Geometry tools¶
- class ase.geometry.Cell(array)[source]¶
Parallel epipedal unit cell of up to three dimensions.
This object resembles a 3x3 array whose [i, j]-th element is the jth Cartesian coordinate of the ith unit vector.
Cells of less than three dimensions are represented by placeholder unit vectors that are zero.
Create cell.
Parameters:
- array: 3x3 arraylike object
The three cell vectors: cell[0], cell[1], and cell[2].
- classmethod ascell(cell)[source]¶
Return argument as a Cell object. See
ase.cell.Cell.new()
.A new Cell object is created if necessary.
- bandpath(path: str = None, npoints: int = None, *, density: float = None, special_points: Mapping[str, Sequence[float]] = None, eps: float = 0.0002, pbc: bool | Sequence[bool] = True) BandPath [source]¶
Build a
BandPath
for this cell.If special points are None, determine the Bravais lattice of this cell and return a suitable Brillouin zone path with standard special points.
If special special points are given, interpolate the path directly from the available data.
Parameters:
- path: string
String of special point names defining the path, e.g. ‘GXL’.
- npoints: int
Number of points in total. Note that at least one point is added for each special point in the path.
- density: float
density of kpoints along the path in Å⁻¹.
- special_points: dict
Dictionary mapping special points to scaled kpoint coordinates. For example
{'G': [0, 0, 0], 'X': [1, 0, 0]}
.- eps: float
Tolerance for determining Bravais lattice.
- pbc: three bools
Whether cell is periodic in each direction. Normally not necessary. If cell has three nonzero cell vectors, use e.g. pbc=[1, 1, 0] to request a 2D bandpath nevertheless.
Example
>>> from ase.cell import Cell
>>> cell = Cell.fromcellpar([4, 4, 4, 60, 60, 60]) >>> cell.bandpath('GXW', npoints=20) BandPath(path='GXW', cell=[3x3], special_points={GKLUWX}, kpts=[20x3])
- cartesian_positions(scaled_positions) ndarray [source]¶
Calculate Cartesian positions from scaled positions.
- cellpar(radians=False)[source]¶
Get unit cell parameters. Sequence of 6 numbers.
First three are unit cell vector lengths and second three are angles between them:
[len(a), len(b), len(c), angle(b,c), angle(a,c), angle(a,b)]
in degrees.
See also
ase.geometry.cell.cell_to_cellpar()
.
- classmethod fromcellpar(cellpar, ab_normal=(0, 0, 1), a_direction=None)[source]¶
Return new Cell from cell lengths and angles.
See also
cellpar_to_cell()
.
- get_bravais_lattice(eps=0.0002, *, pbc=True)[source]¶
Return
BravaisLattice
for this cell:>>> from ase.cell import Cell
>>> cell = Cell.fromcellpar([4, 4, 4, 60, 60, 60]) >>> print(cell.get_bravais_lattice()) FCC(a=5.65685)
Note
The Bravais lattice object follows the AFlow conventions.
cell.get_bravais_lattice().tocell()
may differ from the original cell by a permutation or other operation which maps it to the AFlow convention. For example, the orthorhombic lattice enforces a < b < c.To build a bandpath for a particular cell, use
ase.cell.Cell.bandpath()
instead of this method. This maps the kpoints back to the original input cell.
- property handedness: int¶
Sign of the determinant of the matrix of cell vectors.
1 for right-handed cells, -1 for left, and 0 for cells that do not span three dimensions.
- minkowski_reduce()[source]¶
Minkowski-reduce this cell, returning new cell and mapping.
See also
ase.geometry.minkowski_reduction.minkowski_reduce()
.
- classmethod new(cell=None)[source]¶
Create new cell from any parameters.
If cell is three numbers, assume three lengths with right angles.
If cell is six numbers, assume three lengths, then three angles.
If cell is 3x3, assume three cell vectors.
- niggli_reduce(eps=1e-05)[source]¶
Niggli reduce this cell, returning a new cell and mapping.
