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Fixed chill selector_ice() function

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Sebastian Kloth 2024-02-15 11:49:20 +01:00
parent 0c940115af
commit 6b77ef78e1
1 changed files with 11 additions and 11 deletions
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22
src/mdevaluate/extra/chill.py
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@ -11,7 +11,7 @@ from mdevaluate.coordinates import CoordinateFrame, Coordinates
from mdevaluate.pbc import pbc_points from mdevaluate.pbc import pbc_points
def a_ij(atoms: ArrayLike, N: int = 4, l: int = 3) -> tuple[NDArray, NDArray]: def calc_aij(atoms: ArrayLike, N: int = 4, l: int = 3) -> tuple[NDArray, NDArray]:
tree = KDTree(atoms) tree = KDTree(atoms)
dist, indices = tree.query(atoms, N + 1) dist, indices = tree.query(atoms, N + 1)
@ -84,18 +84,18 @@ def count_ice_types(iceTypes: NDArray) -> NDArray:
def selector_ice( def selector_ice(
start_frame: CoordinateFrame, oxygen_atoms_water: CoordinateFrame,
traj: Coordinates,
chosen_ice_types: ArrayLike, chosen_ice_types: ArrayLike,
combined: bool = True, combined: bool = True,
next_neighbor_distance: float = 0.35,
) -> NDArray: ) -> NDArray:
oxygen = traj.subset(atom_name="OW") atoms = oxygen_atoms_water
atoms = oxygen[start_frame.step] atoms_PBC = pbc_points(atoms, thickness=next_neighbor_distance * 2.2)
atoms = atoms % np.diag(atoms.box) aij, indices = calc_aij(atoms_PBC)
atoms_PBC = pbc_points(atoms, thickness=1)
aij, indices = a_ij(atoms_PBC)
tree = KDTree(atoms_PBC) tree = KDTree(atoms_PBC)
neighbors = tree.query_ball_point(atoms_PBC, 0.35, return_length=True) neighbors = tree.query_ball_point(
atoms_PBC, next_neighbor_distance, return_length=True
) - 1
index_SOL = atoms_PBC.tolist().index(atoms[0].tolist()) index_SOL = atoms_PBC.tolist().index(atoms[0].tolist())
index_SOL = np.arange(index_SOL, index_SOL + len(atoms)) index_SOL = np.arange(index_SOL, index_SOL + len(atoms))
ice_Types = classify_ice(aij, indices, neighbors, index_SOL) ice_Types = classify_ice(aij, indices, neighbors, index_SOL)
@ -117,9 +117,9 @@ def selector_ice(
def ice_types(trajectory: Coordinates, segments: int = 10000) -> pd.DataFrame: def ice_types(trajectory: Coordinates, segments: int = 10000) -> pd.DataFrame:
def ice_types_distribution(frame: CoordinateFrame, selector: Callable) -> NDArray: def ice_types_distribution(frame: CoordinateFrame, selector: Callable) -> NDArray:
atoms_PBC = pbc_points(frame, thickness=1) atoms_PBC = pbc_points(frame, thickness=1)
aij, indices = a_ij(atoms_PBC) aij, indices = calc_aij(atoms_PBC)
tree = KDTree(atoms_PBC) tree = KDTree(atoms_PBC)
neighbors = tree.query_ball_point(atoms_PBC, 0.35, return_length=True) neighbors = tree.query_ball_point(atoms_PBC, 0.35, return_length=True) - 1
index = selector(frame, atoms_PBC) index = selector(frame, atoms_PBC)
ice_types_data = classify_ice(aij, indices, neighbors, index) ice_types_data = classify_ice(aij, indices, neighbors, index)
ice_parts_data = count_ice_types(ice_types_data) ice_parts_data = count_ice_types(ice_types_data)