Fermi-level "
"has not been calculated by Siesta? (option = H.Setup.Only)"
)
)
Ef = 0.0
return Ef * _Ry2eV
[docs]
def read_brillouinzone(self, trs: bool = True) -> MonkhorstPack:
"""Read the Brillouin zone object"""
kcell, kdispl = _siesta.read_hsx_k(self.file)
self._fortran_check("read_brillouinzone", "could not read the file.")
geom = self.read_geometry()
return MonkhorstPack(geom, kcell, displacement=kdispl, trs=trs)
def _r_hamiltonian_v0(self, **kwargs):
geom = self.read_geometry(**kwargs)
# Now read the sizes used...
spin, _, no, no_s, nnz = _siesta.read_hsx_sizes(self.file)
self._fortran_check("read_hamiltonian", "could not read Hamiltonian sizes.")
ncol, col, dH, dS, _ = _siesta.read_hsx_hsx0(self.file, spin, no, no_s, nnz)
col -= 1
self._fortran_check("read_hamiltonian", "could not read Hamiltonian.")
if geom.no != no or geom.no_s != no_s:
raise SileError(
f"{self!r}.read_hamiltonian could not use the "
"passed geometry as the number of atoms or orbitals is "
"inconsistent with HSX file."
)
# Create the Hamiltonian container
H = Hamiltonian(geom, spin, nnzpr=1, orthogonal=False)
# Create the new sparse matrix
H._csr.ncol = ncol.astype(np.int32, copy=False)
H._csr.ptr = _ncol_to_indptr(ncol)
# Correct fortran indices
H._csr.col = col.astype(np.int32, copy=False)
H._csr._nnz = len(col)
H._csr._D = _a.empty([nnz, spin + 1], dtype=dH.dtype)
H._csr._D[:, :spin] = dH[:, :] * _Ry2eV
H._csr._D[:, spin] = dS[:]
_mat_siesta2sisl(H)
H = H.astype(dtype=kwargs.get("dtype", self._stored_dtype), copy=False)
# Convert the supercells to sisl supercells
if no_s // no == np.prod(geom.nsc):
_csr_from_siesta(geom, H._csr)
warn(
MissingFermiLevelWarning(
f"{self.file} does not contain Ef, electronic structure not shifted to Fermi level."
)
)
return H.transpose(spin=False, sort=kwargs.get("sort", True))
def _r_hamiltonian_v1(self, **kwargs):
# Now read the sizes used...
geom = self.read_geometry(**kwargs)
spin, _, no, no_s, nnz = _siesta.read_hsx_sizes(self.file)
self._fortran_check("read_hamiltonian", "could not read Hamiltonian sizes.")
ncol, col, dH, dS, isc = _siesta.read_hsx_hsx1_2(self.file, spin, no, no_s, nnz)
col -= 1
self._fortran_check("read_hamiltonian", "could not read Hamiltonian.")
if geom.no != no or geom.no_s != no_s:
raise SileError(
f"{self!r}.read_hamiltonian could not use the "
"passed geometry as the number of atoms or orbitals is "
"inconsistent with HSX file."
)
# Create the Hamiltonian container
H = Hamiltonian(geom, spin, nnzpr=1, orthogonal=False)
# Create the new sparse matrix
H._csr.ncol = ncol.astype(np.int32, copy=False)
H._csr.ptr = _ncol_to_indptr(ncol).astype(np.int32, copy=False)
# Correct fortran indices
H._csr.col = col.astype(np.int32, copy=False)
H._csr._nnz = len(col)
H._csr._D = _a.empty([nnz, spin + 1], dtype=dH.dtype)
H._csr._D[:, :spin] = dH[:, :] * _Ry2eV
H._csr._D[:, spin] = dS[:]
_mat_siesta2sisl(H)
H = H.astype(dtype=kwargs.get("dtype", self._stored_dtype), copy=False)
# Convert the supercells to sisl supercells
_csr_from_sc_off(H.geometry, isc.T, H._csr)
# Correct the fermi-level here
Ef = self.read_fermi_level()
H.shift(-Ef)
return H.transpose(spin=False, sort=kwargs.get("sort", True))
_r_hamiltonian_v2 = _r_hamiltonian_v1
[docs]
def read_hamiltonian(self, **kwargs) -> Hamiltonian:
"""Returns the electronic structure from the siesta.HSX file"""
return getattr(self, f"_r_hamiltonian_v{self.version}")(**kwargs)
def _r_overlap_v0(self, **kwargs):
"""Returns the overlap matrix from the siesta.HSX file"""
geom = self.read_geometry(**kwargs)
# Now read the sizes used...
spin, _, no, no_s, nnz = _siesta.read_hsx_sizes(self.file)
self._fortran_check("read_overlap", "could not read overlap matrix sizes.")
ncol, col, dS, _ = _siesta.read_hsx_sx0(self.file, spin, no, no_s, nnz)
col -= 1
self._fortran_check("read_overlap", "could not read overlap matrix.")
if geom.no != no or geom.no_s != no_s:
raise SileError(
f"{self!r}.read_overlap could not use the "
"passed geometry as the number of atoms or orbitals is "
"inconsistent with HSX file."
)
# Get dtype
dtype = kwargs.get("dtype", self._stored_dtype)
# Create the Hamiltonian container
S = Overlap(geom, nnzpr=1)
# Create the new sparse matrix
S._csr.ncol = ncol.astype(np.int32, copy=False)
S._csr.ptr = _ncol_to_indptr(ncol)
# Correct fortran indices
S._csr.col = col.astype(np.int32, copy=False)
S._csr._nnz = len(col)
S._csr._D = _a.empty([nnz, 1], dtype=dtype)
S._csr._D[:, 0] = dS[:]
# Convert the supercells to sisl supercells
if no_s // no == np.prod(geom.nsc):
_csr_from_siesta(geom, S._csr)
# not really necessary with Hermitian transposing, but for consistency
return S.transpose(sort=kwargs.get("sort", True))
def _r_overlap_v1(self, **kwargs):
"""Returns the overlap matrix from the siesta.HSX file"""
geom = self.read_geometry(**kwargs)
# Now read the sizes used...
spin, _, no, no_s, nnz = _siesta.read_hsx_sizes(self.file)
self._fortran_check("read_overlap", "could not read overlap matrix sizes.")
ncol, col, dS, isc = _siesta.read_hsx_sx1_2(self.file, spin, no, no_s, nnz)
col -= 1
self._fortran_check("read_overlap", "could not read overlap matrix.")
if geom.no != no or geom.no_s != no_s:
raise SileError(
f"{self!r}.read_overlap could not use the "
"passed geometry as the number of atoms or orbitals is "
"inconsistent with HSX file."
