Merge pull request #647 from materialsvirtuallab/pre-commit-ci-update…

…-config pre-commit autoupdate
materialsvirtuallab · Sep 18, 2024 · 7ae5574 · 7ae5574
2 parents b289424 + 495d58c
commit 7ae5574
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diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
@@ -8,7 +8,7 @@ ci:
 
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.5.0
+    rev: v0.6.3
     hooks:
       - id: ruff
         args: [--fix]
@@ -22,12 +22,12 @@ repos:
       - id: trailing-whitespace
 
   - repo: https://github.com/psf/black
-    rev: 24.4.2
+    rev: 24.8.0
     hooks:
       - id: black
 
   - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v1.10.1
+    rev: v1.11.2
     hooks:
       - id: mypy
 

diff --git a/notebooks/atomsets/composition_models.ipynb b/notebooks/atomsets/composition_models.ipynb
@@ -7,11 +7,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from __future__ import annotations\n",
+    "\n",
     "import pandas as pd\n",
     "\n",
-    "from maml.models import AtomSets\n",
     "from maml.describers import SiteElementProperty\n",
-    "\n",
+    "from maml.models import AtomSets\n",
     "\n",
     "# the data contains the MP formation energy data\n",
     "# 100 formula - formation energy\n",
@@ -33,9 +34,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from pymatgen.core import Element\n",
-    "\n",
     "from megnet.data.crystal import get_elemental_embeddings\n",
+    "from pymatgen.core import Element\n",
     "\n",
     "embeddings = get_elemental_embeddings()\n",
     "embeddings = {Element(i).Z: j for i, j in embeddings.items()}"

diff --git a/notebooks/atomsets/structure_model.ipynb b/notebooks/atomsets/structure_model.ipynb
@@ -7,11 +7,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from mdml.db import MamlDB\n",
-    "import pandas as pd\n",
-    "from pymatgen.core import Structure\n",
+    "from __future__ import annotations\n",
+    "\n",
     "import json\n",
     "\n",
+    "from pymatgen.core import Structure\n",
+    "\n",
     "from maml.models import AtomSets"
    ]
   },
@@ -30,7 +31,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "with open(\"mp_ef_100.json\", \"r\") as f:\n",
+    "with open(\"mp_ef_100.json\") as f:\n",
     "    data = json.load(f)\n",
     "\n",
     "structs = [Structure.from_dict(i) for i in data[\"structure\"]]\n",

