# 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/.
# To check for integers
from __future__ import annotations
import logging
import warnings
from collections import OrderedDict
from functools import singledispatchmethod
from itertools import product
from math import acos
from numbers import Integral, Real
from pathlib import Path
from typing import Iterator, List, Optional, Sequence, Tuple, Union
import numpy as np
from numpy import (
argsort,
bool_,
ceil,
concatenate,
diff,
dot,
floor,
int32,
isin,
ndarray,
split,
sqrt,
square,
tile,
unique,
)
import sisl._array as _a
from sisl._category import Category, GenericCategory
from sisl._dispatch_class import _Dispatchs
from sisl._dispatcher import AbstractDispatch, ClassDispatcher, TypeDispatcher
from sisl._help import isndarray
from sisl._indices import (
indices_gt_le,
indices_in_sphere_with_dist,
indices_le,
list_index_le,
)
from sisl._internal import set_module
from sisl._math_small import cross3, is_ascending
from sisl._namedindex import NamedIndex
from sisl.messages import SislError, deprecate_argument, info, warn
from sisl.shape import Cube, Shape, Sphere
from sisl.typing import ArrayLike, AtomsIndex, NDArray, OrbitalsIndex, SileLike
from sisl.utils import (
angle,
cmd,
default_ArgumentParser,
default_namespace,
direction,
lstranges,
str_spec,
strmap,
)
from sisl.utils.mathematics import fnorm
from .atom import Atom, Atoms
from .lattice import Lattice, LatticeChild
from .orbital import Orbital
__all__ = ["Geometry", "sgeom", "AtomCategory"]
_log = logging.getLogger(__name__)
# It needs to be here otherwise we can't use it in these routines
# Note how we are overwriting the module
@set_module("sisl.geom")
class AtomCategory(Category):
__slots__ = ()
[docs]
@classmethod
def is_class(cls, name, case=True) -> bool:
# Strip off `Atom`
cls_name = cls.__name__[4:]
if case:
return cls_name == name
return cls_name.lower() == name.lower()
@set_module("sisl")
class Geometry(
LatticeChild,
_Dispatchs,
dispatchs=[
ClassDispatcher("new", obj_getattr="error", instance_dispatcher=TypeDispatcher),
ClassDispatcher("to", obj_getattr="error", type_dispatcher=None),
],
when_subclassing="copy",
):
"""Holds atomic information, coordinates, species, lattice vectors
The `Geometry` class holds information regarding atomic coordinates,
the atomic species, the corresponding lattice-vectors.
It enables the interaction and conversion of atomic structures via
simple routine methods.
All lengths are assumed to be in units of Angstrom, however, as
long as units are kept same the exact units are irrespective.
.. code:: python
>>> square = Geometry([[0.5, 0.5, 0.5]], Atom(1),
... lattice=Lattice([1, 1, 10], nsc=[3, 3, 1]))
>>> print(square)
Geometry{na: 1, no: 1,
Atoms{species: 1,
Atom{H, Z: 1, mass(au): 1.00794, maxR: -1.00000,
Orbital{R: -1.00000, q0: 0.0}
}: 1,
},
maxR: -1.00000,
Lattice{volume: 1.0000e+01, nsc: [3 3 1]}
}
Parameters
----------
xyz : array_like
atomic coordinates
``xyz[i, :]`` is the atomic coordinate of the i'th atom.
atoms : array_like or Atoms
atomic species retrieved from the `PeriodicTable`
lattice : Lattice
the unit-cell describing the atoms in a periodic
super-cell
Examples
--------
An atomic cubic lattice of Hydrogen atoms
>>> xyz = [[0, 0, 0],
... [1, 1, 1]]
>>> sc = Lattice([2,2,2])
>>> g = Geometry(xyz, Atom('H'), sc)
The following estimates the lattice vectors from the
atomic coordinates, although possible, it is not recommended
to be used.
>>> xyz = [[0, 0, 0],
... [1, 1, 1]]
>>> g = Geometry(xyz, Atom('H'))
Conversion of geometries to other projects instances can be done via
sisl's dispatch functionality
>>> g.to.ase()
Atoms(...)
converts to an ASE `ase.Atoms` object.
See Also
--------
Atoms : contained atoms ``self.atoms``
Atom : contained atoms are each an object of this
"""
[docs]
@deprecate_argument(
"sc",
"lattice",
"argument sc has been deprecated in favor of lattice, please update your code.",
"0.15",
"0.16",
)
def __init__(self, xyz: ArrayLike, atoms=None, lattice=None, names=None):
# Create the geometry coordinate, be aware that we do not copy!
self.xyz = _a.asarrayd(xyz, order="C").reshape(-1, 3)
# Default value
if atoms is None:
atoms = Atom("H")
# Create the local Atoms object
self._atoms = Atoms(atoms, na=self.na)
# Assign a group specifier
if isinstance(names, NamedIndex):
self._names = names.copy()
else:
self._names = NamedIndex(names)
self._init_lattice(lattice)
def _init_lattice(self, lattice):
"""Initializes the supercell by *calculating* the size if not supplied
If the supercell has not been passed we estimate the unit cell size
by calculating the bond-length in each direction for a square
Cartesian coordinate system.
"""
# We still need the *default* super cell for
# estimating the supercell
self.set_lattice(lattice)
if lattice is not None:
return
# First create an initial guess for the supercell
# It HAS to be VERY large to not interact
closest = self.close(0, R=(0.0, 0.4, 5.0))[2]
if len(closest) < 1:
# We could not find any atoms very close,
# hence we simply return and now it becomes
# the users responsibility
# We create a molecule box with +10 A in each direction
m, M = np.amin(self.xyz, axis=0), np.amax(self.xyz, axis=0) + 10.0
self.set_lattice(M - m)
return
sc_cart = _a.zerosd([3])
cart = _a.zerosd([3])
for i in range(3):
# Initialize cartesian direction
cart[i] = 1.0
# Get longest distance between atoms
max_dist = np.amax(self.xyz[:, i]) - np.amin(self.xyz[:, i])
dist = self.xyz[closest, :] - self.xyz[0, :][None, :]
# Project onto the direction
dd = np.abs(dot(dist, cart))
# Remove all below .4
tmp_idx = (dd >= 0.4).nonzero()[0]
if len(tmp_idx) > 0:
# We have a success
# Add the bond-distance in the Cartesian direction
# to the maximum distance in the same direction
sc_cart[i] = max_dist + np.amin(dd[tmp_idx])
else:
# Default to LARGE array so as no
# interaction occurs (it may be 2D)
sc_cart[i] = max(10.0, max_dist)
cart[i] = 0.0
# Re-set the supercell to the newly found one
self.set_lattice(sc_cart)
@property
def atoms(self) -> Atoms:
"""Atoms associated with the geometry"""
return self._atoms
@property
def names(self):
"""The named index specifier"""
return self._names
@property
def q0(self) -> float:
"""Total initial charge in this geometry (sum of q0 off all atoms)"""
return self.atoms.q0.sum()
@property
def mass(self) -> ndarray:
"""The mass of all atoms as an array"""
return self.atoms.mass
[docs]
def maxR(self, all: bool = False) -> float:
"""Maximum orbital range of the atoms"""
return self.atoms.maxR(all)
@property
def na(self) -> int:
"""Number of atoms in geometry"""
return self.xyz.shape[0]
@property
def na_s(self) -> int:
"""Number of supercell atoms"""
return self.na * self.n_s
def __len__(self) -> int:
"""Number of atoms in geometry in unit cell"""
return self.na
@property
def no(self) -> int:
"""Number of orbitals in unit cell"""
return self.atoms.no
@property
def no_s(self) -> int:
"""Number of supercell orbitals"""
return self.no * self.n_s
@property
def firsto(self) -> NDArray[np.int32]:
"""The first orbital on the corresponding atom"""
return self.atoms.firsto
@property
def lasto(self) -> NDArray[np.int32]:
"""The last orbital on the corresponding atom"""
return self.atoms.lasto
@property
def orbitals(self) -> ndarray:
"""List of orbitals per atom"""
return self.atoms.orbitals
## End size of geometry
@property
def fxyz(self) -> NDArray[np.float64]:
"""Returns geometry coordinates in fractional coordinates"""
return dot(self.xyz, self.icell.T)
def __setitem__(self, atoms, value):
"""Specify geometry coordinates"""
if isinstance(atoms, str):
self.names.add_name(atoms, value)
elif isinstance(value, str):
self.names.add_name(value, atoms)
@singledispatchmethod
def __getitem__(self, atoms) -> ndarray:
"""Geometry coordinates (allows supercell indices)"""
return self.axyz(atoms)
@__getitem__.register
def _(self, atoms: slice) -> ndarray:
if atoms.stop is None:
atoms = atoms.indices(self.na)
else:
atoms = atoms.indices(self.na_s)
return self.axyz(_a.arangei(atoms[0], atoms[1], atoms[2]))
@__getitem__.register
def _(self, atoms: tuple) -> ndarray:
return self[atoms[0]][..., atoms[1]]
@singledispatchmethod
def _sanitize_atoms(self, atoms) -> ndarray:
"""Converts an `atoms` to index under given inputs
`atoms` may be one of the following:
- boolean array -> nonzero()[0]
- name -> self._names[name]
- `Atom` -> self.atoms.index(atom)
- range/list/ndarray -> ndarray
"""
if atoms is None:
return np.arange(self.na)
atoms = _a.asarray(atoms)
if atoms.size == 0:
return _a.asarrayl([])
if atoms.dtype == bool_:
return atoms.nonzero()[0]
return atoms
@_sanitize_atoms.register
def _(self, atoms: ndarray) -> ndarray:
if atoms.dtype == bool_:
return np.flatnonzero(atoms)
return atoms
@_sanitize_atoms.register
def _(self, atoms: slice) -> ndarray:
# TODO consider doing range(self.na)[atoms]
start, stop, step = atoms.start, atoms.stop, atoms.step
if start is None:
start = 0
if stop is None:
stop = self.na
if step is None:
step = 1
return np.arange(start, stop, step)
@_sanitize_atoms.register
def _(self, atoms: str) -> ndarray:
return self.names[atoms]
@_sanitize_atoms.register
def _(self, atoms: Atom) -> ndarray:
return self.atoms.index(atoms)
@_sanitize_atoms.register(AtomCategory)
@_sanitize_atoms.register(GenericCategory)
def _(
self,
atoms_: Union[AtomCategory, GenericCategory],
atoms: AtomsIndex = None,
) -> ndarray:
# First do categorization
cat = atoms_.categorize(self, atoms)
def m(cat):
for ia, c in enumerate(cat):
if c == None:
# we are using NullCategory == None
pass
else:
yield ia
return _a.fromiterl(m(cat))
@_sanitize_atoms.register
def _(self, atoms_: dict, atoms: AtomsIndex = None) -> ndarray:
# First do categorization
return self._sanitize_atoms(AtomCategory.kw(**atoms_), atoms)
@_sanitize_atoms.register
def _(self, atoms: Shape) -> ndarray:
# This is perhaps a bit weird since a shape could
# extend into the supercell.
# Since the others only does this for unit-cell atoms
# then it seems natural to also do that here...
return atoms.within_index(self.xyz)
@_sanitize_atoms.register
def _(self, atoms: bool) -> ndarray:
if atoms:
return np.arange(self.na)
return np.array([], np.int64)
@singledispatchmethod
def _sanitize_orbs(self, orbitals) -> ndarray:
"""Converts an `orbital` to index under given inputs
`orbital` may be one of the following:
- boolean array -> nonzero()[0]
- dict -> {atom: sub_orbital}
"""
if orbitals is None:
return np.arange(self.no)
orbitals = _a.asarray(orbitals)
if orbitals.size == 0:
return _a.asarrayl([])
elif orbitals.dtype == np.bool_:
return orbitals.nonzero()[0]
return orbitals
@_sanitize_orbs.register
def _(self, orbitals: ndarray) -> ndarray:
if orbitals.dtype == bool_:
return np.flatnonzero(orbitals)
return orbitals
@_sanitize_orbs.register
def _(self, orbitals: slice) -> ndarray:
start, stop, step = orbitals.start, orbitals.stop, orbitals.step
if start is None:
start = 0
if stop is None:
stop = self.na
if step is None:
step = 1
return np.arange(start, stop, step)
@_sanitize_orbs.register
def _(self, orbitals: str) -> ndarray:
atoms = self._sanitize_atoms(orbitals)
return self.a2o(atoms, all=True)
@_sanitize_orbs.register
def _(self, orbitals: Atom) -> ndarray:
atoms = self._sanitize_atoms(orbitals)
return self.a2o(atoms, all=True)
@_sanitize_orbs.register
def _(self, orbitals: AtomCategory) -> ndarray:
atoms = self._sanitize_atoms(orbitals)
return self.a2o(atoms, all=True)
@_sanitize_orbs.register
def _(self, orbitals: Shape) -> ndarray:
atoms = self._sanitize_atoms(orbitals)
return self.a2o(atoms, all=True)
@_sanitize_orbs.register
def _(self, orbitals: dict) -> ndarray:
"""A dict has atoms as keys"""
def conv(atom, orbs):
atom = self._sanitize_atoms(atom)
return np.add.outer(self.firsto[atom], orbs).ravel()
return np.concatenate(
tuple(conv(atom, orbs) for atom, orbs in orbitals.items())
)
@_sanitize_orbs.register
def _(self, orbitals: bool) -> ndarray:
if orbitals:
return np.arange(self.no)
return np.array([], dtype=np.int64)
[docs]
def as_primary(
self, na_primary: int, axes: Sequence[int] = (0, 1, 2), ret_super: bool = False
) -> Union[Geometry, Tuple[Geometry, Lattice]]:
"""Reduce the geometry to the primary unit-cell comprising `na_primary` atoms
This will basically try and find the tiling/repetitions required for the geometry to only have
`na_primary` atoms in the unit cell.
Parameters
----------
na_primary :
number of atoms in the primary unit cell
axes :
only search the given directions for supercells, default to all directions
ret_super :
also return the number of supercells used in each direction
Returns
-------
`Geometry`
the primary unit cell
`Lattice`
the tiled supercell numbers used to find the primary unit cell (only if `ret_super` is true)
Raises
------
`SislError`
If the algorithm fails.
"""
na = len(self)
if na % na_primary != 0:
raise ValueError(
f"{self.__class__.__name__}.as_primary requires the number of atoms to be divisable by the "
"total number of atoms."
)
axes = _a.arrayi(axes)
n_supercells = len(self) // na_primary
if n_supercells == 1:
# Return a copy of self
if ret_super:
return self.copy(), self.nsc.copy()
return self.copy()
# Now figure out the repetitions along each direction
fxyz = self.fxyz
# Move to 0
fxyz -= fxyz.min(0)
# Shift a little bit in to account for inaccuracies.
fxyz += (0.5 - (fxyz.max(0) - fxyz.min(0)) / 2) * 0.01
# Default guess to 1 along all directions
supercell = _a.onesi(3)
n_bin = n_supercells
while n_bin > 1:
# Create bins
bins = np.linspace(0, 1, n_bin + 1)
# Loop directions where we need to check
for axis in axes:
if supercell[axis] != 1:
continue
# A histogram should yield an equal splitting for each bins
# if the geometry is a n_bin repetition along the i'th direction.
# Hence if diff == 0 for all elements we have a match.
diff_bin = np.diff(np.histogram(fxyz[:, axis], bins)[0])
if diff_bin.sum() == 0:
supercell[axis] = n_bin
if np.prod(supercell) > n_supercells:
# For geometries with more than 1 atom in the primary unit cell
# we can get false positives (each layer can be split again)
# We will search again the max-value supercell
i_max = supercell.argmax()
n_bin = supercell[i_max]
supercell[i_max] = 1
# Quick escape if hit the correct number of supercells
if np.prod(supercell) == n_supercells:
break
n_bin -= 1
# Check that the number of supercells match
if np.prod(supercell) != n_supercells:
raise SislError(
f"{self.__class__.__name__}.as_primary could not determine the optimal supercell."
