pre-commit autoupdate

.pre-commit-config.yaml

@@ -8,29 +8,29 @@ ci:
 
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.5.0
+    rev: v0.7.2
     hooks:
       - id: ruff
         args: [ --fix, --unsafe-fixes ]
       - id: ruff-format
 
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.6.0
+    rev: v5.0.0
     hooks:
       - id: check-yaml
       - id: end-of-file-fixer
       - id: trailing-whitespace
 
   - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v1.10.1
+    rev: v1.13.0
     hooks:
       - id: mypy
 
   - repo: https://github.com/codespell-project/codespell
     rev: v2.3.0
     hooks:
       - id: codespell
-        stages: [ commit, commit-msg ]
+        stages: [ pre-commit, commit-msg ]
         exclude_types: [ html ]
         additional_dependencies: [ tomli ] # needed to read pyproject.toml below py3.11
 
@@ -42,12 +42,12 @@ repos:
       - id: double-quote-cython-strings
 
   - repo: https://github.com/adamchainz/blacken-docs
-    rev: 1.16.0
+    rev: 1.19.1
     hooks:
       - id: blacken-docs
 
   - repo: https://github.com/igorshubovych/markdownlint-cli
-    rev: v0.41.0
+    rev: v0.42.0
     hooks:
       - id: markdownlint
         # MD013: line too long
@@ -58,12 +58,12 @@ repos:
         args: [ --disable, MD013, MD024, MD025, MD033, MD041, "--" ]
 
   - repo: https://github.com/kynan/nbstripout
-    rev: 0.7.1
+    rev: 0.8.0
     hooks:
       - id: nbstripout
         args: [ --drop-empty-cells, --keep-output ]
 
   - repo: https://github.com/RobertCraigie/pyright-python
-    rev: v1.1.369
+    rev: v1.1.387
     hooks:
       - id: pyright

examples/neb_idpp_solver.ipynb

@@ -6,18 +6,19 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from pymatgen.core import Structure\n",
-    "from pymatgen.analysis.diffusion.neb.pathfinder import IDPPSolver\n",
-    "import numpy as np\n",
+    "from __future__ import annotations\n",
+    "\n",
     "import os\n",
     "\n",
+    "from pymatgen.analysis.diffusion.neb.pathfinder import IDPPSolver\n",
+    "from pymatgen.core import Structure\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",
     "final_struct = Structure.from_file(os.path.join(dirname, \"CONTCAR-1\"), False)\n",
     "\n",
-    "obj = IDPPSolver.from_endpoints(endpoints=[init_struct, final_struct], nimages=3, \n",
-    "                                sort_tol=1.0)\n",
+    "obj = IDPPSolver.from_endpoints(endpoints=[init_struct, final_struct], nimages=3, sort_tol=1.0)\n",
     "new_path = obj.run(maxiter=5000, tol=1e-5, gtol=1e-3, species=[\"Li\"])"
    ]
   }

examples/neb_path_mapping.ipynb

@@ -14,7 +14,10 @@
    "outputs": [],
    "source": [
     "# Start with a lithiated structure\n",
+    "from __future__ import annotations\n",
+    "\n",
     "from pymatgen.core import Structure\n",
+    "\n",
     "struct = Structure.from_file(\"../pymatgen/analysis/diffusion/neb/tests/full_path_files/Li6MnO4.cif\")"
    ]
   },
@@ -25,7 +28,8 @@
    "outputs": [],
    "source": [
     "from pymatgen.analysis.diffusion.neb.full_path_mapper import MigrationGraph\n",
-    "# Instantiate MigrationGraph and consider all hops within 4 Angstroms \n",
+    "\n",
+    "# Instantiate MigrationGraph and consider all hops within 4 Angstroms\n",
     "mg = MigrationGraph.with_distance(structure=struct, migrating_specie=\"Li\", max_distance=4)"
    ]
   },
@@ -316,7 +320,7 @@
    "source": [
     "# Get migration paths through the material of interest using the MigrationGraph hop data\n",
     "paths = []\n",
-    "for u, path in mg.get_path():\n",
+    "for _u, path in mg.get_path():\n",
     "    paths.append(path)"
    ]
   },