diff --git a/notebooks/bowsr/cgcnn_example.ipynb b/notebooks/bowsr/cgcnn_example.ipynb
@@ -6,9 +6,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from __future__ import annotations\n",
+    "\n",
+    "from pymatgen.core.periodic_table import get_el_sp\n",
+    "\n",
     "from maml.apps.bowsr.model.cgcnn import CGCNN\n",
     "from maml.apps.bowsr.optimizer import BayesianOptimizer\n",
-    "from pymatgen.core.periodic_table import get_el_sp\n",
     "\n",
     "model = CGCNN()\n",
     "\n",
@@ -87,11 +90,11 @@
    ],
    "source": [
     "ef_cubic = model.predict_energy(cubic)\n",
-    "print(\"CGCNN predicted formation energy of cubic perovskite: {} eV/atom\".format(ef_cubic))\n",
+    "print(f\"CGCNN predicted formation energy of cubic perovskite: {ef_cubic} eV/atom\")\n",
     "ef_compressed_cubic = model.predict_energy(compressed_cubic)\n",
-    "print(\"CGCNN predicted formation energy of compressed cubic perovskite: {} eV/atom\".format(ef_compressed_cubic))\n",
+    "print(f\"CGCNN predicted formation energy of compressed cubic perovskite: {ef_compressed_cubic} eV/atom\")\n",
     "ef_expanded_cubic = model.predict_energy(expanded_cubic)\n",
-    "print(\"CGCNN predicted formation energy of expanded cubic perovskite: {} eV/atom\".format(ef_expanded_cubic))"
+    "print(f\"CGCNN predicted formation energy of expanded cubic perovskite: {ef_expanded_cubic} eV/atom\")"
    ]
   },
   {
@@ -124,9 +127,7 @@
     "radius = max(round(expected_radius(compressed_optimizer.structure) * 0.6, 2), 1.1)\n",
     "relaxed_from_compressed_cubic, _ = compressed_optimizer.get_optimized_structure_and_energy(radius=radius)\n",
     "print(\n",
-    "    \"CGCNN predicted formation energy of relaxed compressed cubic: {} eV/atom\".format(\n",
-    "        model.predict_energy(relaxed_from_compressed_cubic)\n",
-    "    )\n",
+    "    f\"CGCNN predicted formation energy of relaxed compressed cubic: {model.predict_energy(relaxed_from_compressed_cubic)} eV/atom\"\n",
     ")"
    ]
   },
@@ -160,9 +161,7 @@
     "radius = max(round(expected_radius(expanded_optimizer.structure) * 0.6, 2), 1.1)\n",
     "relaxed_from_expanded_cubic, _ = expanded_optimizer.get_optimized_structure_and_energy(radius=radius)\n",
     "print(\n",
-    "    \"CGCNN predicted formation energy of relaxed expanded cubic: {} eV/atom\".format(\n",
-    "        model.predict_energy(relaxed_from_expanded_cubic)\n",
-    "    )\n",
+    "    f\"CGCNN predicted formation energy of relaxed expanded cubic: {model.predict_energy(relaxed_from_expanded_cubic)} eV/atom\"\n",
     ")"
    ]
   },
@@ -190,16 +189,14 @@
    ],
    "source": [
     "ef_orthorhombic = model.predict_energy(orthorhombic)\n",
-    "print(\"CGCNN predicted formation energy of orthorhombic perovskite: {} eV/atom\".format(ef_orthorhombic))\n",
+    "print(f\"CGCNN predicted formation energy of orthorhombic perovskite: {ef_orthorhombic} eV/atom\")\n",
     "ef_compressed_orthorhombic = model.predict_energy(compressed_orthorhombic)\n",
     "print(\n",
-    "    \"CGCNN predicted formation energy of compressed orthorhombic perovskite: {} eV/atom\".format(\n",
-    "        ef_compressed_orthorhombic\n",
-    "    )\n",
+    "    f\"CGCNN predicted formation energy of compressed orthorhombic perovskite: {ef_compressed_orthorhombic} eV/atom\"\n",
     ")\n",
     "ef_expanded_orthorhombic = model.predict_energy(expanded_orthorhombic)\n",
     "print(\n",
-    "    \"CGCNN predicted formation energy of expanded orthorhombic perovskite: {} eV/atom\".format(ef_expanded_orthorhombic)\n",
+    "    f\"CGCNN predicted formation energy of expanded orthorhombic perovskite: {ef_expanded_orthorhombic} eV/atom\"\n",
     ")"
    ]
   },
@@ -233,9 +230,7 @@
     "radius = max(round(expected_radius(compressed_optimizer.structure) * 0.6, 2), 1.1)\n",
     "relaxed_from_compressed_orthorhombic, _ = compressed_optimizer.get_optimized_structure_and_energy(radius=radius)\n",
     "print(\n",
-    "    \"CGCNN predicted formation energy of relaxed compressed orthorhombic: {} eV/atom\".format(\n",
-    "        model.predict_energy(relaxed_from_compressed_orthorhombic)\n",
-    "    )\n",
+    "    f\"CGCNN predicted formation energy of relaxed compressed orthorhombic: {model.predict_energy(relaxed_from_compressed_orthorhombic)} eV/atom\"\n",
     ")"
    ]
   },
@@ -269,9 +264,7 @@
     "radius = max(round(expected_radius(expanded_optimizer.structure) * 0.6, 2), 1.1)\n",
     "relaxed_from_expanded_orthorhombic, _ = expanded_optimizer.get_optimized_structure_and_energy(radius=radius)\n",
     "print(\n",
-    "    \"CGCNN predicted formation energy of relaxed expanded orthorhombic: {} eV/atom\".format(\n",
-    "        model.predict_energy(relaxed_from_expanded_orthorhombic)\n",
-    "    )\n",
+    "    f\"CGCNN predicted formation energy of relaxed expanded orthorhombic: {model.predict_energy(relaxed_from_expanded_orthorhombic)} eV/atom\"\n",
     ")"
    ]
   },

