jarvis.analysis.structure.neighbors

This module provides classes to specify atomic structure.

Module Contents

Classes

NeighborsAnalysis

Get neighbor informations (RDF,ADF,DDF) for Atoms object.

Functions

check_array(arr[, type_, shape])	Check type and shape of np.ndarray.
special_arange(dims)	Multiple dimensional arange.
calc_structure_data(coords, box, all_symbs, c_size)	Calcuate a dictionary of structure data.

jarvis.analysis.structure.neighbors.check_array(arr, type_=None, shape=None)

Check type and shape of np.ndarray.

Follwing arguments are needed. Args:

x: array to check

type_: the type of the array (int, float), None to not check

shape: the shape of the array, None to not check, -1 to ignore a

dimension

jarvis.analysis.structure.neighbors.special_arange(dims)

Multiple dimensional arange.

This could be extended to any dimension. Args:

dims: sequence of ints of length 3

Returns:

an array of shape (np.prod(dims), 3) with each index having a different arrangement of arange.

This function implements the equivalent of a multidimensional for loop, which feels like multidimensional arange (see the test)

>>> dim0, dim1, dim2 = 2, 4, 3
>>> arr_test = np.zeros((dim0 * dim1 * dim2, 3), dtype=int)
>>> counter = 0
>>> for i in np.arange(dim0):
...     for j in np.arange(dim1):
...         for k in np.arange(dim2):
...             arr_test[counter, 0] = i
...             arr_test[counter, 1] = j
...             arr_test[counter, 2] = k
...             counter += 1

>>> arr_actual = special_arange((dim0, dim1, dim2))
>>> print(arr_actual)
[[0 0 0]
 [0 0 1]
 [0 0 2]
 [0 1 0]
 [0 1 1]
 [0 1 2]
 [0 2 0]
 [0 2 1]
 [0 2 2]
 [0 3 0]
 [0 3 1]
 [0 3 2]
 [1 0 0]
 [1 0 1]
 [1 0 2]
 [1 1 0]
 [1 1 1]
 [1 1 2]
 [1 2 0]
 [1 2 1]
 [1 2 2]
 [1 3 0]
 [1 3 1]
 [1 3 2]]
>>> assert np.all(arr_actual == arr_test)

jarvis.analysis.structure.neighbors.calc_structure_data(coords, box, all_symbs, c_size)

Calcuate a dictionary of structure data.

Args:

coords: the coordinates for each element

box: the lattic matrix

all_symbs: the elements

c_size: the c size

Returns:

a set of structure data

>>> coords = np.array([[0, 0, 0], [0.25, 0.2, 0.25]])
>>> lat = [[2.715, 2.715, 0], [0, 2.715, 2.715], [2.715, 0, 2.715]]
>>> box = np.array(lat)
>>> elements = ["Si", "Si"]
>>> c_size = 10.0
>>> data = calc_structure_data(coords, box, elements, c_size)
>>> assert len(data['coords']) == 128
>>> assert np.allclose(data['coords'][9], [0.    , 0.5   , 0.25  ])
>>> assert np.all(data['dim'] == [4, 4, 4])
>>> assert len(data['new_symbs']) == 128

class jarvis.analysis.structure.neighbors.NeighborsAnalysis(atoms=None, max_n=500, rcut1=None, max_cut=10.0, rcut2=None, verbose=False)

Bases: object

Get neighbor informations (RDF,ADF,DDF) for Atoms object.

property get_all_distributions

Get all distributions.

get_structure_data(c_size=10.0)

Provide non-repetitive structure information.

nbor_list(rcut=10.0, c_size=12.0)

Generate neighbor info.

get_rdf(plot=False)

Calculate radial distribution function.

get_dist_cutoffs()

Get different distance cut-offs.

Args:

s: Structure object

Returns:

rcut: max-cutoff to ensure all the element-combinations are included, used in calculating angluar distribution upto first neighbor

rcut1: decide first cut-off based on total RDF and a buffer (previously used in dihedrals, but not used now in the code)

rcut2: second neighbor cut-off

rcut_dihed: specialized cut-off for dihedrals to avaoid large bonds such as N-N, uses average bond-distance and standard deviations

ang_dist(nbor_info={}, plot=False)

Get angular distribution function upto first neighbor.

Args:

struct_info: struct information

max_n: maximum number of neigbors

c_size: max. cell size

plot: whether to plot distributions

Retruns:

ang_hist1: Angular distribution upto first cut-off

ang_bins1: angle bins

ang_dist_first(plot=False)

Get angular distribution upto first neighbor.

ang_dist_second(plot=False)

Get angular distribution upto second neighbor.

get_ddf(plot=False)

Get dihedral distribution upto first neighbor.

atomwise_radial_dist(rcut=10.0, c_size=0)

Get pair/radial distribution for each atom.

atomwise_angle_dist(rcut=None, nbins=180, c_size=0)

Get angle distribution for each atom.