build: drop python 3.8 (#205)

* drop py38

* cast to float

.github/workflows/ci.yml

@@ -31,9 +31,7 @@ jobs:
         os: [ubuntu-latest, macos-latest, windows-latest]
         python-version: ["3.9", "3.11", "3.12", "3.13"]
         include:
-          - os: ubuntu-latest
-            python-version: "3.8"
-          - os: macos-latest
+          - os: windows-latest
             python-version: "3.10"
 
   test-minreqs:

pyproject.toml

@@ -9,7 +9,7 @@ name = "useq-schema"
 description = "Schema for multi-dimensional microscopy experiments"
 readme = "README.md"
 keywords = ["microscopy", "schema"]
-requires-python = ">=3.8"
+requires-python = ">=3.9"
 license = { text = "BSD 3-Clause License" }
 authors = [
     { email = "[email protected]", name = "Talley Lambert" },
@@ -23,7 +23,6 @@ classifiers = [
     "Operating System :: OS Independent",
     "Programming Language :: Python",
     "Programming Language :: Python :: 3 :: Only",
-    "Programming Language :: Python :: 3.8",
     "Programming Language :: Python :: 3.9",
     "Programming Language :: Python :: 3.10",
     "Programming Language :: Python :: 3.11",
@@ -73,7 +72,7 @@ packages = ["src/useq"]
 # https://beta.ruff.rs/docs/rules/
 [tool.ruff]
 line-length = 88
-target-version = "py38"
+target-version = "py39"
 src = ["src", "tests"]
 
 [tool.ruff.lint]

src/useq/_base_model.py

@@ -5,9 +5,7 @@
     TYPE_CHECKING,
     Any,
     ClassVar,
-    Iterable,
     Optional,
-    Type,
     TypeVar,
     Union,
 )
@@ -16,6 +14,8 @@
 from pydantic import BaseModel, ConfigDict
 
 if TYPE_CHECKING:
+    from collections.abc import Iterable
+
     from typing_extensions import Self
 
     ReprArgs = Iterable[tuple[str | None, Any]]
@@ -84,7 +84,7 @@ class MutableModel(_ReplaceableModel):
 
 class UseqModel(FrozenModel):
     @classmethod
-    def from_file(cls: Type[_Y], path: Union[str, Path]) -> _Y:
+    def from_file(cls: type[_Y], path: Union[str, Path]) -> _Y:
         """Return an instance of this class from a file.  Supports JSON and YAML."""
         path = Path(path)
         if path.suffix in {".yaml", ".yml"}:

src/useq/_grid.py

@@ -3,23 +3,21 @@
 import contextlib
 import math
 import warnings
+from collections.abc import Iterable, Iterator, Sequence
 from enum import Enum
 from typing import (
     TYPE_CHECKING,
+    Annotated,
     Any,
     Callable,
-    Iterable,
-    Iterator,
     Optional,
-    Sequence,
-    Tuple,
     Union,
 )
 
 import numpy as np
 from annotated_types import Ge, Gt
 from pydantic import Field, field_validator, model_validator
-from typing_extensions import Annotated, Self, TypeAlias
+from typing_extensions import Self, TypeAlias
 
 from useq._point_visiting import OrderMode, TraversalOrder
 from useq._position import (
@@ -76,11 +74,11 @@ class _GridPlan(_MultiPointPlan[PositionT]):
         Engines MAY override this even if provided.
     """
 
-    overlap: Tuple[float, float] = Field((0.0, 0.0), frozen=True)
+    overlap: tuple[float, float] = Field((0.0, 0.0), frozen=True)
     mode: OrderMode = Field(OrderMode.row_wise_snake, frozen=True)
 
     @field_validator("overlap", mode="before")
-    def _validate_overlap(cls, v: Any) -> Tuple[float, float]:
+    def _validate_overlap(cls, v: Any) -> tuple[float, float]:
         with contextlib.suppress(TypeError, ValueError):
             v = float(v)
         if isinstance(v, float):
@@ -156,7 +154,7 @@ def iter_grid_positions(
     def __iter__(self) -> Iterator[PositionT]:  # type: ignore [override]
         yield from self.iter_grid_positions()
 
