[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Author: pre-commit-ci[bot]

structuretoolkit/analyse/distance.py

@@ -1,13 +1,11 @@
-from typing import Optional
-
 import numpy as np
 from ase.atoms import Atoms
 
 
 def get_distances_array(
     structure: Atoms,
-    p1: Optional[np.ndarray] = None,
-    p2: Optional[np.ndarray] = None,
+    p1: np.ndarray | None = None,
+    p2: np.ndarray | None = None,
     mic: bool = True,
     vectors: bool = False,
 ) -> np.ndarray:

structuretoolkit/analyse/dscribe.py

@@ -1,24 +1,22 @@
-from typing import Optional
-
 import numpy as np
 from ase.atoms import Atoms
 
 
 def soap_descriptor_per_atom(
     structure: Atoms,
-    r_cut: Optional[float] = None,
-    n_max: Optional[int] = None,
-    l_max: Optional[int] = None,
-    sigma: Optional[float] = 1.0,
+    r_cut: float | None = None,
+    n_max: int | None = None,
+    l_max: int | None = None,
+    sigma: float | None = 1.0,
     rbf: str = "gto",
-    weighting: Optional[np.ndarray] = None,
+    weighting: np.ndarray | None = None,
     average: str = "off",
     compression: dict = None,
-    species: Optional[list] = None,
+    species: list | None = None,
     periodic: bool = True,
     sparse: bool = False,
     dtype: str = "float64",
-    centers: Optional[np.ndarray] = None,
+    centers: np.ndarray | None = None,
     n_jobs: int = 1,
     only_physical_cores: bool = False,
     verbose: bool = False,

structuretoolkit/analyse/neighbors.py

@@ -3,7 +3,6 @@
 
 import itertools
 import warnings
-from typing import Optional, Union
 
 import numpy as np
 from ase.atoms import Atoms
@@ -149,8 +148,8 @@ def copy(self) -> "Tree":
     def _reshape(
         self,
         value: np.ndarray,
-        key: Optional[str] = None,
-        ref_vector: Optional[np.ndarray] = None,
+        key: str | None = None,
+        ref_vector: np.ndarray | None = None,
     ) -> np.ndarray:
         """
         Reshape the given value based on the specified key and reference vector.
@@ -258,7 +257,7 @@ def norm_order(self, value: int) -> None:
             + " with the correct norm_order value"
         )
 
-    def _get_max_length(self, ref_vector: Optional[np.ndarray] = None) -> int:
+    def _get_max_length(self, ref_vector: np.ndarray | None = None) -> int:
         """
         Get the maximum length of the reference vector.
 
@@ -279,7 +278,7 @@ def _get_max_length(self, ref_vector: Optional[np.ndarray] = None) -> int:
         return max(len(dd[dd < np.inf]) for dd in ref_vector)
 
     def _contract(
-        self, value: np.ndarray, ref_vector: Optional[np.ndarray] = None
+        self, value: np.ndarray, ref_vector: np.ndarray | None = None
     ) -> np.ndarray:
         """
         Contract the given value based on the specified reference vector.
@@ -362,7 +361,7 @@ def _get_wrapped_positions(
     def _get_distances_and_indices(
         self,
         positions: np.ndarray,
-        num_neighbors: Optional[int] = None,
+        num_neighbors: int | None = None,
         cutoff_radius: float = np.inf,
         width_buffer: float = 1.2,
     ) -> tuple[np.ndarray, np.ndarray]:
@@ -431,10 +430,10 @@ def numbers_of_neighbors(self) -> int:
     def _get_vectors(
         self,
         positions: np.ndarray,
-        num_neighbors: Optional[int] = None,
+        num_neighbors: int | None = None,
         cutoff_radius: float = np.inf,
-        distances: Optional[np.ndarray] = None,
-        indices: Optional[np.ndarray] = None,
+        distances: np.ndarray | None = None,
+        indices: np.ndarray | None = None,
         width_buffer: float = 1.2,
     ) -> np.ndarray:
         """
@@ -470,7 +469,7 @@ def _get_vectors(
 
     def _estimate_num_neighbors(
         self,
-        num_neighbors: Optional[int] = None,
+        num_neighbors: int | None = None,
         cutoff_radius: float = np.inf,
         width_buffer: float = 1.2,
     ) -> int:
@@ -515,7 +514,7 @@ def _estimate_num_neighbors(
 