diff --git a/notebooks/bowsr/megnet_example.ipynb b/notebooks/bowsr/megnet_example.ipynb
@@ -27,13 +27,15 @@
     }
    ],
    "source": [
+    "from __future__ import annotations\n",
+    "\n",
     "import os\n",
     "\n",
     "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"-1\"\n",
-    "import tensorflow as tf\n",
+    "from pymatgen.core.periodic_table import get_el_sp\n",
+    "\n",
     "from maml.apps.bowsr.model.megnet import MEGNet\n",
     "from maml.apps.bowsr.optimizer import BayesianOptimizer\n",
-    "from pymatgen.core.periodic_table import get_el_sp\n",
     "\n",
     "model = MEGNet()\n",
     "\n",
@@ -119,11 +121,11 @@
    ],
    "source": [
     "ef_cubic = model.predict_energy(cubic)\n",
-    "print(\"MEGNet predicted formation energy of cubic perovskite: {} eV/atom\".format(ef_cubic))\n",
+    "print(f\"MEGNet predicted formation energy of cubic perovskite: {ef_cubic} eV/atom\")\n",
     "ef_compressed_cubic = model.predict_energy(compressed_cubic)\n",
-    "print(\"MEGNet predicted formation energy of compressed cubic perovskite: {} eV/atom\".format(ef_compressed_cubic))\n",
+    "print(f\"MEGNet predicted formation energy of compressed cubic perovskite: {ef_compressed_cubic} eV/atom\")\n",
     "ef_expanded_cubic = model.predict_energy(expanded_cubic)\n",
-    "print(\"MEGNet predicted formation energy of expanded cubic perovskite: {} eV/atom\".format(ef_expanded_cubic))"
+    "print(f\"MEGNet predicted formation energy of expanded cubic perovskite: {ef_expanded_cubic} eV/atom\")"
    ]
   },
   {
@@ -156,9 +158,7 @@
     "radius = max(round(expected_radius(compressed_optimizer.structure) * 0.6, 2), 1.1)\n",
     "relaxed_from_compressed_cubic, _ = compressed_optimizer.get_optimized_structure_and_energy(radius=radius)\n",
     "print(\n",
-    "    \"MEGNet predicted formation energy of relaxed compressed cubic: {} eV/atom\".format(\n",
-    "        model.predict_energy(relaxed_from_compressed_cubic)\n",
-    "    )\n",
+    "    f\"MEGNet predicted formation energy of relaxed compressed cubic: {model.predict_energy(relaxed_from_compressed_cubic)} eV/atom\"\n",
     ")"
    ]
   },
@@ -192,9 +192,7 @@
     "radius = max(round(expected_radius(expanded_optimizer.structure) * 0.6, 2), 1.1)\n",
     "relaxed_from_expanded_cubic, _ = expanded_optimizer.get_optimized_structure_and_energy(radius=radius)\n",
     "print(\n",
-    "    \"MEGNet predicted formation energy of relaxed expanded cubic: {} eV/atom\".format(\n",
-    "        model.predict_energy(relaxed_from_expanded_cubic)\n",
-    "    )\n",
+    "    f\"MEGNet predicted formation energy of relaxed expanded cubic: {model.predict_energy(relaxed_from_expanded_cubic)} eV/atom\"\n",
     ")"
    ]
   },
@@ -222,16 +220,14 @@
    ],
    "source": [
     "ef_orthorhombic = model.predict_energy(orthorhombic)\n",
-    "print(\"MEGNet predicted formation energy of orthorhombic perovskite: {} eV/atom\".format(ef_orthorhombic))\n",
+    "print(f\"MEGNet predicted formation energy of orthorhombic perovskite: {ef_orthorhombic} eV/atom\")\n",
     "ef_compressed_orthorhombic = model.predict_energy(compressed_orthorhombic)\n",
     "print(\n",
-    "    \"MEGNet predicted formation energy of compressed orthorhombic perovskite: {} eV/atom\".format(\n",
-    "        ef_compressed_orthorhombic\n",
-    "    )\n",
+    "    f\"MEGNet predicted formation energy of compressed orthorhombic perovskite: {ef_compressed_orthorhombic} eV/atom\"\n",
     ")\n",
     "ef_expanded_orthorhombic = model.predict_energy(expanded_orthorhombic)\n",
     "print(\n",
-    "    \"MEGNet predicted formation energy of expanded orthorhombic perovskite: {} eV/atom\".format(ef_expanded_orthorhombic)\n",
+    "    f\"MEGNet predicted formation energy of expanded orthorhombic perovskite: {ef_expanded_orthorhombic} eV/atom\"\n",
     ")"
    ]
   },
@@ -265,9 +261,7 @@
     "radius = max(round(expected_radius(compressed_optimizer.structure) * 0.6, 2), 1.1)\n",
     "relaxed_from_compressed_orthorhombic, _ = compressed_optimizer.get_optimized_structure_and_energy(radius=radius)\n",
     "print(\n",
-    "    \"MEGNet predicted formation energy of relaxed compressed orthorhombic: {} eV/atom\".format(\n",
-    "        model.predict_energy(relaxed_from_compressed_orthorhombic)\n",
-    "    )\n",
+    "    f\"MEGNet predicted formation energy of relaxed compressed orthorhombic: {model.predict_energy(relaxed_from_compressed_orthorhombic)} eV/atom\"\n",
     ")"
    ]
   },
@@ -301,9 +295,7 @@
     "radius = max(round(expected_radius(expanded_optimizer.structure) * 0.6, 2), 1.1)\n",
     "relaxed_from_expanded_orthorhombic, _ = expanded_optimizer.get_optimized_structure_and_energy(radius=radius)\n",
     "print(\n",
-    "    \"MEGNet predicted formation energy of relaxed expanded orthorhombic: {} eV/atom\".format(\n",
-    "        model.predict_energy(relaxed_from_expanded_orthorhombic)\n",
-    "    )\n",
+    "    f\"MEGNet predicted formation energy of relaxed expanded orthorhombic: {model.predict_energy(relaxed_from_expanded_orthorhombic)} eV/atom\"\n",
     ")"
    ]
   },