)
# Get dtype
dtype = kwargs.get("dtype", self._stored_dtype)
# Create the Hamiltonian container
S = Overlap(geom, nnzpr=1)
# Create the new sparse matrix
S._csr.ncol = ncol.astype(np.int32, copy=False)
S._csr.ptr = _ncol_to_indptr(ncol)
# Correct fortran indices
S._csr.col = col.astype(np.int32, copy=False)
S._csr._nnz = len(col)
S._csr._D = _a.empty([nnz, 1], dtype=dtype)
S._csr._D[:, 0] = dS[:]
_csr_from_sc_off(S.geometry, isc.T, S._csr)
# not really necessary with Hermitian transposing, but for consistency
return S.transpose(sort=kwargs.get("sort", True))
_r_overlap_v2 = _r_overlap_v1
[docs]
def read_overlap(self, **kwargs) -> Overlap:
"""Returns the electronic structure from the siesta.HSX file"""
return getattr(self, f"_r_overlap_v{self.version}")(**kwargs)
[docs]
def write_hamiltonian(self, H, **kwargs):
"""Writes the Hamiltonian to a siesta.HSX file"""
# we sort below, so no need to do it here
# see hsxSileSiesta.read_overlap for .transpose()
H = H.transpose(spin=False, sort=False)
if H._csr.nnz == 0:
raise SileError(
f"{self!r}.write_hamiltonian cannot write "
"a zero element sparse matrix!"
)
# Default to writing version 2
version = kwargs.get("version", 2)
if version not in (1, 2):
raise NotImplementedError(
f"{self!r}.write_hamiltonian only " "supports one of versions [1, 2]."
)
sort = kwargs.get("sort", True)
bz = kwargs.get("bz", None) # In case the BZ is an argument
dtype = kwargs.get("dtype", np.float64)
if dtype not in (np.float32, np.float64):
raise ValueError(
f"{self!r}.write_hamiltonian only "
"supports float32 or float64 in the HSX file format."
)
# Convert to siesta CSR
_csr_to_siesta(H.geometry, H._csr, diag=True)
H.finalize(sort=sort)
# we have the required dtype above.
# However, the fortran interface requires a double, so we have to
# use a bool to signal the stored data.
H = H.astype(dtype=np.float64, copy=False)
_mat_sisl2siesta(H)
csr = H._csr
# Extract the data to pass to the fortran routine
cell = H.geometry.cell
xyz = H.geometry.xyz
# Get H and S
if H.orthogonal:
self._log("writing orthogonal hamiltonian")
s = csr.copy(dims=0)
s.empty(keep_nnz=True)
s += s.diags(1)
missing_diags = s.nnz - csr.nnz
if missing_diags:
# This should never happen as _csr_to_siesta should ensure
# that the diagonal entries always exists.
# Hence it gets changed before writing.
# Not compeletely optimal, but for now this is OK
raise SislError(
f"{self.__class__.__name__}.write_hamiltonian "
"The diagonal elements of your orthogonal Hamiltonian "
f"have not been defined. Got {len(csr) - missing_diags} elements, expected {len(csr)}."
)
h = csr._D
s = s._D
else:
self._log("writing non-orthogonal hamiltonian")
h = csr._D[:, : H.S_idx]
s = csr._D[:, H.S_idx]
# Get shorter variants
nsc = H.geometry.nsc[:].astype(np.int32)
isc = _siesta.siesta_sc_off(*nsc)
# Correct arguments depending on BZ
kcell = _a.zerosi([3, 3])
kdispl = _a.zerosd([3])
if isinstance(bz, MonkhorstPack):
kcell = np.diag(bz._diag)
kdispl = bz._displ
else:
# TODO, perhaps do something based on the type of BZ
for i in range(3):
if nsc[i] > 1:
kcell[i, i] = 2
else:
kcell[i, i] = 1
geom = H.geometry
atoms = geom.atoms
# Populate arguments
args = []
def func(atom_):
atom, _ = atom_
return f"{atom.tag:20s}"[:20], atom.Z, len(atom)
args.extend(list(map(list, zip(*map(func, atoms.iter(species=True))))))
# Next values
no_max = (atoms.lasto - atoms.firsto[:-1]).max() + 1
# Atoms require 'n' and 'zeta' to be >=1
n = _a.zerosi([no_max, atoms.nspecies], order="F") + 1
l = np.zeros_like(n)
zeta = np.zeros_like(n) + 1
try:
for i_s, (atom, _) in enumerate(atoms.iter(species=True)):
for io, orb in enumerate(atom):
n[io, i_s] = orb.n
l[io, i_s] = orb.l
zeta[io, i_s] = orb.zeta
except Exception:
pass # should we warn?
args.extend([n, l, zeta])
args.extend(
[
_toF(xyz.T, np.float64, 1 / _Bohr2Ang),
atoms.species + 1,
atoms.firsto[:-1],
]
)
# Now cell etc.
args.append(_toF(cell.T, np.float64, 1 / _Bohr2Ang))
args.append(nsc)
args.append(isc) # already in correct shape!
# Now the matrices
args.extend([csr.ncol, csr.col + 1])
args.append(_toF(h, np.float64, _eV2Ry))
args.append(_toF(s, np.float64))
args.append(dtype == np.float64)
args.extend([0.0, 0.0, 0.0])
if version == 2:
args.append(kcell)
args.append(kdispl)
# see hsxSileSiesta.read_lattice for .T
write_hsx = getattr(_siesta, f"write_hsx{version}")
write_hsx(self.file, *args)
self._fortran_check(
"write_hamiltonian", "could not write Hamiltonian and overlap matrix."