examples/probability_analysis.ipynb

@@ -29,22 +29,25 @@
     }
    ],
    "source": [
+    "from __future__ import annotations\n",
+    "\n",
+    "import numpy as np\n",
+    "\n",
     "from pymatgen.analysis.diffusion.aimd.pathway import ProbabilityDensityAnalysis\n",
     "from pymatgen.core import Structure\n",
-    "import numpy as np\n",
     "\n",
-    "#First prepare the structure and ionic trajectories files\n",
+    "# First prepare the structure and ionic trajectories files\n",
     "trajectories = np.load(\"../pymatgen/analysis/diffusion/aimd/tests/cNa3PS4_trajectories.npy\")\n",
     "structure = Structure.from_file(\"../pymatgen/analysis/diffusion/aimd/tests/cNa3PS4.cif\", False)\n",
     "\n",
-    "#ProbabilityDensityAnalysis object\n",
+    "# ProbabilityDensityAnalysis object\n",
     "pda = ProbabilityDensityAnalysis(structure, trajectories, interval=0.5)\n",
-    "#Save probability distribution to a CHGCAR-like file\n",
+    "# Save probability distribution to a CHGCAR-like file\n",
     "pda.to_chgcar(filename=\"CHGCAR_new.vasp\")\n",
     "\n",
-    "print(\"Maximum: %s, Minimum: %s\" % (pda.Pr.max(), pda.Pr.min()))\n",
+    "print(f\"Maximum: {pda.Pr.max()}, Minimum: {pda.Pr.min()}\")\n",
     "# \\int P(r)d^3r = 1\n",
-    "print(\"Total probability: %s\" % np.sum(pda.Pr * pda.structure.lattice.volume / pda.lens[0]/pda.lens[1]/pda.lens[2]))\n"
+    "print(f\"Total probability: {np.sum(pda.Pr * pda.structure.lattice.volume / pda.lens[0]/pda.lens[1]/pda.lens[2])}\")"
    ]
   },
   {
@@ -69,23 +72,23 @@
     }
    ],
    "source": [
-    "from pymatgen.analysis.diffusion.analyzer import DiffusionAnalyzer\n",
     "import json\n",
     "\n",
-    "#ProbabilityDensityAnalysis object\n",
-    "filename=\"../pymatgen/analysis/diffusion/aimd/tests/cNa3PS4_pda.json\"\n",
+    "from pymatgen.analysis.diffusion.analyzer import DiffusionAnalyzer\n",
+    "\n",
+    "# ProbabilityDensityAnalysis object\n",
+    "filename = \"../pymatgen/analysis/diffusion/aimd/tests/cNa3PS4_pda.json\"\n",
     "\n",
-    "data = json.load(open(\"../pymatgen/analysis/diffusion/aimd/tests/cNa3PS4_pda.json\", \"r\"))\n",
+    "data = json.load(open(\"../pymatgen/analysis/diffusion/aimd/tests/cNa3PS4_pda.json\"))\n",
     "diff_analyzer = DiffusionAnalyzer.from_dict(data)\n",
     "\n",
-    "pda = ProbabilityDensityAnalysis.from_diffusion_analyzer(diff_analyzer, interval=0.5,\n",
-    "                                                         species=(\"Na\", \"Li\"))\n",
-    "#Save probability distribution to a CHGCAR-like file\n",
+    "pda = ProbabilityDensityAnalysis.from_diffusion_analyzer(diff_analyzer, interval=0.5, species=(\"Na\", \"Li\"))\n",
+    "# Save probability distribution to a CHGCAR-like file\n",
     "pda.to_chgcar(filename=\"CHGCAR_new2.vasp\")\n",
     "\n",
-    "print(\"Maximum: %s, Minimum: %s\" % (pda.Pr.max(), pda.Pr.min()))\n",
+    "print(f\"Maximum: {pda.Pr.max()}, Minimum: {pda.Pr.min()}\")\n",
     "# \\int P(r)d^3r = 1\n",
-    "print(\"Total probability: %s\" % np.sum(pda.Pr * pda.structure.lattice.volume / pda.lens[0]/pda.lens[1]/pda.lens[2]))\n"
+    "print(f\"Total probability: {np.sum(pda.Pr * pda.structure.lattice.volume / pda.lens[0]/pda.lens[1]/pda.lens[2])}\")"
    ]
   }
  ],

