Miscellaneous

Fingerprints of finite point sets

AMDs and PDDs also work for finite point sets. The functions calculate.finite_AMD() and calculate.finite_PDD() accept just a numpy array containing the points, returning the fingerprint of the finite point set. Unlike amd.AMD and amd.PDD no integer k is passed; instead the distances to all neighbours are found (number of columns = no of points - 1).

Simplex-wise distance distributions

As the name suggests

Inverse design

It is possible to reconstruct a periodic set up to isometry from its PDD if the periodic set satisfies certain conditions (a ‘general position’) and the PDD has enough columns. This is implemented via the functions calculate.PDD_reconstructable(), which returns the PDD of a periodic set with enough columns, and reconstruct.reconstruct() which returns the motif given the PDD and unit cell.