)
# Cut down the supercell (TODO this does not correct the number of supercell connections!)
lattice = self.lattice.copy()
for i in range(3):
lattice = lattice.untile(supercell[i], i)
# Now we need to find the atoms that are in the primary cell
# We do this by finding all coordinates within the primary unit-cell
fxyz = dot(self.xyz, lattice.icell.T)
# Move to 0 and shift in 0.05 Ang in each direction
fxyz -= fxyz.min(0)
# Find minimal distance in each direction
sc_idx = (supercell > 1).nonzero()[0]
min_fxyz = _a.zerosd(3)
for i in sc_idx:
s_fxyz = np.sort(fxyz[:, i])
min_fxyz[i] = s_fxyz[(s_fxyz < 1e-4).nonzero()[0][-1] + 1]
fxyz += min_fxyz * 0.05
# Find all fractional indices that are below 1
ind = np.logical_and.reduce(fxyz < 1.0, axis=1).nonzero()[0]
geom = self.sub(ind)
geom.set_lattice(lattice)
if ret_super:
return geom, supercell
return geom
[docs]
def as_supercell(self) -> Geometry:
"""Create a new geometry equivalent to ``self * self.nsc``, where the indices are ordered as the supercells
Returns
-------
`Geometry`
the supercell expanded and reordered Geometry
"""
# Get total number of atoms
na = len(self)
# create the big supercell geometry in the simplest (linear) way
sc = self * self.nsc
# remove nsc, this supercell should hold all information
sc.set_nsc([1, 1, 1])
# get off-set for first atom
# this is used to correct the indices created after having shifted
# everything
f0 = self.fxyz[0]
# translate the supercell such that the 0, 0, 0 (primary cell)
# is located at the origin.
sc = sc.translate(-(self.nsc // 2) @ self.cell)
# Calculate the translation table such that the ordering in `sc` can
# be made to look like the `self` supercell indices
isc_sc = np.rint(sc.xyz[::na] @ self.icell.T - f0).astype(np.int32)
isc_self = self.a2isc(np.arange(self.n_s) * na)
def new_sub(isc):
return (abs(isc_sc - isc).sum(1) == 0).nonzero()[0][0]
# Create the translation table for the indices
translate = np.array([new_sub(isc) for isc in isc_self])
# make sure all atoms are present
translate = np.repeat(translate * na, na).reshape(-1, na) + np.arange(na)
# re-arrange the atoms and return
return sc.sub(translate.ravel())
[docs]
def reorder(self) -> None:
"""Reorders atoms according to first occurence in the geometry
The atoms gets reordered according to their placement in the geometry.
For instance, if the first atom is the 2nd species in the geometry. Then
this routine will swap the 2nd and 1st species in the `self.atoms` object.
Notes
-----
This is an in-place operation.
"""
self._atoms = self.atoms.reorder(inplace=True)
[docs]
def reduce(self) -> None:
"""Remove all atoms not currently used in the ``self.atoms`` object
Notes
-----
This is an in-place operation.
"""
self._atoms = self.atoms.reduce(inplace=True)
[docs]
def rij(self, ia: AtomsIndex, ja: AtomsIndex) -> ndarray:
r"""Distance between atom `ia` and `ja`, atoms can be in super-cell indices
Returns the distance between two atoms:
.. math::
r^{IJ} = |\mathbf r^J - \mathbf r^I|
Parameters
----------
ia :
atomic index of first atom
ja :
atomic indices
"""
R = self.Rij(ia, ja)
if len(R.shape) == 1:
return (R[0] ** 2.0 + R[1] ** 2 + R[2] ** 2) ** 0.5
return fnorm(R)
[docs]
def Rij(self, ia: AtomsIndex, ja: AtomsIndex) -> ndarray:
r"""Vector between atom `ia` and `ja`, atoms can be in super-cell indices
Returns the vector between two atoms:
.. math::
\mathbf r^{IJ} = \mathbf r^J - \mathbf r^I
Parameters
----------
ia :
atomic index of first atom
ja :
atomic indices
"""
xi = self.axyz(ia)
xj = self.axyz(ja)
if isinstance(ja, Integral):
return xj[:] - xi[:]
elif np.allclose(xi.shape, xj.shape):
return xj - xi
return xj - xi[None, :]
[docs]
def orij(self, orbitals1: OrbitalsIndex, orbitals2: OrbitalsIndex) -> ndarray:
r"""Distance between orbital `orbitals1` and `orbitals2`, orbitals can be in super-cell indices
Returns the distance between two orbitals:
.. math::
r^{ij} = |\mathbf r^j - \mathbf r^i|
Parameters
----------
orbitals1 :
orbital index of first orbital
orbitals2 :
orbital indices
"""
return self.rij(self.o2a(orbitals1), self.o2a(orbitals2))
[docs]
def oRij(self, orbitals1: OrbitalsIndex, orbitals2: OrbitalsIndex) -> ndarray:
r"""Vector between orbital `orbitals1` and `orbitals2`, orbitals can be in super-cell indices
Returns the vector between two orbitals:
.. math::
\mathbf r^{ij} = \mathbf r^j - \mathbf r^i
Parameters
----------
orbitals1 :
orbital index of first orbital
orbitals2 :
orbital indices
"""
return self.Rij(self.o2a(orbitals1), self.o2a(orbitals2))
[docs]
@staticmethod
def read(sile: SileLike, *args, **kwargs) -> Geometry:
"""Reads geometry from the `Sile` using `Sile.read_geometry`
Parameters
----------
sile :
a `Sile` object which will be used to read the geometry
if it is a string it will create a new sile using `get_sile`.
See Also
--------
write : writes a `Geometry` to a given `Sile`/file
"""
# This only works because, they *must*
# have been imported previously
from sisl.io import BaseSile, get_sile
if isinstance(sile, BaseSile):
return sile.read_geometry(*args, **kwargs)
else:
with get_sile(sile, mode="r") as fh:
return fh.read_geometry(*args, **kwargs)
def __str__(self) -> str:
"""str of the object"""
s = f"{self.__class__.__name__}{{na: {self.na}, no: {self.no},\n "
s += str(self.atoms).replace("\n", "\n ")
if len(self.names) > 0:
s += ",\n " + str(self.names).replace("\n", "\n ")
return (
s
+ ",\n maxR: {0:.5f},\n {1}\n}}".format(
self.maxR(), str(self.lattice).replace("\n", "\n ")
)
).strip()
def __repr__(self) -> str:
"""A simple, short string representation."""
return f"<{self.__module__}.{self.__class__.__name__} na={self.na}, no={self.no}, nsc={self.nsc}>"
[docs]
def iter(self) -> Iterator[int]:
"""An iterator over all atomic indices
This iterator is the same as:
>>> for ia in range(len(self)):
... <do something>
or equivalently
>>> for ia in self:
... <do something>
See Also
--------
iter_species : iterate across indices and atomic species
iter_orbitals : iterate across atomic indices and orbital indices
"""
yield from range(len(self))
__iter__ = iter
[docs]
def iter_species(self, atoms: AtomsIndex = None) -> Iterator[int, Atom, int]:
"""Iterator over all atoms (or a subset) and species as a tuple in this geometry
>>> for ia, a, idx_species in self.iter_species():
... isinstance(ia, int) == True
... isinstance(a, Atom) == True
... isinstance(idx_species, int) == True
with ``ia`` being the atomic index, ``a`` the `Atom` object, ``idx_species``
is the index of the specie
Parameters
----------
atoms :
only loop on the given atoms, default to all atoms
See Also
--------
iter : iterate over atomic indices
iter_orbitals : iterate across atomic indices and orbital indices
"""
if atoms is None:
for ia in self:
yield ia, self.atoms[ia], self.atoms.species[ia]
else:
for ia in self._sanitize_atoms(atoms).ravel():
yield ia, self.atoms[ia], self.atoms.species[ia]
[docs]
def iter_orbitals(
self, atoms: AtomsIndex = None, local: bool = True
) -> Iterator[int, int]:
r"""Returns an iterator over all atoms and their associated orbitals
>>> for ia, io in self.iter_orbitals():
with ``ia`` being the atomic index, ``io`` the associated orbital index on atom ``ia``.
Note that ``io`` will start from ``0``.
Parameters
----------
atoms :
only loop on the given atoms, default to all atoms
local :
whether the orbital index is the global index, or the local index relative to
the atom it resides on.
Yields
------
ia
atomic index
io
orbital index
See Also
--------
iter : iterate over atomic indices
iter_species : iterate across indices and atomic species
"""
if atoms is None:
if local:
for ia, IO in enumerate(zip(self.firsto, self.lasto + 1)):
for io in range(IO[1] - IO[0]):
yield ia, io
else:
for ia, IO in enumerate(zip(self.firsto, self.lasto + 1)):
for io in range(IO[0], IO[1]):
yield ia, io
else:
atoms = self._sanitize_atoms(atoms).ravel()
if local:
for ia, io1, io2 in zip(
atoms, self.firsto[atoms], self.lasto[atoms] + 1
):
for io in range(io2 - io1):
yield ia, io
else:
for ia, io1, io2 in zip(
atoms, self.firsto[atoms], self.lasto[atoms] + 1
):
for io in range(io1, io2):
yield ia, io
[docs]
def iR(self, na: int = 1000, iR: int = 20, R: Optional[float] = None) -> int:
"""Return an integer number of maximum radii (``self.maxR()``) which holds approximately `na` atoms
Parameters
----------
na :
number of atoms within the radius
iR :
initial `iR` value, which the sphere is estitametd from
R :
the value used for atomic range (defaults to ``self.maxR()``)
Returns
-------
int
number of radius needed to contain `na` atoms. Minimally 2 will be returned.
"""
ia = np.random.randint(len(self))
# default block iterator
if R is None:
R = self.maxR() + 0.001
if R < 0:
raise ValueError(
f"{self.__class__.__name__}.iR unable to determine a number of atoms within a sphere with negative radius, is maxR() defined?"
)
# Number of atoms within 20 * R
naiR = max(1, len(self.close(ia, R=R * iR)))
# Convert to na atoms spherical radii
iR = int(4 / 3 * np.pi * R**3 / naiR * na)
return max(2, iR)
[docs]
def iter_block_rand(
self,
iR: int = 20,
R: Optional[float] = None,
atoms: AtomsIndex = None,
) -> Iterator[Tuple[ndarray, ndarray]]:
"""Perform the *random* block-iteration by randomly selecting the next center of block"""
# We implement yields as we can then do nested iterators
# create a boolean array
na = len(self)
if atoms is not None:
not_passed = np.zeros(na, dtype=bool)
# Reverse the values
not_passed[atoms] = True
else:
not_passed = np.ones(na, dtype=bool)
# Figure out how many we need to loop on
not_passed_N = np.sum(not_passed)
if iR < 2:
raise SislError(f"{self.__class__.__name__}.iter_block_rand too small iR!")
if R is None:
R = self.maxR() + 0.001
# The boundaries (ensure complete overlap)
R = np.array([iR - 0.5, iR + 0.501]) * R
# loop until all passed are true
while not_passed_N > 0:
# Take a random non-passed element
all_true = not_passed.nonzero()[0]
# Shuffle should increase the chance of hitting a
# completely "fresh" segment, thus we take the most
# atoms at any single time.
# Shuffling will cut down needed iterations.
np.random.shuffle(all_true)
# take one element, after shufling, we can take the first
idx = all_true[0]
del all_true
# Now we have found a new index, from which
# we want to create the index based stuff on
# get all elements within two radii
all_idx = self.close(idx, R=R)
# Get unit-cell atoms, we are drawing a circle, and this
# circle only encompasses those already in the unit-cell.
all_idx[1] = np.union1d(
self.sc2uc(all_idx[0], unique=True), self.sc2uc(all_idx[1], unique=True)
)
# If we translated stuff into the unit-cell, we could end up in situations
# where the supercell atom is in the circle, but not the UC-equivalent
# of that one.
all_idx[0] = all_idx[0][all_idx[0] < na]
# Only select those who have not been runned yet
all_idx[0] = all_idx[0][not_passed[all_idx[0]].nonzero()[0]]
if len(all_idx[0]) == 0:
continue
# Tell the next loop to skip those passed
not_passed[all_idx[0]] = False
# Update looped variables
not_passed_N -= len(all_idx[0])
# Now we want to yield the stuff revealed
# all_idx[0] contains the elements that should be looped
# all_idx[1] contains the indices that can be searched
yield all_idx[0], all_idx[1]
if np.any(not_passed):
print(not_passed.nonzero()[0])
print(np.sum(not_passed), len(self))
raise SislError(
f"{self.__class__.__name__}.iter_block_rand error on iterations. Not all atoms have been visited."
)
[docs]
def iter_block_shape(
self, shape=None, iR: int = 20, atoms: AtomsIndex = None
) -> Iterator[Tuple[ndarray, ndarray]]:
"""Perform the *grid* block-iteration by looping a grid"""
# We implement yields as we can then do nested iterators
# create a boolean array
na = len(self)
if atoms is not None:
not_passed = np.zeros(na, dtype=bool)
# Reverse the values
not_passed[atoms] = True
else:
not_passed = np.ones(na, dtype=bool)
# Figure out how many we need to loop on
not_passed_N = np.sum(not_passed)
if iR < 2:
raise SislError(f"{self.__class__.__name__}.iter_block_shape too small iR!")
R = self.maxR() + 0.001
if shape is None:
# we default to the Cube shapes
dS = (Cube((iR - 0.5) * R), Cube((iR + 1.501) * R))
else:
if isinstance(shape, Shape):
dS = (shape,)
else:
dS = tuple(shape)
if len(dS) == 1:
dS += (dS[0].expand(R),)
if len(dS) != 2:
raise ValueError(
f"{self.__class__.__name__}.iter_block_shape, number of Shapes *must* be one or two"
)
# Now create the Grid
# convert the radius to a square Grid
# We do this by examining the x, y, z coordinates
xyz_m = np.amin(self.xyz, axis=0)
xyz_M = np.amax(self.xyz, axis=0)
dxyz = xyz_M - xyz_m
# Currently iterating different shapes only works for
# Sphere and Cube
for s in dS:
if not isinstance(s, (Cube, Sphere)):
raise ValueError(
f"{self.__class__.__name__}.iter_block_shape currently only works for "
"Cube or Sphere objects. Please change sources."
)
# Retrieve the internal diameter
if isinstance(dS[0], Cube):
ir = dS[0].edge_length
elif isinstance(dS[0], Sphere):
ir = [dS[0].radius * 0.5**0.5 * 2] * 3
elif isinstance(dS[0], Shape):
# Convert to spheres (which probably should be cubes for performance)
dS = [s.to.Sphere() for s in dS]
# Now do the same with spheres
ir = [dS[0].radius * 0.5**0.5 * 2] * 3
# Figure out number of segments in each iteration
# (minimum 1)
ixyz = _a.arrayi(ceil(dxyz / ir + 0.0001))
# Calculate the steps required for each iteration
for i in (0, 1, 2):
dxyz[i] = dxyz[i] / ixyz[i]
# Correct the initial position to offset the initial displacement
# so that we are at the border.
xyz_m[i] += min(dxyz[i], ir[i]) / 2
if xyz_m[i] > xyz_M[i]:
# This is the case where one of the cell dimensions
# is far too great.
# In this case ixyz[i] should be 1
xyz_m[i] = (xyz_M[i] - xyz_m[i]) / 2
# Shorthand function
where = np.where
# Now we loop in each direction
for x, y, z in product(range(ixyz[0]), range(ixyz[1]), range(ixyz[2])):
# Create the new center
center = xyz_m + [x * dxyz[0], y * dxyz[1], z * dxyz[2]]
# Correct in case the iteration steps across the maximum
center = where(center < xyz_M, center, xyz_M)
dS[0].center = center[:]
dS[1].center = center[:]
# Now perform the iteration
# get all elements within two radii
all_idx = self.within(dS)
# Get unit-cell atoms, we are drawing a circle, and this
# circle only encompasses those already in the unit-cell.
all_idx[1] = np.union1d(
self.sc2uc(all_idx[0], unique=True), self.sc2uc(all_idx[1], unique=True)
)
# If we translated stuff into the unit-cell, we could end up in situations
# where the supercell atom is in the circle, but not the UC-equivalent
# of that one.
all_idx[0] = all_idx[0][all_idx[0] < na]
# Only select those who have not been runned yet
all_idx[0] = all_idx[0][not_passed[all_idx[0]].nonzero()[0]]
if len(all_idx[0]) == 0:
continue
# Tell the next loop to skip those passed
not_passed[all_idx[0]] = False
# Update looped variables
not_passed_N -= len(all_idx[0])
# Now we want to yield the stuff revealed
# all_idx[0] contains the elements that should be looped
# all_idx[1] contains the indices that can be searched
yield all_idx[0], all_idx[1]
if np.any(not_passed):
not_passed = not_passed.nonzero()[0]
raise SislError(
f"{self.__class__.__name__}.iter_block_shape error on iterations. Not all atoms have been visited "
f"{not_passed}"
)
[docs]
def iter_block(
self,
iR: int = 20,
R: Optional[float] = None,
atoms: AtomsIndex = None,
method: str = "rand",
) -> Iterator[Tuple[ndarray, ndarray]]:
"""Iterator for performance critical loops
NOTE: This requires that `R` has been set correctly as the maximum interaction range.
