type: remove deprecated typing aliases as described in PEP-585

examples/CollectTips.ipynb

@@ -23,7 +23,6 @@
     "import os\n",
     "from functools import reduce\n",
     "from operator import add\n",
-    "from typing import List\n",
     "\n",
     "import numpy as np\n",
     "\n",
@@ -37,11 +36,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def collect_tips(cur: swcgeom.Tree.Node, children: List[int]) -> int:\n",
+    "def collect_tips(cur: swcgeom.Tree.Node, children: list[int]) -> int:\n",
     "    cur[\"tips\"] = max(1, reduce(add, children, 0))\n",
     "    return cur[\"tips\"]\n",
     "\n",
-    "def collect_length(cur: swcgeom.Tree.Node, children: List[swcgeom.Tree.Node]) -> swcgeom.Tree.Node:\n",
+    "def collect_length(cur: swcgeom.Tree.Node, children: list[swcgeom.Tree.Node]) -> swcgeom.Tree.Node:\n",
     "    def length(acc: float, child: swcgeom.Tree.Node) -> float:\n",
     "        return acc + cur.distance(child) + child[\"length\"]\n",
     "\n",

examples/CutTree.ipynb

@@ -30,7 +30,6 @@
    "outputs": [],
    "source": [
     "import math\n",
-    "from typing import List\n",
     "\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
@@ -138,7 +137,7 @@
     "\n",
     "    removals = []\n",
     "\n",
-    "    def collect_cut(n: Tree.Node, children: List[int]) -> int:\n",
+    "    def collect_cut(n: Tree.Node, children: list[int]) -> int:\n",
     "        if len(children) <= 1:\n",
     "            return n.id\n",
     "\n",

examples/Features.ipynb

@@ -20,7 +20,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from typing import Any, cast, Dict\n",
+    "from typing import Any, cast\n",
     "\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
@@ -88,7 +88,7 @@
    ],
    "source": [
     "features = extract_feature(tree3)\n",
-    "all_features: Dict[Feature, Dict] = {\n",
+    "all_features: dict[Feature, dict] = {\n",
     "    \"length\": {},\n",
     "    \"node_radial_distance\": {},\n",
     "    \"node_branch_order\": {},\n",

examples/dgl/graph.py

@@ -1,6 +1,6 @@
 """Dgl grapth transforms."""
 
-from typing import List, cast
+from typing import cast
 
 import dgl
 
@@ -19,21 +19,21 @@ class ToDGLGraph(Transform[Tree, dgl.DGLGraph]):
     this class is more of a toy and template.
     """
 
-    keys: List[str] | None
+    keys: list[str] | None
     to_bidirected: bool
 
     def __init__(
         self,
         to_bidirected: bool = False,
-        keys: List[str] | None = None,
+        keys: list[str] | None = None,
     ) -> None:
         """Transofrm tree to dgl graph.
 
         Parameters
         ----------
         to_bidirected : bool, default to `False`
             If True, return bidirected graph.
-        keys : List[str], optional
+        keys : list[str], optional
             Copy these keys as ndata of graph.
         """
 

examples/pytorch/branch.py

@@ -1,6 +1,7 @@
 """Transformations in branch requires pytorch."""
 
-from typing import Callable, Literal
+from collections.abc import Callable
+from typing import Literal
 
 import numpy as np
 import numpy.typing as npt

examples/pytorch/branch_dataset.py

@@ -2,7 +2,8 @@
 
 import os
 import warnings
-from typing import Generic, Iterable, TypeVar, cast
+from collections.abc import Iterable
+from typing import Generic, TypeVar, cast
 
 import torch
 import torch.utils.data

swcgeom/analysis/feature_extractor.py

@@ -7,10 +7,11 @@
 """
 
 from abc import ABC, abstractmethod
+from collections.abc import Callable
 from functools import cached_property
 from itertools import chain
 from os.path import basename
-from typing import Any, Callable, Dict, List, Literal, Tuple, overload
+from typing import Any, Literal, overload
 
 import numpy as np
 import numpy.typing as npt
@@ -54,7 +55,7 @@
 ]
 
 NDArrayf32 = npt.NDArray[np.float32]
-FeatAndKwargs = Feature | Tuple[Feature, Dict[str, Any]]
+FeatAndKwargs = Feature | tuple[Feature, dict[str, Any]]
 
 Feature1D = set(["length", "volume", "node_count", "bifurcation_count", "tip_count"])
 