diff --git a/notebooks/describer/site_describer.ipynb b/notebooks/describer/site_describer.ipynb
@@ -6,7 +6,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from pymatgen.core import Structure, Lattice\n",
+    "from __future__ import annotations\n",
+    "\n",
+    "from pymatgen.core import Lattice, Structure\n",
     "\n",
     "s = Structure(Lattice.cubic(3.147), [\"Mo\", \"Mo\"], [[0, 0, 0], [0.5, 0.5, 0.5]])  # a simple Mo bcc lattice"
    ]

diff --git a/notebooks/direct/Example1_MPF2021.2.8.all.ipynb b/notebooks/direct/Example1_MPF2021.2.8.all.ipynb
@@ -18,11 +18,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from maml.sampling.direct import DIRECTSampler, BirchClustering, SelectKFromClusters\n",
+    "from __future__ import annotations\n",
+    "\n",
     "import pickle\n",
-    "import numpy as np\n",
+    "\n",
     "import matplotlib.pyplot as plt\n",
-    "import matplotlib.ticker as mtick"
+    "import matplotlib.ticker as mtick\n",
+    "import numpy as np\n",
+    "\n",
+    "from maml.sampling.direct import BirchClustering, DIRECTSampler, SelectKFromClusters"
    ]
   },
   {
@@ -258,7 +262,7 @@
     "    explained_variance[:30] * 100,\n",
     "    \"o-\",\n",
     ")\n",
-    "plt.xlabel(\"i$^{\\mathrm{th}}$ PC\", size=20)\n",
+    "plt.xlabel(r\"i$^{\\mathrm{th}}$ PC\", size=20)\n",
     "plt.ylabel(\"Explained variance\", size=20)\n",
     "ax = plt.gca()\n",
     "ax.yaxis.set_major_formatter(mtick.PercentFormatter())"
@@ -468,13 +472,13 @@
     "    x + 0.6,\n",
     "    scores_MPF_DIRECT,\n",
     "    width=0.3,\n",
-    "    label=f\"DIRECT, $\\overline{{\\mathrm{{Coverage\\ score}}}}$ = {np.mean(scores_MPF_DIRECT):.3f}\",\n",
+    "    label=rf\"DIRECT, $\\overline{{\\mathrm{{Coverage\\ score}}}}$ = {np.mean(scores_MPF_DIRECT):.3f}\",\n",
     ")\n",
     "plt.bar(\n",
     "    x + 0.3,\n",
     "    scores_MPF_MS,\n",
     "    width=0.3,\n",
-    "    label=f\"Manual, $\\overline{{\\mathrm{{Coverage\\ score}}}}$ = {np.mean(scores_MPF_MS):.3f}\",\n",
+    "    label=rf\"Manual, $\\overline{{\\mathrm{{Coverage\\ score}}}}$ = {np.mean(scores_MPF_MS):.3f}\",\n",
     ")\n",
     "plt.xticks(x + 0.45, x_ticks, size=16)\n",
     "plt.yticks(np.linspace(0, 1.0, 6), size=16)\n",