I.e. the loop would look like this:
>>> for ias, idxs in self.iter_block():
... for ia in ias:
... idx_a = self.close(ia, R = R, idx = idxs)
This iterator is intended for systems with more than 1000 atoms.
Remark that the iterator used is non-deterministic, i.e. any two iterators need
not return the same atoms in any way.
Parameters
----------
iR :
the number of `R` ranges taken into account when doing the iterator
R :
enables overwriting the local R quantity. Defaults to ``self.maxR() + 0.001``
atoms :
enables only effectively looping a subset of the full geometry
method : {'rand', 'sphere', 'cube'}
select the method by which the block iteration is performed.
Possible values are:
`rand`: a spherical object is constructed with a random center according to the internal atoms
`sphere`: a spherical equispaced shape is constructed and looped
`cube`: a cube shape is constructed and looped
Yields
-------
numpy.ndarray
current list of atoms currently searched
numpy.ndarray
atoms that needs searching
"""
if iR < 2:
raise SislError(f"{self.__class__.__name__}.iter_block too small iR!")
method = method.lower()
if method in ("rand", "random"):
yield from self.iter_block_rand(iR, R, atoms)
elif method in ("sphere", "cube"):
if R is None:
R = self.maxR() + 0.001
# Create shapes
if method == "sphere":
dS = (Sphere((iR - 0.5) * R), Sphere((iR + 0.501) * R))
elif method == "cube":
dS = (
Cube((2 * iR - 0.5) * R),
# we need an extra R here since it needs to extend on both sides
Cube((2 * iR + 1.501) * R),
)
yield from self.iter_block_shape(dS)
else:
raise ValueError(
f"{self.__class__.__name__}.iter_block got unexpected 'method' argument: {method}"
)
[docs]
@deprecate_argument(
"eps",
"atol",
"argument eps has been deprecated in favor of atol",
"0.15",
"0.16",
)
def overlap(
self,
other: GeometryLikeType,
atol: float = 0.1,
offset: Sequence[float] = (0.0, 0.0, 0.0),
offset_other: Sequence[float] = (0.0, 0.0, 0.0),
) -> Tuple[ndarray, ndarray]:
"""Calculate the overlapping indices between two geometries
Find equivalent atoms (in the primary unit-cell only) in two geometries.
This routine finds which atoms have the same atomic positions in `self` and `other`.
Note that this will return duplicate overlapping atoms if one atoms lies within `eps`
of more than 1 atom in `other`.
Parameters
----------
other :
Geometry to compare with `self`
atol :
atoms within this distance will be considered *equivalent*
offset :
offset for `self.xyz` before comparing
offset_other :
offset for `other.xyz` before comparing
Examples
--------
>>> gr22 = sisl.geom.graphene().tile(2, 0).tile(2, 1)
>>> gr44 = gr22.tile(2, 0).tile(2, 1)
>>> offset = np.array([0.2, 0.4, 0.4])
>>> gr22 = gr22.translate(offset)
>>> idx = np.arange(len(gr22))
>>> np.random.shuffle(idx)
>>> gr22 = gr22.sub(idx)
>>> idx22, idx44 = gr22.overlap(gr44, offset=-offset)
>>> assert idx22 == np.arange(len(gr22))
>>> assert idx44 == idx
Returns
-------
idx_self : numpy.ndarray of int
indices in `self` that are equivalent with `idx_other`
idx_other : numpy.ndarray of int
indices in `other` that are equivalent with `idx_self`
"""
# sanitize `other`
other = self.new(other)
s_xyz = self.xyz + (_a.arrayd(offset) - _a.arrayd(offset_other))
idx_self = []
self_extend = idx_self.extend
idx_other = []
other_extend = idx_other.extend
for ia, xyz in enumerate(s_xyz):
# only search in the primary unit-cell
idx = other.close_sc(xyz, R=(atol,))
self_extend([ia] * idx.size)
other_extend(idx)
return _a.arrayi(idx_self), _a.arrayi(idx_other)
[docs]
def optimize_nsc(
self,
axes: Optional[Union[int, Sequence[int]]] = None,
R: Optional[float] = None,
) -> ndarray:
"""Optimize the number of supercell connections based on ``self.maxR()``
After this routine the number of supercells may not necessarily be the same.
This is an in-place operation.
Parameters
----------
axes :
only optimize the specified axes (default to all)
R :
the maximum connection radius for each atom
"""
if axes is None:
axes = [0, 1, 2]
else:
axes = _a.asarrayi(axes).ravel()
if len(axes) == 0:
return self.nsc
if R is None:
R = self.maxR() + 0.001
if R < 0:
R = 0.00001
warn(
f"{self.__class__.__name__}"
".optimize_nsc could not determine the radius from the "
"internal atoms (defaulting to zero radius)."
)
ic = self.icell
nrc = 1 / fnorm(ic)
idiv = floor(np.maximum(nrc / (2 * R), 1.1)).astype(np.int32, copy=False)
imcell = ic * idiv.reshape(-1, 1)
# We know this is the maximum
nsc = self.nsc.copy()
# We need to subtract one to ensure we are not taking into account
# too big supercell connections.
# I don't think we need anything other than this.
# However, until I am sure that this wouldn't change, regardless of the
# cell. I will keep it.
Rimcell = R * fnorm(imcell)[axes]
nsc[axes] = (floor(Rimcell) + ceil(Rimcell % 0.5 - 0.5)).astype(np.int32)
# Since for 1 it is not sure that it is a connection or not, we limit the search by
# removing it.
nsc[axes] = np.where(nsc[axes] > 1, nsc[axes], 0)
for i in axes:
# Initialize the isc for this direction
# (note we do not take non-orthogonal directions
# into account)
isc = _a.zerosi(3)
isc[i] = nsc[i]
# Initialize the actual number of supercell connections
# along this direction.
prev_isc = isc[i]
while prev_isc == isc[i]:
# Try next supercell connection
isc[i] += 1
with warnings.catch_warnings():
warnings.simplefilter("ignore")
for ia in self:
idx = self.close_sc(ia, isc=isc, R=R)
if len(idx) > 0:
prev_isc = isc[i]
break
# Save the reached supercell connection
nsc[i] = prev_isc * 2 + 1
self.set_nsc(nsc)
return nsc
[docs]
def sub_orbital(self, atoms: AtomsIndex, orbitals: OrbitalsIndex) -> Geometry:
r"""Retain only a subset of the orbitals on `atoms` according to `orbitals`
This allows one to retain only a given subset of geometry.
Parameters
----------
atoms : array_like of int or Atom
indices of atoms or `Atom` that will be reduced in size according to `orbitals`
orbitals : array_like of int or Orbital
indices of the orbitals on `atoms` that are retained in the geometry, the list of
orbitals will be sorted.
Notes
-----
Future implementations may allow one to re-arange orbitals using this method.
When using this method the internal species list will be populated by another specie
that is named after the orbitals removed. This is to distinguish different atoms.
Examples
--------
>>> # a Carbon atom with 2 orbitals
>>> C = sisl.Atom('C', [1., 2.])
>>> # an oxygen atom with 3 orbitals
>>> O = sisl.Atom('O', [1., 2., 2.4])
>>> geometry = sisl.Geometry([[0] * 3, [1] * 3]], 2, [C, O])
Now ``geometry`` is a geometry with 2 different species and 6 atoms (3 of each).
They are ordered ``[C, O, C, O, C, O]``. In the following we
will note species that are different from the original by a ``'`` in the list.
Retain 2nd orbital on the 2nd atom: ``[C, O', C, O, C, O]``
>>> new_geom = geometry.sub_orbital(1, 1)
Retain 2nd orbital on 1st and 2nd atom: ``[C', O', C, O, C, O]``
>>> new_geom = geometry.sub_orbital([0, 1], 1)
Retain 2nd orbital on the 1st atom and 3rd orbital on 4th atom: ``[C', O, C, O', C, O]``
>>> new_geom = geometry.sub_orbital(0, 1).sub_orbital(3, 2)
Retain 2nd orbital on all atoms equivalent to the first atom: ``[C', O, C', O, C', O]``
>>> new_geom = geometry.sub_orbital(obj.geometry.atoms[0], 1)
Retain 1st orbital on 1st atom, and 2nd orbital on 3rd and 5th atom: ``[C', O, C'', O, C'', O]``
>>> new_geom = geometry.sub_orbital(0, 0).sub_orbital([2, 4], 1)
See Also
--------
remove_orbital : removing a set of orbitals (opposite of this)
"""
atoms = self._sanitize_atoms(atoms).ravel()
# Figure out if all atoms have the same species
species = self.atoms.species[atoms]
uniq_species, indices = unique(species, return_inverse=True)
if len(uniq_species) > 1:
# In case there are multiple different species but one wishes to
# retain the same orbital index, then we loop on the unique species
new = self
for i in range(uniq_species.size):
idx = (indices == i).nonzero()[0]
# now determine whether it is the whole atom
# or only part of the geometry
new = new.sub_orbital(atoms[idx], orbitals)
return new
# At this point we are sure that uniq_species is *only* one specie!
geom = self.copy()
# Get the atom object we wish to reduce
old_atom = geom.atoms[atoms[0]]
old_atom_species = geom.atoms.species_index(old_atom)
old_atom_count = (geom.atoms.species == old_atom_species).sum()
if isinstance(orbitals, (Orbital, Integral)):
orbitals = [orbitals]
if isinstance(orbitals[0], Orbital):
orbitals = [old_atom.index(orb) for orb in orbitals]
orbitals = np.sort(orbitals)
if len(orbitals) == 0:
raise ValueError(
f"{self.__class__.__name__}.sub_orbital trying to retain 0 orbitals on a given atom. This is not allowed!"
)
# create the new atom
new_atom = old_atom.sub(orbitals)
# Rename the new-atom to <>_1_2 for orbital == [1, 2]
new_atom._tag += "_" + "_".join(map(str, orbitals))
# There are now 2 cases.
# 1. we replace all atoms of a given specie
# 2. we replace a subset of atoms of a given specie
if len(atoms) == old_atom_count:
# We catch the warning about reducing the number of orbitals!
with warnings.catch_warnings():
warnings.filterwarnings("ignore")
# this is in-place operation and we don't need to worry about
geom.atoms.replace_atom(old_atom, new_atom)
else:
# we have to add the new one (in case it does not exist)
try:
new_atom_species = geom.atoms.species_index(new_atom)
except Exception:
new_atom_species = geom.atoms.nspecies
# the above checks that it is indeed a new atom
geom._atoms._atom.append(new_atom)
# transfer specie index
geom.atoms._species[atoms] = new_atom_species
geom.atoms._update_orbitals()
return geom
[docs]
def remove_orbital(self, atoms: AtomsIndex, orbitals: OrbitalsIndex) -> Geometry:
"""Remove a subset of orbitals on `atoms` according to `orbitals`
For more detailed examples, please see the equivalent (but opposite) method
`sub_orbital`.
Parameters
----------
atoms : array_like of int or Atom
indices of atoms or `Atom` that will be reduced in size according to `orbitals`
orbitals : array_like of int or Orbital
indices of the orbitals on `atoms` that are removed from the geometry.
See Also
--------
sub_orbital : retaining a set of orbitals (see here for examples)
"""
# Get specie index of the atom (convert to list of indices)
atoms = self._sanitize_atoms(atoms).ravel()
# Figure out if all atoms have the same species
species = self.atoms.species[atoms]
uniq_species, indices = unique(species, return_inverse=True)
if len(uniq_species) > 1:
# In case there are multiple different species but one wishes to
# retain the same orbital index, then we loop on the unique species
new = self
for i in range(uniq_species.size):
idx = (indices == i).nonzero()[0]
# now determine whether it is the whole atom
# or only part of the geometry
new = new.remove_orbital(atoms[idx], orbitals)
return new
# Get the atom object we wish to reduce
# We know np.all(geom.atoms[atom] == old_atom)
old_atom = self.atoms[atoms[0]]
if isinstance(orbitals, (Orbital, Integral)):
orbitals = [orbitals]
if isinstance(orbitals[0], Orbital):
orbitals = [old_atom.index(orb) for orb in orbitals]
orbitals = np.delete(_a.arangei(len(old_atom)), np.asarray(orbitals).ravel())
orbitals = np.sort(orbitals)
# now call sub_orbital
return self.sub_orbital(atoms, orbitals)
def __mul__(self, m, method="tile") -> Geometry:
"""Implement easy tile/repeat function
Parameters
----------
m : int or tuple or list or (tuple, str) or (list, str)
a tuple/list may be of length 2 or 3. A length of 2 corresponds
to tuple[0] == *number of multiplications*, tuple[1] is the
axis.
A length of 3 corresponds to each of the directions.
An optional string may be used to specify the `tile` or `repeat` function.
The default is the `tile` function.
Examples
--------
>>> geometry = Geometry([0.] * 3, lattice=[1.5, 3, 4])
>>> geometry * 2 == geometry.tile(2, 0).tile(2, 1).tile(2, 2)
True
>>> geometry * [2, 1, 2] == geometry.tile(2, 0).tile(2, 2)
True
>>> geometry * [2, 2] == geometry.tile(2, 2)
True
>>> geometry * ([2, 1, 2], 'repeat') == geometry.repeat(2, 0).repeat(2, 2)
True
>>> geometry * ([2, 1, 2], 'r') == geometry.repeat(2, 0).repeat(2, 2)
True
>>> geometry * ([2, 0], 'r') == geometry.repeat(2, 0)
True
>>> geometry * ([2, 2], 'r') == geometry.repeat(2, 2)
True
See Also
--------
tile : specific method to enlarge the geometry
repeat : specific method to enlarge the geometry
"""
# Simple form
if isinstance(m, Integral):
return self * [m, m, m]
# Error in argument, fall-back
if len(m) == 1:
return self * m[0]
# Look-up table
method_tbl = {"r": "repeat", "repeat": "repeat", "t": "tile", "tile": "tile"}
# Determine the type
if len(m) == 2:
# either
# (r, axis)
# ((...), method
if isinstance(m[1], str):
method = method_tbl[m[1]]
m = m[0]
g = self
if len(m) == 1:
# r
m = m[0]
for i in range(3):
g = getattr(g, method)(m, i)
elif len(m) == 2:
# (r, axis)
g = getattr(g, method)(m[0], m[1])
elif len(m) == 3:
# (r, r, r)
for i in range(3):
g = getattr(g, method)(m[i], i)
else:
raise ValueError(f"Multiplying a geometry got an unexpected value: {m}")
return g
def __rmul__(self, m) -> Geometry:
"""Default to repeating the atomic structure"""
return self.__mul__(m, "repeat")
[docs]
def angle(
self,
atoms: AtomsIndex,
dir: Union[str, int, Sequence[int]] = (1.0, 0, 0),
ref: Optional[Union[int, Sequence[float]]] = None,
rad: bool = False,
) -> Union[float, ndarray]:
r"""The angle between atom `atoms` and the direction `dir`, with possibility of a reference coordinate `ref`
The calculated angle can be written as this
.. math::
\theta = \arccos \frac{(\mathbf r^I - \mathbf{r^{\mathrm{ref}}})\cdot \mathbf{d}}
{|\mathbf r^I-\mathbf{r^{\mathrm{ref}}}||\mathbf{d}|}
and thus lies in the interval :math:`[0 ; \pi]` as one cannot distinguish orientation without
additional vectors.
