Require future annotations import, fix ruff fallout

contrib/pov-nodes/nodal_points.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import numpy as np
 from pov import (
     Background,

contrib/pov-nodes/pov.py

@@ -1,4 +1,6 @@
 # http://aspn.activestate.com/ASPN/Cookbook/Python/Recipe/205451
+from __future__ import annotations
+
 import os
 
 import numpy as np

contrib/weights-from-festa-sommariva.py

@@ -11,6 +11,7 @@
 
 These rules are licensed under the GPL and are not included by default.
 """
+from __future__ import annotations
 
 import re
 

contrib/weights-from-fortran.py

@@ -1,4 +1,5 @@
 #! /usr/bin/env python
+from __future__ import annotations
 
 import re
 

contrib/weights-from-jaskowiec-sukumar.py

@@ -10,12 +10,12 @@
 The quadrature rules can be found in the supplemental material at the same
 URL and should be a file named ``nme6528-sup-0002-supinfo.tar``.
 """
+from __future__ import annotations
 
 import pathlib
 import re
 import sys
 import tarfile
-from typing import Optional
 
 
 FILE_ORDER_REGEX = re.compile(r"cubature_tet_sym_p(\d{1,2})_n\d{1,3}_expand.txt")
@@ -64,7 +64,7 @@ def extract_rules_if_not(path: pathlib.Path) -> pathlib.Path:
 
 
 def generate_jaskowiec_sukumar_quadrature_rules(
-    infolder: pathlib.Path, outfile: Optional[pathlib.Path] = None
+    infolder: pathlib.Path, outfile: pathlib.Path | None = None
 ) -> None:
     import numpy as np
 

contrib/weights-from-txt.py

@@ -1,4 +1,5 @@
 #! /usr/bin/env python
+from __future__ import annotations
 
 import sys
 

contrib/weights-from-witherden-vincent.py

@@ -9,6 +9,7 @@
 
 They are available under a Creative Commons license.
 """
+from __future__ import annotations
 
 import os
 

doc/conf.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 from importlib import metadata
 from urllib.request import urlopen
 

examples/derivative.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import numpy as np
 
 import modepy as mp

examples/plot-basis.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import matplotlib.pyplot as pt
 import numpy as np
 

examples/plot-nodes.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import matplotlib.pyplot as pt
 import numpy as np
 

examples/plot-quadrature-nodes.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import matplotlib.pyplot as plt
 import numpy as np
 

examples/plot-warp.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import matplotlib.pyplot as pt
 import numpy as np
 

modepy/__init__.py

@@ -1,3 +1,6 @@
+from __future__ import annotations
+
+
 __copyright__ = """Copyright (C) 2009, 2010, 2013 Andreas Kloeckner, Tim Warburton,
     Jan Hesthaven, Xueyu Zhu"""
 

modepy/matrices.py

@@ -1,3 +1,6 @@
+from __future__ import annotations
+
+
 __copyright__ = "Copyright (C) 2013 Andreas Kloeckner"
 
 __license__ = """
@@ -21,7 +24,7 @@
 """
 
 
-from typing import Callable, Optional, Sequence, Tuple, Union
+from typing import Callable, Sequence
 from warnings import warn
 
 import numpy as np
@@ -117,7 +120,7 @@ def vandermonde(
 def multi_vandermonde(
             functions: Sequence[Callable[[np.ndarray], Sequence[np.ndarray]]],
             nodes: np.ndarray
-        ) -> Tuple[np.ndarray, ...]:
+        ) -> tuple[np.ndarray, ...]:
     """Evaluate multiple (generalized) Vandermonde matrices.
 
     The Vandermonde Matrix is given by :math:`V_{i,j} := f_j(x_i)`
@@ -213,8 +216,8 @@ def differentiation_matrices(
             basis: Sequence[Callable[[np.ndarray], np.ndarray]],
             grad_basis: Sequence[Callable[[np.ndarray], Sequence[np.ndarray]]],
             nodes: np.ndarray,
-            from_nodes: Optional[np.ndarray] = None
-        ) -> Tuple[np.ndarray, ...]:
+            from_nodes: np.ndarray | None = None
+        ) -> tuple[np.ndarray, ...]:
     """Return matrices carrying out differentiation on nodal values in the
     :math:`(r,s,t)` unit directions. (See :ref:`tri-coords` and
     :ref:`tet-coords`.)
@@ -253,7 +256,7 @@ def differentiation_matrices(
 def diff_matrices(
             basis: Basis,
             nodes: np.ndarray,
-            from_nodes: Optional[np.ndarray] = None
+            from_nodes: np.ndarray | None = None
         ):
     """Like :func:`differentiation_matrices`, but for a given :class:`~modepy.Basis`.
 
@@ -264,7 +267,7 @@ def diff_matrices(
 
 
 def diff_matrix_permutation(
-            node_tuples: Sequence[Tuple[int, ...]],
+            node_tuples: Sequence[tuple[int, ...]],
             ref_axis: int
         ) -> np.ndarray:
     """Return a :mod:`numpy` array *permutation* of integers so that::
@@ -290,7 +293,7 @@ def diff_matrix_permutation(
 
 
 def inverse_mass_matrix(
-            basis: Union[Basis, Sequence[Callable[[np.ndarray], np.ndarray]]],
+            basis: Basis | Sequence[Callable[[np.ndarray], np.ndarray]],
             nodes: np.ndarray
         ) -> np.ndarray:
     """Return a matrix :math:`A=M^{-1}`, which is the inverse of the one returned
@@ -325,7 +328,7 @@ def inverse_mass_matrix(
 
 
 def mass_matrix(
-            basis: Union[Basis, Sequence[Callable[[np.ndarray], np.ndarray]]],
+            basis: Basis | Sequence[Callable[[np.ndarray], np.ndarray]],
             nodes: np.ndarray
         ) -> np.ndarray:
     r"""Return a mass matrix :math:`M`, which obeys
@@ -370,7 +373,7 @@ def modal_quad_mass_matrix(
 def nodal_quad_mass_matrix(
             quadrature: Quadrature,
             test_functions: Sequence[Callable[[np.ndarray], np.ndarray]],
-            nodes: Optional[np.ndarray] = None,
+            nodes: np.ndarray | None = None,
         ) -> np.ndarray:
     r"""Using the *quadrature*, provide a matrix :math:`M` that
     satisfies:

modepy/modal_decay.py

@@ -1,3 +1,6 @@
+from __future__ import annotations
+
+
 __copyright__ = "Copyright (C) 2010-2012 Andreas Kloeckner"
 
 __license__ = """
@@ -20,7 +23,6 @@
 THE SOFTWARE.
 """
 
-from typing import Tuple
 
 import numpy as np
 import numpy.linalg as la
@@ -88,7 +90,7 @@ def simplex_interp_error_coefficient_estimator_matrix(
 
 
 def make_mode_number_vector(
-        mode_order_tuples: Tuple[Tuple[int, ...], ...],
+        mode_order_tuples: tuple[tuple[int, ...], ...],
         ignored_modes: int) -> np.ndarray:
     node_cnt = len(mode_order_tuples)
 
@@ -152,7 +154,7 @@ def skyline_pessimize(modal_values: np.ndarray) -> np.ndarray:
 
 def fit_modal_decay(
         coeffs: np.ndarray, dims: int, n: int,
-        ignored_modes: int = 1) -> Tuple[np.ndarray, np.ndarray]:
+        ignored_modes: int = 1) -> tuple[np.ndarray, np.ndarray]:
     """Fit a curve to the modal decay on each element.
 
     :arg coeffs: an array of shape ``(num_elements, num_modes)`` containing modal