diff --git a/notebooks/direct/Example2_Ti-H.ipynb b/notebooks/direct/Example2_Ti-H.ipynb
@@ -18,11 +18,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from maml.sampling.direct import DIRECTSampler, BirchClustering, SelectKFromClusters, M3GNetStructure\n",
+    "from __future__ import annotations\n",
+    "\n",
     "import pickle\n",
-    "import numpy as np\n",
+    "\n",
     "import matplotlib.pyplot as plt\n",
-    "import matplotlib.ticker as mtick"
+    "import matplotlib.ticker as mtick\n",
+    "import numpy as np\n",
+    "\n",
+    "from maml.sampling.direct import BirchClustering, DIRECTSampler, SelectKFromClusters"
    ]
   },
   {
@@ -182,7 +186,7 @@
     "    explained_variance[:30] * 100,\n",
     "    \"o-\",\n",
     ")\n",
-    "plt.xlabel(\"i$^{\\mathrm{th}}$ PC\", size=20)\n",
+    "plt.xlabel(r\"i$^{\\mathrm{th}}$ PC\", size=20)\n",
     "plt.ylabel(\"Explained variance\", size=20)\n",
     "ax = plt.gca()\n",
     "ax.yaxis.set_major_formatter(mtick.PercentFormatter())"
@@ -421,10 +425,10 @@
     "    x + 0.6,\n",
     "    scores_DIRECT,\n",
     "    width=0.3,\n",
-    "    label=f\"DIRECT, $\\overline{{\\mathrm{{Coverage\\ score}}}}$ = {np.mean(scores_DIRECT):.3f}\",\n",
+    "    label=rf\"DIRECT, $\\overline{{\\mathrm{{Coverage\\ score}}}}$ = {np.mean(scores_DIRECT):.3f}\",\n",
     ")\n",
     "plt.bar(\n",
-    "    x + 0.3, scores_MS, width=0.3, label=f\"Manual, $\\overline{{\\mathrm{{Coverage\\ score}}}}$ = {np.mean(scores_MS):.3f}\"\n",
+    "    x + 0.3, scores_MS, width=0.3, label=rf\"Manual, $\\overline{{\\mathrm{{Coverage\\ score}}}}$ = {np.mean(scores_MS):.3f}\"\n",
     ")\n",
     "plt.xticks(x + 0.45, x_ticks, size=16)\n",
     "plt.yticks(np.linspace(0, 1.0, 6), size=16)\n",
@@ -570,7 +574,7 @@
     "    explained_variance[:30] * 100,\n",
     "    \"o-\",\n",
     ")\n",
-    "plt.xlabel(\"i$^{\\mathrm{th}}$ PC\", size=20)\n",
+    "plt.xlabel(r\"i$^{\\mathrm{th}}$ PC\", size=20)\n",
     "plt.ylabel(\"Explained variance\", size=20)\n",
     "ax = plt.gca()\n",
     "ax.yaxis.set_major_formatter(mtick.PercentFormatter())"
@@ -747,8 +751,8 @@
     "x_ticks = [f\"PC {n+1}\" for n in range(len(x))]\n",
     "\n",
     "plt.figure(figsize=(15,4))\n",
-    "plt.bar(x+0.6, scores_TiH_DIRECT_birch, width=0.3, label=f\"DIRECT, $\\overline{{\\mathrm{{Coverage\\ score}}}}$ = {np.mean(scores_TiH_DIRECT_birch):.3f}\")\n",
-    "plt.bar(x+0.3, scores_TiH_MS, width=0.3, label=f\"MS, $\\overline{{\\mathrm{{Coverage\\ score}}}}$ = {np.mean(scores_TiH_MS):.3f}\")\n",
+    "plt.bar(x+0.6, scores_TiH_DIRECT_birch, width=0.3, label=rf\"DIRECT, $\\overline{{\\mathrm{{Coverage\\ score}}}}$ = {np.mean(scores_TiH_DIRECT_birch):.3f}\")\n",
+    "plt.bar(x+0.3, scores_TiH_MS, width=0.3, label=rf\"MS, $\\overline{{\\mathrm{{Coverage\\ score}}}}$ = {np.mean(scores_TiH_MS):.3f}\")\n",
     "plt.xticks(x+0.45, x_ticks,size=16)\n",
     "plt.yticks(np.linspace(0,1.,6),size=16)\n",
     "plt.ylabel(\"Coverage score\", size=20)\n",