examples/radial_distribution_function.ipynb

@@ -6,9 +6,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from pymatgen.analysis.diffusion.aimd.rdf import RadialDistributionFunction, RadialDistributionFunctionFast\n",
-    "from monty.serialization import loadfn\n",
+    "from __future__ import annotations\n",
+    "\n",
     "import matplotlib.pyplot as plt\n",
+    "from monty.serialization import loadfn\n",
+    "\n",
+    "from pymatgen.analysis.diffusion.aimd.rdf import RadialDistributionFunction, RadialDistributionFunctionFast\n",
+    "\n",
     "%matplotlib inline"
    ]
   },
@@ -27,7 +31,7 @@
     }
    ],
    "source": [
-    "obj = loadfn('../pymatgen/analysis/diffusion/aimd/tests/cNa3PS4_pda.json')"
+    "obj = loadfn(\"../pymatgen/analysis/diffusion/aimd/tests/cNa3PS4_pda.json\")"
    ]
   },
   {
@@ -39,7 +43,8 @@
     "structure_list = []\n",
     "for i, s in enumerate(obj.get_drift_corrected_structures()):\n",
     "    structure_list.append(s)\n",
-    "    if i == 9: break"
+    "    if i == 9:\n",
+    "        break"
    ]
   },
   {
@@ -49,12 +54,11 @@
    "outputs": [],
    "source": [
     "rdf_func = RadialDistributionFunction.from_species(\n",
-    "           structures=structure_list, ngrid=101, rmax=10.0, cell_range=1,\n",
-    "           sigma=0.2, species=['Na'], reference_species=['S'])\n",
+    "    structures=structure_list, ngrid=101, rmax=10.0, cell_range=1, sigma=0.2, species=[\"Na\"], reference_species=[\"S\"]\n",
+    ")\n",
     "\n",
     "# fewer parameters in new implementations\n",
-    "rdf_func_faster = RadialDistributionFunctionFast(structures=structure_list, \n",
-    "                                                ngrid=101, rmax=10.0, sigma=0.2)"
+    "rdf_func_faster = RadialDistributionFunctionFast(structures=structure_list, ngrid=101, rmax=10.0, sigma=0.2)"
    ]
   },
   {
@@ -152,7 +156,7 @@
     }
    ],
    "source": [
-    "r, rdf2 = rdf_func_faster.get_rdf('S', 'Na')\n",
+    "r, rdf2 = rdf_func_faster.get_rdf(\"S\", \"Na\")\n",
     "plt.plot(r, rdf2)\n",
     "plt.title(\"New RDF implementation\")"
    ]
@@ -186,9 +190,9 @@
     }
    ],
    "source": [
-    "r, cn = rdf_func_faster.get_coordination_number('S', 'Na')\n",
+    "r, cn = rdf_func_faster.get_coordination_number(\"S\", \"Na\")\n",
     "plt.plot(r[:70], cn[:70])\n",
-    "plt.title('New coordination number')"
+    "plt.title(\"New coordination number\")"
    ]
   },
   {
@@ -220,18 +224,16 @@
     }
    ],
    "source": [
+    "r, rdf_na_p_s = rdf_func_faster.get_rdf(\"S\", [\"Na\", \"P\", \"S\"])\n",
+    "r, rdf_na = rdf_func_faster.get_rdf(\"S\", [\"Na\"])\n",
+    "r, rdf_p = rdf_func_faster.get_rdf(\"S\", [\"P\"])\n",
+    "r, rdf_s = rdf_func_faster.get_rdf(\"S\", [\"S\"])\n",
     "\n",
-    "r, rdf_na_p_s = rdf_func_faster.get_rdf('S', ['Na', 'P', 'S'])\n",
-    "r, rdf_na = rdf_func_faster.get_rdf('S', ['Na'])\n",
-    "r, rdf_p = rdf_func_faster.get_rdf('S', ['P'])\n",
-    "r, rdf_s = rdf_func_faster.get_rdf('S', ['S'])\n",
-    "\n",
-    "plt.plot(r, rdf_na_p_s, label='S-[Na, P, S]')\n",
-    "plt.plot(r, rdf_na, label='S-Na')\n",
-    "plt.plot(r, rdf_p, label='S-P')\n",
-    "plt.plot(r, rdf_s, label='S-S')\n",
-    "plt.legend()\n",
-    "\n"
+    "plt.plot(r, rdf_na_p_s, label=\"S-[Na, P, S]\")\n",
+    "plt.plot(r, rdf_na, label=\"S-Na\")\n",
+    "plt.plot(r, rdf_p, label=\"S-P\")\n",
+    "plt.plot(r, rdf_s, label=\"S-S\")\n",
+    "plt.legend()"
    ]
   },
   {
@@ -264,8 +266,14 @@
    ],
    "source": [
     "rdf_func = RadialDistributionFunction.from_species(\n",
-    "           structures=structure_list, ngrid=101, rmax=10.0, cell_range=1,\n",
-    "           sigma=0.2, species=['Na','P', 'S'], reference_species=['S'])\n",
+    "    structures=structure_list,\n",
+    "    ngrid=101,\n",
+    "    rmax=10.0,\n",
+    "    cell_range=1,\n",
+    "    sigma=0.2,\n",
+    "    species=[\"Na\", \"P\", \"S\"],\n",
+    "    reference_species=[\"S\"],\n",
+    ")\n",
     "\n",
     "plt.plot(rdf_func.interval, rdf_func.rdf)\n",
     "plt.ylim([-0.2, 6.2])"
@@ -288,8 +296,8 @@
     "%%timeit -n 1\n",
     "\n",
     "rdf_func = RadialDistributionFunction.from_species(\n",
-    "           structures=structure_list, ngrid=101, rmax=10.0, cell_range=1,\n",
-    "           sigma=0.2, species=['Na'], reference_species=['S'])"
+    "    structures=structure_list, ngrid=101, rmax=10.0, cell_range=1, sigma=0.2, species=[\"Na\"], reference_species=[\"S\"]\n",
+    ")"
    ]
   },
   {
@@ -308,8 +316,7 @@
    "source": [
     "%%timeit -n 1\n",
     "# fewer parameters in new implementations\n",
-    "rdf_func_faster = RadialDistributionFunctionFast(structures=structure_list, \n",
-    "                                                ngrid=101, rmax=10.0, sigma=0.2)"
+    "rdf_func_faster = RadialDistributionFunctionFast(structures=structure_list, ngrid=101, rmax=10.0, sigma=0.2)"
    ]
   }
  ],