Parameters
----------
atoms :
indices/boolean of all atoms where angles should be calculated on
dir :
the direction from which the angle is calculated from, default to ``x``.
An integer specifies the corresponding lattice vector as the direction.
ref :
the reference point from which the vectors are drawn, default to origin
An integer specifies an atomic index.
rad :
whether the returned value is in radians
"""
xi = self.axyz(atoms)
if isinstance(dir, (str, Integral)):
dir = direction(dir, abc=self.cell, xyz=np.diag([1] * 3))
else:
dir = _a.asarrayd(dir)
# Normalize so we don't have to have this in the
# below formula
dir = dir / fnorm(dir)
if ref is None:
pass
elif isinstance(ref, Integral):
xi -= self.axyz(ref)[None, :]
else:
xi -= _a.asarrayd(ref)[None, :]
nx = sqrt(square(xi).sum(1))
ang = np.zeros_like(nx)
idx = (nx > 1e-6).nonzero()[0]
ang[idx] = np.arccos(xi[idx] @ dir / nx[idx])
if rad:
return ang
return np.degrees(ang)
[docs]
def rotate_miller(self, m, v) -> Geometry:
"""Align Miller direction along ``v``
Rotate geometry and cell such that the Miller direction
points along the Cartesian vector ``v``.
"""
# Create normal vector to miller direction and cartesian
# direction
cp = _a.arrayd(
[
m[1] * v[2] - m[2] * v[1],
m[2] * v[0] - m[0] * v[2],
m[0] * v[1] - m[1] * v[0],
]
)
cp /= fnorm(cp)
lm = _a.arrayd(m)
lm /= fnorm(lm)
lv = _a.arrayd(v)
lv /= fnorm(lv)
# Now rotate the angle between them
a = acos(np.sum(lm * lv))
return self.rotate(a, cp, rad=True)
[docs]
def translate2uc(
self,
atoms: AtomsIndex = None,
axes: Optional[Union[int, bool, Sequence[int]]] = None,
) -> Geometry:
"""Translates atoms in the geometry into the unit cell
One can translate a subset of the atoms or axes by appropriate arguments.
Warning
-------
When coordinates are lying on one of the edges, they may move to the other
side of the unit-cell due to small rounding errors.
In such situations you are encouraged to shift all coordinates by a small
amount to remove numerical errors, in the following case we have atomic
coordinates lying close to the lower side of each lattice vector.
>>> geometry.translate(1e-8).translate2uc().translate(-1e-8)
Notes
-----
By default only the periodic axes will be translated to the UC. If
translation is required for all axes, supply them directly.
Parameters
----------
atoms :
only translate the given atomic indices, if not specified, all
atoms will be translated
axes :
only translate certain lattice directions, `None` specifies
only the directions with supercells, `True` specifies all
directions.
"""
if axes is None:
axes = (self.lattice.nsc > 1).nonzero()[0]
elif isinstance(axes, bool):
if axes:
axes = (0, 1, 2)
else:
raise ValueError(
"translate2uc with a bool argument can only be True to signal all axes"
)
fxyz = self.fxyz
# move to unit-cell
fxyz[:, axes] %= 1
g = self.copy()
# convert back
if atoms is None:
g.xyz[:, :] = fxyz @ self.cell
else:
idx = self._sanitize_atoms(atoms).ravel()
g.xyz[idx] = fxyz[idx] @ self.cell
return g
[docs]
def add_vacuum(
self, vacuum: float, axis: int, offset: Sequence[float] = (0, 0, 0)
) -> Geometry:
"""Add vacuum along the `axis` lattice vector
When the vacuum is bigger than the maximum orbital ranges the
number of supercells along that axis will be truncated to 1 (de-couple
images).
Parameters
----------
vacuum :
amount of vacuum added, in Ang
axis :
the lattice vector to add vacuum along
offset :
offset in geometry when adding the vacuum.
Returns
-------
Geometry : a new geometry with added vacuum
"""
new = self.copy()
new.xyz += _a.arrayd(offset)
new.set_lattice(self.lattice.add_vacuum(vacuum, axis))
if vacuum > self.maxR() + 0.001:
# only overwrite along axis
nsc = [None for _ in range(3)]
nsc[axis] = 1
new.lattice.set_nsc(nsc)
return new
def __add__(self, b) -> Geometry:
"""Merge two geometries (or geometry and supercell)
Parameters
----------
self, b : Geometry or Lattice or tuple or list
when adding a Geometry with a Geometry it defaults to using `add` function
with the LHS retaining the cell-vectors.
a tuple/list may be of length 2 with the first element being a Geometry and the second
being an integer specifying the lattice vector where it is appended.
One may also use a `Lattice` instead of a `Geometry` which behaves similarly.
Examples
--------
>>> A + B == A.add(B)
>>> A + (B, 1) == A.append(B, 1)
>>> A + (B, 2) == A.append(B, 2)
>>> (A, 1) + B == A.append(B, 1)
See Also
--------
add : add geometries
append : appending geometries
prepend : prending geometries
"""
if isinstance(b, (Lattice, Geometry)):
return self.add(b)
return self.append(b[0], b[1])
def __radd__(self, b) -> Geometry:
"""Merge two geometries (or geometry and supercell)
Parameters
----------
self, b : Geometry or Lattice or tuple or list
when adding a Geometry with a Geometry it defaults to using `add` function
with the LHS retaining the cell-vectors.
a tuple/list may be of length 2 with the first element being a Geometry and the second
being an integer specifying the lattice vector where it is appended.
One may also use a `Lattice` instead of a `Geometry` which behaves similarly.
Examples
--------
>>> A + B == A.add(B)
>>> A + (B, 1) == A.append(B, 1)
>>> A + (B, 2) == A.append(B, 2)
>>> (A, 1) + B == A.append(B, 1)
See Also
--------
add : add geometries
append : appending geometries
prepend : prending geometries
"""
if isinstance(b, (Lattice, Geometry)):
return b.add(self)
return self + b
[docs]
def attach(
self,
atom: int,
other: GeometryLike,
other_atom: int,
dist="calc",
axis: Optional[int] = None,
) -> Geometry:
"""Attaches another `Geometry` at the `atom` index with respect to `other_atom` using different methods.
The attached geometry will be inserted at the end of the geometry via `add`.
Parameters
----------
atom : int
atomic index which is the base position of the attachment. The distance
between `atom` and `other_atom` is `dist`.
other : Geometry
the other Geometry to attach at the given point. In this case `dist` from
`atom`.
other_atom : int
the index of the atom in `other` that is inserted at `atom`.
dist : array_like or float or str, optional
the distance (in `Ang`) between the attached coordinates.
If `dist` is `array_like` it should be the vector between
the atoms;
if `dist` is `float` the argument `axis` is required
and the vector will be calculated along the corresponding latticevector;
else if `dist` is `str` this will correspond to the
`method` argument of the `Atom.radius` class of the two
atoms. Here `axis` is also required.
axis : int
specify the direction of the lattice vectors used.
Not used if `dist` is an array-like argument.
"""
other = self.new(other)
if isinstance(dist, Real):
# We have a single rational number
if axis is None:
raise ValueError(
f"{self.__class__.__name__}.attach, `axis` has not been specified, please specify the axis when using a distance"
)
# Now calculate the vector that we should have
# between the atoms
v = self.cell[axis, :]
v = v / (v @ v) ** 0.5 * dist
elif isinstance(dist, str):
# We have a single rational number
if axis is None:
raise ValueError(
f"{self.__class__.__name__}.attach, `axis` has not been specified, please specify the axis when using a distance"
)
# This is the empirical distance between the atoms
d = self.atoms[atom].radius(dist) + other.atoms[other_atom].radius(dist)
if isinstance(axis, Integral):
v = self.cell[axis, :]
else:
v = np.array(axis)
v = v / (v @ v) ** 0.5 * d
else:
# The user *must* have supplied a vector
v = np.array(dist)
# Now create a copy of the other geometry
# so that we move it...
# Translate to origin, then back to position in new cell
o = other.translate(-other.xyz[other_atom] + self.xyz[atom] + v)
# We do not know how to handle the lattice-vectors,
# so we will do nothing...
return self.add(o)
[docs]
def replace(
self,
atoms: AtomsIndex,
other: GeometryLike,
offset: Sequence[float] = (0.0, 0.0, 0.0),
) -> Geometry:
"""Create a new geometry from `self` and replace `atoms` with `other`
Parameters
----------
atoms :
atoms in `self` to be removed and replaced by other
`other` will be placed in the geometry at the lowest index of `atoms`
other :
the other Geometry to insert instead, the unit-cell will not
be used.
offset :
the offset for `other` when adding its coordinates, default to no offset
"""
# Find lowest value in atoms
atoms = self._sanitize_atoms(atoms)
index = atoms.min()
if offset is None:
offset = _a.zerosd(3)
# remove atoms, preparing for inserting new geometry
out = self.remove(atoms)
other = self.new(other)
# insert new positions etc.
out.xyz = np.insert(out.xyz, index, other.xyz + offset, axis=0)
out._atoms = out.atoms.insert(index, other.atoms)
return out
[docs]
def reverse(self, atoms: AtomsIndex = None) -> Geometry:
"""Returns a reversed geometry
Also enables reversing a subset of the atoms.
Parameters
----------
atoms :
only reverse the given atomic indices, if not specified, all
atoms will be reversed
"""
if atoms is None:
xyz = self.xyz[::-1, :]
else:
atoms = self._sanitize_atoms(atoms).ravel()
xyz = np.copy(self.xyz)
xyz[atoms, :] = self.xyz[atoms[::-1], :]
return self.__class__(
xyz, atoms=self.atoms.reverse(atoms), lattice=self.lattice.copy()
)
[docs]
def mirror(
self,
method,
atoms: AtomsIndex = None,
point: Sequence[float] = (0, 0, 0),
) -> Geometry:
r"""Mirrors the atomic coordinates about a plane given by its normal vector
This will typically move the atomic coordinates outside of the unit-cell.
This method should be used with care.
Parameters
----------
method : {'xy'/'z', ..., 'ab', ..., v}
mirror the structure about a Cartesian direction (``x``, ``y``, ``z``),
plane (``xy``, ``xz``, ``yz``) or about user defined vectors (``v``).
A vector may also be specified by ``'ab'`` which is the vector normal
to the plane spanned by the first and second lattice vector.
or user defined vector (`v`) which is defining a plane.
atoms :
only mirror a subset of atoms
point:
mirror coordinates around the plane that intersects the *method* vector
and this point
Examples
--------
>>> geom = geom.graphene()
>>> out = geom.mirror('x')
>>> out.xyz[:, 0]
[0. -1.42]
>>> out = geom.mirror('x', point=(1.42/2, 0, 0))
>>> out.xyz[:, 0]
[1.42 0.]
"""
atoms = self._sanitize_atoms(atoms)
point = _a.asarrayd(point)
if isinstance(method, str):
method = "".join(sorted(method.lower()))
if method in ("z", "xy"):
method = _a.arrayd([0, 0, 1])
elif method in ("x", "yz"):
method = _a.arrayd([1, 0, 0])
elif method in ("y", "xz"):
method = _a.arrayd([0, 1, 0])
elif method == "a":
method = self.cell[0]
elif method == "b":
method = self.cell[1]
elif method == "c":
method = self.cell[2]
elif method == "ab":
method = cross3(self.cell[0], self.cell[1])
elif method == "ac":
method = cross3(self.cell[0], self.cell[2])
elif method == "bc":
method = cross3(self.cell[1], self.cell[2])
else:
raise ValueError(
f"{self.__class__.__name__}.mirror unrecognized 'method' value"
)
# it has to be an array of length 3
# Mirror about a user defined vector
method = _a.asarrayd(method).copy()
method /= fnorm(method)
# project onto vector
vp = (self.xyz[atoms, :] - point).dot(method) * 2
# convert coordinates
# first subtract the projection, then its mirror position
g = self.copy()
g.xyz[atoms, :] -= vp.reshape(-1, 1) * method.reshape(1, 3)
return g
[docs]
def axyz(self, atoms: AtomsIndex = None, isc=None) -> ndarray:
"""Return the atomic coordinates in the supercell of a given atom.
The ``Geometry[...]`` slicing is calling this function with appropriate options.
Parameters
----------
atoms :
atom(s) from which we should return the coordinates, the atomic indices
may be in supercell format.
isc : array_like, optional
Returns the atomic coordinates shifted according to the integer
parts of the cell. Defaults to the unit-cell
Examples
--------
>>> geom = Geometry([[0, 0, 0], [0.5, 0, 0]], lattice=1.)
>>> print(geom.axyz(isc=[1,0,0]))
[[1. 0. 0. ]
[1.5 0. 0. ]]
>>> geom = Geometry([[0, 0, 0], [0.5, 0, 0]], lattice=1.)
>>> print(geom.axyz(0))
[0. 0. 0.]
"""
if atoms is None and isc is None:
return self.xyz
atoms = self._sanitize_atoms(atoms)
# If only atoms has been specified
if isc is None:
# get offsets from atomic indices (note that this will be per atom)
isc = self.a2isc(atoms)
offset = self.lattice.offset(isc)
return self.xyz[self.sc2uc(atoms)] + offset
# Neither of atoms, or isc are `None`, we add the offset to all coordinates
return self.axyz(atoms) + self.lattice.offset(isc)
[docs]
def within_sc(
self,
shapes,
isc=None,
atoms: AtomsIndex = None,
atoms_xyz=None,
ret_xyz: bool = False,
ret_rij: bool = False,
):
"""Indices of atoms in a given supercell within a given shape from a given coordinate
This returns a set of atomic indices which are within a
sphere of radius ``R``.
If R is a tuple/list/array it will return the indices:
in the ranges:
>>> ( x <= R[0] , R[0] < x <= R[1], R[1] < x <= R[2] )
Parameters
----------
shapes : Shape or list of Shape
A list of increasing shapes that define the extend of the geometric
volume that is searched.
It is vital that::
shapes[0] in shapes[1] in shapes[2] ...
isc : array_like, optional
The super-cell which the coordinates are checked in. Defaults to ``[0, 0, 0]``
atoms :
List of atoms that will be considered. This can
be used to only take out a certain atoms.
atoms_xyz : array_like, optional
The atomic coordinates of the equivalent `idx` variable (`idx` must also be passed)
ret_xyz :
If True this method will return the coordinates
for each of the couplings.
ret_rij :
If True this method will return the distance to the center of the shapes
Returns
-------
index
indices of atoms (in supercell indices) within the shape
xyz
atomic coordinates of the indexed atoms (only for true `ret_xyz`)
rij
distance of the indexed atoms to the center of the shape (only for true `ret_rij`)
"""
# Ensure that `shapes` is a list
if isinstance(shapes, Shape):
shapes = [shapes]
nshapes = len(shapes)
# Convert to actual array
if atoms is not None:
atoms = self._sanitize_atoms(atoms)
else:
# If idx is None, then idx_xyz cannot be used!
# So we force it to None
atoms_xyz = None
# Get shape centers
off = shapes[-1].center[:]
# Get the supercell offset
soff = self.lattice.offset(isc)[:]
# Get atomic coordinate in principal cell
if atoms_xyz is None:
xa = self[atoms, :] + soff[None, :]
else:
# For extremely large systems re-using the
# idx_xyz is faster than indexing
# a very large array
# However, this idx_xyz should not
# be offset by any supercell
xa = atoms_xyz[:, :] + soff[None, :]
# Get indices and coordinates of the largest shape
# The largest part of the calculation are to calculate
# the content in the largest shape.
ix = shapes[-1].within_index(xa)
# Reduce search space
xa = xa[ix, :]
if atoms is None:
# This is because of the pre-check of the distance checks
atoms = ix
else:
atoms = atoms[ix]
if len(xa) == 0:
# Quick return if there are no entries...
ret = [[np.empty([0], np.int32)] * nshapes]
if ret_xyz:
ret.append([np.empty([0, 3], np.float64)] * nshapes)
if ret_rij:
ret.append([np.empty([0], np.float64)] * nshapes)
if nshapes == 1:
if ret_xyz and ret_rij:
return [ret[0][0], ret[1][0], ret[2][0]]
elif ret_xyz or ret_rij:
return [ret[0][0], ret[1][0]]
return ret[0][0]
if ret_xyz or ret_rij:
return ret
return ret[0]
# Calculate distance
if ret_rij:
d = sqrt(square(xa - off[None, :]).sum(1))
# Create the initial lists that we will build up
# Then finally, we will return the reversed lists
# Quick return
if nshapes == 1:
ret = [[atoms]]
if ret_xyz:
ret.append([xa])
if ret_rij:
ret.append([d])
if ret_xyz or ret_rij:
return ret
return ret[0]
# TODO Check that all shapes coincide with the following shapes
# Now we create a list of indices which coincide
# in each of the shapes
# Do a reduction on each of the list elements
ixS = []
cum = np.array([], atoms.dtype)
for i, s in enumerate(shapes):
x = s.within_index(xa)
if i > 0:
x = np.setdiff1d(x, cum, assume_unique=True)
# Update elements to remove in next loop
cum = np.append(cum, x)
ixS.append(x)
# Do for the first shape
ret = [[_a.asarrayi(atoms[ixS[0]]).ravel()]]
rc = 0
if ret_xyz:
rc = rc + 1
ret.append([xa[ixS[0], :]])
if ret_rij:
rd = rc + 1
ret.append([d[ixS[0]]])
for i in range(1, nshapes):
ret[0].append(_a.asarrayi(atoms[ixS[i]]).ravel())
if ret_xyz:
ret[rc].append(xa[ixS[i], :])
if ret_rij:
ret[rd].append(d[ixS[i]])
if ret_xyz or ret_rij:
return ret
return ret[0]
[docs]
def close_sc(
self,
xyz_ia,
isc=(0, 0, 0),
R=None,
atoms: AtomsIndex = None,
atoms_xyz=None,
ret_xyz=False,
ret_rij=False,
):
"""Indices of atoms in a given supercell within a given radius from a given coordinate
This returns a set of atomic indices which are within a
sphere of radius `R`.
