pymatgen.optimization package

Optimization utilities.

Submodules

pymatgen.optimization.fast_parser module

parse_n_doubles(file, out, nelem=-1)

Parse doubles from a binary file object into out.

The file is read in 1 MiB chunks. On return, the file position follows the stop pointer returned by the parser without rewinding trailing whitespace.

The file must be a seekable binary stream (the function uses tell()/seek() to leave the cursor right after the last parsed number). Backward seeks on compressed streams (e.g. gzip.GzipFile, bz2.BZ2File) are implemented as decompress-from-start and can be dramatically slower than on uncompressed files; prefer decompressing upstream when parsing large compressed inputs.

Returns:

Parsed element count.

Return type:

int

Raises:

• TypeError – If file is not a binary stream.

• ValueError – If nelem exceeds out.shape[0].

pymatgen.optimization.neighbors module

find_points_in_spheres(all_coords, center_coords, r, pbc, lattice, tol=1e-08, min_r=1.0)

For each point in center_coords, get all the neighboring points in all_coords that are within the cutoff radius r. All the coordinates should be Cartesian.

Parameters:

• all_coords – (np.ndarray[double, dim=2]) all available points. When periodic boundary is considered, this is all the points in the lattice.

• center_coords – (np.ndarray[double, dim=2]) all centering points

• r – (float) cutoff radius

• pbc – (np.ndarray[np.int64_t, dim=1]) whether to set periodic boundaries

• lattice – (np.ndarray[double, dim=2]) 3x3 lattice matrix

• tol – (float) numerical tolerance

• min_r – (float) minimal cutoff to calculate the neighbor list directly. If the cutoff is less than this value, the algorithm will calculate neighbor list using min_r as cutoff and discard those that have larger distances.

Returns:

Indexes of center_coords. index2 (n, ): Indexes of all_coords that form the neighbor pair. offset_vectors (n, 3): The periodic image offsets for all_coords. distances (n, ).

Return type:

index1 (n, )