Enable Ruff flake8-import-conventions (ICN)

pyproject.toml

@@ -51,6 +51,7 @@ select = [
     "EXE", # flake8-executable
     "FA", # flake8-future-annotations
     "I", # isort
+    "ICN", # flake8-import-conventions
     "N", # pep8-naming
     "PGH", # pygrep-hooks
     "PLC", # Pylint Convention
@@ -207,6 +208,12 @@ ignore = [
 [tool.ruff.lint.pydocstyle]
 convention = "pep257" # https://docs.astral.sh/ruff/settings/#lint_pydocstyle_convention
 
+[tool.ruff.lint.flake8-import-conventions.extend-aliases]
+# Using "tk" causes name conflict in stdlib/tkinter/ttk.py with Style.tk
+"tkinter" = "tkinter"
+# Added
+"numpy.typing" = "npt"
+
 [tool.ruff.lint.isort]
 split-on-trailing-comma = false
 combine-as-imports = true

stubs/JACK-Client/jack/__init__.pyi

@@ -4,7 +4,7 @@ from collections.abc import Callable, Generator, Iterable, Iterator, Sequence
 from typing import Any, Literal, NoReturn, overload
 from typing_extensions import Self
 
-import numpy
+import numpy as np
 from _cffi_backend import _CDataBase
 from numpy.typing import NDArray
 
@@ -224,7 +224,7 @@ class OwnPort(Port):
     def disconnect(self, other: str | Port | None = None) -> None: ...
     def unregister(self) -> None: ...
     def get_buffer(self) -> _CBufferType: ...
-    def get_array(self) -> NDArray[numpy.float32]: ...
+    def get_array(self) -> NDArray[np.float32]: ...
 
 class OwnMidiPort(MidiPort, OwnPort):
     def __init__(self, port_ptr: _CDataBase, client: Client) -> None: ...

stubs/networkx/networkx/classes/graph.pyi

@@ -4,7 +4,7 @@ from functools import cached_property
 from typing import Any, ClassVar, TypeVar, overload
 from typing_extensions import Self, TypeAlias
 
-import numpy
+import numpy as np
 from networkx.classes.coreviews import AdjacencyView, AtlasView
 from networkx.classes.digraph import DiGraph
 from networkx.classes.reportviews import DiDegreeView, NodeView, OutEdgeView
@@ -22,7 +22,7 @@ _Data: TypeAlias = (
     | dict[_Node, dict[_Node, dict[str, Any]]]
     | dict[_Node, Iterable[_Node]]
     | Iterable[_EdgePlus[_Node]]
-    | numpy.ndarray[Any, Any]
+    | np.ndarray[Any, Any]
     # | scipy.sparse.base.spmatrix
 )
 

stubs/networkx/networkx/convert_matrix.pyi

@@ -3,7 +3,7 @@ from collections.abc import Callable, Collection, Hashable, Iterable
 from typing import Literal, TypeVar, overload
 from typing_extensions import TypeAlias
 
-import numpy
+import numpy as np
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
 
@@ -21,8 +21,8 @@ _G = TypeVar("_G", bound=Graph[Hashable])
 def to_pandas_adjacency(
     G: Graph[_Node],
     nodelist: _Axes[_Node] | None = None,
-    dtype: numpy.dtype[Incomplete] | None = None,
-    order: numpy._OrderCF = None,
+    dtype: np.dtype[Incomplete] | None = None,
+    order: np._OrderCF = None,
     multigraph_weight: Callable[[list[float]], float] = ...,
     weight: str = "weight",
     nonedge: float = 0.0,
@@ -72,17 +72,17 @@ def from_pandas_edgelist(
 def to_numpy_array(
     G: Graph[_Node],
     nodelist: Collection[_Node] | None = None,
-    dtype: numpy.dtype[Incomplete] | None = None,
-    order: numpy._OrderCF = None,
+    dtype: np.dtype[Incomplete] | None = None,
+    order: np._OrderCF = None,
     multigraph_weight: Callable[[list[float]], float] = ...,
     weight: str = "weight",
     nonedge: float = 0.0,
-) -> numpy.ndarray[Incomplete, numpy.dtype[Incomplete]]: ...
+) -> np.ndarray[Incomplete, np.dtype[Incomplete]]: ...
 @overload
 def from_numpy_array(
-    A: numpy.ndarray[Incomplete, Incomplete], parallel_edges: bool = False, create_using: None = None
+    A: np.ndarray[Incomplete, Incomplete], parallel_edges: bool = False, create_using: None = None
 ) -> Graph[Incomplete]: ...
 @overload
-def from_numpy_array(A: numpy.ndarray[Incomplete, Incomplete], parallel_edges: bool = False, *, create_using: type[_G]) -> _G: ...
+def from_numpy_array(A: np.ndarray[Incomplete, Incomplete], parallel_edges: bool = False, *, create_using: type[_G]) -> _G: ...
 @overload
-def from_numpy_array(A: numpy.ndarray[Incomplete, Incomplete], parallel_edges: bool, create_using: type[_G]) -> _G: ...
+def from_numpy_array(A: np.ndarray[Incomplete, Incomplete], parallel_edges: bool, create_using: type[_G]) -> _G: ...

stubs/networkx/networkx/drawing/layout.pyi

@@ -1,6 +1,6 @@
 from _typeshed import Incomplete
 
-import numpy
+import numpy as np
 
 def random_layout(G, center: Incomplete | None = None, dim: int = 2, seed: Incomplete | None = None): ...
 def circular_layout(G, scale: float = 1, center: Incomplete | None = None, dim: int = 2): ...
@@ -57,7 +57,7 @@ def arf_layout(
     dt: float = 0.001,
     max_iter: int = 1000,
     *,
-    seed: int | numpy.random.RandomState | None = None,
+    seed: int | np.random.RandomState | None = None,
 ): ...
 def rescale_layout(pos, scale: float = 1): ...
 def rescale_layout_dict(pos, scale: float = 1): ...

stubs/networkx/networkx/utils/misc.pyi

@@ -3,7 +3,7 @@ from _typeshed import Incomplete
 from types import ModuleType
 from typing_extensions import TypeAlias
 
-import numpy
+import numpy as np
 
 __all__ = [
     "flatten",
@@ -23,7 +23,7 @@ __all__ = [
 ]
 
 _RandomNumberGenerator: TypeAlias = (
-    ModuleType | random.Random | numpy.random.RandomState | numpy.random.Generator | PythonRandomInterface
+    ModuleType | random.Random | np.random.RandomState | np.random.Generator | PythonRandomInterface
 )
 _RandomState: TypeAlias = int | _RandomNumberGenerator | None
 
@@ -36,7 +36,7 @@ def groups(many_to_one): ...
 def create_random_state(random_state: Incomplete | None = None): ...
 
 class PythonRandomViaNumpyBits(random.Random):
-    def __init__(self, rng: numpy.random.Generator | None = None) -> None: ...
+    def __init__(self, rng: np.random.Generator | None = None) -> None: ...
     def getrandbits(self, k: int) -> int: ...
 
 class PythonRandomInterface:

stubs/tensorflow/tensorflow/_aliases.pyi

@@ -5,7 +5,7 @@ from collections.abc import Iterable, Mapping, Sequence
 from typing import Any, Protocol, TypeVar
 from typing_extensions import TypeAlias
 
-import numpy  # pytype needs the unaliased import to resolve DTypeLike
+import numpy  # noqa: ICN001 # pytype needs the unaliased import to resolve DTypeLike
 import numpy as np
 import numpy.typing as npt
 import tensorflow as tf