     def _estimate_width(
         self,
-        num_neighbors: Optional[int] = None,
+        num_neighbors: int | None = None,
         cutoff_radius: float = np.inf,
         width_buffer: float = 1.2,
     ) -> float:
@@ -551,7 +550,7 @@ def _estimate_width(
     def get_neighborhood(
         self,
         positions: np.ndarray,
-        num_neighbors: Optional[int] = None,
+        num_neighbors: int | None = None,
         cutoff_radius: float = np.inf,
         width_buffer: float = 1.2,
     ) -> "Tree":
@@ -647,7 +646,7 @@ def get_spherical_harmonics(
         l: np.ndarray,
         m: np.ndarray,
         cutoff_radius: float = np.inf,
-        rotation: Optional[np.ndarray] = None,
+        rotation: np.ndarray | None = None,
     ) -> np.ndarray:
         """
         Args:
@@ -868,8 +867,8 @@ def shells(self) -> np.ndarray:
 
     def get_local_shells(
         self,
-        mode: Optional[str] = None,
-        tolerance: Optional[int] = None,
+        mode: str | None = None,
+        tolerance: int | None = None,
         cluster_by_distances: bool = False,
         cluster_by_vecs: bool = False,
     ) -> np.ndarray:
@@ -947,8 +946,8 @@ def get_local_shells(
 
     def get_global_shells(
         self,
-        mode: Optional[str] = None,
-        tolerance: Optional[int] = None,
+        mode: str | None = None,
+        tolerance: int | None = None,
         cluster_by_distances: bool = False,
         cluster_by_vecs: bool = False,
     ) -> np.ndarray:
@@ -1008,7 +1007,7 @@ def get_global_shells(
 
     def get_shell_matrix(
         self,
-        chemical_pair: Optional[list[str]] = None,
+        chemical_pair: list[str] | None = None,
         cluster_by_distances: bool = False,
         cluster_by_vecs: bool = False,
     ):
@@ -1107,8 +1106,8 @@ def find_neighbors_by_vector(
 
     def cluster_by_vecs(
         self,
-        distance_threshold: Optional[float] = None,
-        n_clusters: Optional[int] = None,
+        distance_threshold: float | None = None,
+        n_clusters: int | None = None,
         linkage: str = "complete",
         metric: str = "euclidean",
     ):
@@ -1152,8 +1151,8 @@ def cluster_by_vecs(
 
     def cluster_by_distances(
         self,
-        distance_threshold: Optional[float] = None,
-        n_clusters: Optional[int] = None,
+        distance_threshold: float | None = None,
+        n_clusters: int | None = None,
         linkage: str = "complete",
         metric: str = "euclidean",
         use_vecs: bool = False,
@@ -1225,7 +1224,7 @@ def reset_clusters(self, vecs: bool = True, distances: bool = True):
 
     def cluster_analysis(
         self, id_list: list, return_cluster_sizes: bool = False
-    ) -> Union[dict[int, list[int]], tuple[dict[int, list[int]], list[int]]]:
+    ) -> dict[int, list[int]] | tuple[dict[int, list[int]], list[int]]:
         """
         Perform cluster analysis on a list of atom IDs.
 
@@ -1283,7 +1282,7 @@ def __probe_cluster(
     def get_bonds(
         self,
         radius: float = np.inf,
-        max_shells: Optional[int] = None,
+        max_shells: int | None = None,
         prec: float = 0.1,
     ) -> list[dict[str, list[list[int]]]]:
         """
@@ -1361,7 +1360,7 @@ def get_neighbors(
     structure: Atoms,
     num_neighbors: int = 12,
     tolerance: int = 2,
-    id_list: Optional[list] = None,
+    id_list: list | None = None,
     cutoff_radius: float = np.inf,
     width_buffer: float = 1.2,
     mode: str = "filled",
@@ -1400,12 +1399,12 @@ def _get_neighbors(
     structure: Atoms,
     num_neighbors: int = 12,
     tolerance: int = 2,
-    id_list: Optional[list] = None,
+    id_list: list | None = None,
     cutoff_radius: float = np.inf,
     width_buffer: float = 1.2,
     get_tree: bool = False,
     norm_order: int = 2,
-) -> Union[Neighbors, Tree]:
+) -> Neighbors | Tree:
     """
     Get the neighbors of atoms in a structure.
 

structuretoolkit/analyse/pyscal.py

@@ -1,7 +1,7 @@
 # Copyright (c) Max-Planck-Institut für Eisenforschung GmbH - Computational Materials Design (CM) Department
 # Distributed under the terms of "New BSD License", see the LICENSE file.
 
