GridBased

This is the pydoc code for the gridbased module.

# molli.descriptor.gridbased

Grid based conformer averaged descriptors This is a foundational file

molli.descriptor.gridbased.prune(grid: ndarray, struct_or_ens: ConformerEnsemble | CartesianGeometry, max_dist: float = 2.0, eps: float = 0.5)

This function prunes the grid to return only the points closer than (max_dist + eps) from an ensemble or molecule. This is a very fast (!) implementation that utilizes a KDTree structure for fast queries

molli.descriptor.gridbased.nearest_atom_index(grid: ndarray, struct_or_ens: ConformerEnsemble | CartesianGeometry, max_dist: float = 2.0)

Returns an array of atom indexes that are closest to the grid points in the shape of (n_conformers, n_gridpoints) for ensembles and in the shape of (n_gridpoints,) for StructureLikes where the number corresponds to the atom index (or -1 if the closest atom is farther than max_dist)

Uses KDTree data structure to increase the rate of computations significantly

molli.descriptor.gridbased.atomic_indicator_field(ens: ConformerEnsemble, grid: ndarray, indicator_values: ndarray, atomic_radii: ndarray, nearest_atom_idx: ndarray = None, weighted: bool = False) → ndarray

Main workhorse function for indicator field calculation that takes advantage of C++ backend code whenever possible.

molli.descriptor.gridbased.aso(ens: ConformerEnsemble, grid: ndarray, weighted: bool = False) → ndarray

Main workhorse function for ASO calculation that takes advantage of C++ backend code whenever possible. With large grids can be relatively memory intensive, so breaking up the grid into smaller pieces is recommended (see “chunky” calculation strategy)

molli.descriptor.gridbased.aeif(ens: ConformerEnsemble, grid: ndarray, nearest_atom_idx: ndarray = None, weighted: bool = False)

` Average electronic indicator field