)
@set_module("sisl.io.siesta")
class wfsxSileSiesta(SileBinSiesta):
r"""Binary WFSX file reader for Siesta
The WFSX file assumes that users initialize the object with
a `parent` argument (or one of the other geometry related objects as
shown below).
The `parent` argument is necessary to convert WFSX k-points from 1/Ang to
reduced coordinates.
When returning `EigenstateElectron` objects the parent of these objects
are the equivalent of the `parent` argument upon initialization.
Therefore please remember to pass a correct `parent`.
Parameters
----------
parent : obj, optional
a parent may contain a geometry, and/or a supercell
geometry : Geometry, optional
a geometry contains a cell with corresponding lattice vectors
used to convert k [1/Ang] -> [b]
lattice : Lattice, optional
a supercell contains the lattice vectors to convert k
"""
def _setup(self, *args, **kwargs):
"""Simple setup that needs to be overwritten
All _r_next_* methods expect the fortran file unit to be handled
and that the position in the file is correct.
"""
super()._setup(*args, **kwargs)
# default lattice
lattice = None
# In case the instantiation was called with wfsxSileSiesta("path", geometry=geometry)
parent = kwargs.get("parent")
if parent is None:
geometry = None
elif isinstance(parent, Geometry):
geometry = parent
elif isinstance(parent, Lattice):
lattice = parent
else:
geometry = parent.geometry
geometry = kwargs.get("geometry", geometry)
if geometry is not None and lattice is None:
lattice = geometry.lattice
lattice = kwargs.get("lattice", kwargs.get("sc", lattice))
if lattice is None and geometry is not None:
raise ValueError(
f"{self.__class__.__name__}(geometry=Geometry, lattice=None) is not an allowed argument combination."
)
if parent is None:
parent = geometry
if parent is None:
parent = lattice
self._parent = parent
self._geometry = geometry
self._lattice = lattice
if self._parent is None and self._geometry is None and self._lattice is None:
def conv(k):
if not np.allclose(k, 0.0):
warn(
f"{self.__class__.__name__} cannot convert stored k-points from 1/Ang to reduced coordinates. "
"Please ensure parent=Hamiltonian, geometry=Geometry, or lattice=Lattice to ensure reduced k."
)
return k / _Bohr2Ang
else:
def conv(k):
return (k @ lattice.cell.T) / (2 * np.pi * _Bohr2Ang)
self._convert_k = conv
def _open_wfsx(self, mode, rewind=False):
"""Open the file unit for the WFSX file.
Here we also initialize some variables to keep track of the state of the read.
"""
self._fortran_open(mode, rewind=rewind)
# Here we initialize the variables that will keep track of the state of the read.
# The process for identification is done on this basis:
# _ik is the current (Python) index for the k-point to be read
# _ispin is the current (Python) index for the spin-index to be read (only has meaning for a spin-polarized
# WFSX files)
# _state is:
# -1 : the file-descriptor has just been opened (i.e. in front of header)
# 0 : it means that the file-descriptor is in front of basis information
# 1 : it means that the file-descriptor is in front of k point information
# 2 : it means that the file-descriptor is in front of k point WFSX values
#
self._state = -1
self._ispin = 0
self._ik = 0
def _close_wfsx(self):
"""Close the file unit for the WFSX file.
We clean the variables used to keep track of read state.
"""
self._fortran_close()
# Clean variables
del self._state
del self._ik
del self._ispin
try:
del self._sizes
except Exception:
pass
try:
del self._basis
except Exception:
pass
try:
del self._funcs
except Exception:
pass
def _setup_parsing(self, close=True, skip_basis=True):
"""Gets all the things needed to parse the wfsx file.
Parameters
-----------
close: bool, optional
Whether the file unit should be closed afterwards.
skip_basis : bool, optional
whether to also read the basis or not
"""
self._open_wfsx("r")
# Read the sizes relevant to the file.
# We also read whether there's only gamma point information or there are multiple points
self._sizes = self._r_next_sizes(skip_basis=skip_basis)
if not skip_basis:
self._basis = self._r_next_basis()
# Get the functions that should be used to parse state values.
if self._sizes.nspin in (4, 8):
# We will have twice as many coefficients.
func_index = 4
elif self._sizes.Gamma:
# State values will be in double precision floats
func_index = 1
else:
# State values will be in double precision complex
func_index = 2
Funcs = namedtuple("WFSXReads", ["read_index", "read_next"])
self._funcs = Funcs(
getattr(_siesta, f"read_wfsx_index_{func_index}"),
getattr(_siesta, f"read_wfsx_next_{func_index}"),
)
if close:
self._close_wfsx()
def _r_next_sizes(self, skip_basis=False):
"""Reads the sizes if they are the next thing to be read.
Parameters
-----------
skip_basis: boolean, optional
Whether this method should also skip over the basis information.
Returns
-------
namedtuple :
- 'nspin': int. Number of spin components.
- 'no_u': int. Number of orbitals in the unit cell.
- 'nk': int. Number of k points in the file.
- 'Gamma': bool. Whether the file contains only the gamma point.
"""
# Check that we are in the right position in the file
if self._state != -1:
raise SileError(
f"We are not in a position to read the sizes. State is: {self._state}"
)
# Read the sizes that we can find in the WFSX file
Sizes = namedtuple("Sizes", ["nspin", "no_u", "nk", "Gamma"])
sizes = _siesta.read_wfsx_next_sizes(self._iu, skip_basis)
# Inform that we are now in front of k point information
self._state = 1 if skip_basis else 0
# Check that everything went fine
self._fortran_check("_r_next_sizes", "could not read sizes")
return Sizes(*sizes)
def _r_next_basis(self):
"""Reads the basis if it is the next thing to be read.
Returns
-------
Atoms:
the basis read.