If `R` is a tuple/list/array it will return the indices:
in the ranges:
>>> ( x <= R[0] , R[0] < x <= R[1], R[1] < x <= R[2] )
Parameters
----------
xyz_ia : array_like of floats or int
Either a point in space or an index of an atom.
If an index is passed it is the equivalent of passing
the atomic coordinate ``close_sc(self.xyz[xyz_ia,:])``.
isc : (3,), optional
Integer super-cell offsets in which the coordinates are checked in.
I.e. ``isc=[0, 0, 0]`` is the primary cell (default).
R : float or array_like, optional
The radii parameter to where the atomic connections are found.
If `R` is an array it will return the indices:
in the ranges ``( x <= R[0] , R[0] < x <= R[1], R[1] < x <= R[2] )``.
If a single float it will return ``x <= R``.
atoms :
List of atoms that will be considered. This can
be used to only take out a certain atom.
atoms_xyz : array_like of float, optional
The atomic coordinates of the equivalent `atoms` variable (`atoms` must also be passed)
ret_xyz :
If True this method will return the coordinates
for each of the couplings.
ret_rij :
If True this method will return the distance
for each of the couplings.
Returns
-------
index
indices of atoms (in supercell indices) within the shells of radius `R`
xyz
atomic coordinates of the indexed atoms (only for true `ret_xyz`)
rij
distance of the indexed atoms to the center coordinate (only for true `ret_rij`)
"""
maxR = self.maxR() + 0.001
if R is None:
R = np.array([maxR], np.float64)
elif not isndarray(R):
R = _a.asarrayd(R).ravel()
# Maximum distance queried
max_R = R[-1]
if atoms is not None and max_R > maxR + 0.1:
warn(
f"{self.__class__.__name__}.close_sc has been passed an 'atoms' argument "
"together with an R value larger than the orbital ranges. "
"If used together with 'sparse-matrix.construct' this can result in wrong couplings.",
register=True,
)
# Convert to actual array
if atoms is not None:
atoms = self._sanitize_atoms(atoms).ravel()
else:
# If atoms is None, then atoms_xyz cannot be used!
atoms_xyz = None
if isinstance(xyz_ia, Integral):
off = self.xyz[xyz_ia]
elif not isndarray(xyz_ia):
off = _a.asarrayd(xyz_ia)
elif xyz_ia.ndim == 0:
off = self.xyz[xyz_ia]
else:
off = xyz_ia
# Calculate the complete offset
foff = self.lattice.offset(isc) - off
# Get distances between `xyz_ia` and `atoms`
if atoms_xyz is None:
dxa = self.axyz(atoms) + foff
else:
# For extremely large systems re-using the
# atoms_xyz is faster than indexing
# a very large array
dxa = atoms_xyz + foff
# Immediately downscale by easy checking
# This will reduce the computation of the vector-norm
# which is the main culprit of the time-consumption
# This abstraction will _only_ help very large
# systems.
# For smaller ones this will actually be a slower
# method..
if atoms is None:
atoms, d = indices_in_sphere_with_dist(dxa, max_R)
dxa = dxa[atoms].reshape(-1, 3)
else:
ix, d = indices_in_sphere_with_dist(dxa, max_R)
atoms = atoms[ix]
dxa = dxa[ix].reshape(-1, 3)
del ix
if len(atoms) == 0:
# Create default return
ret = [[_a.emptyi([0]) for _ in R]]
if ret_xyz:
ret.append([_a.emptyd([0, 3]) for _ in R])
if ret_rij:
ret.append([_a.emptyd([0]) for _ in R])
# Quick return if there are
# no entries...
if len(R) == 1:
if ret_xyz and ret_rij:
return [ret[0][0], ret[1][0], ret[2][0]]
elif ret_xyz or ret_rij:
return [ret[0][0], ret[1][0]]
return ret[0][0]
if ret_xyz or ret_rij:
return ret
return ret[0]
if ret_xyz:
xa = dxa + off
del dxa # just because this array could be very big...
# Check whether we only have one range to check.
# If so, we need not reduce the index space
if len(R) == 1:
ret = [atoms]
if ret_xyz:
ret.append(xa)
if ret_rij:
ret.append(d)
if ret_xyz or ret_rij:
return ret
return ret[0]
if not is_ascending(R):
raise ValueError(
f"{self.__class__.__name__}.close_sc proximity checks for several "
"quantities at a time requires ascending R values."
)
# The more neigbours you wish to find the faster this becomes
# We only do "one" heavy duty search,
# then we immediately reduce search space to this subspace
tidx = indices_le(d, R[0])
ret = [[atoms[tidx]]]
r_app = ret[0].append
if ret_xyz:
ret.append([xa[tidx]])
r_appx = ret[1].append
if ret_rij:
ret.append([d[tidx]])
r_appd = ret[-1].append
if ret_xyz and ret_rij:
for i in range(1, len(R)):
# Search in the sub-space
# Notice that this sub-space reduction will never
# allow the same indice to be in two ranges (due to
# numerics)
tidx = indices_gt_le(d, R[i - 1], R[i])
r_app(atoms[tidx])
r_appx(xa[tidx])
r_appd(d[tidx])
elif ret_xyz:
for i in range(1, len(R)):
tidx = indices_gt_le(d, R[i - 1], R[i])
r_app(atoms[tidx])
r_appx(xa[tidx])
elif ret_rij:
for i in range(1, len(R)):
tidx = indices_gt_le(d, R[i - 1], R[i])
r_app(atoms[tidx])
r_appd(d[tidx])
else:
for i in range(1, len(R)):
tidx = indices_gt_le(d, R[i - 1], R[i])
r_app(atoms[tidx])
if ret_xyz or ret_rij:
return ret
return ret[0]
[docs]
def bond_correct(
self, ia: int, atoms: AtomsIndex, method: Union[str, float] = "calc"
) -> None:
"""Corrects the bond between `ia` and the `atoms`.
Corrects the bond-length between atom `ia` and `atoms` in such
a way that the atomic radius is preserved.
I.e. the sum of the bond-lengths minimizes the distance matrix.
Only atom `ia` is moved.
Parameters
----------
ia :
The atom to be displaced according to the atomic radius
atoms :
The atom(s) from which the radius should be reduced.
method :
If str will use that as lookup in `Atom.radius`.
Else it will be the new bond-length.
"""
# Decide which algorithm to choose from
atoms = self._sanitize_atoms(atoms).ravel()
if len(atoms) == 1:
algo = atoms[0]
else:
# signal a list of atoms
algo = -1
if algo >= 0:
# We have a single atom
# Get bond length in the closest direction
# A bond-length HAS to be below 10
atoms, c, d = self.close(
ia, R=(0.1, 10.0), atoms=algo, ret_xyz=True, ret_rij=True
)
i = np.argmin(d[1])
# Convert to unitcell atom (and get the one atom)
atoms = self.sc2uc(atoms[1][i])
c = c[1][i]
d = d[1][i]
# Calculate the bond vector
bv = self.xyz[ia, :] - c
try:
# If it is a number, we use that.
rad = float(method)
except Exception:
# get radius
rad = self.atoms[atoms].radius(method) + self.atoms[ia].radius(method)
# Update the coordinate
self.xyz[ia, :] = c + bv / d * rad
else:
raise NotImplementedError(
"Changing bond-length dependent on several lacks implementation."
)
[docs]
def within(
self,
shapes,
atoms: AtomsIndex = None,
atoms_xyz=None,
ret_xyz: bool = False,
ret_rij: bool = False,
ret_isc: bool = False,
):
"""Indices of atoms in the entire supercell within a given shape from a given coordinate
This heavily relies on the `within_sc` method.
Note that if a connection is made in a neighboring super-cell
then the atomic index is shifted by the super-cell index times
number of atoms.
This allows one to decipher super-cell atoms from unit-cell atoms.
Parameters
----------
shapes : Shape, list of Shape
atoms :
List of indices for atoms that are to be considered
atoms_xyz : array_like, optional
The atomic coordinates of the equivalent `atoms` variable (`atoms` must also be passed)
ret_xyz :
If true this method will return the coordinates
for each of the couplings.
ret_rij :
If true this method will return the distances from the `xyz_ia`
for each of the couplings.
ret_isc :
If true this method will return the supercell offsets for each of the couplings.
Returns
-------
index
indices of atoms (in supercell indices) within the shape
xyz
atomic coordinates of the indexed atoms (only for true `ret_xyz`)
rij
distance of the indexed atoms to the center of the shape (only for true `ret_rij`)
isc
supercell indices of the couplings (only for true `ret_isc`)
"""
# Ensure that `shapes` is a list
if isinstance(shapes, Shape):
shapes = [shapes]
nshapes = len(shapes)
ret = [[np.empty([0], np.int32)] * nshapes]
i = 0
if ret_xyz:
ixyz = i + 1
i += 1
ret.append([np.empty([0, 3], np.float64)] * nshapes)
if ret_rij:
irij = i + 1
i += 1
ret.append([np.empty([0], np.float64)] * nshapes)
if ret_isc:
iisc = i + 1
i += 1
ret.append([np.empty([0, 3], np.int32)] * nshapes)
# number of special returns
n_ret = i
listify = n_ret == 0 or (n_ret == 1 and ret_isc)
def isc_tile(isc, n):
return tile(isc.reshape(1, -1), (n, 1))
for s in range(self.n_s):
na = self.na * s
isc = self.lattice.sc_off[s, :]
sret = self.within_sc(
shapes,
self.lattice.sc_off[s, :],
atoms=atoms,
atoms_xyz=atoms_xyz,
ret_xyz=ret_xyz,
ret_rij=ret_rij,
)
if listify:
# This is to "fake" the return
# of a list (we will do indexing!)
sret = [sret]
if isinstance(sret[0], list):
# we have a list of arrays (nshapes > 1)
for i, x in enumerate(sret[0]):
ret[0][i] = concatenate((ret[0][i], x + na), axis=0)
if ret_xyz:
ret[ixyz][i] = concatenate(
(ret[ixyz][i], sret[ixyz][i]), axis=0
)
if ret_rij:
ret[irij][i] = concatenate(
(ret[irij][i], sret[irij][i]), axis=0
)
if ret_isc:
ret[iisc][i] = concatenate(
(ret[iisc][i], isc_tile(isc, len(x))), axis=0
)
elif len(sret[0]) > 0:
# We can add it to the list (nshapes == 1)
# We add the atomic offset for the supercell index
ret[0][0] = concatenate((ret[0][0], sret[0] + na), axis=0)
if ret_xyz:
ret[ixyz][0] = concatenate((ret[ixyz][0], sret[ixyz]), axis=0)
if ret_rij:
ret[irij][0] = concatenate((ret[irij][0], sret[irij]), axis=0)
if ret_isc:
ret[iisc][0] = concatenate(
(ret[iisc][0], isc_tile(isc, len(sret[0]))), axis=0
)
if nshapes == 1:
if n_ret == 0:
return ret[0][0]
return tuple(ret[i][0] for i in range(n_ret + 1))
if n_ret == 0:
return ret[0]
return ret
[docs]
def close(
self,
xyz_ia,
R=None,
atoms: AtomsIndex = None,
atoms_xyz=None,
ret_xyz: bool = False,
ret_rij: bool = False,
ret_isc: bool = False,
):
"""Indices of atoms in the entire supercell within a given radius from a given coordinate
This heavily relies on the `close_sc` method.
Note that if a connection is made in a neighboring super-cell
then the atomic index is shifted by the super-cell index times
number of atoms.
This allows one to decipher super-cell atoms from unit-cell atoms.
Parameters
----------
xyz_ia : coordinate/index
Either a point in space or an index of an atom.
If an index is passed it is the equivalent of passing
the atomic coordinate ``close_sc(self.xyz[xyz_ia,:])``.
R : (None), float/tuple of float
The radii parameter to where the atomic connections are found.
If `R` is an array it will return the indices:
in the ranges:
>>> ( x <= R[0] , R[0] < x <= R[1], R[1] < x <= R[2] )
If a single float it will return:
>>> x <= R
atoms :
List of indices for atoms that are to be considered
atoms_xyz : array_like, optional
The atomic coordinates of the equivalent `atoms` variable (`atoms` must also be passed)
ret_xyz :
If true this method will return the coordinates
for each of the couplings.
ret_rij :
If true this method will return the distances from the `xyz_ia`
for each of the couplings.
ret_isc :
If true this method will return the lattice offset from `xyz_ia`
for each of the couplings.
