Drop Python 3.8

.github/workflows/ci.yml

@@ -8,7 +8,7 @@ on:
       - published
 
 env:
-  MINIMUM_PYTHON_VERSION: "3.8"
+  MINIMUM_PYTHON_VERSION: "3.9"
 
 concurrency:
   group: ${{ github.head_ref || github.run_id }}
@@ -36,7 +36,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
+        python-version: ["3.9", "3.10", "3.11", "3.12"]
     steps:
       - uses: actions/checkout@v3
       - name: Setup Python ${{ matrix.python-version }}

.pre-commit-config.yaml

@@ -12,7 +12,7 @@ repos:
     hooks:
       - id: pyupgrade
         language: python
-        args: [--py38-plus]
+        args: [--py39-plus]
   - repo: https://github.com/pycqa/isort
     rev: 5.13.2
     hooks:

HISTORY.md

@@ -4,6 +4,7 @@
 
 - Removed templating
 - Added support for Python 3.12
+- Dropped support for Python 3.8
 
 ## 0.4.2 (2023-06-09)
 

README.md

@@ -14,13 +14,12 @@ containers for grand-challenge.org.
 
 ## Features
 
-  - Validation of submitted predictions
-  - Interface to SciKit-Learn metrics and Pandas aggregations
+  - Interface to SciKit-Learn metrics
   - Bounding box annotations with Jaccard Index calculations
 
 ## Getting Started
 
-[evalutils](https://github.com/comic/evalutils) requires Python 3.8 or
+[evalutils](https://github.com/comic/evalutils) requires Python 3.9 or
 above, and can be installed from `pip`.
 Please see the [Getting Started](https://comic.github.io/evalutils/usage.html)
 documentation for more details.

docs/conf.py

@@ -20,7 +20,6 @@
 import os
 import sys
 from importlib.metadata import version as _get_version
-from typing import Dict, List
 
 evalutils_version = _get_version("evalutils")
 
@@ -111,7 +110,7 @@ def setup(app):
 # Add any paths that contain custom static files (such as style sheets) here,
 # relative to this directory. They are copied after the builtin static files,
 # so a file named "default.css" will overwrite the builtin "default.css".
-html_static_path: List[str] = []
+html_static_path: list[str] = []
 
 
 # -- Options for HTMLHelp output ---------------------------------------
@@ -122,7 +121,7 @@ def setup(app):
 
 # -- Options for LaTeX output ------------------------------------------
 
-latex_elements: Dict[str, str] = {
+latex_elements: dict[str, str] = {
     # The paper size ('letterpaper' or 'a4paper').
     #
     # 'papersize': 'letterpaper',

evalutils/roc.py

@@ -1,4 +1,4 @@
-from typing import List, NamedTuple, Tuple
+from typing import NamedTuple
 
 import numpy as np
 from numpy import ndarray
@@ -55,9 +55,9 @@ def get_bootstrapped_roc_ci_curves(
     """
 
     rng_seed = 40  # control reproducibility
-    bootstrapped_az_scores: List[float] = []
+    bootstrapped_az_scores: list[float] = []
 
-    tprs_list: List[ndarray] = []
+    tprs_list: list[ndarray] = []
     base_fpr = np.linspace(0, 1, 101)
     rng = np.random.RandomState(rng_seed)
 
@@ -114,7 +114,7 @@ def get_bootstrapped_roc_ci_curves(
 
 
 def average_roc_curves(
-    roc_curves: List[BootstrappedROCCICurves], bins: int = 200
+    roc_curves: list[BootstrappedROCCICurves], bins: int = 200
 ) -> BootstrappedROCCICurves:
     """
     Averages ROC curves using vertical averaging (fixed FP rates),
@@ -237,8 +237,8 @@ def get_bootstrapped_ci_point_error(
         The fpr vals (one per ROC point) representing the highest val in CI
     """
     rng_seed = 40  # control reproducibility
-    tprs_list: List[ndarray] = []
-    fprs_list: List[ndarray] = []
+    tprs_list: list[ndarray] = []
+    fprs_list: list[ndarray] = []
     rng = np.random.RandomState(rng_seed)
 