"""
# Check that we are in the right position in the file
if self._state != 0:
raise SileError(
f"We are not in a position to read the basis. State is: {self._state}"
)
# Read the basis information that we can find in the WFSX file
basis_info = _siesta.read_wfsx_next_basis(self._iu, self._sizes.no_u)
# Inform that we are now in front of k point information
self._state = 1
# Check that everything went fine
self._fortran_check("_r_next_basis", "could not read basis information")
# Convert the information to a dict so that code is easier to follow.
basis_info = dict(
zip(
(
"atom_indices",
"atom_labels",
"orb_index_atom",
"orb_n",
"orb_symmetry",
),
basis_info,
)
)
# Sanitize the string information
for char_key in ("atom_labels", "orb_symmetry"):
basis_info[char_key] = np.array(
["".join(label).rstrip() for label in basis_info[char_key].astype(str)]
)
# Find out the unique atom indices
unique_ats = np.unique(basis_info["atom_indices"])
def _get_atom_object(at):
"""Given an atom index, generates an Atom object with all the information we have about it"""
atom_orbs = np.where(basis_info["atom_indices"] == at)[0]
at_label = basis_info["atom_labels"][atom_orbs[0]]
orbitals = [
AtomicOrbital(f"{n}{symmetry}")
for n, symmetry in zip(
basis_info["orb_n"][atom_orbs],
basis_info["orb_symmetry"][atom_orbs],
)
]
return Atom(at_label, orbitals=orbitals)
# Generate the Atoms oject.
return Atoms([_get_atom_object(at) for at in unique_ats])
def _r_next_info(self, ispin, ik):
"""Reads the next eigenstate information.
This function should only be called after reading the sizes
or reading the previous states values.
Parameters
----------
ispin: integer
the (python) spin index of the next eigenstate.
ik: integer
the (python) k index of the next eigenstate.
Returns
-------
array of shape (3,):
The k point of the state.
float:
The weight of the k point.
int:
Number of wavefunctions that the state contains. It is needed by the
function that reads the eigenstates values.
"""
# Store the indices of the current state
self._ik = ik
self._ispin = ispin
# Check that we are in a position where we will read state information
if self._state != 1:
raise SileError(
f"We are not in a position to read k point information. State is: {self._state}"
)
# Read the state information
file_ispin, file_ik, k, weight, nwf = _siesta.read_wfsx_next_info(self._iu)
# Inform that we are now in front of state values
self._state = 2
# Check that the read went fine
self._fortran_check(
"_r_next_info",
f"could not read next eigenstate info [{ispin + 1}, {ik + 1}]",
)
# Check that the read indices match the indices that we were expecting.
if file_ispin != ispin + 1 or file_ik != ik + 1:
self._ik = file_ik - 1
self._ispin = file_ispin - 1
raise SileError(
f"WFSX indices do not match the expected ones. Expected: [{ispin + 1}, {ik + 1}], found [{file_ispin}, {file_ik}]"
)
return k, weight, nwf
def _r_next_values(self, ispin, ik, nwf):
"""Reads the next eigenstate values.
This function should only be called after reading the states information
Parameters
----------
ispin: integer
the (python) spin index of the next eigenstate.
ik: integer
the (python) k index of the next eigenstate.
nwf: integer
The number of wavefunctions that the next eigenstate contains.
Should have been obtained by reading the states info with `_r_next_info`.
Returns
-------
array of shape (nwf,):
The indices of each wavefunction that the state contains.
array of shape (nwf,):
The eigenvalues (in eV) of each wavefunction that the state contains.
array of shape (norbitals, nwf):
The coefficients for each wavefunction that the state contains.
"""
# Check that we are in the right position in the file
if self._state != 2:
raise SileError(
f"We are not in a position to read k point WFSX values. State is: {self._state}"
)
# Read the state values
idx, eig, state = self._funcs.read_next(self._iu, self._sizes.no_u, nwf)
# Inform that we are now in front of the next state info
self._state = 1
# Check that everything went fine
self._fortran_check(
"_r_next_values",
f"could not read next eigenstate values [{ispin + 1}, {ik + 1}]",
)
return idx, eig, state
def _r_next_eigenstate(self, ispin, ik):
"""Reads the next eigenstate in the WFSX file.
Parameters
----------
ispin: integer
the (python) spin index of the next eigenstate.
ik: integer
the (python) k index of the next eigenstate.
Returns
--------
EigenstateElectron:
The next eigenstate.
"""
# Get the information of this eigenstate
k, weight, nwf = self._r_next_info(ispin, ik)
# Now that we have the information, we can read the values because
# we know the number of wavefunctions stored in the k point (`nwf`)
idx, eig, state = self._r_next_values(ispin, ik, nwf)
# Build the info dictionary for the eigenstate to know how it was calculated
# We include the spin index if needed.
info = dict(k=self._convert_k(k), weight=weight, gauge="orbital", index=idx - 1)
if self._sizes.nspin == 2:
info["spin"] = ispin
# `eig` is already in eV
# See onlysSileSiesta.read_lattice to understand why we transpose `state`
return EigenstateElectron(state.T, eig, parent=self._parent, **info)
[docs]
def read_sizes(self):
"""Reads the sizes related to this WFSX file
Returns
-------
int : number of spin components
int : number of orbitals in the unit-cell
int : number of k-points
bool : True if the file only contains the Gamma-point
"""
self._open_wfsx("r")
sizes = self._r_next_sizes()
self._close_wfsx()
return sizes
[docs]
def read_basis(self) -> Atoms:
"""Reads the basis contained in the WFSX file.
The WFSX file only contains information about the atom labels, which atom
each orbital belongs to and the orbital quantum numbers. It is thus not
complete in every sense.
Returns
-------
Atoms:
the basis read.
"""
self._open_wfsx("r")
self._sizes = self._r_next_sizes(skip_basis=False)
basis = self._r_next_basis()
self._close_wfsx()
return basis
[docs]
def yield_eigenstate(self):
r"""Iterates over the states in the WFSX file
Yields
------
EigenstateElectron
"""
# Open file and get parsing information
self._setup_parsing(close=False)
if self._sizes.nspin == 2:
itt_spin = range(2)
else:
itt_spin = range(1)
try:
# Iterate over all eigenstates in the WFSX file, yielding control to the caller at
# each iteration.
for ik, ispin in product(range(self._sizes.nk), itt_spin):
yield self._r_next_eigenstate(ispin, ik)
# We ran out of eigenstates
self._close_wfsx()
except GeneratorExit:
# The loop in which the generator was used has been broken.
self._close_wfsx()
[docs]
@deprecate_argument(
"ktol",
"atol",
"use atol instead of ktol",
"0.15",
"0.17",
)
def read_eigenstate(
self, k=(0, 0, 0), spin: int = 0, atol: float = 1e-4
) -> EigenstateElectron:
"""Reads a specific eigenstate from the file.