@@ -121,9 +122,9 @@ class FeatureExtractor(ABC):
     @overload
     def get(self, feature: Feature, **kwargs) -> NDArrayf32: ...
     @overload
-    def get(self, feature: List[FeatAndKwargs]) -> List[NDArrayf32]: ...
+    def get(self, feature: list[FeatAndKwargs]) -> list[NDArrayf32]: ...
     @overload
-    def get(self, feature: Dict[Feature, Dict[str, Any]]) -> Dict[str, NDArrayf32]: ...
+    def get(self, feature: dict[Feature, dict[str, Any]]) -> dict[str, NDArrayf32]: ...
     # fmt:on
     def get(self, feature, **kwargs):
         """Get feature.
@@ -168,7 +169,7 @@ def plot(self, feature: FeatAndKwargs, title: str | bool = True, **kwargs) -> Ax
 
     # Custom Plots
 
-    def plot_node_branch_order(self, feature_kwargs: Dict[str, Any], **kwargs) -> Axes:
+    def plot_node_branch_order(self, feature_kwargs: dict[str, Any], **kwargs) -> Axes:
         vals = self._get("node_branch_order", **feature_kwargs)
         bin_edges = np.arange(int(np.ceil(vals.max() + 1))) + 0.5
         return self._plot_histogram_impl(vals, bin_edges, **kwargs)
@@ -234,7 +235,7 @@ def get_sholl(self, **kwargs) -> NDArrayf32:
 
     def plot_sholl(
         self,
-        feature_kwargs: Dict[str, Any],  # pylint: disable=unused-argument
+        feature_kwargs: dict[str, Any],  # pylint: disable=unused-argument
         **kwargs,
     ) -> Axes:
         _, ax = self._features.sholl.plot(**kwargs)
@@ -264,7 +265,7 @@ class PopulationFeatureExtractor(FeatureExtractor):
     """Extract features from population."""
 
     _population: Population
-    _features: List[Features]
+    _features: list[Features]
 
     def __init__(self, population: Population) -> None:
         super().__init__()
@@ -279,7 +280,7 @@ def get_sholl(self, **kwargs) -> NDArrayf32:
 
     # Custom Plots
 
-    def plot_sholl(self, feature_kwargs: Dict[str, Any], **kwargs) -> Axes:
+    def plot_sholl(self, feature_kwargs: dict[str, Any], **kwargs) -> Axes:
         vals, rs = self._get_sholl_impl(**feature_kwargs)
         ax = self._lineplot(xs=rs, ys=vals.flatten(), **kwargs)
         ax.set_ylabel("Count of Intersections")
@@ -295,7 +296,7 @@ def _get_impl(self, feature: Feature, **kwargs) -> NDArrayf32:
 
     def _get_sholl_impl(
         self, steps: int = 20, **kwargs
-    ) -> Tuple[NDArrayf32, NDArrayf32]:
+    ) -> tuple[NDArrayf32, NDArrayf32]:
         rmax = max(t.sholl.rmax for t in self._features)
         rs = Sholl.get_rs(rmax=rmax, steps=steps)
         vals = self._get_impl("sholl", steps=rs, **kwargs)
@@ -333,7 +334,7 @@ class PopulationsFeatureExtractor(FeatureExtractor):
     """Extract feature from population."""
 
     _populations: Populations
-    _features: List[List[Features]]
+    _features: list[list[Features]]
 
     def __init__(self, populations: Populations) -> None:
         super().__init__()
@@ -348,7 +349,7 @@ def get_sholl(self, **kwargs) -> NDArrayf32:
 
     # Custom Plots
 
-    def plot_sholl(self, feature_kwargs: Dict[str, Any], **kwargs) -> Axes:
+    def plot_sholl(self, feature_kwargs: dict[str, Any], **kwargs) -> Axes:
         vals, rs = self._get_sholl_impl(**feature_kwargs)
         ax = self._lineplot(xs=rs, ys=vals, **kwargs)
         ax.set_ylabel("Count of Intersections")
@@ -369,7 +370,7 @@ def _get_impl(self, feature: Feature, **kwargs) -> NDArrayf32:
 
     def _get_sholl_impl(
         self, steps: int = 20, **kwargs
-    ) -> Tuple[NDArrayf32, NDArrayf32]:
+    ) -> tuple[NDArrayf32, NDArrayf32]:
         rmaxs = chain.from_iterable((t.sholl.rmax for t in p) for p in self._features)
         rmax = max(rmaxs)
         rs = Sholl.get_rs(rmax=rmax, steps=steps)

swcgeom/analysis/features.py

@@ -2,7 +2,7 @@
 
 from abc import ABC, abstractmethod
 from functools import cached_property
-from typing import List, TypeVar
+from typing import TypeVar
 
 import numpy as np
 import numpy.typing as npt
@@ -163,7 +163,7 @@ def get_tortuosity(self) -> npt.NDArray[np.float32]:
         return np.array([path.tortuosity() for path in self._paths], dtype=np.float32)
 