     num_possible_scores = len(np.unique(y_score))
@@ -302,7 +302,7 @@ def get_bootstrapped_ci_point_error(
 
 def _get_confidence_intervals(
     *, n_bootstraps: int, one_sided_ci: float, points_array
-) -> Tuple[ndarray, ndarray]:
+) -> tuple[ndarray, ndarray]:
     ci_upper = []
     ci_lower = []
 

evalutils/scorers.py

@@ -1,5 +1,4 @@
 from collections import namedtuple
-from typing import List, Tuple
 
 from numpy import array
 from sklearn.neighbors import BallTree
@@ -11,8 +10,8 @@
 
 def score_detection(
     *,
-    ground_truth: List[Tuple[float, ...]],
-    predictions: List[Tuple[float, ...]],
+    ground_truth: list[tuple[float, ...]],
+    predictions: list[tuple[float, ...]],
     radius: float = 1.0,
 ) -> DetectionScore:
     """
@@ -97,10 +96,10 @@ def score_detection(
 
 def find_hits_for_targets(
     *,
-    targets: List[Tuple[float, ...]],
-    predictions: List[Tuple[float, ...]],
+    targets: list[tuple[float, ...]],
+    predictions: list[tuple[float, ...]],
     radius: float,
-) -> List[Tuple[int, ...]]:
+) -> list[tuple[int, ...]]:
     """
     Generates a list of the predicted points that are within a radius r of the
     targets. The indicies are returned in sorted order, from closest to

evalutils/stats.py

@@ -1,5 +1,5 @@
 from collections import namedtuple
-from typing import List, Optional, Tuple, Union
+from typing import Optional, Union
 
 import numpy as np
 from numpy import ndarray
@@ -11,12 +11,12 @@
 )
 
 VOXELSPACING_TYPE = Optional[
-    Union[Tuple[Union[float, int], ...], List[Union[float, int]], float, int]
+    Union[tuple[Union[float, int], ...], list[Union[float, int]], float, int]
 ]
 
 
 def calculate_confusion_matrix(
-    y_true: ndarray, y_pred: ndarray, labels: List[int]
+    y_true: ndarray, y_pred: ndarray, labels: list[int]
 ) -> ndarray:
     """
     Efficient confusion matrix calculation, based on sklearn interface

pyproject.toml

@@ -17,7 +17,7 @@ classifiers = [
 evalutils = "evalutils.__main__:main"
 
 [tool.poetry.dependencies]
-python = "^3.8"
+python = "^3.9"
 scipy = "*"
 numpy = "*"
 scikit-learn = "*"
@@ -42,7 +42,7 @@ line_length = 79
 
 [tool.black]
 line-length = 79
-target-version = ['py38']
+target-version = ['py39']
 
 [tool.pytest.ini_options]
 minversion = "6.0"
@@ -64,11 +64,10 @@ filterwarnings = [
 legacy_tox_ini = """
 [tox]
 isolated_build = True
-envlist = py38, py39, py310, py311, py312
+envlist = py39, py310, py311, py312
 
 [gh-actions]
 python =
-    3.8: py38
     3.9: py39
     3.10: py310
     3.11: py311

tests/test_stats.py

@@ -1,5 +1,3 @@
-from typing import Tuple
-
 import numpy as np
 import pytest
 import scipy.ndimage as ndimage
@@ -20,13 +18,11 @@ def reset_seeds():
 @pytest.mark.parametrize(
     "shape, dtype", (((12,), np.int32), ((10, 2), np.int32), ((10,), np.int8))
 )
-def test_edt32_indices_wrong_format(shape: Tuple[int, ...], dtype: np.dtype):
+def test_edt32_indices_wrong_format(shape: tuple[int, ...], dtype: np.dtype):
     x = np.random.random((10,)) > 0.5
     indices = np.zeros((x.ndim,) + shape, dtype=dtype)
     with pytest.raises(RuntimeError):
-        stats.distance_transform_edt_float32(
-            input=x, sampling=[1.2], indices=indices
-        )
+        stats.distance_transform_edt(input=x, sampling=[1.2], indices=indices)
 
 
 @pytest.mark.parametrize(