Returns
-------
index
indices of atoms (in supercell indices) within the shells of radius `R`
xyz
atomic coordinates of the indexed atoms (only for true `ret_xyz`)
rij
distance of the indexed atoms to the center coordinate (only for true `ret_rij`)
isc
integer lattice offsets for the couplings (related to `rij` without atomic coordinates)
"""
if R is None:
R = self.maxR() + 0.001
R = _a.asarrayd(R).ravel()
nR = R.size
# Convert index coordinate to point
if isinstance(xyz_ia, Integral):
xyz_ia = self.xyz[xyz_ia]
elif not isndarray(xyz_ia):
xyz_ia = _a.asarrayd(xyz_ia)
ret = [[np.empty([0], np.int32)] * nR]
i = 0
if ret_xyz:
ixyz = i + 1
i += 1
ret.append([np.empty([0, 3], np.float64)] * nR)
if ret_rij:
irij = i + 1
i += 1
ret.append([np.empty([0], np.float64)] * nR)
if ret_isc:
iisc = i + 1
i += 1
ret.append([np.empty([0, 3], np.int32)] * nR)
# number of special returns
n_ret = i
listify = n_ret == 0 or (n_ret == 1 and ret_isc)
def isc_tile(isc, n):
return tile(isc.reshape(1, -1), (n, 1))
for s in range(self.n_s):
na = self.na * s
isc = self.lattice.sc_off[s]
sret = self.close_sc(
xyz_ia,
isc,
R=R,
atoms=atoms,
atoms_xyz=atoms_xyz,
ret_xyz=ret_xyz,
ret_rij=ret_rij,
)
if listify:
# This is to "fake" the return
# of a list (we will do indexing!)
sret = [sret]
if isinstance(sret[0], list):
# we have a list of arrays (len(R) > 1)
for i, x in enumerate(sret[0]):
ret[0][i] = concatenate((ret[0][i], x + na), axis=0)
if ret_xyz:
ret[ixyz][i] = concatenate(
(ret[ixyz][i], sret[ixyz][i]), axis=0
)
if ret_rij:
ret[irij][i] = concatenate(
(ret[irij][i], sret[irij][i]), axis=0
)
if ret_isc:
ret[iisc][i] = concatenate(
(ret[iisc][i], isc_tile(isc, len(x))), axis=0
)
elif len(sret[0]) > 0:
# We can add it to the list (len(R) == 1)
# We add the atomic offset for the supercell index
ret[0][0] = concatenate((ret[0][0], sret[0] + na), axis=0)
if ret_xyz:
ret[ixyz][0] = concatenate((ret[ixyz][0], sret[ixyz]), axis=0)
if ret_rij:
ret[irij][0] = concatenate((ret[irij][0], sret[irij]), axis=0)
if ret_isc:
ret[iisc][0] = concatenate(
(ret[iisc][0], isc_tile(isc, len(sret[0]))), axis=0
)
if nR == 1:
if n_ret == 0:
return ret[0][0]
return tuple(ret[i][0] for i in range(n_ret + 1))
if n_ret == 0:
return ret[0]
return ret
[docs]
def a2transpose(
self, atoms1: AtomsIndex, atoms2: AtomsIndex = None
) -> Tuple[ndarray, ndarray]:
"""Transposes connections from `atoms1` to `atoms2` such that supercell connections are transposed
When handling supercell indices it is useful to get the *transposed* connection. I.e. if you have
a connection from site ``i`` (in unit cell indices) to site ``j`` (in supercell indices) it may be
useful to get the equivalent supercell connection such for site ``j`` (in unit cell indices) to
site ``i`` (in supercell indices) such that they correspond to the transposed coupling.
Note that since this transposes couplings the indices returned are always expanded to the full
length if either of the inputs are a single index.
Examples
--------
>>> gr = geom.graphene()
>>> atoms = gr.close(0, 1.5)
>>> atoms
array([0, 1, 5, 9], dtype=int32)
>>> gr.a2transpose(0, atoms)
(array([0, 1, 1, 1], dtype=int32), array([ 0, 0, 14, 10], dtype=int32))
Parameters
----------
atoms1 :
atomic indices must have same length as `atoms2` or length 1
atoms2 :
atomic indices must have same length as `atoms1` or length 1.
If not present then only `atoms1` will be returned in transposed indices.
Returns
-------
atoms2 : array_like
transposed indices for atoms2 (only returned if `atoms2` is not None)
atoms1 : array_like
transposed indices for atoms1
"""
# First check whether they have the same size, if so then do not pre-process
atoms1 = self._sanitize_atoms(atoms1)
if atoms2 is None:
# we only need to transpose atoms1
offset = self.lattice.sc_index(-self.a2isc(atoms1)) * self.na
return atoms1 % self.na + offset
atoms2 = self._sanitize_atoms(atoms2)
if atoms1.size == atoms2.size:
pass
elif atoms1.size == 1: # typical case where atoms1 is a single number
atoms1 = np.tile(atoms1, atoms2.size)
elif atoms2.size == 1:
atoms2 = np.tile(atoms2, atoms1.size)
else:
raise ValueError(
f"{self.__class__.__name__}.a2transpose only allows length 1 or same length arrays."
)
# Now convert atoms
na = self.na
sc_index = self.lattice.sc_index
isc1 = self.a2isc(atoms1)
isc2 = self.a2isc(atoms2)
atoms1 = atoms1 % na + sc_index(-isc2) * na
atoms2 = atoms2 % na + sc_index(-isc1) * na
return atoms2, atoms1
[docs]
def o2transpose(
self, orb1: OrbitalsIndex, orb2: Optional[OrbitalsIndex] = None
) -> Tuple[ndarray, ndarray]:
"""Transposes connections from `orb1` to `orb2` such that supercell connections are transposed
When handling supercell indices it is useful to get the *transposed* connection. I.e. if you have
a connection from site ``i`` (in unit cell indices) to site ``J`` (in supercell indices) it may be
useful to get the equivalent supercell connection such for site ``j`` (in unit cell indices) to
site ``I`` (in supercell indices) such that they correspond to the transposed coupling.
Note that since this transposes couplings the indices returned are always expanded to the full
length if either of the inputs are a single index.
Examples
--------
>>> gr = geom.graphene() # one orbital per site
>>> atoms = gr.close(0, 1.5)
>>> atoms
array([0, 1, 5, 9], dtype=int32)
>>> gr.o2transpose(0, atoms)
(array([0, 1, 1, 1], dtype=int32), array([ 0, 0, 14, 10], dtype=int32))
Parameters
----------
orb1 :
orbital indices must have same length as `orb2` or length 1
orb2 :
orbital indices must have same length as `orb1` or length 1.
If not present then only `orb1` will be returned in transposed indices.
Returns
-------
orb2 : array_like
transposed indices for orb2 (only returned if `orb2` is not None)
orb1 : array_like
transposed indices for orb1
"""
# First check whether they have the same size, if so then do not pre-process
orb1 = self._sanitize_orbs(orb1)
if orb2 is None:
# we only need to transpose orb1
offset = self.lattice.sc_index(-self.o2isc(orb1)) * self.no
return orb1 % self.no + offset
orb2 = self._sanitize_orbs(orb2)
if orb1.size == orb2.size:
pass
elif orb1.size == 1: # typical case where orb1 is a single number
orb1 = np.tile(orb1, orb2.size)
elif orb2.size == 1:
orb2 = np.tile(orb2, orb1.size)
else:
raise ValueError(
f"{self.__class__.__name__}.o2transpose only allows length 1 or same length arrays."
)
# Now convert orbs
no = self.no
sc_index = self.lattice.sc_index
isc1 = self.o2isc(orb1)
isc2 = self.o2isc(orb2)
orb1 = orb1 % no + sc_index(-isc2) * no
orb2 = orb2 % no + sc_index(-isc1) * no
return orb2, orb1
[docs]
def a2o(self, atoms: AtomsIndex, all: bool = False) -> ndarray:
"""
Returns an orbital index of the first orbital of said atom.
This is particularly handy if you want to create
TB models with more than one orbital per atom.
Note that this will preserve the super-cell offsets.
Parameters
----------
atoms :
Atomic indices
all :
``False``, return only the first orbital corresponding to the atom,
``True``, returns list of the full atom(s), will always return a 1D array.
"""
# we must not alter `atoms` as it may come from outside
off, atoms = np.divmod(self._sanitize_atoms(atoms), self.na)
is_integral = isinstance(atoms, Integral)
off *= self.no
if not all:
return self.firsto[atoms] + off
ob = (self.firsto[atoms] + off).ravel()
oe = (self.lasto[atoms] + off + 1).ravel()
# Create ranges
if is_integral:
return _a.arangei(ob, oe)
return _a.array_arange(ob, oe)
[docs]
def o2a(self, orbitals: OrbitalsIndex, unique: bool = False) -> ndarray:
"""Atomic index corresponding to the orbital indicies.
Note that this will preserve the super-cell offsets.
Parameters
----------
orbitals :
List of orbital indices to return the atoms for
unique :
If True only return the unique atoms.
"""
orbitals = self._sanitize_orbs(orbitals)
if orbitals.ndim == 0:
# must only be 1 number (an Integral)
return (
np.argmax(orbitals % self.no <= self.lasto)
+ (orbitals // self.no) * self.na
)
isc, orbitals = np.divmod(_a.asarrayi(orbitals.ravel()), self.no)
a = list_index_le(orbitals, self.lasto)
if unique:
return np.unique(a + isc * self.na)
return a + isc * self.na
[docs]
def uc2sc(self, atoms: AtomsIndex, unique: bool = False) -> ndarray:
"""Returns atom from unit-cell indices to supercell indices, possibly removing dublicates
Parameters
----------
atoms :
the atomic unit-cell indices to be converted to supercell indices
unique :
If True the returned indices are unique and sorted.
"""
atoms = self._sanitize_atoms(atoms) % self.na
atoms = (
atoms.reshape(1, -1) + _a.arangei(self.n_s).reshape(-1, 1) * self.na
).ravel()
if unique:
return np.unique(atoms)
return atoms
auc2sc = uc2sc
[docs]
def sc2uc(self, atoms: AtomsIndex, unique: bool = False) -> ndarray:
"""Returns atoms from supercell indices to unit-cell indices, possibly removing dublicates
Parameters
----------
atoms :
the atomic supercell indices to be converted to unit-cell indices
unique :
If True the returned indices are unique and sorted.
"""
atoms = self._sanitize_atoms(atoms) % self.na
if unique:
return np.unique(atoms)
return atoms
asc2uc = sc2uc
[docs]
def osc2uc(self, orbitals: OrbitalsIndex, unique: bool = False) -> ndarray:
"""Orbitals from supercell indices to unit-cell indices, possibly removing dublicates
Parameters
----------
orbitals :
the orbital supercell indices to be converted to unit-cell indices
unique :
If True the returned indices are unique and sorted.
"""
orbitals = self._sanitize_orbs(orbitals) % self.no
if unique:
return np.unique(orbitals)
return orbitals
[docs]
def ouc2sc(self, orbitals: OrbitalsIndex, unique: bool = False) -> ndarray:
"""Orbitals from unit-cell indices to supercell indices, possibly removing dublicates
Parameters
----------
orbitals :
the orbital unit-cell indices to be converted to supercell indices
unique :
If True the returned indices are unique and sorted.
"""
orbitals = self._sanitize_orbs(orbitals) % self.no
orbitals = (
orbitals.reshape(1, *orbitals.shape)
+ _a.arangei(self.n_s).reshape(-1, *([1] * orbitals.ndim)) * self.no
).ravel()
if unique:
return np.unique(orbitals)
return orbitals
[docs]
def a2isc(self, atoms: AtomsIndex) -> ndarray:
"""Super-cell indices for a specific/list atom
Returns a vector of 3 numbers with integers.
Any multi-dimensional input will be flattened before return.
The returned indices will thus always be a 2D matrix or a 1D vector.
Parameters
----------
atoms :
atom indices to extract the supercell locations of
"""
atoms = self._sanitize_atoms(atoms) // self.na
if atoms.ndim > 1:
atoms = atoms.ravel()
return self.lattice.sc_off[atoms, :]
# This function is a bit weird, it returns a real array,
# however, there should be no ambiguity as it corresponds to th
# offset and "what else" is there to query?
[docs]
def a2sc(self, atoms: AtomsIndex) -> ndarray:
"""Returns the super-cell offset for a specific atom
Parameters
----------
atoms :
atom indices to extract the supercell offsets of
"""
return self.lattice.offset(self.a2isc(atoms))
[docs]
def o2isc(self, orbitals: OrbitalsIndex) -> ndarray:
"""
Returns the super-cell index for a specific orbital.
Returns a vector of 3 numbers with integers.
"""
orbitals = self._sanitize_orbs(orbitals) // self.no
if orbitals.ndim > 1:
orbitals = orbitals.ravel()
return self.lattice.sc_off[orbitals, :]
[docs]
def o2sc(self, orbitals: OrbitalsIndex) -> ndarray:
"""
Returns the super-cell offset for a specific orbital.
"""
return self.lattice.offset(self.o2isc(orbitals))
[docs]
def equal(self, other: GeometryLike, R: bool = True, tol: float = 1e-4) -> bool:
"""Whether two geometries are the same (optional not check of the orbital radius)
Parameters
----------
other :
the other Geometry to check against
R :
if True also check if the orbital radii are the same (see `Atom.equal`)
tol :
tolerance for checking the atomic coordinates
"""
other = self.new(other)
if not isinstance(other, Geometry):
return False
same = self.lattice.equal(other.lattice, tol=tol)
same = same and np.allclose(self.xyz, other.xyz, atol=tol)
same = same and self.atoms.equal(other.atoms, R)
return same
def __eq__(self, other):
return self.equal(other)
def __ne__(self, other):
return not (self == other)
[docs]
def sparserij(self, dtype=np.float64, na_iR: int = 1000, method: str = "rand"):
"""Return the sparse matrix with all distances in the matrix
The sparse matrix will only be defined for the elements which have
orbitals overlapping with other atoms.
Parameters
----------
dtype : numpy.dtype, numpy.float64
the data-type of the sparse matrix
na_iR :
number of atoms within the sphere for speeding
up the `iter_block` loop.
method :
see `iter_block` for details
Returns
-------
SparseAtom
sparse matrix with all rij elements
See Also
--------
iter_block : the method for looping the atoms
distance : create a list of distances
"""
from .sparse_geometry import SparseAtom
rij = SparseAtom(self, nnzpr=20, dtype=dtype)
# Get R
R = (0.1, self.maxR() + 0.001)
iR = self.iR(na_iR)
# Do the loop
for ias, atoms in self.iter_block(iR=iR, method=method):
# Get all the indexed atoms...
# This speeds up the searching for
# coordinates...
atoms_xyz = self[atoms, :]
# Loop the atoms inside
for ia in ias:
idx, r = self.close(
ia, R=R, atoms=atoms, atoms_xyz=atoms_xyz, ret_rij=True
)
rij[ia, ia] = 0.0
rij[ia, idx[1]] = r[1]
return rij
[docs]
def distance(
self,
atoms: AtomsIndex = None,
R: Optional[float] = None,
tol: float = 0.1,
method: Union[
Callable[[Sequence[float]], float],
Literal["average", "mode", "<numpy.method>"],
] = "average",
) -> Union[float, ndarray]:
"""Calculate the distances for all atoms in shells of radius `tol` within `max_R`
Parameters
----------
atoms :
only create list of distances from the given atoms, default to all atoms
R :
the maximum radius to consider, default to ``self.maxR()``.
To retrieve all distances for atoms within the supercell structure
you can pass `numpy.inf`.
tol : float or array_like, optional
the tolerance for grouping a set of atoms.
This parameter sets the shell radius for each shell.
I.e. the returned distances between two shells will be maximally
``2*tol``, but only if atoms are within two consecutive lists.
If this is a list, the shells will be of unequal size.
The first shell size will be ``tol * .5`` or ``tol[0] * .5`` if `tol` is a list.
method :
How the distance in each shell is determined.
A list of distances within each shell is gathered and the equivalent
method will be used to extract a single quantity from the list of
distances in the shell.
If `'mode'` is chosen it will use `scipy.stats.mode`.
If another string is given it will correspond to ``getattr(numpy, method)``,
while any callable function may be passed. The passed function
will only be passed a list of unsorted distances that needs to be
processed.
Notes
-----
Using ``method='mode'`` requires ``scipy>=1.9``.
Examples
--------
>>> geom = Geometry([0]*3, Atom(1, R=1.), lattice=Lattice(1., nsc=[5, 5, 1]))
>>> geom.distance()
array([1.])
>>> geom.distance(tol=[0.5, 0.4, 0.3, 0.2])
array([1.])
>>> geom.distance(R=2, tol=[0.5, 0.4, 0.3, 0.2])
array([1. , 1.41421356, 2. ])
>>> geom.distance(R=2, tol=[0.5, 0.7]) # the R = 1 and R = 2 ** .5 gets averaged
array([1.20710678, 2. ])
Returns
-------
numpy.ndarray
an array of positive numbers yielding the distances from the atoms in reduced form
See Also
--------
sparserij : return a sparse matrix will all distances between atoms
"""
atoms = self._sanitize_atoms(atoms).ravel()
# Figure out maximum distance
if R is None:
R = self.maxR()
if R < 0:
raise ValueError(
f"{self.__class__.__name__}"
".distance cannot determine the `R` parameter. "
"The internal `maxR()` is negative and thus not set. "
"Set an explicit value for `R`."