-    def _step_size(self, fov_width: float, fov_height: float) -> Tuple[float, float]:
+    def _step_size(self, fov_width: float, fov_height: float) -> tuple[float, float]:
         dx = fov_width - (fov_width * self.overlap[0]) / 100
         dy = fov_height - (fov_height * self.overlap[1]) / 100
         return dx, dy
@@ -409,7 +407,7 @@ def __iter__(self) -> Iterator[RelativePosition]:  # type: ignore [override]
         seed = np.random.RandomState(self.random_seed)
         func = _POINTS_GENERATORS[self.shape]
 
-        points: list[Tuple[float, float]] = []
+        points: list[tuple[float, float]] = []
         needed_points = self.num_points
         start_at = self.start_at
         if isinstance(start_at, RelativePosition):
@@ -454,7 +452,7 @@ def num_positions(self) -> int:
 
 
 def _is_a_valid_point(
-    points: list[Tuple[float, float]],
+    points: list[tuple[float, float]],
     x: float,
     y: float,
     min_dist_x: float,

src/useq/_hardware_autofocus.py

@@ -1,4 +1,4 @@
-from typing import Any, Dict, Optional, Tuple
+from typing import Any, Optional
 
 from pydantic import PrivateAttr
 
@@ -42,7 +42,7 @@ def event(self, event: MDAEvent) -> Optional[MDAEvent]:
         if not self.should_autofocus(event):
             return None
 
-        updates: Dict[str, Any] = {"action": self.as_action()}
+        updates: dict[str, Any] = {"action": self.as_action()}
         if event.z_pos is not None and event.sequence is not None:
             zplan = event.sequence.z_plan
             if zplan and zplan.is_relative and "z" in event.index:
@@ -71,7 +71,7 @@ class AxesBasedAF(AutoFocusPlan):
         is change, (in other words: every time the position is changed.).
     """
 
-    axes: Tuple[str, ...]
+    axes: tuple[str, ...]
     _previous: dict = PrivateAttr(default_factory=dict)
 
     def should_autofocus(self, event: MDAEvent) -> bool:

src/useq/_iter_sequence.py

@@ -1,9 +1,9 @@
 from __future__ import annotations
 
-from functools import lru_cache
+from functools import cache
 from itertools import product
 from types import MappingProxyType
-from typing import TYPE_CHECKING, Any, Iterator, cast
+from typing import TYPE_CHECKING, Any, cast
 
 from typing_extensions import TypedDict
 
@@ -14,6 +14,8 @@
 from useq._z import AnyZPlan  # noqa: TCH001  # noqa: TCH001
 
 if TYPE_CHECKING:
+    from collections.abc import Iterator
+
     from useq._mda_sequence import MDASequence
     from useq._position import Position, PositionBase, RelativePosition
 
@@ -39,17 +41,17 @@ class PositionDict(TypedDict, total=False):
     z_pos: float
 
 
-@lru_cache(maxsize=None)
+@cache
 def _iter_axis(seq: MDASequence, ax: str) -> tuple[Channel | float | PositionBase, ...]:
     return tuple(seq.iter_axis(ax))
 
 
-@lru_cache(maxsize=None)
+@cache
 def _sizes(seq: MDASequence) -> dict[str, int]:
     return {k: len(list(_iter_axis(seq, k))) for k in seq.axis_order}
 
 
-@lru_cache(maxsize=None)
+@cache
 def _used_axes(seq: MDASequence) -> str:
     return "".join(k for k in seq.axis_order if _sizes(seq)[k])
 

src/useq/_mda_event.py

@@ -1,17 +1,13 @@
 # don't add __future__.annotations here
 # pydantic2 isn't rebuilding the model correctly
 
+from collections.abc import Mapping, Sequence
 from types import MappingProxyType
 from typing import (
     TYPE_CHECKING,
     Any,
-    Dict,
-    List,
-    Mapping,
     NamedTuple,
     Optional,
-    Sequence,
-    Tuple,
 )
 
 import numpy as np
@@ -29,7 +25,7 @@
 if TYPE_CHECKING:
     from useq._mda_sequence import MDASequence
 
-    ReprArgs = Sequence[Tuple[Optional[str], Any]]
+    ReprArgs = Sequence[tuple[Optional[str], Any]]
 
 
 class Channel(UseqModel):
@@ -182,7 +178,6 @@ class MDAEvent(UseqModel):
         `False`.
     """
 
