Apply flake8+pycodestyles autofixes

pyproject.toml

@@ -27,24 +27,33 @@ line-length = 130
 target-version = "py39"
 
 [tool.ruff.lint]
-# TODO: Use extend-select instead to get base E and F rules that don't conflict with the formatter
-select = [
+extend-select = [
     "FA", # flake8-future-annotations
     "I", # isort
     "PYI", # flake8-pyi
     "UP", # pyupgrade
+    "W", # pycodestyle Warning
 ]
 ignore = [
     ###
     # Rules we don't want or don't agree with
     ###
 
+    # Used for direct, non-subclass type comparison, for example: `type(val) is str`
+    # see https://github.com/astral-sh/ruff/issues/6465
+    "E721", # Do not compare types, use `isinstance()`
+
     # Typeshed doesn't want complex or non-literal defaults, or long strings, for maintenance and testing reasons.
     # This doesn't affect us, let's have more complete stubs.
     "PYI011",
     "PYI014",
     "PYI053",
 
+    # TODO: Fixing these would change which symbols are even visible for Pylance.
+    # Which may negatively affect users, especially if the symbol wasn't meant to be re-exported.
+    # Manually evaluate each violation.
+    "F401",
+
     # TODO: Investigate and fix or configure
     "PYI001",
     "PYI002",
@@ -55,8 +64,18 @@ ignore = [
     "PYI051", # Request for autofix: https://github.com/astral-sh/ruff/issues/14185
     "PYI052",
 ]
-
 [tool.ruff.lint.per-file-ignores]
+"*.pyi" = [
+    ###
+    # Rules that are out of the control of stub authors:
+    ###
+    "E743", # Ambiguous function name; stubs must follow implementation
+    "F403", # `from . import *` used; unable to detect undefined names
+    # Stubs can sometimes re-export entire modules.
+    # Issues with using a star-imported name will be caught by type-checkers.
+    "F405", # may be undefined, or defined from star imports
+]
+
 # We keep docstrings in sklearn
 "stubs/sklearn/**" = ["PYI021"]
 

stubs/matplotlib/pyplot.pyi

@@ -11,7 +11,6 @@ import numpy as np
 from matplotlib import rcParams as rcParams, style as style
 from matplotlib.contour import QuadContourSet
 
-from . import rcParams
 from ._typing import *
 from .artist import Artist
 from .axes import Axes as Axes
@@ -780,7 +779,7 @@ def tick_params(axis: Literal["x", "y", "both"] = ..., **kwargs): ...
 def ticklabel_format(
     *,
     axis: Literal["x", "y", "both"] = ...,
-    style: Literal["sci", "scientific", "plain"] = ...,
+    style: Literal["sci", "scientific", "plain"] = ...,  # noqa: F811
     scilimits=...,
     useOffset: bool | float = ...,
     useLocale: bool = ...,

stubs/matplotlib/widgets.pyi

@@ -9,9 +9,6 @@ import numpy as np
 from matplotlib.axes._axes import Axes
 from matplotlib.backend_bases import DrawEvent, Event, FigureCanvasBase, KeyEvent, MouseButton, MouseEvent
 from matplotlib.figure import Figure
-from matplotlib.lines import Line2D
-from matplotlib.patches import Ellipse, Rectangle
-from matplotlib.transforms import Affine2D
 from numpy import float64, ndarray
 from numpy.typing import ArrayLike
 from PIL.Image import Image