See also
ase.build.tools.niggli_reduce_cell()
.
- normal(i)[source]¶
Normal vector of the two vectors with index different from i.
This is the cross product of those vectors in cyclic order from i.
- property rank: int¶
“Return the dimension of the cell.
Equal to the number of nonzero lattice vectors.
- reciprocal() Cell [source]¶
Get reciprocal lattice as a Cell object.
The reciprocal cell is defined such that
cell.reciprocal() @ cell.T == np.diag(cell.mask())
within machine precision.
Does not include factor of 2 pi.
- scaled_positions(positions) ndarray [source]¶
Calculate scaled positions from Cartesian positions.
The scaled positions are the positions given in the basis of the cell vectors. For the purpose of defining the basis, cell vectors that are zero will be replaced by unit vectors as per
complete()
.
- standard_form()[source]¶
Rotate axes such that unit cell is lower triangular. The cell handedness is preserved.
A lower-triangular cell with positive diagonal entries is a canonical (i.e. unique) description. For a left-handed cell the diagonal entries are negative.
Returns:
rcell: the standardized cell object
- Q: ndarray
The orthogonal transformation. Here, rcell @ Q = cell, where cell is the input cell and rcell is the lower triangular (output) cell.
- ase.geometry.cell_to_cellpar(cell, radians=False)[source]¶
Returns the cell parameters [a, b, c, alpha, beta, gamma].
Angles are in degrees unless radian=True is used.
- ase.geometry.cellpar_to_cell(cellpar, ab_normal=(0, 0, 1), a_direction=None)[source]¶
Return a 3x3 cell matrix from cellpar=[a,b,c,alpha,beta,gamma].
Angles must be in degrees.
The returned cell is orientated such that a and b are normal to \(ab_normal\) and a is parallel to the projection of \(a_direction\) in the a-b plane.
Default \(a_direction\) is (1,0,0), unless this is parallel to \(ab_normal\), in which case default \(a_direction\) is (0,0,1).
The returned cell has the vectors va, vb and vc along the rows. The cell will be oriented such that va and vb are normal to \(ab_normal\) and va will be along the projection of \(a_direction\) onto the a-b plane.
Example:
>>> from ase.geometry.cell import cellpar_to_cell
>>> cell = cellpar_to_cell([1, 2, 4, 10, 20, 30], (0, 1, 1), (1, 2, 3)) >>> np.round(cell, 3) array([[ 0.816, -0.408, 0.408], [ 1.992, -0.13 , 0.13 ], [ 3.859, -0.745, 0.745]])
- ase.geometry.complete_cell(cell)[source]¶
Calculate complete cell with missing lattice vectors.
Returns a new 3x3 ndarray.
- ase.geometry.conditional_find_mic(vectors, cell, pbc)[source]¶
Return list of vector arrays and corresponding list of vector lengths for a given list of vector arrays. The minimum image convention is applied if cell and pbc are set. Can be used like a simple version of get_distances.
- ase.geometry.distance(s1, s2, permute=True)[source]¶
Get the distance between two structures s1 and s2.
The distance is defined by the Frobenius norm of the spatial distance between all coordinates (see numpy.linalg.norm for the definition).
permute: minimise the distance by ‘permuting’ same elements
- ase.geometry.find_mic(v, cell, pbc=True)[source]¶
Finds the minimum-image representation of vector(s) v using either one of two find mic algorithms depending on the given cell, v and pbc.
- ase.geometry.get_angles(v0, v1, cell=None, pbc=None)[source]¶
Get angles formed by two lists of vectors.
Calculate angle in degrees between vectors v0 and v1
Set a cell and pbc to enable minimum image convention, otherwise angles are taken as-is.
- ase.geometry.get_angles_derivatives(v0, v1, cell=None, pbc=None)[source]¶
Get derivatives of angles formed by two lists of vectors (v0, v1) w.r.t. Cartesian coordinates in degrees.