This method iterates over the states until it finds a match. Do not call
this method repeatedly. If you want to loop eigenstates, use `yield_eigenstate`.
Parameters
----------
k: array-like of shape (3,), optional
The k point of the state you want to find.
spin:
The spin index of the state you want to find. Only meaningful for polarized
calculations.
atol:
The threshold value for considering two k-points the same (i.e. to match
the query k point with the states k point).
See Also
--------
yield_eigenstate
Returns
-------
EigenstateElectron or None:
If found, the state that was queried.
Raises
------
LookupError :
in case the requested k-point can not be found in the file.
"""
# Iterate over all eigenstates in the file
for state in self.yield_eigenstate():
info = state.info
if info.get("spin", 0) == spin and np.allclose(info["k"], k, atol=atol):
# This is the state that the user requested
return state
raise LookupError(
f"{self.__class__.__name__}.read_eigenstate could not find k-point: {k!s} eigenstate"
)
[docs]
def read_info(self):
"""Reads the information for all the k points contained in the file
Returns
-------
k: array of shape (nk, 3)
k values of the k points contained in the file.
weight: array of shape (nk,)
weight of each k point
nwf: array of shape (nspin, nk)
number of wavefunctions that each kpoint(-spin) contains.
"""
# Open file and get parsing information
self._setup_parsing(close=False, skip_basis=True)
# Check if we are in the correct position in the file (we should be just after the header)
if self._state != 1:
raise ValueError(
f"We are not in a position to read eigenstate info in the file. State: {self._state}"
)
if self._sizes.nspin == 2:
nspin = 2
else:
nspin = 1
# Read all the information. Parse here the k values obtained.
# Store the information that should be returned under `returns`.
k, kw, nwf = _siesta.read_wfsx_next_all_info(self._iu, nspin, self._sizes.nk)
# Close the file unit
self._close_wfsx()
# Check for errors in the read.
self._fortran_check("read_info", "could not read file information.")
return self._convert_k(k), kw, nwf
[docs]
def read_brillouinzone(self) -> BrillouinZone:
"""Read the brillouin zone object"""
k, weight, _ = self.read_info()
return BrillouinZone(self._parent, k=k, weight=weight)
@set_module("sisl.io.siesta")
class _gridSileSiesta(SileBinSiesta):
r"""Binary real-space grid file
The Siesta binary grid sile will automatically convert the units from Siesta
units (Bohr, Ry) to sisl units (Ang, eV) provided the correct extension is present.
"""
def read_lattice(self, *args, **kwargs) -> Lattice:
r"""Return the cell contained in the file"""
cell = _siesta.read_grid_cell(self.file).T * _Bohr2Ang
self._fortran_check("read_lattice", "could not read cell.")
return Lattice(cell)
def read_grid_size(self):
r"""Query grid size information such as the grid size and number of spin components
Returns
-------
int : number of spin-components
mesh : 3 values for the number of mesh-elements
"""
# Read the sizes
nspin, mesh = _siesta.read_grid_sizes(self.file)
self._fortran_check("read_grid_size", "could not read grid sizes.")
return nspin, mesh
def read_grid(self, index=0, dtype=np.float64, *args, **kwargs) -> Grid:
"""Read grid contained in the Grid file
Parameters
----------
index : int or str or array_like, optional
the spin-index for retrieving one of the components. If a vector
is passed it refers to the fraction per indexed component. I.e.
``[0.5, 0.5]`` will return sum of half the first two components.
Default to the first component.
dtype : numpy.float64, optional
default data-type precision
spin : optional
same as `index` argument. `spin` argument has precedence.
"""
index = kwargs.get("spin", index)
# Read the sizes and cell
nspin, mesh = self.read_grid_size()
lattice = self.read_lattice()
grid = _siesta.read_grid(self.file, nspin, mesh[0], mesh[1], mesh[2])
self._fortran_check("read_grid", "could not read grid.")
if isinstance(index, str):
index = index.lower()
# convert to factors
if index == "z":
index = [1, -1]
elif index == "x":
index = [0, 0, 2]
elif index == "y":
index = [0, 0, 0, 2]
elif index == "total":
index = [1]
if nspin > 1:
index.append(1)
if len(index) > nspin:
raise ValueError(
f"{self.__class__.__name__}.read_grid got a wrong spin request for the grid values."
)
if isinstance(index, Integral):
grid = grid[:, :, :, index]
else:
grid = grid_reduce_indices(grid, index, axis=-1)
# Simply create the grid (with no information)
# We will overwrite the actual grid
g = Grid([1, 1, 1], lattice=lattice)
# NOTE: there is no need to swap-axes since the returned array is in F ordering
# and thus the first axis is the fast (x, y, z) is retained
g.grid = grid * self.grid_unit
return g
def write_grid(self, *grids: Grid) -> None:
"""Write the grid to the file"""
self._fortran_open("w")
_siesta.write_grid_header(
self._iu,
len(grids),
*grids[0].shape,
_toF(grids[0].cell.T, np.float64, 1.0 / _Bohr2Ang),
)
for grid in grids:
if grid.shape != grids[0].shape:
raise ValueError(
f"{self.__class__.__name__}.write_grid got grids with different shapes."
)
if grid.dtype in (np.float32, np.float64):
_siesta.write_grid_sp(
self._iu,
_toF(grid.grid, np.float32, 1.0 / self.grid_unit),
*grid.shape,
)
else:
raise ValueError(
f"{self.__class__.__name__}.write_grid can only write real-valued grids."
)
self._fortran_close()
@set_module("sisl.io.siesta")
class _gfSileSiesta(SileBinSiesta):
"""Surface Green function file containing, Hamiltonian, overlap matrix and self-energies
Do not mix read and write statements when using this code. Complete one or the other
before doing the other thing. Fortran does not allow the same file opened twice, if this
is needed you are recommended to make a symlink to the file and thus open two different
files.