-from typing import Any, Optional, Union
+from typing import Any
 
 import numpy as np
 from ase.atoms import Atoms
@@ -24,10 +24,10 @@ def get_steinhardt_parameters(
     structure: Atoms,
     neighbor_method: str = "cutoff",
     cutoff: float = 0.0,
-    n_clusters: Optional[int] = 2,
-    q: Optional[tuple] = None,
+    n_clusters: int | None = 2,
+    q: tuple | None = None,
     averaged: bool = False,
-) -> Union[tuple[np.ndarray], tuple[np.ndarray, np.ndarray]]:
+) -> tuple[np.ndarray] | tuple[np.ndarray, np.ndarray]:
     """
     Calculate Steinhardts parameters
 
@@ -80,7 +80,7 @@ def get_centro_symmetry_descriptors(
 
 def get_diamond_structure_descriptors(
     structure: Atoms, mode: str = "total", ovito_compatibility: bool = False
-) -> Union[dict[str, int], np.ndarray]:
+) -> dict[str, int] | np.ndarray:
     """
     Analyse diamond structure
 
@@ -244,7 +244,7 @@ def find_solids(
     q: int = 6,
     right: bool = True,
     return_sys: bool = False,
-) -> Union[int, Any]:
+) -> int | Any:
     """
     Get the number of solids or the corresponding pyscal system.
     Calls necessary pyscal methods as described in https://pyscal.org/en/latest/methods/03_solidliquid.html.

structuretoolkit/analyse/snap.py

@@ -1,5 +1,4 @@
 from ctypes import POINTER, c_double, c_int, cast
-from typing import Optional, Union
 
 import numpy as np
 from ase.atoms import Atoms
@@ -70,7 +69,7 @@ def get_snap_descriptors_per_atom(
     rmin0: float = 0.0,
     bzeroflag: bool = False,
     quadraticflag: bool = False,
-    weights: Optional[Union[list, np.ndarray]] = None,
+    weights: list | np.ndarray | None = None,
     cutoff: float = 10.0,
 ) -> np.ndarray:
     """
@@ -121,7 +120,7 @@ def get_snap_descriptor_derivatives(
     rmin0: float = 0.0,
     bzeroflag: bool = False,
     quadraticflag: bool = False,
-    weights: Optional[Union[list, np.ndarray]] = None,
+    weights: list | np.ndarray | None = None,
     cutoff: float = 10.0,
 ):
     """
@@ -659,7 +658,7 @@ def _get_default_parameters(
     rmin0: float = 0.0,
     bzeroflag: bool = False,
     quadraticflag: bool = False,
-    weights: Optional[Union[list, np.ndarray]] = None,
+    weights: list | np.ndarray | None = None,
     cutoff: float = 10.0,
 ):
     """

structuretoolkit/analyse/spatial.py

@@ -1,7 +1,7 @@
 # Copyright (c) Max-Planck-Institut für Eisenforschung GmbH - Computational Materials Design (CM) Department
 # Distributed under the terms of "New BSD License", see the LICENSE file.
 
-from typing import Callable, Optional
+from collections.abc import Callable
 
 import numpy as np
 from ase.atoms import Atoms
@@ -225,7 +225,7 @@ def __init__(
         l_values: np.ndarray = np.arange(2, 13),
         q_eps: float = 0.3,
         var_ratio: float = 5.0,
-        min_samples: Optional[int] = None,
+        min_samples: int | None = None,
         neigh_args: dict = None,
         **kwargs,
     ):
@@ -294,7 +294,7 @@ def __init__(
         self.structure = structure
 
     def run_workflow(
-        self, positions: Optional[np.ndarray] = None, steps: int = -1
+        self, positions: np.ndarray | None = None, steps: int = -1
     ) -> np.ndarray:
         """
         Run the workflow to obtain the interstitial positions.
@@ -407,7 +407,7 @@ def get_interstitials(
     l_values: np.ndarray = np.arange(2, 13),
     q_eps: float = 0.3,
     var_ratio: float = 5.0,
-    min_samples: Optional[int] = None,
+    min_samples: int | None = None,
     neigh_args: dict = None,
     **kwargs,
 ) -> Interstitials:
@@ -457,7 +457,7 @@ def get_interstitials(
 def get_layers(
     structure: Atoms,
     distance_threshold: float = 0.01,
-    id_list: Optional[list[int]] = None,
+    id_list: list[int] | None = None,
     wrap_atoms: bool = True,
     planes: np.ndarray = None,
     cluster_method: str = None,
@@ -690,9 +690,9 @@ def get_delaunay_neighbors(structure: Atoms, width_buffer: float = 10.0) -> np.n
 
 def get_cluster_positions(
     structure: Atoms,
-    positions: Optional[np.ndarray] = None,
+    positions: np.ndarray | None = None,
     eps: float = 1.0,
-    buffer_width: Optional[float] = None,
+    buffer_width: float | None = None,
     return_labels: bool = False,
 ) -> np.ndarray:
     """