Set a cell and pbc to enable minimum image convention, otherwise derivatives of angles are taken as-is.
There is a singularity in the derivatives for sin(angle) -> 0 for which a ZeroDivisionError is raised.
Derivative output format: [[dx_a0, dy_a0, dz_a0], […], […, dz_a2].
- ase.geometry.get_dihedrals(v0, v1, v2, cell=None, pbc=None)[source]¶
Get dihedral angles formed by three lists of vectors.
Calculate dihedral angle (in degrees) between the vectors a0->a1, a1->a2 and a2->a3, written as v0, v1 and v2.
Set a cell and pbc to enable minimum image convention, otherwise angles are taken as-is.
- ase.geometry.get_dihedrals_derivatives(v0, v1, v2, cell=None, pbc=None)[source]¶
Get derivatives of dihedrals formed by three lists of vectors (v0, v1, v2) w.r.t Cartesian coordinates in degrees.
Set a cell and pbc to enable minimum image convention, otherwise dihedrals are taken as-is.
Derivative output format: [[dx_a0, dy_a0, dz_a0], …, […, dz_a3]].
- ase.geometry.get_distances(p1, p2=None, cell=None, pbc=None)[source]¶
Return distance matrix of every position in p1 with every position in p2
If p2 is not set, it is assumed that distances between all positions in p1 are desired. p2 will be set to p1 in this case.
Use set cell and pbc to use the minimum image convention.
- ase.geometry.get_distances_derivatives(v0, cell=None, pbc=None)[source]¶
Get derivatives of distances for all vectors in v0 w.r.t. Cartesian coordinates in Angstrom.
Set cell and pbc to use the minimum image convention.
There is a singularity for distances -> 0 for which a ZeroDivisionError is raised. Derivative output format: [[dx_a0, dy_a0, dz_a0], [dx_a1, dy_a1, dz_a1]].
- ase.geometry.get_duplicate_atoms(atoms, cutoff=0.1, delete=False)[source]¶
Get list of duplicate atoms and delete them if requested.
Identify all atoms which lie within the cutoff radius of each other. Delete one set of them if delete == True.
- ase.geometry.get_layers(atoms, miller, tolerance=0.001)[source]¶
Returns two arrays describing which layer each atom belongs to and the distance between the layers and origo.
Parameters:
- miller: 3 integers
The Miller indices of the planes. Actually, any direction in reciprocal space works, so if a and b are two float vectors spanning an atomic plane, you can get all layers parallel to this with miller=np.cross(a,b).
- tolerance: float
The maximum distance in Angstrom along the plane normal for counting two atoms as belonging to the same plane.
Returns:
- tags: array of integres
Array of layer indices for each atom.
- levels: array of floats
Array of distances in Angstrom from each layer to origo.
Example:
>>> import numpy as np
>>> from ase.spacegroup import crystal >>> from ase.geometry.geometry import get_layers
>>> atoms = crystal('Al', [(0,0,0)], spacegroup=225, cellpar=4.05) >>> np.round(atoms.positions, decimals=5) array([[ 0. , 0. , 0. ], [ 0. , 2.025, 2.025], [ 2.025, 0. , 2.025], [ 2.025, 2.025, 0. ]]) >>> get_layers(atoms, (0,0,1)) (array([0, 1, 1, 0]...), array([ 0. , 2.025]))
- ase.geometry.is_minkowski_reduced(cell, pbc=True)[source]¶
Tests if a cell is Minkowski-reduced.
Parameters:
- cell: array
The lattice basis to test (in row-vector format).
- pbc: array, optional
The periodic boundary conditions of the cell (Default \(True\)). If \(pbc\) is provided, only periodic cell vectors are tested.
Returns:
- is_reduced: bool
True if cell is Minkowski-reduced, False otherwise.
- ase.geometry.minkowski_reduce(cell, pbc=True)[source]¶
Calculate a Minkowski-reduced lattice basis. The reduced basis has the shortest possible vector lengths and has norm(a) <= norm(b) <= norm(c).