stubs/networkx/algorithms/community/__init__.pyi

@@ -1,22 +1,11 @@
-from .asyn_fluid import asyn_fluidc as asyn_fluidc
-from .centrality import girvan_newman as girvan_newman
-from .community_utils import is_partition as is_partition
-from .kclique import k_clique_communities as k_clique_communities
-from .kernighan_lin import kernighan_lin_bisection as kernighan_lin_bisection
-from .label_propagation import (
-    asyn_lpa_communities as asyn_lpa_communities,
-    asyn_lpa_communities as label_propagation_communities,
-    label_propagation_communities as label_propagation_communities,
-)
-from .louvain import louvain_communities as louvain_communities, louvain_partitions as louvain_partitions
-from .lukes import lukes_partitioning as lukes_partitioning
-from .modularity_max import (
-    greedy_modularity_communities as greedy_modularity_communities,
-    naive_greedy_modularity_communities as naive_greedy_modularity_communities,
-)
-from .quality import (
-    coverage as coverage,
-    modularity as modularity,
-    partition_quality as partition_quality,
-    performance as performance,
-)
+from networkx.algorithms.community.asyn_fluid import *
+from networkx.algorithms.community.centrality import *
+from networkx.algorithms.community.community_utils import *
+from networkx.algorithms.community.divisive import *
+from networkx.algorithms.community.kclique import *
+from networkx.algorithms.community.kernighan_lin import *
+from networkx.algorithms.community.label_propagation import *
+from networkx.algorithms.community.louvain import *
+from networkx.algorithms.community.lukes import *
+from networkx.algorithms.community.modularity_max import *
+from networkx.algorithms.community.quality import *

stubs/networkx/algorithms/connectivity/connectivity.pyi

@@ -6,11 +6,10 @@ from operator import itemgetter
 # connectivity algorithms.
 from ...algorithms.flow import boykov_kolmogorov, build_residual_network, dinitz, edmonds_karp, shortest_augmenting_path
 from ...classes.graph import Graph
+from .utils import build_auxiliary_edge_connectivity, build_auxiliary_node_connectivity
 
 default_flow_func = ...
 
-from .utils import build_auxiliary_edge_connectivity, build_auxiliary_node_connectivity
-
 __all__ = [
     "average_node_connectivity",
     "local_node_connectivity",

stubs/networkx/algorithms/connectivity/cuts.pyi

@@ -4,11 +4,10 @@ import itertools
 # cut algorithms.
 from ...algorithms.flow import build_residual_network, edmonds_karp
 from ...classes.graph import Graph
+from .utils import build_auxiliary_edge_connectivity, build_auxiliary_node_connectivity
 
 default_flow_func = ...
 
-from .utils import build_auxiliary_edge_connectivity, build_auxiliary_node_connectivity
-
 __all__ = [
     "minimum_st_node_cut",
     "minimum_node_cut",

stubs/networkx/algorithms/connectivity/disjoint_paths.pyi

@@ -1,15 +1,16 @@
 # Define the default maximum flow function to use for the undelying
 # maximum flow computations
+from itertools import filterfalse as _filterfalse
+
 from ...algorithms.flow import edmonds_karp, preflow_push, shortest_augmenting_path
 from ...classes.graph import Graph
 from ...exception import NetworkXNoPath
 
-default_flow_func = ...
-from itertools import filterfalse as _filterfalse
-
 # Functions to build auxiliary data structures.
 from .utils import build_auxiliary_edge_connectivity, build_auxiliary_node_connectivity
 
+default_flow_func = ...
+
 __all__ = ["edge_disjoint_paths", "node_disjoint_paths"]
 
 def edge_disjoint_paths(

stubs/networkx/algorithms/tree/branchings.pyi

@@ -24,7 +24,6 @@ from operator import itemgetter
 from queue import PriorityQueue
 from typing import Literal
 
-from ...algorithms.tree.branchings import ArborescenceIterator
 from ...classes.digraph import DiGraph
 from ...classes.graph import Graph
 from ...classes.multidigraph import MultiDiGraph

stubs/networkx/algorithms/tree/decomposition.pyi

@@ -3,7 +3,6 @@ from itertools import combinations
 from networkx import Graph
 
 from ...algorithms import chordal_graph_cliques, complete_to_chordal_graph, moral
-from ...classes.graph import Graph
 from ...utils import not_implemented_for
 
 __all__ = ["junction_tree"]

stubs/networkx/algorithms/tree/mst.pyi

@@ -6,7 +6,6 @@ from math import isnan
 from operator import itemgetter
 from queue import PriorityQueue
 
