From b66e920758975ade59a973df0c578a3638684eed Mon Sep 17 00:00:00 2001
From: Sebastian Kloth <sebastian.kloth@pkm.tu-darmstadt.de>
Date: Thu, 28 Dec 2023 12:00:40 +0100
Subject: [PATCH] Updated type hints

---
 src/mdevaluate/distribution.py | 28 ++++++++++++++--------------
 1 file changed, 14 insertions(+), 14 deletions(-)

diff --git a/src/mdevaluate/distribution.py b/src/mdevaluate/distribution.py
index ea7c42f..dd069a7 100644
--- a/src/mdevaluate/distribution.py
+++ b/src/mdevaluate/distribution.py
@@ -1,7 +1,7 @@
 from typing import Callable, Optional, Union
 
 import numpy as np
-from numpy.typing import ArrayLike
+from numpy.typing import ArrayLike, NDArray
 from scipy import spatial
 from scipy.spatial import KDTree
 from scipy.sparse.csgraph import connected_components
@@ -24,7 +24,7 @@ def time_average(
     coordinates_b: Optional[Coordinates] = None,
     skip: float = 0.1,
     segments: int = 100,
-) -> np.ndarray:
+) -> NDArray:
     """
     Compute the time average of a function.
 
@@ -58,7 +58,7 @@ def gr(
     bins: Optional[ArrayLike] = None,
     remove_intra: bool = False,
     **kwargs
-) -> np.ndarray:
+) -> NDArray:
     r"""
     Compute the radial pair distribution of one or two sets of atoms.
 
@@ -121,7 +121,7 @@ def gr(
 
 def distance_distribution(
     atoms: CoordinateFrame, bins: Optional[int, ArrayLike]
-) -> np.ndarray:
+) -> NDArray:
     connection_vectors = atoms[:-1, :] - atoms[1:, :]
     connection_lengths = (connection_vectors**2).sum(axis=1) ** 0.5
     return np.histogram(connection_lengths, bins)[0]
@@ -129,7 +129,7 @@ def distance_distribution(
 
 def tetrahedral_order(
     atoms: CoordinateFrame, reference_atoms: CoordinateFrame = None
-) -> np.ndarray:
+) -> NDArray:
     if reference_atoms is None:
         reference_atoms = atoms
     indices = next_neighbors(
@@ -175,7 +175,7 @@ def tetrahedral_order_distribution(
     atoms: CoordinateFrame,
     reference_atoms: Optional[CoordinateFrame] = None,
     bins: Optional[ArrayLike] = None,
-) -> np.ndarray:
+) -> NDArray:
     assert bins is not None, "Bin edges of the distribution have to be specified."
     Q = tetrahedral_order(atoms, reference_atoms=reference_atoms)
     return np.histogram(Q, bins=bins)[0]
@@ -186,7 +186,7 @@ def radial_density(
     bins: Optional[ArrayLike] = None,
     symmetry_axis: ArrayLike = (0, 0, 1),
     origin: Optional[ArrayLike] = None,
-) -> np.ndarray:
+) -> NDArray:
     """
     Calculate the radial density distribution.
 
@@ -223,7 +223,7 @@ def shell_density(
     shell_thickness: float = 0.5,
     symmetry_axis: ArrayLike = (0, 0, 1),
     origin: Optional[ArrayLike] = None,
-) -> np.ndarray:
+) -> NDArray:
     """
     Compute the density distribution on a cylindrical shell.
 
@@ -258,7 +258,7 @@ def next_neighbor_distribution(
     number_of_neighbors: int = 4,
     bins: Optional[ArrayLike] = None,
     normed: bool = True,
-) -> np.ndarray:
+) -> NDArray:
     """
     Compute the distribution of next neighbors with the same residue name.
     """
@@ -281,7 +281,7 @@ def hbonds(
     HA_lim: float = 0.35,
     min_cos: float = np.cos(30 * np.pi / 180),
     full_output: bool = False,
-) -> Union[np.ndarray, tuple[np.ndarray, np.ndarray, np.ndarray]]:
+) -> Union[NDArray, tuple[NDArray, NDArray, NDArray]]:
     """
     Compute h-bond pairs
 
@@ -303,7 +303,7 @@ def hbonds(
 
     def dist_DltA(
         D: CoordinateFrame, A: CoordinateFrame, max_dist: float = 0.35
-    ) -> np.ndarray:
+    ) -> NDArray:
         ppoints, pind = pbc_points(D, thickness=max_dist + 0.1, index=True)
         Dtree = spatial.cKDTree(ppoints)
         Atree = spatial.cKDTree(A)
@@ -315,7 +315,7 @@ def hbonds(
 
     def dist_AltD(
         D: CoordinateFrame, A: CoordinateFrame, max_dist: float = 0.35
-    ) -> np.ndarray:
+    ) -> NDArray:
         ppoints, pind = pbc_points(A, thickness=max_dist + 0.1, index=True)
         Atree = spatial.cKDTree(ppoints)
         Dtree = spatial.cKDTree(D)
@@ -357,7 +357,7 @@ def hbonds(
         return pairs[is_bond]
 
 
-def calc_cluster_sizes(atoms: CoordinateFrame, r_max: float = 0.35) -> np.ndarray:
+def calc_cluster_sizes(atoms: CoordinateFrame, r_max: float = 0.35) -> NDArray:
     frame_PBC, indices_PBC = pbc_points(atoms, thickness=r_max + 0.1, index=True)
     tree = KDTree(frame_PBC)
     matrix = tree.sparse_distance_matrix(tree, r_max, output_type="ndarray")
@@ -372,7 +372,7 @@ def calc_cluster_sizes(atoms: CoordinateFrame, r_max: float = 0.35) -> np.ndarra
     return np.array(cluster_sizes).flatten()
 
 
-def gyration_radius(position: CoordinateFrame) -> np.ndarray:
+def gyration_radius(position: CoordinateFrame) -> NDArray:
     r"""
     Calculates a list of all radii of gyration of all molecules given in the coordinate
     frame, weighted with the masses of the individual atoms.