Implements the method described in:
Low-dimensional Lattice Basis Reduction Revisited Nguyen, Phong Q. and Stehlé, Damien, ACM Trans. Algorithms 5(4) 46:1–46:48, 2009 doi: 10.1145/1597036.1597050
Parameters:
- cell: array
The lattice basis to reduce (in row-vector format).
- pbc: array, optional
The periodic boundary conditions of the cell (Default \(True\)). If \(pbc\) is provided, only periodic cell vectors are reduced.
Returns:
- rcell: array
The reduced lattice basis.
- op: array
The unimodular matrix transformation (rcell = op @ cell).
- ase.geometry.permute_axes(atoms, permutation)[source]¶
Permute axes of unit cell and atom positions. Considers only cell and atomic positions. Other vector quantities such as momenta are not modified.
- ase.geometry.wrap_positions(positions, cell, pbc=True, center=(0.5, 0.5, 0.5), pretty_translation=False, eps=1e-07)[source]¶
Wrap positions to unit cell.
Returns positions changed by a multiple of the unit cell vectors to fit inside the space spanned by these vectors. See also the
ase.Atoms.wrap()
method.Parameters:
- positions: float ndarray of shape (n, 3)
Positions of the atoms
- cell: float ndarray of shape (3, 3)
Unit cell vectors.
- pbc: one or 3 bool
For each axis in the unit cell decides whether the positions will be moved along this axis.
- center: three float
The positons in fractional coordinates that the new positions will be nearest possible to.
- pretty_translation: bool
Translates atoms such that fractional coordinates are minimized.
- eps: float
Small number to prevent slightly negative coordinates from being wrapped.
Example:
>>> from ase.geometry import wrap_positions >>> wrap_positions([[-0.1, 1.01, -0.5]], ... [[1, 0, 0], [0, 1, 0], [0, 0, 4]], ... pbc=[1, 1, 0]) array([[ 0.9 , 0.01, -0.5 ]])
Analysis tools¶
Provides the class Analysis
for structural analysis of any Atoms
object or list thereof (trajectories).
Example:
>>> import numpy as np
>>> from ase.build import molecule
>>> from ase.geometry.analysis import Analysis
>>> mol = molecule('C60')
>>> ana = Analysis(mol)
>>> CCBonds = ana.get_bonds('C', 'C', unique=True)
>>> CCCAngles = ana.get_angles('C', 'C', 'C', unique=True)
>>> print("There are {} C-C bonds in C60.".format(len(CCBonds[0])))
>>> print("There are {} C-C-C angles in C60.".format(len(CCCAngles[0])))
>>> CCBondValues = ana.get_values(CCBonds)
>>> CCCAngleValues = ana.get_values(CCCAngles)
>>> print("The average C-C bond length is {}.".format(np.average(CCBondValues)))
>>> print("The average C-C-C angle is {}.".format(np.average(CCCAngleValues)))
The Analysis
class provides a getter and setter for the images.
This allows you to use the same neighbourlist for different images, e.g. to analyze two MD simulations at different termperatures but constant bonding patterns.
Using this approach saves the time to recalculate all bonds, angles and dihedrals and therefore speeds up your analysis.
Using the Analysis.clear_cache()
function allows you to clear the calculated matrices/lists to reduce your memory usage.
The entire class can be used with few commands:
To retrieve tuples of bonds/angles/dihedrals (they are calculated the first time they are accessed) use
instance.all_xxx
where xxx is one of bonds/angles/dihedrals.If you only want those one-way (meaning e.g. not bonds i-j and j-i but just i-j) use
instance.unique_xxx
.To get selected bonds/angles/dihedrals use
instance.get_xxx(A,B,...)
, see the API section for details on which arguments you can pass.To get the actual value of a bond/angle/dihedral use
instance.get_xxx_value(tuple)
.To get a lot of bond/angle/dihedral values at once use
Analysis.get_values()
.There is also a wrapper to get radial distribution functions
Analysis.get_rdf()
.
