Merge branch 'master' of github.com:materialsvirtuallab/pymatgen-analysis-diffusion

Author: shyuep

.pre-commit-config.yaml

@@ -8,21 +8,21 @@ ci:
 
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.12.7
+    rev: v0.13.3
     hooks:
       - id: ruff
         args: [ --fix, --unsafe-fixes ]
       - id: ruff-format
 
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v5.0.0
+    rev: v6.0.0
     hooks:
       - id: check-yaml
       - id: end-of-file-fixer
       - id: trailing-whitespace
 
   - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v1.17.1
+    rev: v1.18.2
     hooks:
       - id: mypy
 
@@ -35,14 +35,14 @@ repos:
         additional_dependencies: [ tomli ] # needed to read pyproject.toml below py3.11
 
   - repo: https://github.com/MarcoGorelli/cython-lint
-    rev: v0.16.7
+    rev: v0.17.0
     hooks:
       - id: cython-lint
         args: [ --no-pycodestyle ]
       - id: double-quote-cython-strings
 
   - repo: https://github.com/adamchainz/blacken-docs
-    rev: 1.19.1
+    rev: 1.20.0
     hooks:
       - id: blacken-docs
 
@@ -64,6 +64,6 @@ repos:
         args: [ --drop-empty-cells, --keep-output ]
 
   - repo: https://github.com/RobertCraigie/pyright-python
-    rev: v1.1.403
+    rev: v1.1.406
     hooks:
       - id: pyright

examples/neb_idpp_solver.ipynb

@@ -10,9 +10,10 @@
     "\n",
     "import os\n",
     "\n",
-    "from pymatgen.analysis.diffusion.neb.pathfinder import IDPPSolver\n",
     "from pymatgen.core import Structure\n",
     "\n",
+    "from pymatgen.analysis.diffusion.neb.pathfinder import IDPPSolver\n",
+    "\n",
     "dirname = \"../pymatgen/analysis/diffusion/neb/tests/pathfinder_files/\"\n",
     "\n",
     "init_struct = Structure.from_file(os.path.join(dirname, \"CONTCAR-0\"), False)\n",

examples/probability_analysis.ipynb

@@ -32,9 +32,9 @@
     "from __future__ import annotations\n",
     "\n",
     "import numpy as np\n",
+    "from pymatgen.core import Structure\n",
     "\n",
     "from pymatgen.analysis.diffusion.aimd.pathway import ProbabilityDensityAnalysis\n",
-    "from pymatgen.core import Structure\n",
     "\n",
     "# First prepare the structure and ionic trajectories files\n",
     "trajectories = np.load(\"../pymatgen/analysis/diffusion/aimd/tests/cNa3PS4_trajectories.npy\")\n",

requirements.txt

@@ -1,4 +1,4 @@
-pymatgen==2025.6.14
+pymatgen==2025.10.7
 joblib==1.5.2
 ase==3.26.0
 seaborn==0.13.2

src/pymatgen/analysis/diffusion/aimd/clustering.py

@@ -10,7 +10,6 @@
 import warnings
 
 import numpy as np
-
 from pymatgen.util.coord import all_distances, pbc_diff
 
 __author__ = "Shyue Ping Ong"
@@ -161,15 +160,15 @@ def get_centroids(self, points, labels, k, centroids):  # noqa: ANN001,ANN201
             k: Number of means
             centroids: List of centroids
         """
-        m, n = points.shape
+        _m, n = points.shape
         labels = np.array(labels)
         new_centroids = []
         for i in range(k):
             ind = np.where(labels == i)[0]
             if len(ind) > 0:
                 c = np.zeros(n)
                 for j in ind:
-                    dist, image = self.lattice.get_distance_and_image(centroids[i], points[j])
+                    _dist, image = self.lattice.get_distance_and_image(centroids[i], points[j])
                     c += points[j] + image
                 c /= len(ind)
                 c = np.mod(c, 1)
@@ -203,7 +202,7 @@ def get_random_centroid(points: np.ndarray) -> np.ndarray:
     Args:
         points: List of points.
     """
-    m, n = points.shape
+    _m, n = points.shape
     maxd = np.max(points, axis=0)
     mind = np.min(points, axis=0)
     return np.array([random.uniform(mind[i], maxd[i]) for i in range(n)])

src/pymatgen/analysis/diffusion/aimd/pathway.py

@@ -13,10 +13,11 @@
 if TYPE_CHECKING:
     from collections.abc import Sequence
 
-    from pymatgen.analysis.diffusion.analyzer import DiffusionAnalyzer
     from pymatgen.core.structure import Structure
     from pymatgen.util.typing import PathLike, SpeciesLike
 
+    from pymatgen.analysis.diffusion.analyzer import DiffusionAnalyzer
+
 
 class ProbabilityDensityAnalysis:
     r"""

src/pymatgen/analysis/diffusion/aimd/rdf.py

@@ -12,13 +12,12 @@
 
 import numpy as np
 from joblib import Parallel, delayed
+from pymatgen.core import Structure
+from pymatgen.util.plotting import pretty_plot
 from scipy.ndimage import gaussian_filter1d
 from scipy.signal import find_peaks
 from scipy.stats import norm
 
-from pymatgen.core import Structure
-from pymatgen.util.plotting import pretty_plot
-
 if TYPE_CHECKING:
     from collections.abc import Sequence
 

src/pymatgen/analysis/diffusion/aimd/van_hove.py

@@ -14,21 +14,20 @@
 import numpy as np
 import pandas as pd
 import seaborn as sns
-from scipy.stats import norm
-
 from pymatgen.util.plotting import pretty_plot
+from scipy.stats import norm
 
 from .rdf import RadialDistributionFunction
 
 if TYPE_CHECKING:
     from collections.abc import Callable, Sequence
 
     from matplotlib.axes import Axes
-
-    from pymatgen.analysis.diffusion.analyzer import DiffusionAnalyzer
     from pymatgen.core import Structure
     from pymatgen.util.typing import SpeciesLike
 
+    from pymatgen.analysis.diffusion.analyzer import DiffusionAnalyzer
+
 __author__ = "Iek-Heng Chu"
 __version__ = "1.0"
 __date__ = "Aug 9, 2017"
@@ -87,7 +86,7 @@ def __init__(
         if step_skip <= 0:
             raise ValueError("skip_step should be >=1!")
 