This small snippet reads/writes the GF file
>>> with sisl.io._gfSileSiesta("hello.GF") as f:
... nspin, no, k, E = f.read_header()
... for ispin, new_k, k, E in f:
... if new_k:
... H, S = f.read_hamiltonian()
... SeHSE = f.read_self_energy()
To write a file do:
>>> with sisl.io._gfSileSiesta("hello.GF") as f:
... f.write_header(sisl.MonkhorstPack(...), E)
... for ispin, new_k, k, E in f:
... if new_k:
... f.write_hamiltonian(H, S)
... f.write_self_energy(SeHSE)
"""
def _open_gf(self, mode, rewind=False):
self._fortran_open(mode, rewind=rewind)
# They will at any given time
# correspond to the current Python indices that is to be read
# The process for identification is done on this basis:
# iE is the current (Python) index for the energy-point to be read
# ik is the current (Python) index for the k-point to be read
# ispin is the current (Python) index for the spin-index to be read (only has meaning for a spin-polarized
# GF files)
# state is:
# -1 : the file-descriptor has just been opened (i.e. in front of header)
# 0 : it means that the file-descriptor IS in front of H and S
# 1 : it means that the file-descriptor is NOT in front of H and S but somewhere in front of a self-energy
# is_read is:
# 0 : means that the current indices HAVE NOT been read
# 1 : means that the current indices HAVE been read
#
# All routines in the gf_read/write sources requires input in Python indices
self._state = -1
self._is_read = 0
self._ispin = 0
self._ik = 0
self._iE = 0
def _close_gf(self):
if not self._fortran_is_open():
return
self._fortran_close()
# Clean variables
del self._state
del self._iE
del self._ik
del self._ispin
try:
del self._no_u
except Exception:
pass
try:
del self._nspin
except Exception:
pass
def _step_counter(self, method, **kwargs):
"""Method for stepping values *must* be called before doing the actual read to check correct values"""
opt = {"method": method}
if kwargs.get("header", False):
# The header only exists once, so check whether it is the correct place to read/write
if self._state != -1 or self._is_read == 1:
raise SileError(
f"{self.__class__.__name__}.{method} failed because the header has already "
"been read."
)
self._state = -1
self._ispin = 0
self._ik = 0
self._iE = 0
# print("HEADER: ", self._state, self._ispin, self._ik, self._iE)
elif kwargs.get("HS", False):
# Correct for the previous state and jump values
if self._state == -1:
# We have just read the header
if self._is_read != 1:
raise SileError(
f"{self.__class__.__name__}.{method} failed because the file descriptor "
"has not read the header."
)
# Reset values as though the header has just been read
self._state = 0
self._ispin = 0
self._ik = 0
self._iE = 0
elif self._state == 0:
if self._is_read == 1:
raise SileError(
f"{self.__class__.__name__}.{method} failed because the file descriptor "
"has already read the current HS for the given k-point."
)
elif self._state == 1:
# We have just read from the last energy-point
if self._iE + 1 != self._nE or self._is_read != 1:
raise SileError(
f"{self.__class__.__name__}.{method} failed because the file descriptor "
"has not read all energy-points for a given k-point."
)
self._state = 0
self._ik += 1
if self._ik >= self._nk:
# We need to step spin
self._ispin += 1
self._ik = 0
self._iE = 0
# print("HS: ", self._state, self._ispin, self._ik, self._iE)
if self._ispin >= self._nspin:
opt["spin"] = self._ispin + 1
opt["nspin"] = self._nspin
raise SileError(
f"{self.__class__.__name__}.{method} failed because of missing information, "
"a non-existing entry has been requested! spin={spin} max_spin={nspin}."
)
else:
# We are reading an energy-point
if self._state == -1:
raise SileError(
f"{self.__class__.__name__}.{method} failed because the file descriptor "
"has an unknown state."
)
elif self._state == 0:
if self._is_read == 1:
# Fine, we have just read the HS, ispin and ik are correct
self._state = 1
self._iE = 0
else:
raise SileError(
f"{self.__class__.__name__}.{method} failed because the file descriptor "
"has an unknown state."
)
elif self._state == 1:
if self._is_read == 0 and self._iE < self._nE:
# we have not read the current energy-point.and self._iE + 1 < self._nE:
pass
elif self._is_read == 1 and self._iE + 1 < self._nE:
self._iE += 1
else:
raise SileError(
f"{self.__class__.__name__}.{method} failed because the file descriptor "
"has an unknown state."
)
if self._iE >= self._nE:
# You are trying to read beyond the entry
opt["iE"] = self._iE + 1
opt["NE"] = self._nE
raise SileError(
f"{self.__class__.__name__}.{method} failed because of missing information, "
f"a non-existing energy-point has been requested! E_index={self._iE+1} max_E_index={self._nE}."
)
# print("SE: ", self._state, self._ispin, self._ik, self._iE)
# Always signal (when stepping) that we have not yet read the thing
if kwargs.get("read", False):
self._is_read = 1
else:
self._is_read = 0
def Eindex(self, E):
"""Return the closest energy index corresponding to the energy ``E``
Parameters
----------
E : float or int
if ``int``, return it-self, else return the energy index which is
closests to the energy.
"""
if isinstance(E, Integral):
return E
idxE = np.abs(self._E - E).argmin()
ret_E = self._E[idxE]
if abs(ret_E - E) > 5e-3:
warn(
self.__class__.__name__
+ " requesting energy "
+ f"{E:.5f} eV, found {ret_E:.5f} eV as the closest energy!"
)
elif abs(ret_E - E) > 1e-3:
info(
self.__class__.__name__
+ " requesting energy "
+ f"{E:.5f} eV, found {ret_E:.5f} eV as the closest energy!"
)
return idxE
def kindex(self, k):
"""Return the index of the k-point that is closests to the queried k-point (in reduced coordinates)
Parameters
----------
k : array_like of float or int
the queried k-point in reduced coordinates :math:`]-0.5;0.5]`. If ``int``
return it-self.