The main difference between properties (getters) and functions here is, that getters provide data that is cached.
This means that getting information from Analysis.all_bonds
more than once is instantaneous, since the information is cached in Analysis._cache
.
If you call any Analysis.get_xxx()
the information is calculated from the cached data, meaning each call will take the same amount of time.
API:
- class ase.geometry.analysis.Analysis(images, nl=None, **kwargs)[source]¶
Analysis class
Parameters for initialization:
- images:
Atoms
object or list of such Images to analyze.
- nl: None,
NeighborList
object or list of such Neighborlist(s) for the given images. One or nImages, depending if bonding pattern changes or is constant. Using one Neigborlist greatly improves speed.
- kwargs: options, dict
Arguments for constructing
NeighborList
object ifnl
is None.
The choice of
bothways=True
for theNeighborList
object will not influence the amount of bonds/angles/dihedrals you get, all are reported in both directions. Use the unique-labeled properties to get lists without duplicates.- property adjacency_matrix¶
The adjacency/connectivity matrix.
If not already done, build a list of adjacency matrices for all
nl
.No setter or deleter, only getter
- property all_angles¶
All angles
A list with indices of atoms in angles for each neighborlist in self. Atom i forms an angle to the atoms inside the tuples in result[i]: i – result[i][x][0] – result[i][x][1] where x is in range(number of angles from i). See also
unique_angles
.No setter or deleter, only getter
- property all_bonds¶
All Bonds.
A list with indices of bonded atoms for each neighborlist in self. Atom i is connected to all atoms inside result[i]. Duplicates from PBCs are removed. See also
unique_bonds
.No setter or deleter, only getter
- property all_dihedrals¶
All dihedrals
Returns a list with indices of atoms in dihedrals for each neighborlist in this instance. Atom i forms a dihedral to the atoms inside the tuples in result[i]: i – result[i][x][0] – result[i][x][1] – result[i][x][2] where x is in range(number of dihedrals from i). See also
unique_dihedrals
.No setter or deleter, only getter
- property distance_matrix¶
The distance matrix.
If not already done, build a list of distance matrices for all
nl
. Seease.neighborlist.get_distance_matrix()
.No setter or deleter, only getter
- get_angle_value(imIdx, idxs, mic=True, **kwargs)[source]¶
Get angle.
Parameters:
- imIdx: int
Index of Image to get value from.
- idxs: tuple or list of integers
Get angle between atoms idxs[0]-idxs[1]-idxs[2].
- mic: bool
Passed on to
ase.Atoms.get_angle()
for retrieving the value, defaults to True. If the cell of the image is correctly set, there should be no reason to change this.- kwargs: options or dict
Passed on to
ase.Atoms.get_angle()
.
Returns:
- return: float
Value returned by image.get_angle.
- get_angles(A, B, C, unique=True)[source]¶
Get angles from given elements A-B-C.
Parameters:
- A, B, C: str
Get Angles between elements A, B and C. B will be the central atom.
- unique: bool
Return the angles both ways or just one way (A-B-C and C-B-A or only A-B-C)
Returns:
- return: list of lists of tuples
return[imageIdx][atomIdx][angleI], each tuple starts with atomIdx.
Use
get_values()
to convert the returned list to values.
- get_bond_value(imIdx, idxs, mic=True, **kwargs)[source]¶
Get bond length.
Parameters:
- imIdx: int
Index of Image to get value from.
- idxs: tuple or list of integers
Get distance between atoms idxs[0]-idxs[1].
- mic: bool
Passed on to
ase.Atoms.get_distance()
for retrieving the value, defaults to True. If the cell of the image is correctly set, there should be no reason to change this.- kwargs: options or dict
Passed on to
ase.Atoms.get_distance()
.
Returns:
- return: float
Value returned by image.get_distance.