-from ...algorithms.tree.mst import SpanningTreeIterator
 from ...classes.graph import Graph
 from ...utils import UnionFind, not_implemented_for, py_random_state
 

stubs/networkx/classes/graph.pyi

@@ -7,7 +7,6 @@ from numpy.typing import ArrayLike
 
 from ..classes.coreviews import AdjacencyView
 from ..classes.digraph import DiGraph
-from ..classes.graph import Graph
 from ..classes.reportviews import DegreeView, EdgeView, NodeView
 from ..exception import NetworkXError
 

stubs/networkx/classes/graphviews.pyi

@@ -4,7 +4,6 @@ from networkx import DiGraph, Graph
 
 from ..classes.coreviews import FilterAdjacency, FilterAtlas, FilterMultiAdjacency, UnionAdjacency, UnionMultiAdjacency
 from ..classes.filters import no_filter
-from ..classes.graph import Graph
 from ..exception import NetworkXError
 from ..utils import not_implemented_for
 

stubs/networkx/classes/reportviews.pyi

@@ -2,7 +2,6 @@ from collections.abc import Mapping, Set as AbstractSet
 from typing import Any
 
 from ..classes.graph import Graph
-from ..classes.reportviews import NodeDataView
 
 __all__ = [
     "NodeView",

stubs/networkx/generators/classic.pyi

@@ -3,7 +3,6 @@ import numbers
 from collections.abc import Iterable
 
 from ..classes import Graph
-from ..classes.graph import Graph
 from ..exception import NetworkXError
 from ..utils import nodes_or_number, pairwise
 

stubs/networkx/generators/triads.pyi

@@ -4,7 +4,6 @@ from networkx import DiGraph
 # Copyright 2011 Reya Group <http://www.reyagroup.com>
 # Copyright 2011 Alex Levenson <[email protected]>
 # Copyright 2011 Diederik van Liere <[email protected]>
-from ..classes import DiGraph
 
 __all__ = ["triad_graph"]
 

stubs/skimage/__init__.pyi

@@ -1,11 +1,9 @@
-__version__: str = ...
-
-submodules: list = ...
-
 from ._shared import lazy as lazy
 from ._shared.tester import PytestTester as PytestTester  # noqa
 from ._shared.version_requirements import ensure_python_version as ensure_python_version
 
+__version__: str = ...
+submodules: list = ...
 __getattr__ = ...
 __lazy_dir__ = ...
 

stubs/skimage/future/graph/rag.pyi

@@ -8,7 +8,6 @@ from numpy.lib.stride_tricks import as_strided
 from scipy import ndimage as ndi, sparse
 
 from ..._shared.version_requirements import require
-from .rag import RAG
 
 def _edge_generator_from_csr(csr_matrix): ...
 def min_weight(graph: RAG, src: int, dst: int, n: int) -> Mapping: ...

stubs/skimage/measure/_regionprops.pyi

@@ -12,7 +12,6 @@ from scipy.spatial.distance import pdist
 from . import _moments
 from ._find_contours import find_contours
 from ._marching_cubes_lewiner import marching_cubes
-from ._regionprops import RegionProperties
 from ._regionprops_utils import euler_number, perimeter, perimeter_crofton
 
 __all__ = ["regionprops", "euler_number", "perimeter", "perimeter_crofton"]

stubs/sklearn/base.pyi

@@ -1,7 +1,13 @@
+import copy
+import inspect
+import platform
+import re
+import warnings
 from collections import defaultdict as defaultdict
 from collections.abc import Iterable
 from typing import Any, ClassVar, TypeVar
 
+import numpy as np
 from numpy import ndarray
 
 from ._config import get_config as get_config
@@ -17,14 +23,6 @@ BaseEstimator_Self = TypeVar("BaseEstimator_Self", bound=BaseEstimator)
 # Author: Gael Varoquaux <[email protected]>
 # License: BSD 3 clause
 
-import copy
-import inspect
-import platform
-import re
-import warnings
-
-import numpy as np
-
 def clone(estimator: BaseEstimator | Iterable[BaseEstimator], *, safe: bool = True) -> Any: ...
 