examples/van_hove.ipynb

@@ -52,20 +52,24 @@
     }
    ],
    "source": [
+    "from __future__ import annotations\n",
+    "\n",
     "import json\n",
-    "import numpy as np\n",
+    "\n",
     "import matplotlib.pyplot as plt\n",
-    "from pymatgen.analysis.diffusion.analyzer import DiffusionAnalyzer\n",
+    "\n",
     "from pymatgen.analysis.diffusion.aimd.van_hove import VanHoveAnalysis\n",
+    "from pymatgen.analysis.diffusion.analyzer import DiffusionAnalyzer\n",
     "\n",
     "%matplotlib inline\n",
     "\n",
-    "data = json.load(open(\"../pymatgen/analysis/diffusion/aimd/tests/cNa3PS4_pda.json\", \"r\"))\n",
+    "data = json.load(open(\"../pymatgen/analysis/diffusion/aimd/tests/cNa3PS4_pda.json\"))\n",
     "\n",
     "new_obj = DiffusionAnalyzer.from_dict(data)\n",
     "\n",
-    "vhfunc = VanHoveAnalysis(diffusion_analyzer=new_obj, avg_nsteps=5, ngrid=101, rmax=10.0, \n",
-    "                         step_skip=5, sigma=0.1, species = [\"Li\", \"Na\"])\n",
+    "vhfunc = VanHoveAnalysis(\n",
+    "    diffusion_analyzer=new_obj, avg_nsteps=5, ngrid=101, rmax=10.0, step_skip=5, sigma=0.1, species=[\"Li\", \"Na\"]\n",
+    ")\n",
     "\n",
     "vhfunc.get_3d_plot(mode=\"self\")\n",
     "vhfunc.get_3d_plot(mode=\"distinct\")"
@@ -126,21 +130,22 @@
    ],
    "source": [
     "import json\n",
-    "import numpy as np\n",
+    "\n",
     "from pymatgen.analysis.diffusion.aimd.rdf import RadialDistributionFunction\n",
     "\n",
     "%matplotlib inline\n",
     "\n",
     "# The generated structure list in this example is for illustration purposes only.\n",
-    "data = json.load(open(\"../pymatgen/analysis/diffusion/aimd/tests/cNa3PS4_pda.json\", \"r\"))\n",
+    "data = json.load(open(\"../pymatgen/analysis/diffusion/aimd/tests/cNa3PS4_pda.json\"))\n",
     "new_obj = DiffusionAnalyzer.from_dict(data)\n",
     "\n",
     "structure_list = []\n",
     "for i, s in enumerate(new_obj.get_drift_corrected_structures()):\n",
     "    structure_list.append(s)\n",
-    "    if i == 9: break\n",
+    "    if i == 9:\n",
+    "        break\n",
     "\n",
-    "# Compute and plot RDF for Na and S        \n",
+    "# Compute and plot RDF for Na and S\n",
     "species = [\"Na\", \"S\"]\n",
     "obj = RadialDistributionFunction(structure_list, ngrid=101, rmax=10.0, sigma=0.1, indices=[0], reference_indices=[2, 3])\n",
     "plt = obj.get_rdf_plot(label=\"RDF\")"

pymatgen/analysis/diffusion/utils/tests/test_maggma.py

@@ -16,7 +16,7 @@
 __date__ = "April 10, 2019"
 
 
-@pytest.fixture()
+@pytest.fixture
 def maggma_stores() -> dict[str, JSONStore]:
     return {
         "sgroups": JSONStore(f"{dir_path}/maggma_sgroup_store.json", key="group_id"),