"""
if isinstance(k, Integral):
return k
ik = np.sum(np.abs(self._k - _a.asarrayd(k)[None, :]), axis=1).argmin()
ret_k = self._k[ik, :]
if not np.allclose(ret_k, k, atol=0.0001):
warn(
SileWarning(
self.__class__.__name__
+ " requesting k-point "
+ "[{:.3f}, {:.3f}, {:.3f}]".format(*k)
+ " found "
+ "[{:.3f}, {:.3f}, {:.3f}]".format(*ret_k)
)
)
return ik
def read_header(self):
"""Read the header of the file and open it for reading subsequently
NOTES: this method may change in the future
Returns
-------
nspin : number of spin-components stored (1 or 2)
no_u : size of the matrices returned
k : k points in the GF file
E : energy points in the GF file
"""
# Ensure it is open (in read-mode)
if self._fortran_is_open():
_siesta.io_m.rewind_file(self._iu)
else:
self._open_gf("r")
nspin, no_u, nkpt, NE = _siesta.read_gf_sizes(self._iu)
self._fortran_check("read_header", "could not read sizes.")
self._nspin = nspin
self._nk = nkpt
self._nE = NE
# We need to rewind (because of k and energy -points)
_siesta.io_m.rewind_file(self._iu)
self._step_counter("read_header", header=True, read=True)
k, E = _siesta.read_gf_header(self._iu, nkpt, NE)
self._fortran_check("read_header", "could not read header information.")
if self._nspin > 2: # non-colinear
self._no_u = no_u * 2
else:
self._no_u = no_u
self._E = E * _Ry2eV
self._k = k.T
return nspin, no_u, self._k, self._E
def disk_usage(self):
"""Calculate the estimated size of the resulting file
Returns
-------
estimated disk-space used in GB
"""
is_open = self._fortran_is_open()
if not is_open:
self.read_header()
# HS are only stored per k-point
HS = 2 * self._nspin * self._nk
SE = HS / 2 * self._nE
# Now calculate the full size
# no_u ** 2 = matrix size
# 16 = bytes in double complex
# 1024 ** 3 = B -> GB
mem = (HS + SE) * self._no_u**2 * 16 / 1024**3
if not is_open:
self._close_gf()
return mem
def read_hamiltonian(self) -> tuple[np.ndarray, np.ndarray]:
"""Return current Hamiltonian and overlap matrix from the GF file
Returns
-------
complex128 : Hamiltonian matrix
complex128 : Overlap matrix
"""
self._step_counter("read_hamiltonian", HS=True, read=True)
H, S = _siesta.read_gf_hs(self._iu, self._no_u)
self._fortran_check(
"read_hamiltonian", "could not read Hamiltonian and overlap matrices."
)
# we don't convert to C order!
return H * _Ry2eV, S
def read_self_energy(self) -> np.ndarray:
r"""Read the currently reached bulk self-energy
The returned self-energy is:
.. math::
\boldsymbol \Sigma_{\mathrm{bulk}}(E) = \mathbf S E - \mathbf H - \boldsymbol \Sigma(E)
Returns
-------
complex128 : Self-energy matrix
"""
self._step_counter("read_self_energy", read=True)
SE = _siesta.read_gf_se(self._iu, self._no_u, self._iE)
self._fortran_check("read_self_energy", "could not read self-energy.")
# we don't convert to C order!
return SE * _Ry2eV
def HkSk(self, k=(0, 0, 0), spin: int = 0) -> tuple[np.ndarray, np.ndarray]:
"""Retrieve H and S for the given k-point
Parameters
----------
k : int or array_like of float, optional
k-point to read the corresponding Hamiltonian and overlap matrices
for. If a specific k-point is passed `kindex` will be used to find
the corresponding index.
spin :
spin-index for the Hamiltonian and overlap matrices
"""
if not self._fortran_is_open():
self.read_header()
# find k-index that is requested
ik = self.kindex(k)
_siesta.read_gf_find(
self._iu,
self._nspin,
self._nk,
self._nE,
self._state,
self._ispin,
self._ik,
self._iE,
self._is_read,
0,
spin,
ik,
0,
)
self._fortran_check("HkSk", "could not find Hamiltonian and overlap matrix.")
self._state = 0
self._ispin = spin
self._ik = ik
self._iE = 0
self._is_read = 0 # signal this is to be read
return self.read_hamiltonian()
def self_energy(self, E, k=0, spin: int = 0) -> np.ndarray:
"""Retrieve self-energy for a given energy-point and k-point
Parameters
----------
E : int or float
energy to retrieve self-energy at
k : int or array_like of float, optional
k-point to retrieve k-point at
spin :
spin-index to retrieve self-energy at
"""
if not self._fortran_is_open():
self.read_header()
ik = self.kindex(k)
iE = self.Eindex(E)
_siesta.read_gf_find(
self._iu,
self._nspin,
self._nk,
self._nE,
self._state,
self._ispin,
self._ik,
self._iE,
self._is_read,
1,
spin,
ik,
iE,
)
self._fortran_check("self_energy", "could not find requested self-energy.")
self._state = 1
self._ispin = spin
self._ik = ik
self._iE = iE
self._is_read = 0 # signal this is to be read
return self.read_self_energy()
def write_header(self, bz, E, mu: float = 0.0, obj=None):
"""Write to the binary file the header of the file
Parameters
----------
bz : BrillouinZone
contains the k-points, the weights and possibly the parent Hamiltonian (if `obj` is None)s
E : array_like of cmplx or float
the energy points. If `obj` is an instance of `SelfEnergy` where an
associated ``eta`` is defined then `E` may be float, otherwise
it *has* to be a complex array.
mu :
chemical potential in the file
obj : ..., optional
an object that contains the Hamiltonian definitions, defaults to ``bz.parent``
"""
if obj is None:
obj = bz.parent
nspin = obj.spin.size(obj.dtype)
cell = obj.geometry.lattice.cell
na_u = obj.geometry.na
no_u = obj.geometry.no
xa = obj.geometry.xyz
# The lasto in siesta requires lasto(0) == 0
# and secondly, the Python index to fortran
# index makes firsto behave like fortran lasto
lasto = obj.geometry.firsto
bloch = _a.onesi(3)
NE = len(E)
if E.dtype not in [np.complex64, np.complex128]:
E = E + 1j * obj.eta
Nk = len(bz)
k = bz.k
w = bz.weight
sizes = {
"na_used": na_u,
"nkpt": Nk,
"ne": NE,
}
self._nspin = nspin
self._E = E
self._k = np.copy(k)
self._nE = len(E)
self._nk = len(k)
if self._nspin > 2:
self._no_u = no_u * 2
else:
self._no_u = no_u
# Ensure it is open (in write mode)
self._close_gf()
self._open_gf("w")
# Now write to it...
self._step_counter("write_header", header=True, read=True)
# see onlysSileSiesta.read_lattice for .T
_siesta.write_gf_header(
self._iu,
nspin,
_toF(cell.T, np.float64, 1.0 / _Bohr2Ang),
na_u,
no_u,
no_u,
_toF(xa.T, np.float64, 1.0 / _Bohr2Ang),
lasto,
bloch,
0,
mu * _eV2Ry,
_toF(k.T, np.float64),
w,
self._E / _Ry2eV,
**sizes,
)
self._fortran_check("write_header", "could not write header information.")