)
elif np.any(self.nsc > 1):
maxR = fnorm(self.cell).max()
# These loops could be leveraged if we look at angles...
for i, j, k in product(
[0, self.nsc[0] // 2], [0, self.nsc[1] // 2], [0, self.nsc[2] // 2]
):
if i == 0 and j == 0 and k == 0:
continue
sc = [i, j, k]
off = self.lattice.offset(sc)
for ii, jj, kk in product([0, 1], [0, 1], [0, 1]):
o = self.cell[0] * ii + self.cell[1] * jj + self.cell[2] * kk
maxR = max(maxR, fnorm(off + o))
if R > maxR:
R = maxR
# Convert to list
tol = _a.asarrayd(tol).ravel()
if len(tol) == 1:
# Now we are in a position to determine the sizes
dR = _a.aranged(tol[0] * 0.5, R + tol[0] * 0.55, tol[0])
else:
dR = tol.copy()
dR[0] *= 0.5
# The first tolerance, is for it-self, the second
# has to have the first tolerance as the field
dR = _a.cumsumd(np.insert(dR, 1, tol[0]))
if dR[-1] < R:
# Now finalize dR by ensuring all remaining segments are captured
t = tol[-1]
dR = concatenate((dR, _a.aranged(dR[-1] + t, R + t * 0.55, t)))
# Reduce to the largest value above R
# This ensures that R, truly is the largest considered element
dR = dR[: (dR > R).nonzero()[0][0] + 1]
# Now we can figure out the list of atoms in each shell
# First create the initial lists of shell atoms
# The inner shell will never be used, because it should correspond
# to the atom it-self.
shells = [[] for i in range(len(dR) - 1)]
for a in atoms:
_, r = self.close(a, R=dR, ret_rij=True)
for i, rlist in enumerate(r[1:]):
shells[i].extend(rlist)
# Now parse all of the shells with the correct routine
# First we grap the routine:
if isinstance(method, str):
if method == "median":
def func(lst):
return np.median(lst, overwrite_input=True)
elif method == "mode":
from scipy.stats import mode
def func(lst):
return mode(lst, keepdims=False)[0]
else:
try:
func = getattr(np, method)
except AttributeError as e:
raise ValueError(
f"{self.__class__.__name__}.distance `method` got wrong input value."
) from e
else:
func = method
# Reduce lists
for i in range(len(shells)):
lst = shells[i]
if len(lst) == 0:
continue
# Reduce elements
shells[i] = func(lst)
# Convert to flattened numpy array and ensure shape
d = np.hstack(shells).ravel()
return d
[docs]
def within_inf(
self,
lattice: Lattice,
periodic: Optional[Sequence[bool]] = None,
tol: float = 1e-5,
origin: Sequence[float] = (0.0, 0.0, 0.0),
) -> Tuple[ndarray, ndarray, ndarray]:
"""Find all atoms within a provided supercell
Note this function is rather different from `close` and `within`.
Specifically this routine is returning *all* indices for the infinite
periodic system (where ``self.nsc > 1`` or `periodic` is true).
Atomic coordinates lying on the boundary of the supercell will be duplicated
on the neighboring supercell images. Thus performing `geom.within_inf(geom.lattice)`
may result in more atoms than in the structure.
Notes
-----
The name of this function may change. Currently it should only be used
internally in sisl.
Parameters
----------
lattice : LatticeLike
the supercell in which this geometry should be expanded into.
periodic :
explicitly define the periodic directions, by default the periodic
directions are only where ``self.nsc > 1 & self.pbc``.
tol :
length tolerance for the fractional coordinates to be on a duplicate site (in Ang).
This allows atoms within `tol` of the cell boundaries to be taken as *inside* the
cell.
origin :
origin that is the basis for comparison, default to 0.
Returns
-------
numpy.ndarray
unit-cell atomic indices which are inside the `lattice` cell
numpy.ndarray
atomic coordinates for the `ia` atoms (including supercell offsets)
numpy.ndarray
integer supercell offsets for `ia` atoms
"""
lattice = Lattice.new(lattice)
if periodic is None:
periodic = np.logical_and(self.pbc, self.nsc > 1)
else:
periodic = list(periodic)
if origin is None:
origin = _a.zerosd(3)
# Our first task is to construct a geometry large
# enough to fully encompass the supercell
# 1. Number of times each lattice vector must be expanded to fit
# inside the "possibly" larger `lattice`.
idx = dot(lattice.cell, self.icell.T)
tile_min = floor(idx.min(0))
tile_max = ceil(idx.max(0)).astype(dtype=int32)
# Intrinsic offset (when atomic coordinates are outside primary unit-cell)
idx = dot(self.xyz, self.icell.T)
tmp = floor(idx.min(0))
tile_min = np.where(tile_min < tmp, tile_min, tmp).astype(dtype=int32)
tmp = ceil(idx.max(0))
tile_max = np.where(tmp < tile_max, tile_max, tmp).astype(dtype=int32)
del idx, tmp
# 1a) correct for origin displacement
idx = floor(dot(lattice.origin, self.icell.T))
tile_min = np.where(tile_min < idx, tile_min, idx).astype(dtype=int32)
idx = floor(dot(origin, self.icell.T))
tile_min = np.where(tile_min < idx, tile_min, idx).astype(dtype=int32)
# 2. Reduce tiling along non-periodic directions
tile_min = np.where(periodic, tile_min, 0)
tile_max = np.where(periodic, tile_max, 1)
# 3. Find the *new* origin according to the *negative* tilings.
# This is important for skewed cells as the placement of the new
# larger geometry has to be shifted to have lattice inside
big_origin = (tile_min.reshape(3, 1) * self.cell).sum(0)
# The xyz geometry that fully encompass the (possibly) larger supercell
tile = tile_max - tile_min
full_geom = (self * tile).translate(big_origin - origin)
# Now we have to figure out all atomic coordinates within
cuboid = lattice.to.Cuboid()
# Make sure that full_geom doesn't return coordinates outside the unit cell
# for non periodic directions
full_geom.set_nsc([full_geom.nsc[i] if periodic[i] else 1 for i in range(3)])
# Now retrieve all atomic coordinates from the full geometry
xyz = full_geom.axyz(_a.arangei(full_geom.na_s))
idx = cuboid.within_index(xyz)
xyz = xyz[idx, :]
del full_geom
# Figure out supercell connections in the smaller indices
# Since we have shifted all coordinates into the primary unit cell we
# are sure that these fxyz are [0:1[
fxyz = dot(xyz, self.icell.T)
# Since there are numerical errors for the above operation
# we *have* to account for possible sign-errors
# This is done by a length tolerance
ftol = tol / fnorm(self.cell).reshape(1, 3)
isc = floor(fxyz - ftol).astype(int32)
# Now we can extract the indices where the two are non-matching.
# At these indices we have some "errors" that we have to fix and
# thus select the correct isc.
idx_diff = (isc - floor(fxyz + ftol).astype(int32)).nonzero()
# For these indices we can use the nearest integer as that
# selects the closest. floor will ONLY be wrong for -0.0000, 0.99999, ...
isc[idx_diff] = np.rint(fxyz[idx_diff]).astype(int32)
# Convert indices to unit-cell indices and also return coordinates and
# infinite supercell indices
return self.sc2uc(idx), xyz, isc
# Create pickling routines
def __getstate__(self):
"""Returns the state of this object"""
d = self.lattice.__getstate__()
d["xyz"] = self.xyz
d["atoms"] = self.atoms.__getstate__()
return d
def __setstate__(self, d):
"""Re-create the state of this object"""
lattice = Lattice([1, 1, 1])
lattice.__setstate__(d)
atoms = Atoms()
atoms.__setstate__(d["atoms"])
self.__init__(d["xyz"], atoms=atoms, lattice=lattice)
@classmethod
def _ArgumentParser_args_single(cls):
"""Returns the options for `Geometry.ArgumentParser` in case they are the only options"""
return {
"limit_arguments": False,
"short": True,
"positional_out": True,
}
# Hook into the Geometry class to create
# an automatic ArgumentParser which makes actions
# as the options are read.
@default_ArgumentParser(description="Manipulate a Geometry object in sisl.")
def ArgumentParser(self, p=None, *args, **kwargs):
"""Create and return a group of argument parsers which manipulates it self `Geometry`.
Parameters
----------
parser : ArgumentParser, optional
in case the arguments should be added to a specific parser. It defaults
to create a new.
limit_arguments : bool, optional
If ``False`` additional options will be created which are similar to other options.
For instance ``--repeat-x <>`` which is equivalent to ``--repeat <> x``.
Default `True`.
short : bool, optional
Create short options for a selected range of options.
positional_out : bool, optional
If ``True``, adds a positional argument which acts as --out. This may be handy if only the geometry is in the argument list.
"""
limit_args = kwargs.get("limit_arguments", True)
short = kwargs.get("short", False)
if short:
def opts(*args):
return args
else:
def opts(*args):
return [arg for arg in args if arg.startswith("--")]
# We limit the import to occur here
import argparse
# The first thing we do is adding the geometry to the NameSpace of the
# parser.
# This will enable custom actions to interact with the geometry in a
# straight forward manner.
if isinstance(self, Geometry):
g = self.copy()
else:
g = None
namespace = default_namespace(
_geometry=g,
_stored_geometry=False,
)
# Create actions
class Format(argparse.Action):
def __call__(self, parser, ns, value, option_string=None):
ns._geom_fmt = value[0]
p.add_argument(
*opts("--format"),
action=Format,
nargs=1,
default=".8f",
help="Specify output format for coordinates.",
)
class MoveOrigin(argparse.Action):
def __call__(self, parser, ns, no_value, option_string=None):
ns._geometry.xyz[:, :] -= np.amin(ns._geometry.xyz, axis=0)[None, :]
p.add_argument(
*opts("--origin", "-O"),
action=MoveOrigin,
nargs=0,
help="Move all atoms such that the smallest value along each Cartesian direction will be at the origin.",
)
class MoveCenterOf(argparse.Action):
def __call__(self, parser, ns, value, option_string=None):
xyz = ns._geometry.center(what="xyz")
ns._geometry = ns._geometry.translate(
ns._geometry.center(what=value) - xyz
)
p.add_argument(
*opts("--center-of", "-co"),
choices=["mass", "mass:pbc", "xyz", "position", "cell", "mm:xyz"],
action=MoveCenterOf,
help="Move coordinates to the center of the designated choice.",
)
class MoveUnitCell(argparse.Action):
def __call__(self, parser, ns, value, option_string=None):
if value in ("translate", "tr", "t"):
# Simple translation
tmp = np.amin(ns._geometry.xyz, axis=0)
ns._geometry = ns._geometry.translate(-tmp)
elif value == "mod":
g = ns._geometry
# Change all coordinates using the reciprocal cell and move to unit-cell (% 1.)
fxyz = g.fxyz % 1.0
ns._geometry.xyz[:, :] = dot(fxyz, g.cell)
p.add_argument(
*opts("--unit-cell", "-uc"),
choices=["translate", "tr", "t", "mod"],
action=MoveUnitCell,
help="Moves the coordinates into the unit-cell by translation or the mod-operator",
)
# Rotation
class Rotation(argparse.Action):
def __call__(self, parser, ns, values, option_string=None):
# Convert value[0] to the direction
# The rotate function expects degree
ang = angle(values[0], rad=False, in_rad=False)
ns._geometry = ns._geometry.rotate(ang, values[1], what="abc+xyz")
p.add_argument(
*opts("--rotate", "-R"),
nargs=2,
metavar=("ANGLE", "DIR"),
action=Rotation,
help='Rotate coordinates and lattice vectors around given axis (x|y|z|a|b|c). ANGLE defaults to be specified in degree. Prefix with "r" for input in radians.',
)
if not limit_args:
class RotationX(argparse.Action):
def __call__(self, parser, ns, value, option_string=None):
# The rotate function expects degree
ang = angle(value, rad=False, in_rad=False)
ns._geometry = ns._geometry.rotate(ang, "x", what="abc+xyz")
p.add_argument(
*opts("--rotate-x", "-Rx"),
metavar="ANGLE",
action=RotationX,
help='Rotate coordinates and lattice vectors around x axis. ANGLE defaults to be specified in degree. Prefix with "r" for input in radians.',
)
class RotationY(argparse.Action):
def __call__(self, parser, ns, value, option_string=None):
# The rotate function expects degree
ang = angle(value, rad=False, in_rad=False)
ns._geometry = ns._geometry.rotate(ang, "y", what="abc+xyz")
p.add_argument(
*opts("--rotate-y", "-Ry"),
metavar="ANGLE",
action=RotationY,
help='Rotate coordinates and lattice vectors around y axis. ANGLE defaults to be specified in degree. Prefix with "r" for input in radians.',
)
class RotationZ(argparse.Action):
def __call__(self, parser, ns, value, option_string=None):
# The rotate function expects degree
ang = angle(value, rad=False, in_rad=False)
ns._geometry = ns._geometry.rotate(ang, "z", what="abc+xyz")
p.add_argument(
*opts("--rotate-z", "-Rz"),
metavar="ANGLE",
action=RotationZ,
help='Rotate coordinates and lattice vectors around z axis. ANGLE defaults to be specified in degree. Prefix with "r" for input in radians.',
)
# Reduce size of geometry
class ReduceSub(argparse.Action):
def __call__(self, parser, ns, value, option_string=None):
# Get atomic indices
rng = lstranges(strmap(int, value))
ns._geometry = ns._geometry.sub(rng)
p.add_argument(
"--sub",
metavar="RNG",
action=ReduceSub,
help="Retains specified atoms, can be complex ranges.",
)
class ReduceRemove(argparse.Action):
def __call__(self, parser, ns, value, option_string=None):
# Get atomic indices
rng = lstranges(strmap(int, value))
ns._geometry = ns._geometry.remove(rng)
p.add_argument(
"--remove",
metavar="RNG",
action=ReduceRemove,
help="Removes specified atoms, can be complex ranges.",
)
# Swaps atoms
class AtomSwap(argparse.Action):
def __call__(self, parser, ns, value, option_string=None):
# Get atomic indices
a = lstranges(strmap(int, value[0]))
b = lstranges(strmap(int, value[1]))
if len(a) != len(b):
raise ValueError(
"swapping atoms requires equal number of LHS and RHS atomic ranges"
)
ns._geometry = ns._geometry.swap(a, b)
p.add_argument(
*opts("--swap"),
metavar=("A", "B"),
nargs=2,
action=AtomSwap,
help="Swaps groups of atoms (can be complex ranges). The groups must be of equal length.",
)
# Add an atom
class AtomAdd(argparse.Action):
def __call__(self, parser, ns, values, option_string=None):
# Create an atom from the input
g = Geometry(
[float(x) for x in values[0].split(",")], atoms=Atom(values[1])
)
ns._geometry = ns._geometry.add(g)
p.add_argument(
*opts("--add"),
nargs=2,
metavar=("COORD", "Z"),
action=AtomAdd,
help="Adds an atom, coordinate is comma separated (in Ang). Z is the atomic number.",
)
class Translate(argparse.Action):
def __call__(self, parser, ns, values, option_string=None):
# Create an atom from the input
if "," in values[0]:
xyz = [float(x) for x in values[0].split(",")]
else:
xyz = [float(x) for x in values[0].split()]
ns._geometry = ns._geometry.translate(xyz)
p.add_argument(
*opts("--translate", "-t"),
nargs=1,
metavar="COORD",
action=Translate,
help="Translates the coordinates via a comma separated list (in Ang).",
)
# Periodicly increase the structure
class PeriodRepeat(argparse.Action):
def __call__(self, parser, ns, values, option_string=None):
r = int(values[0])
d = direction(values[1])
ns._geometry = ns._geometry.repeat(r, d)
p.add_argument(
*opts("--repeat", "-r"),
nargs=2,
metavar=("TIMES", "DIR"),
action=PeriodRepeat,
help="Repeats the geometry in the specified direction.",
)
if not limit_args:
class PeriodRepeatX(argparse.Action):
def __call__(self, parser, ns, value, option_string=None):
ns._geometry = ns._geometry.repeat(int(value), 0)
p.add_argument(
*opts("--repeat-x", "-rx"),
metavar="TIMES",
action=PeriodRepeatX,
help="Repeats the geometry along the first cell vector.",
)
class PeriodRepeatY(argparse.Action):
def __call__(self, parser, ns, value, option_string=None):
ns._geometry = ns._geometry.repeat(int(value), 1)
p.add_argument(
*opts("--repeat-y", "-ry"),
metavar="TIMES",
action=PeriodRepeatY,
help="Repeats the geometry along the second cell vector.",
)
class PeriodRepeatZ(argparse.Action):
def __call__(self, parser, ns, value, option_string=None):
ns._geometry = ns._geometry.repeat(int(value), 2)
p.add_argument(
*opts("--repeat-z", "-rz"),
metavar="TIMES",
action=PeriodRepeatZ,
help="Repeats the geometry along the third cell vector.",
)
class ReduceUnrepeat(argparse.Action):
def __call__(self, parser, ns, values, option_string=None):
s = int(values[0])
d = direction(values[1])
ns._geometry = ns._geometry.unrepeat(s, d)
p.add_argument(
*opts("--unrepeat", "-ur"),
nargs=2,
metavar=("REPS", "DIR"),
action=ReduceUnrepeat,
help="Unrepeats the geometry into `reps` parts along the unit-cell direction `dir` (opposite of --repeat).",
)
class PeriodTile(argparse.Action):
def __call__(self, parser, ns, values, option_string=None):
r = int(values[0])
d = direction(values[1])
ns._geometry = ns._geometry.tile(r, d)
p.add_argument(
*opts("--tile"),
nargs=2,
metavar=("TIMES", "DIR"),
action=PeriodTile,
help="Tiles the geometry in the specified direction.",
)
if not limit_args:
class PeriodTileX(argparse.Action):
def __call__(self, parser, ns, value, option_string=None):
ns._geometry = ns._geometry.tile(int(value), 0)
p.add_argument(
*opts("--tile-x", "-tx"),
metavar="TIMES",
action=PeriodTileX,
help="Tiles the geometry along the first cell vector.",
)
class PeriodTileY(argparse.Action):
def __call__(self, parser, ns, value, option_string=None):
ns._geometry = ns._geometry.tile(int(value), 1)
p.add_argument(
*opts("--tile-y", "-ty"),
metavar="TIMES",
action=PeriodTileY,
help="Tiles the geometry along the second cell vector.",
)
class PeriodTileZ(argparse.Action):
def __call__(self, parser, ns, value, option_string=None):
ns._geometry = ns._geometry.tile(int(value), 2)
p.add_argument(
*opts("--tile-z", "-tz"),
metavar="TIMES",
action=PeriodTileZ,
help="Tiles the geometry along the third cell vector.",
)
class ReduceUntile(argparse.Action):
def __call__(self, parser, ns, values, option_string=None):
s = int(values[0])
d = direction(values[1])
ns._geometry = ns._geometry.untile(s, d)
p.add_argument(
*opts("--untile", "--cut", "-ut"),
nargs=2,
metavar=("REPS", "DIR"),
action=ReduceUntile,
help="Untiles the geometry into `reps` parts along the unit-cell direction `dir` (opposite of --tile).",
)
# append another geometry
class Geometryend(argparse.Action):
def __call__(self, parser, ns, values, option_string=None):
# Create an atom from the input
f = Path(values[0])
geom = Geometry.read(values[0])
d = direction(values[1])
ns._geometry = getattr(ns._geometry, self._method_pend)(geom, d)
class GeometryAppend(Geometryend):
_method_pend = "append"
p.add_argument(
*opts("--append"),
nargs=2,
metavar=("GEOM", "DIR"),
action=GeometryAppend,
help="Appends another Geometry along direction DIR.",
)
class GeometryPrepend(Geometryend):
_method_pend = "prepend"
p.add_argument(
*opts("--prepend"),
nargs=2,
metavar=("GEOM", "DIR"),
action=GeometryPrepend,
help="Prepends another Geometry along direction DIR.",
)
# Sort
class Sort(argparse.Action):
def __call__(self, parser, ns, values, option_string=None):
# call geometry.sort(...) using appropriate keywords (and ordered dict)
kwargs = OrderedDict()
opts = values[0].split(";")
for i, opt in enumerate(opts):
# Split for equal
opt = opt.split("=", 1)
if len(opt) > 1:
opt, val = opt
else:
opt = opt[0]
val = "True"
if val.lower() in ("t", "true"):
val = True
elif val.lower() in ("f", "false"):
val = False
elif opt == "atol":
# float values
val = float(val)
elif opt == "group":
pass
else:
# it must be a range/tuple
val = lstranges(strmap(int, val))
# we always add integers to allow users to use the same keywords on commandline
kwargs[opt.strip() + str(i)] = val
ns._geometry = ns._geometry.sort(**kwargs)
p.add_argument(
*opts("--sort"),
nargs=1,
metavar="SORT",
action=Sort,
help='Semi-colon separated options for sort, please always encapsulate in quotation ["axis=0;descend;lattice=(1, 2);group=Z"].',
)
# Print some common information about the
# geometry (to stdout)
class PrintInfo(argparse.Action):
def __call__(self, parser, ns, no_value, option_string=None):