     @cached_property
-    def _paths(self) -> List[Tree.Path]:
+    def _paths(self) -> list[Tree.Path]:
         return self.tree.get_paths()
 
 
@@ -201,7 +201,7 @@ def get_angle(self, eps: float = 1e-7) -> npt.NDArray[np.float32]:
         return self.calc_angle(self._branches, eps=eps)
 
     @staticmethod
-    def calc_angle(branches: List[T], eps: float = 1e-7) -> npt.NDArray[np.float32]:
+    def calc_angle(branches: list[T], eps: float = 1e-7) -> npt.NDArray[np.float32]:
         """Calc agnle between branches.
 
         Returns
@@ -219,5 +219,5 @@ def calc_angle(branches: List[T], eps: float = 1e-7) -> npt.NDArray[np.float32]:
         return angle
 
     @cached_property
-    def _branches(self) -> List[Tree.Branch]:
+    def _branches(self) -> list[Tree.Branch]:
         return self.tree.get_branches()

swcgeom/analysis/lmeasure.py

@@ -1,7 +1,7 @@
 """L-Measure analysis."""
 
 import math
-from typing import Literal, Tuple
+from typing import Literal
 
 import numpy as np
 import numpy.typing as npt
@@ -274,7 +274,7 @@ def rall_power(self, bif: Tree.Node) -> float:
         rall_power, _, _, _ = self._rall_power(bif)
         return rall_power
 
-    def _rall_power_d(self, bif: Tree.Node) -> Tuple[float, float, float]:
+    def _rall_power_d(self, bif: Tree.Node) -> tuple[float, float, float]:
         children = bif.children()
         assert len(children) == 2, "Rall Power is only defined for bifurcations"
         parent = bif.parent()
@@ -284,7 +284,7 @@ def _rall_power_d(self, bif: Tree.Node) -> Tuple[float, float, float]:
         da, db = 2 * children[0].r, 2 * children[1].r
         return dp, da, db
 
-    def _rall_power(self, bif: Tree.Node) -> Tuple[float, float, float, float]:
+    def _rall_power(self, bif: Tree.Node) -> tuple[float, float, float, float]:
         dp, da, db = self._rall_power_d(bif)
         start, stop, step = 0, 5, 5 / 1000
         xs = np.arange(start, stop, step)
@@ -501,7 +501,7 @@ def bif_torque_remote(self, bif: Tree.Node) -> float:
 
     def _bif_vector_local(
         self, bif: Tree.Node
-    ) -> Tuple[npt.NDArray[np.float32], npt.NDArray[np.float32]]:
+    ) -> tuple[npt.NDArray[np.float32], npt.NDArray[np.float32]]:
         children = bif.children()
         assert len(children) == 2, "Only defined for bifurcations"
 
@@ -511,7 +511,7 @@ def _bif_vector_local(
 
     def _bif_vector_remote(
         self, bif: Tree.Node
-    ) -> Tuple[npt.NDArray[np.float32], npt.NDArray[np.float32]]:
+    ) -> tuple[npt.NDArray[np.float32], npt.NDArray[np.float32]]:
         children = bif.children()
         assert len(children) == 2, "Only defined for bifurcations"
 

swcgeom/analysis/sholl.py

@@ -1,7 +1,7 @@
 """Sholl analysis."""
 
 import warnings
-from typing import List, Literal, Optional, Tuple
+from typing import Literal, Optional
 
 import numpy as np
 import numpy.typing as npt
@@ -84,18 +84,18 @@ def intersect(self, r: float) -> int:
 
     def plot(  # pylint: disable=too-many-arguments
         self,
-        steps: List[float] | int = 20,
+        steps: list[float] | int = 20,
         plot_type: str | None = None,
         kind: Literal["bar", "linechart", "circles"] = "circles",
         fig: Figure | None = None,
         ax: Axes | None = None,
         **kwargs,
-    ) -> Tuple[Figure, Axes]:
+    ) -> tuple[Figure, Axes]:
         """Plot Sholl analysis.
 
         Parameters
         ----------
-        steps : int or List[float], default to 20
+        steps : int or list[float], default to 20
             Steps of raius of circle. If steps is int, then it will be
             evenly divided into n radii.
         kind : "bar" | "linechart" | "circles", default `circles`