-        n_ions, nsteps, ndim = diffusion_analyzer.disp.shape
+        _n_ions, nsteps, _ndim = diffusion_analyzer.disp.shape
 
         if nsteps <= avg_nsteps:
             raise ValueError("Number of timesteps is too small!")
@@ -515,7 +514,7 @@ def plot_evolution_from_data(
         plt.rcParams["ytick.labelsize"] = 20
         plt.rcParams["xtick.major.pad"] = 10
 
-        fig, ax = plt.subplots(figsize=(12, 8), facecolor="w")
+        _fig, ax = plt.subplots(figsize=(12, 8), facecolor="w")
         ax = sns.heatmap(
             df,
             linewidths=0,

src/pymatgen/analysis/diffusion/analyzer.py

@@ -24,20 +24,18 @@
 import numpy as np
 import scipy.constants as const
 from monty.json import MSONable
-from scipy.optimize import curve_fit
-
 from pymatgen.analysis.structure_matcher import OrderDisorderElementComparator, StructureMatcher
 from pymatgen.core.periodic_table import get_el_sp
 from pymatgen.core.structure import Structure
 from pymatgen.io.vasp.outputs import Vasprun
 from pymatgen.util.coord import pbc_diff
 from pymatgen.util.plotting import pretty_plot
+from scipy.optimize import curve_fit
 
 if TYPE_CHECKING:
     from collections.abc import Generator, Sequence
 
     from matplotlib.axes import Axes
-
     from pymatgen.util.typing import PathLike, SpeciesLike
 
 __author__ = "Will Richards, Shyue Ping Ong"
@@ -250,7 +248,7 @@ def __init__(
             # drift corrected position
             dc = self.disp - drift
 
-            nions, nsteps, dim = dc.shape
+            _nions, nsteps, _dim = dc.shape
 
             self.indices = indices
 
@@ -404,7 +402,7 @@ def get_drift_corrected_structures(
         coords = np.array(self.structure.cart_coords)
         species = self.structure.species_and_occu
         lattices = self.lattices
-        nsites, nsteps, dim = self.corrected_displacements.shape
+        _nsites, nsteps, _dim = self.corrected_displacements.shape
 
         for i in range(start or 0, stop or nsteps, step or 1):
             latt = lattices[0] if len(lattices) == 1 else lattices[i]
@@ -960,7 +958,7 @@ def weighted_lstsq(a: np.ndarray, b: np.ndarray) -> tuple:
     # Get self diffusivity
     a = np.ones((len(dt), 2))
     a[:, 0] = dt
-    (m, c), res, rank, s = weighted_lstsq(a, msd)
+    (m, _c), res, _rank, _s = weighted_lstsq(a, msd)
     # m shouldn't be negative
     m = max(m, 1e-15)
 

src/pymatgen/analysis/diffusion/neb/full_path_mapper.py

@@ -18,17 +18,17 @@
 import networkx as nx
 import numpy as np
 from monty.json import MSONable
-
-from pymatgen.analysis.diffusion.neb.pathfinder import ChgcarPotential, MigrationHop, NEBPathfinder
-from pymatgen.analysis.diffusion.neb.periodic_dijkstra import get_optimal_pathway_rev, periodic_dijkstra
-from pymatgen.analysis.diffusion.utils.parse_entries import process_entries
 from pymatgen.analysis.graphs import StructureGraph
 from pymatgen.analysis.local_env import MinimumDistanceNN, NearNeighbors
 from pymatgen.core import Composition, PeriodicSite, Structure
 from pymatgen.io.vasp import VolumetricData
 from pymatgen.symmetry.analyzer import SpacegroupAnalyzer
 from pymatgen.symmetry.structure import SymmetrizedStructure
 
+from pymatgen.analysis.diffusion.neb.pathfinder import ChgcarPotential, MigrationHop, NEBPathfinder
+from pymatgen.analysis.diffusion.neb.periodic_dijkstra import get_optimal_pathway_rev, periodic_dijkstra
+from pymatgen.analysis.diffusion.utils.parse_entries import process_entries
+
 if TYPE_CHECKING:
     from collections.abc import Callable, Generator
 
@@ -515,7 +515,7 @@ def _setup_grids(self) -> None:
         bb = np.linspace(0, 1, len(self.potential_field.get_axis_grid(1)), endpoint=False)
         cc = np.linspace(0, 1, len(self.potential_field.get_axis_grid(2)), endpoint=False)
         # move the grid points to the center
-        aa, bb, dd = map(_shift_grid, [aa, bb, cc])
+        aa, bb, _dd = map(_shift_grid, [aa, bb, cc])
 
         # mesh grid for each unit cell
         AA, BB, CC = np.meshgrid(aa, bb, cc, indexing="ij")