# We fake that it has been stored...
ns._stored_geometry = True
print(ns._geometry)
p.add_argument(
*opts("--info"),
nargs=0,
action=PrintInfo,
help="Print, to stdout, some regular information about the geometry.",
)
class Out(argparse.Action):
def __call__(self, parser, ns, value, option_string=None):
if value is None:
return
if len(value) == 0:
return
# If the vector, exists, we should write it
kwargs = {}
if hasattr(ns, "_geom_fmt"):
kwargs["fmt"] = ns._geom_fmt
if hasattr(ns, "_vector"):
v = getattr(ns, "_vector")
vs = getattr(ns, "_vector_scale")
if isinstance(vs, bool):
if vs:
vs = 1.0 / np.max(sqrt(square(v).sum(1)))
info(f"Scaling vector by: {vs}")
else:
vs = 1.0
# Store the vectors with the scaling
kwargs["data"] = v * vs
ns._geometry.write(value[0], **kwargs)
# Issue to the namespace that the geometry has been written, at least once.
ns._stored_geometry = True
p.add_argument(
*opts("--out", "-o"),
nargs=1,
action=Out,
help="Store the geometry (at its current invocation) to the out file.",
)
# If the user requests positional out arguments, we also add that.
if kwargs.get("positional_out", False):
p.add_argument(
"out",
nargs="*",
default=None,
action=Out,
help="Store the geometry (at its current invocation) to the out file.",
)
# We have now created all arguments
return p, namespace
new_dispatch = Geometry.new
to_dispatch = Geometry.to
# Define base-class for this
class GeometryNewDispatch(AbstractDispatch):
"""Base dispatcher from class passing arguments to Geometry class
This forwards all `__call__` calls to `dispatch`
"""
def __call__(self, *args, **kwargs):
return self.dispatch(*args, **kwargs)
# Bypass regular Geometry to be returned as is
class GeometryNewGeometryDispatch(GeometryNewDispatch):
def dispatch(self, geometry, copy=False):
"""Return geometry as-is (no copy), for sanitization purposes"""
if copy:
return geometry.copy()
return geometry
new_dispatch.register(Geometry, GeometryNewGeometryDispatch)
class GeometryNewFileDispatch(GeometryNewDispatch):
def dispatch(self, *args, **kwargs):
"""Defer the `Geometry.read` method by passing down arguments"""
cls = self._get_class()
return cls.read(*args, **kwargs)
new_dispatch.register(str, GeometryNewFileDispatch)
new_dispatch.register(Path, GeometryNewFileDispatch)
# see sisl/__init__.py for new_dispatch.register(BaseSile, GeometryNewFileDispatcher)
class GeometryNewAseDispatch(GeometryNewDispatch):
def dispatch(self, aseg, **kwargs):
"""Convert an ``ase`` object into a `Geometry`"""
cls = self._get_class()
Z = aseg.get_atomic_numbers()
xyz = aseg.get_positions()
cell = aseg.get_cell()
nsc = [3 if pbc else 1 for pbc in aseg.pbc]
lattice = Lattice(cell, nsc=nsc)
return cls(xyz, atoms=Z, lattice=lattice, **kwargs)
new_dispatch.register("ase", GeometryNewAseDispatch)
# currently we can't ensure the ase Atoms type
# to get it by type(). That requires ase to be importable.
try:
from ase import Atoms as ase_Atoms
new_dispatch.register(ase_Atoms, GeometryNewAseDispatch)
# ensure we don't pollute name-space
del ase_Atoms
except Exception:
pass
class GeometryNewpymatgenDispatch(GeometryNewDispatch):
def dispatch(self, struct, **kwargs):
"""Convert a ``pymatgen`` structure/molecule object into a `Geometry`"""
from pymatgen.core import Structure
cls = self._get_class(allow_instance=True)
Z = []
xyz = []
for site in struct.sites:
Z.append(site.specie.Z)
xyz.append(site.coords)
xyz = np.array(xyz)
if isinstance(struct, Structure):
# we also have the lattice
cell = struct.lattice.matrix
nsc = [3, 3, 3] # really, this is unknown
else:
cell = xyz.max() - xyz.min(0) + 15.0
nsc = [1, 1, 1]
lattice = Lattice(cell, nsc=nsc)
return cls(xyz, atoms=Z, lattice=lattice, **kwargs)
new_dispatch.register("pymatgen", GeometryNewpymatgenDispatch)
# currently we can't ensure the pymatgen classes
# to get it by type(). That requires pymatgen to be importable.
try:
from pymatgen.core import Molecule as pymatgen_Molecule
from pymatgen.core import Structure as pymatgen_Structure
new_dispatch.register(pymatgen_Molecule, GeometryNewpymatgenDispatch)
new_dispatch.register(pymatgen_Structure, GeometryNewpymatgenDispatch)
# ensure we don't pollute name-space
del pymatgen_Molecule, pymatgen_Structure
except Exception:
pass
class GeometryToDispatch(AbstractDispatch):
"""Base dispatcher from class passing from Geometry class"""
class GeometryToSileDispatch(GeometryToDispatch):
def dispatch(self, *args, **kwargs):
geom = self._get_object()
return geom.write(*args, **kwargs)
to_dispatch.register("str", GeometryToSileDispatch)
to_dispatch.register("Path", GeometryToSileDispatch)
# to do geom.to[Path](path)
to_dispatch.register(str, GeometryToSileDispatch)
to_dispatch.register(Path, GeometryToSileDispatch)
class GeometryToAseDispatch(GeometryToDispatch):
def dispatch(self, **kwargs):
from ase import Atoms as ase_Atoms
geom = self._get_object()
return ase_Atoms(
symbols=geom.atoms.Z,
positions=geom.xyz.tolist(),
cell=geom.cell.tolist(),
pbc=geom.nsc > 1,
**kwargs,
)
to_dispatch.register("ase", GeometryToAseDispatch)
try:
from ase import Atoms as ase_Atoms
to_dispatch.register(ase_Atoms, GeometryToAseDispatch)
del ase_Atoms
except ImportError:
pass
class GeometryTopymatgenDispatch(GeometryToDispatch):
def dispatch(self, **kwargs):
from pymatgen.core import Lattice, Molecule, Structure
from sisl._core.atom import PeriodicTable
# ensure we have an object
geom = self._get_object()
lattice = Lattice(geom.cell)
# get atomic letters and coordinates
PT = PeriodicTable()
xyz = geom.xyz
species = [PT.Z_label(Z) for Z in geom.atoms.Z]
if all(self.nsc == 1):
# we define a molecule
return Molecule(species, xyz, **kwargs)
return Structure(lattice, species, xyz, coords_are_cartesian=True, **kwargs)
to_dispatch.register("pymatgen", GeometryTopymatgenDispatch)
class GeometryToDataframeDispatch(GeometryToDispatch):
def dispatch(self, *args, **kwargs):
import pandas as pd
geom = self._get_object()
# Now create data-frame
# Currently we will populate it with
# - xyz
# - symbol
# - Z
# - tag
# - R
# - mass
# - valence q
# - norbs
data = {}
x, y, z = geom.xyz.T
data["x"] = x
data["y"] = y
data["z"] = z
atoms = geom.atoms
data["Z"] = atoms.Z
data["mass"] = atoms.mass
data["R"] = atoms.maxR(all=True)
data["q0"] = atoms.q0
data["norbitals"] = atoms.orbitals
return pd.DataFrame(data)
to_dispatch.register("dataframe", GeometryToDataframeDispatch)
try:
from pandas import DataFrame as pd_DataFrame
to_dispatch.register(pd_DataFrame, GeometryToDataframeDispatch)
del pd_DataFrame
except ImportError:
pass
# Clean up
del new_dispatch, to_dispatch
@set_module("sisl")
def sgeom(geometry=None, argv=None, ret_geometry=False):
"""Main script for sgeom.
This routine may be called with `argv` and/or a `Sile` which is the geometry at hand.
Parameters
----------
geom : Geometry or BaseSile
this may either be the geometry, as-is, or a `Sile` which contains
the geometry.
argv : list of str
the arguments passed to sgeom
ret_geometry : bool, optional
whether the function should return the geometry
"""
import argparse
import sys
from pathlib import Path
from sisl.io import BaseSile, get_sile
# The geometry-file *MUST* be the first argument
# (except --help|-h)
exe = Path(sys.argv[0]).name
# We cannot create a separate ArgumentParser to retrieve a positional arguments
# as that will grab the first argument for an option!
# Start creating the command-line utilities that are the actual ones.
description = f"""
This manipulation utility is highly advanced and one should note that the ORDER of
options is determining the final structure. For instance:
{exe} geom.xyz --repeat 2 x --repeat 2 y
is NOT equivalent to:
{exe} geom.xyz --repeat 2 y --repeat 2 x
This may be unexpected but enables one to do advanced manipulations.
Additionally, in between arguments, one may store the current state of the geometry
by writing to a standard file.
{exe} geom.xyz --repeat 2 y geom_repy.xyz --repeat 2 x geom_repy_repx.xyz
will create two files:
geom_repy.xyz
will only be repeated 2 times along the second lattice vector, while:
geom_repy_repx.xyz
will be repeated 2 times along the second lattice vector, and then the first
lattice vector.
"""
if argv is not None:
if len(argv) == 0:
argv = ["--help"]
elif len(sys.argv) == 1:
# no arguments
# fake a help
argv = ["--help"]
else:
argv = sys.argv[1:]
# Ensure that the arguments have pre-pended spaces
argv = cmd.argv_negative_fix(argv)
p = argparse.ArgumentParser(
exe,
formatter_class=argparse.RawDescriptionHelpFormatter,
description=description,
)
# Add default sisl version stuff
cmd.add_sisl_version_cite_arg(p)
# First read the input "Sile"
stdout_geom = True
if geometry is None:
from os.path import isfile
argv, input_file = cmd.collect_input(argv)
if input_file is None:
stdout_geom = False
geometry = Geometry([0] * 3)
else:
# Extract specification of the input file
i_file, spec = str_spec(input_file)
if isfile(i_file):
geometry = get_sile(input_file).read_geometry()
else:
info(f"Cannot find file '{input_file}'!")
geometry = Geometry
stdout_geom = False
elif isinstance(geometry, Geometry):
# Do nothing, the geometry is already created
pass
elif isinstance(geometry, BaseSile):
geometry = geometry.read_geometry()
# Store the input file...
input_file = geometry.file
# Do the argument parser
p, ns = geometry.ArgumentParser(p, **geometry._ArgumentParser_args_single())
# Now the arguments should have been populated
# and we will sort out if the input options
# is only a help option.
try:
if not hasattr(ns, "_input_file"):
setattr(ns, "_input_file", input_file)
except Exception:
pass
# Now try and figure out the actual arguments
p, ns, argv = cmd.collect_arguments(
argv, input=False, argumentparser=p, namespace=ns
)
# We are good to go!!!
args = p.parse_args(argv, namespace=ns)
g = args._geometry
if stdout_geom and not args._stored_geometry:
# We should write out the information to the stdout
# This is merely for testing purposes and may not be used for anything.
print("Cell:")
for i in (0, 1, 2):
print(" {:10.6f} {:10.6f} {:10.6f}".format(*g.cell[i, :]))
print("Lattice:")
print(" {:d} {:d} {:d}".format(*g.nsc))
print(" {:>10s} {:>10s} {:>10s} {:>3s}".format("x", "y", "z", "Z"))
for ia in g:
print(
" {1:10.6f} {2:10.6f} {3:10.6f} {0:3d}".format(
g.atoms[ia].Z, *g.xyz[ia, :]
)
)
if ret_geometry:
return g
return 0