-    # MappingProxyType is not subscriptable on Python 3.8
     index: Mapping[str, int] = Field(default_factory=lambda: MappingProxyType({}))
     channel: Optional[Channel] = None
     exposure: Optional[float] = Field(default=None, gt=0.0)
@@ -193,8 +188,8 @@ class MDAEvent(UseqModel):
     z_pos: Optional[float] = None
     slm_image: Optional[SLMImage] = None
     sequence: Optional["MDASequence"] = Field(default=None, repr=False)
-    properties: Optional[List[PropertyTuple]] = None
-    metadata: Dict[str, Any] = Field(default_factory=dict)
+    properties: Optional[list[PropertyTuple]] = None
+    metadata: dict[str, Any] = Field(default_factory=dict)
     action: AnyAction = Field(default_factory=AcquireImage)
     keep_shutter_open: bool = False
     reset_event_timer: bool = False

src/useq/_mda_sequence.py

@@ -1,16 +1,11 @@
 from __future__ import annotations
 
+from collections.abc import Iterable, Iterator, Mapping, Sequence
 from contextlib import suppress
 from typing import (
     TYPE_CHECKING,
     Any,
-    Dict,
-    Iterable,
-    Iterator,
-    Mapping,
     Optional,
-    Sequence,
-    Tuple,
     Union,
 )
 from uuid import UUID, uuid4
@@ -182,24 +177,24 @@ class MDASequence(UseqModel):
     ```
     """
 
-    metadata: Dict[str, Any] = Field(default_factory=dict)
-    axis_order: Tuple[str, ...] = AXES
+    metadata: dict[str, Any] = Field(default_factory=dict)
+    axis_order: tuple[str, ...] = AXES
     # note that these are BOTH just `Sequence[Position]` but we retain the distinction
     # here so that WellPlatePlans are preserved in the model instance.
-    stage_positions: Union[WellPlatePlan, Tuple[Position, ...]] = Field(
+    stage_positions: Union[WellPlatePlan, tuple[Position, ...]] = Field(
         default_factory=tuple, union_mode="left_to_right"
     )
     grid_plan: Optional[MultiPointPlan] = Field(
         default=None, union_mode="left_to_right"
     )
-    channels: Tuple[Channel, ...] = Field(default_factory=tuple)
+    channels: tuple[Channel, ...] = Field(default_factory=tuple)
     time_plan: Optional[AnyTimePlan] = None
     z_plan: Optional[AnyZPlan] = None
     autofocus_plan: Optional[AnyAutofocusPlan] = None
-    keep_shutter_open_across: Tuple[str, ...] = Field(default_factory=tuple)
+    keep_shutter_open_across: tuple[str, ...] = Field(default_factory=tuple)
 
     _uid: UUID = PrivateAttr(default_factory=uuid4)
-    _sizes: Optional[Dict[str, int]] = PrivateAttr(default=None)
+    _sizes: Optional[dict[str, int]] = PrivateAttr(default=None)
 
     @property
     def uid(self) -> UUID:
@@ -225,7 +220,7 @@ def _validate_keep_shutter_open_across(cls, v: tuple[str, ...]) -> tuple[str, ..
         return v
 
     @field_validator("channels", mode="before")
-    def _validate_channels(cls, value: Any) -> Tuple[Channel, ...]:
+    def _validate_channels(cls, value: Any) -> tuple[Channel, ...]:
         if isinstance(value, str) or not isinstance(
             value, Sequence
         ):  # pragma: no cover
@@ -245,7 +240,7 @@ def _validate_channels(cls, value: Any) -> Tuple[Channel, ...]:
     @field_validator("stage_positions", mode="before")
     def _validate_stage_positions(
         cls, value: Any
-    ) -> Union[WellPlatePlan, Tuple[Position, ...]]:
+    ) -> Union[WellPlatePlan, tuple[Position, ...]]:
         if isinstance(value, np.ndarray):
             if value.ndim == 1:
                 value = [value]
@@ -402,7 +397,7 @@ def _check_order(
                     raise ValueError(err)  # pragma: no cover
 
     @property
-    def shape(self) -> Tuple[int, ...]:
+    def shape(self) -> tuple[int, ...]:
         """Return the shape of this sequence.
 
         !!! note