def write_hamiltonian(self, H, S=None):
"""Write the current energy, k-point and H and S to the file
Parameters
----------
H : matrix
a square matrix corresponding to the Hamiltonian
S : matrix, optional
a square matrix corresponding to the overlap, for efficiency reasons
it may be advantageous to specify this argument for orthogonal cells.
"""
no = len(H)
if S is None:
S = np.eye(no, dtype=np.complex128, order="F")
self._step_counter("write_hamiltonian", HS=True, read=True)
_siesta.write_gf_hs(
self._iu,
self._ik,
self._E[self._iE] / _Ry2eV,
_toF(H, np.complex128, _eV2Ry),
_toF(S, np.complex128),
no_u=no,
)
self._fortran_check(
"write_hamiltonian", "could not write Hamiltonian and overlap matrices."
)
def write_self_energy(self, SE):
r"""Write the current self energy, k-point and H and S to the file
The self-energy must correspond to the *bulk* self-energy
.. math::
\boldsymbol \Sigma_{\mathrm{bulk}}(E) = \mathbf S E - \mathbf H - \boldsymbol \Sigma(E)
Parameters
----------
SE : matrix
a square matrix corresponding to the self-energy (Green function)
"""
no = len(SE)
self._step_counter("write_self_energy", read=True)
_siesta.write_gf_se(
self._iu,
self._ik,
self._iE,
self._E[self._iE] / _Ry2eV,
_toF(SE, np.complex128, _eV2Ry),
no_u=no,
)
self._fortran_check("write_self_energy", "could not write self-energy.")
def __len__(self):
return self._nE * self._nk * self._nspin
def __iter__(self):
"""Iterate through the energies and k-points that this GF file is associated with
Yields
------
bool, list of float, float
"""
# get everything
e = self._E
if self._nspin in [1, 2]:
GFStep = namedtuple("GFStep", ["spin", "do_HS", "k", "E"])
for ispin in range(self._nspin):
for k in self._k:
yield GFStep(ispin, True, k, e[0])
for E in e[1:]:
yield GFStep(ispin, False, k, E)
else:
GFStep = namedtuple("GFStep", ["do_HS", "k", "E"])
for k in self._k:
yield GFStep(True, k, e[0])
for E in e[1:]:
yield GFStep(False, k, E)
# We will automatically close once we hit the end
self._close_gf()
def _type(name, obj, dic=None):
if dic is None:
dic = {}
# Always pass the docstring
if not "__doc__" in dic:
try:
dic["__doc__"] = obj.__doc__.replace(obj.__name__, name)
except Exception:
pass
return type(name, (obj,), dic)
# Faster than class ... \ pass
tsgfSileSiesta = _type("tsgfSileSiesta", _gfSileSiesta)
gridSileSiesta = _type("gridSileSiesta", _gridSileSiesta, {"grid_unit": 1.0})
if has_fortran_module:
add_sile("TSHS", tshsSileSiesta)
add_sile("onlyS", onlysSileSiesta)
add_sile("TSDE", tsdeSileSiesta)
add_sile("DM", dmSileSiesta)
add_sile("HSX", hsxSileSiesta)
add_sile("TSGF", tsgfSileSiesta)
add_sile("WFSX", wfsxSileSiesta)
# These have unit-conversions
add_sile(
"RHO",
_type("rhoSileSiesta", _gridSileSiesta, {"grid_unit": 1.0 / _Bohr2Ang**3}),
)
add_sile(
"LDOS",
_type("ldosSileSiesta", _gridSileSiesta, {"grid_unit": 1.0 / _Bohr2Ang**3}),
)
add_sile(
"RHOINIT",
_type("rhoinitSileSiesta", _gridSileSiesta, {"grid_unit": 1.0 / _Bohr2Ang**3}),
)
add_sile(
"RHOXC",
_type("rhoxcSileSiesta", _gridSileSiesta, {"grid_unit": 1.0 / _Bohr2Ang**3}),
)
add_sile(
"DRHO",
_type("drhoSileSiesta", _gridSileSiesta, {"grid_unit": 1.0 / _Bohr2Ang**3}),
)
add_sile(
"BADER",
_type("baderSileSiesta", _gridSileSiesta, {"grid_unit": 1.0 / _Bohr2Ang**3}),
)
add_sile(
"IOCH",
_type("iorhoSileSiesta", _gridSileSiesta, {"grid_unit": 1.0 / _Bohr2Ang**3}),
)
add_sile(
"TOCH",
_type("totalrhoSileSiesta", _gridSileSiesta, {"grid_unit": 1.0 / _Bohr2Ang**3}),
)
# The following two files *require* that
# STM.DensityUnits Ele/bohr**3
# which I can't check!
# They are however the default
add_sile(
"STS",
_type("stsSileSiesta", _gridSileSiesta, {"grid_unit": 1.0 / _Bohr2Ang**3}),
)
add_sile(
"STM.LDOS",
_type("stmldosSileSiesta", _gridSileSiesta, {"grid_unit": 1.0 / _Bohr2Ang**3}),
)
add_sile("VH", _type("hartreeSileSiesta", _gridSileSiesta, {"grid_unit": _Ry2eV}))
add_sile(
"VNA",
_type("neutralatomhartreeSileSiesta", _gridSileSiesta, {"grid_unit": _Ry2eV}),
)
add_sile(
"VT", _type("totalhartreeSileSiesta", _gridSileSiesta, {"grid_unit": _Ry2eV})
)