- get_bonds(A, B, unique=True)[source]¶
Get bonds from element A to element B.
Parameters:
- A, B: str
Get Bonds between elements A and B
- unique: bool
Return the bonds both ways or just one way (A-B and B-A or only A-B)
Returns:
- return: list of lists of tuples
return[imageIdx][atomIdx][bondI], each tuple starts with atomIdx.
Use
get_values()
to convert the returned list to values.
- get_dihedral_value(imIdx, idxs, mic=True, **kwargs)[source]¶
Get dihedral.
Parameters:
- imIdx: int
Index of Image to get value from.
- idxs: tuple or list of integers
Get angle between atoms idxs[0]-idxs[1]-idxs[2]-idxs[3].
- mic: bool
Passed on to
ase.Atoms.get_dihedral()
for retrieving the value, defaults to True. If the cell of the image is correctly set, there should be no reason to change this.- kwargs: options or dict
Passed on to
ase.Atoms.get_dihedral()
.
Returns:
- return: float
Value returned by image.get_dihedral.
- get_dihedrals(A, B, C, D, unique=True)[source]¶
Get dihedrals A-B-C-D.
Parameters:
- A, B, C, D: str
Get Dihedralss between elements A, B, C and D. B-C will be the central axis.
- unique: bool
Return the dihedrals both ways or just one way (A-B-C-D and D-C-B-A or only A-B-C-D)
Returns:
- return: list of lists of tuples
return[imageIdx][atomIdx][dihedralI], each tuple starts with atomIdx.
Use
get_values()
to convert the returned list to values.
- get_rdf(rmax, nbins, imageIdx=None, elements=None, return_dists=False, volume: float | None = None)[source]¶
Get RDF.
Wrapper for
ase.ga.utilities.get_rdf()
with more selection possibilities.Parameters:
- rmax: float
Maximum distance of RDF.
- nbins: int
Number of bins to divide RDF.
- imageIdx: int/slice/None
Images to analyze, see
_get_slice()
for details.- elements: str/int/list/tuple
Make partial RDFs.
If elements is None, a full RDF is calculated. If elements is an integer or a list/tuple of integers, only those atoms will contribute to the RDF (like a mask). If elements is a string or a list/tuple of strings, only Atoms of those elements will contribute.
Returns:
- return: list of lists / list of tuples of lists
If return_dists is True, the returned tuples contain (rdf, distances). Otherwise only rdfs for each image are returned.
- get_values(inputList, imageIdx=None, mic=True, **kwargs)[source]¶
Get Bond/Angle/Dihedral values.
Parameters:
- inputList: list of lists of tuples
Can be any list provided by
get_bonds()
,get_angles()
orget_dihedrals()
.- imageIdx: integer or slice
The images from
images
to be analyzed. If None, all frames will be analyzed. See_get_slice()
for details.- mic: bool
Passed on to
Atoms
for retrieving the values, defaults to True. If the cells of the images are correctly set, there should be no reason to change this.- kwargs: options or dict
Passed on to the
Atoms
classes functions for retrieving the values.
Returns:
- return: list of lists of floats
return[imageIdx][valueIdx]. Has the same shape as the inputList, instead of each tuple there is a float with the value this tuple yields.
The type of value requested is determined from the length of the tuple inputList[0][0]. The methods from the
Atoms
class are used.
- property images¶
Images.
Set during initialization but can also be set later.
- property nImages¶
Number of Images in this instance.
Cannot be set, is determined automatically.
- property nl¶
Neighbor Lists in this instance.
Set during initialization.
No setter or deleter, only getter
- property unique_angles¶
Get Unique Angles.
all_angles
i-j-k without k-j-i.
- property unique_bonds¶
Get Unique Bonds.
all_bonds
i-j without j-i. This is the upper triangle of the connectivity matrix (i,j), \(i < j\)
- property unique_dihedrals¶
Get Unique Dihedrals.
all_dihedrals
i-j-k-l without l-k-j-i.
- images: