Merge pull request #72 from jet-net/pre-commit-ci-update-config

chore: update pre-commit hooks

.pre-commit-config.yaml

@@ -7,20 +7,20 @@ exclude: ".*key"
 
 repos:
   - repo: https://github.com/psf/black-pre-commit-mirror
-    rev: 24.3.0
+    rev: 24.8.0
     hooks:
       - id: black-jupyter
         language_version: python3
         args: [--line-length=100]
 
   - repo: https://github.com/adamchainz/blacken-docs
-    rev: "1.16.0"
+    rev: "1.18.0"
     hooks:
       - id: blacken-docs
         additional_dependencies: [black==23.*]
 
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: "v4.5.0"
+    rev: "v4.6.0"
     hooks:
       - id: check-added-large-files
       - id: check-case-conflict
@@ -50,7 +50,7 @@ repos:
         args: [--prose-wrap=preserve]
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: "v0.3.5"
+    rev: "v0.6.3"
     hooks:
       - id: ruff
         args: ["--fix", "--show-fixes"]
@@ -65,7 +65,7 @@ repos:
   #         - pytest
 
   - repo: https://github.com/codespell-project/codespell
-    rev: "v2.2.6"
+    rev: "v2.3.0"
     hooks:
       - id: codespell
         types_or: [python, rst, markdown]
@@ -76,12 +76,12 @@ repos:
       - id: shellcheck
 
   - repo: https://github.com/abravalheri/validate-pyproject
-    rev: v0.16
+    rev: v0.19
     hooks:
       - id: validate-pyproject
 
   - repo: https://github.com/python-jsonschema/check-jsonschema
-    rev: 0.28.1
+    rev: 0.29.2
     hooks:
       - id: check-dependabot
       - id: check-github-workflows

jetnet/evaluation/gen_metrics.py

@@ -82,8 +82,9 @@ def _calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6):
     covmean, _ = linalg.sqrtm(sigma1.dot(sigma2), disp=False)
     if not np.isfinite(covmean).all():
         msg = (
-            "fid calculation produces singular product; " "adding %s to diagonal of cov estimates"
-        ) % eps
+            "fid calculation produces singular product; "
+            f"adding {eps} to diagonal of cov estimates"
+        )
         logger.debug(msg)
         offset = np.eye(sigma1.shape[0]) * eps
         covmean = linalg.sqrtm((sigma1 + offset).dot(sigma2 + offset))

tests/datasets/test_qgjets.py

@@ -26,7 +26,7 @@
 num_particles = 153
 
 
-@pytest.mark.slow()
+@pytest.mark.slow
 @pytest.mark.parametrize(
     ("jet_types", "split", "expected_length", "class_id"),
     [
@@ -63,7 +63,7 @@ def test_getData(jet_types, split, expected_length, class_id, file_list):
         assert np.all(jf[:, 0] == class_id)
 
 
-@pytest.mark.slow()
+@pytest.mark.slow
 @pytest.mark.parametrize("file_list", [test_file_list_withbc, test_file_list_withoutbc])
 def test_getDataFeatures(file_list):
     pf, jf = DataClass.getData(data_dir=data_dir, jet_features=None, file_list=file_list)
@@ -82,7 +82,7 @@ def test_getDataFeatures(file_list):
     assert np.max(pf[:, :, 1]) == approx(550, rel=0.2)
 
 
-@pytest.mark.slow()
+@pytest.mark.slow
 def test_getDataErrors():
     with pytest.raises(AssertionError):
         DataClass.getData(jet_type="f")

tests/datasets/test_toptagging.py

@@ -18,7 +18,7 @@
 split = "valid"  # for faster testing
 
 
-@pytest.mark.slow()
+@pytest.mark.slow
 @pytest.mark.parametrize(
     ("jet_types", "split", "expected_length", "class_id"),
     [
@@ -61,7 +61,7 @@ def test_getData(jet_types, split, expected_length, class_id):
         assert np.all(jf[:, 0] == class_id)
 
 
-@pytest.mark.slow()
+@pytest.mark.slow
 def test_getDataFeatures():
     pf, jf = DataClass.getData(data_dir=data_dir, jet_features=["E", "type"], split=split)
     assert pf.shape == (valid_length, num_particles, 4)
@@ -82,7 +82,7 @@ def test_getDataFeatures():
     assert np.max(pf[:, :, 1]) == approx(2000, rel=0.2)
 
 
-@pytest.mark.slow()
+@pytest.mark.slow
 def test_DataClassNormalisation():
     X = DataClass(
         data_dir=data_dir,
@@ -99,7 +99,7 @@ def test_DataClassNormalisation():
     assert np.max(X.jet_data[:, 0]) == 1
 
 
-@pytest.mark.slow()
+@pytest.mark.slow
 def test_getDataErrors():
     with pytest.raises(AssertionError):
         DataClass.getData(jet_type="f", split=split)

tests/datasets/test_utils.py

@@ -9,7 +9,7 @@
 test_data_3d = rng.random((5, 4, 3))
 
 
-@pytest.fixture()
+@pytest.fixture
 def features_order():
     return ["eta", "phi", "pt"]
 

tutorials/pyhep-data-access.ipynb

@@ -77,6 +77,8 @@
     }
    ],
    "source": [
+    "from __future__ import annotations\n",
+    "\n",
     "from jetnet.datasets import JetNet\n",
     "\n",
     "print(f\"Particle features: {JetNet.ALL_PARTICLE_FEATURES}\")\n",
@@ -175,8 +177,8 @@
     }
    ],
    "source": [
-    "from jetnet.utils import to_image\n",
     "import matplotlib.pyplot as plt\n",
+    "from jetnet.utils import to_image\n",
     "\n",
     "num_images = 5\n",
     "num_types = len(data_args[\"jet_type\"])\n",
@@ -254,8 +256,8 @@
     }
    ],
    "source": [
-    "from jetnet.utils import jet_features\n",
     "import numpy as np\n",
+    "from jetnet.utils import jet_features\n",
     "\n",
     "fig = plt.figure(figsize=(12, 12))\n",
     "plt.ticklabel_format(axis=\"y\", scilimits=(0, 0), useMathText=True)\n",
@@ -291,10 +293,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import numpy as np\n",
     "from jetnet.datasets import JetNet\n",
     "from jetnet.datasets.normalisations import FeaturewiseLinear\n",
-    "\n",
-    "import numpy as np\n",
     "from sklearn.preprocessing import OneHotEncoder\n",
     "\n",
     "\n",