Drop support for Python 3.9, require Python >= 3.10 (#2132)

* Drop support for Python 3.9, require Python >= 3.10

Starting development for Mesa 3.0 and following SPEC 0 we can drop Python 3.9 support and require Python 3.10 or higher for future development. This allows adopting more modern Python features and simplifies the testing and CI configurations.

See the Python 3.10 release notes: https://docs.python.org/3/whatsnew/3.10.html

The 2.3.x release series will keep supporting Python 3.9.

* Update type hinting for Python 3.10

`X | Y` can now be used for type annotations, including making a variable optional with `X | None`

* intro_tutorial.ipynb: Require Python 3.10

* typing: Replace Union with pipe (|)

We can do this now in Python 3.10!

Author: EwoutH

.github/workflows/build_lint.yml

@@ -37,8 +37,6 @@ jobs:
             python-version: "3.11"
           - os: ubuntu
             python-version: "3.10"
-          - os: ubuntu
-            python-version: "3.9"
           # Disabled for now. See https://github.com/projectmesa/mesa/issues/1253
           #- os: ubuntu
           #  python-version: 'pypy-3.8'

.pre-commit-config.yaml

@@ -17,7 +17,7 @@ repos:
     rev: v3.15.2
     hooks:
     -   id: pyupgrade
-        args: [--py38-plus]
+        args: [--py310-plus]
 -   repo: https://github.com/pre-commit/pre-commit-hooks
     rev: v4.5.0  # Use the ref you want to point at
     hooks:

docs/tutorials/intro_tutorial.ipynb

@@ -60,7 +60,7 @@
    "source": [
     "### Tutorial Setup\n",
     "\n",
-    "Create and activate a [virtual environment](http://docs.python-guide.org/en/latest/dev/virtualenvs/). *Python version 3.9 or higher is required*.\n",
+    "Create and activate a [virtual environment](http://docs.python-guide.org/en/latest/dev/virtualenvs/). *Python version 3.10 or higher is required*.\n",
     "\n",
     "Install Mesa:\n",
     "\n",

mesa/agent.py

@@ -14,11 +14,11 @@
 import warnings
 import weakref
 from collections import defaultdict
-from collections.abc import Iterable, Iterator, MutableSet, Sequence
+from collections.abc import Callable, Iterable, Iterator, MutableSet, Sequence
 from random import Random
 
 # mypy
-from typing import TYPE_CHECKING, Any, Callable
+from typing import TYPE_CHECKING, Any
 
 if TYPE_CHECKING:
     # We ensure that these are not imported during runtime to prevent cyclic

mesa/batchrunner.py

@@ -2,7 +2,7 @@
 from collections.abc import Iterable, Mapping
 from functools import partial
 from multiprocessing import Pool
-from typing import Any, Optional, Union
+from typing import Any
 
 from tqdm.auto import tqdm
 
@@ -11,9 +11,9 @@
 
 def batch_run(
     model_cls: type[Model],
-    parameters: Mapping[str, Union[Any, Iterable[Any]]],
+    parameters: Mapping[str, Any | Iterable[Any]],
     # We still retain the Optional[int] because users may set it to None (i.e. use all CPUs)
-    number_processes: Optional[int] = 1,
+    number_processes: int | None = 1,
     iterations: int = 1,
     data_collection_period: int = -1,
     max_steps: int = 1000,
@@ -76,7 +76,7 @@ def batch_run(
 
 
 def _make_model_kwargs(
-    parameters: Mapping[str, Union[Any, Iterable[Any]]],
+    parameters: Mapping[str, Any | Iterable[Any]],
 ) -> list[dict[str, Any]]:
     """Create model kwargs from parameters dictionary.
 

mesa/datacollection.py

@@ -196,7 +196,7 @@ def collect(self, model):
         if self.model_reporters:
             for var, reporter in self.model_reporters.items():
                 # Check if lambda or partial function
-                if isinstance(reporter, (types.LambdaType, partial)):
+                if isinstance(reporter, types.LambdaType | partial):
                     self.model_vars[var].append(reporter(model))
                 # Check if model attribute
                 elif isinstance(reporter, str):

mesa/experimental/cell_space/cell_collection.py

@@ -1,10 +1,10 @@
 from __future__ import annotations
 
 import itertools
-from collections.abc import Iterable, Mapping
+from collections.abc import Callable, Iterable, Mapping
 from functools import cached_property
 from random import Random
-from typing import TYPE_CHECKING, Callable, Generic, TypeVar
+from typing import TYPE_CHECKING, Generic, TypeVar
 
 if TYPE_CHECKING:
     from mesa.experimental.cell_space.cell import Cell

mesa/experimental/cell_space/grid.py

@@ -60,7 +60,7 @@ def _validate_parameters(self):
             raise ValueError("Dimensions must be a list of positive integers.")
         if not isinstance(self.torus, bool):
             raise ValueError("Torus must be a boolean.")
-        if self.capacity is not None and not isinstance(self.capacity, (float, int)):
+        if self.capacity is not None and not isinstance(self.capacity, float | int):
             raise ValueError("Capacity must be a number or None.")
 
     def select_random_empty_cell(self) -> T:

mesa/experimental/cell_space/network.py

@@ -1,5 +1,5 @@
 from random import Random
-from typing import Any, Optional
+from typing import Any
 
 from mesa.experimental.cell_space.cell import Cell
 from mesa.experimental.cell_space.discrete_space import DiscreteSpace
@@ -11,8 +11,8 @@ class Network(DiscreteSpace):
     def __init__(
         self,
         G: Any,  # noqa: N803
-        capacity: Optional[int] = None,
-        random: Optional[Random] = None,
+        capacity: int | None = None,
+        random: Random | None = None,
         cell_klass: type[Cell] = Cell,
     ) -> None:
         """A Networked grid

mesa/experimental/components/altair.py

@@ -1,5 +1,4 @@
 import contextlib
-from typing import Optional
 
 import solara
 
@@ -8,7 +7,7 @@
 
 
 @solara.component
-def SpaceAltair(model, agent_portrayal, dependencies: Optional[list[any]] = None):
+def SpaceAltair(model, agent_portrayal, dependencies: list[any] | None = None):
     space = getattr(model, "grid", None)
     if space is None:
         # Sometimes the space is defined as model.space instead of model.grid