Functions for creating distributions of atomic indices using brute-force approaches.


brute_uniform_idx(mol, idx[, n, operation, ...])

Brute force approach to creating uniform or clustered distributions.


CAT.attachment.distribution_brute.brute_uniform_idx(mol, idx, n=2, operation='min', weight=<function <lambda>>)[source]

Brute force approach to creating uniform or clustered distributions.

Explores, and evaluates, all valid combinations of size \(n\) constructed from the \(k\) atoms in neighbor closest to each atom in center.

The combination where the \(n\) atoms are closest (operation = 'max') or furthest removed from each other (operation = 'min') is returned.

  • mol (array-like [float], shape \((m,3)\)) – An array-like object with Cartesian coordinate representing a collection of central atoms.

  • idx (array-like [int], shape \((l,p)\)) – An array-like object with indices in mol. Combinations will be explored and evaluated along axis -1 of the passed array.

  • n (int) – The number of to-be returned opposing atoms. Should be larger than or equal to 1.

  • operation (str) – Whether to evaluate the weighted distance using argmin() or argmax(). Accepted values are "min" and "max".

  • weight (Callable) – A callable for applying weights to the distance; default: \(e^{-x}\). The callable should take an array as argument and return a new array, e.g. numpy.exp().


An array with indices of opposing atoms.

Return type:

numpy.ndarray [int], shape \((m, n)\)

See also


Yield the column-indices of dist which yield a uniform or clustered distribution.