 class BaseEstimator:

stubs/sklearn/calibration.pyi

@@ -1,10 +1,12 @@
+import warnings
 from collections.abc import Iterable
 from functools import partial as partial
 from inspect import signature as signature
 from math import log as log
 from numbers import Integral as Integral
 from typing import ClassVar, Literal, TypeVar
 
+import numpy as np
 from matplotlib.artist import Artist
 from matplotlib.axes import Axes
 from matplotlib.figure import Figure
@@ -34,10 +36,6 @@ from .utils.validation import check_consistent_length as check_consistent_length
 CalibratedClassifierCV_Self = TypeVar("CalibratedClassifierCV_Self", bound=CalibratedClassifierCV)
 _SigmoidCalibration_Self = TypeVar("_SigmoidCalibration_Self", bound=_SigmoidCalibration)
 
-import warnings
-
-import numpy as np
-
 class CalibratedClassifierCV(ClassifierMixin, MetaEstimatorMixin, BaseEstimator):
     calibrated_classifiers_: list = ...
     feature_names_in_: ndarray = ...

stubs/sklearn/cluster/_affinity_propagation.pyi

@@ -1,6 +1,8 @@
+import warnings
 from numbers import Integral as Integral, Real as Real
 from typing import Any, ClassVar, Literal, TypeVar
 
+import numpy as np
 from numpy import ndarray
 from numpy.random import RandomState
 
@@ -15,10 +17,6 @@ from ..utils.validation import check_is_fitted as check_is_fitted
 
 AffinityPropagation_Self = TypeVar("AffinityPropagation_Self", bound=AffinityPropagation)
 
-import warnings
-
-import numpy as np
-
 ###############################################################################
 # Public API
 

stubs/sklearn/cluster/_agglomerative.pyi

@@ -1,7 +1,9 @@
+import warnings
 from heapq import heapify as heapify, heappop as heappop, heappush as heappush, heappushpop as heappushpop
 from numbers import Integral as Integral, Real as Real
 from typing import Any, Callable, ClassVar, Literal, TypeVar
 
+import numpy as np
 from joblib import Memory
 from numpy import ndarray
 from scipy import sparse as sparse
@@ -22,10 +24,6 @@ from ._feature_agglomeration import AgglomerationTransform
 FeatureAgglomeration_Self = TypeVar("FeatureAgglomeration_Self", bound=FeatureAgglomeration)
 AgglomerativeClustering_Self = TypeVar("AgglomerativeClustering_Self", bound=AgglomerativeClustering)
 
-import warnings
-
-import numpy as np
-
 ###############################################################################
 # Hierarchical tree building functions
 

stubs/sklearn/cluster/_bicluster.pyi

@@ -2,6 +2,7 @@ from abc import ABCMeta, abstractmethod
 from numbers import Integral as Integral
 from typing import Any, ClassVar, Literal, TypeVar
 
+import numpy as np
 from numpy import ndarray
 from numpy.random import RandomState
 from scipy.linalg import norm as norm
@@ -22,8 +23,6 @@ from . import KMeans as KMeans, MiniBatchKMeans as MiniBatchKMeans
 
 BaseSpectral_Self = TypeVar("BaseSpectral_Self", bound=BaseSpectral)
 
-import numpy as np
-
 __all__ = ["SpectralCoclustering", "SpectralBiclustering"]
 
 class BaseSpectral(BiclusterMixin, BaseEstimator, metaclass=ABCMeta):

stubs/sklearn/cluster/_birch.pyi

@@ -1,7 +1,9 @@
+import warnings
 from math import sqrt as sqrt
 from numbers import Integral as Integral, Real as Real
 from typing import Any, ClassVar, TypeVar
 
+import numpy as np
 from numpy import ndarray
 from scipy import sparse as sparse
 from scipy.sparse import spmatrix
@@ -24,10 +26,6 @@ Birch_Self = TypeVar("Birch_Self", bound=Birch)
 #          Joel Nothman <[email protected]>
 # License: BSD 3 clause
 
-import warnings
-
-import numpy as np
-
 class _CFNode:
     squared_norm_: ndarray = ...
     centroids_: ndarray = ...