fix scipy-stubs squigglies

Author: jorenham

pytensor/link/numba/dispatch/linalg/decomposition/lu.py

@@ -1,5 +1,5 @@
 from collections.abc import Callable
-from typing import cast as typing_cast
+from typing import Literal
 
 import numpy as np
 from numba import njit as numba_njit
@@ -37,9 +37,9 @@ def _lu_factor_to_lu(a, dtype, overwrite_a):
 
 def _lu_1(
     a: np.ndarray,
-    permute_l: bool,
+    permute_l: Literal[True],
     check_finite: bool,
-    p_indices: bool,
+    p_indices: Literal[False],
     overwrite_a: bool,
 ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
     """
@@ -48,23 +48,20 @@ def _lu_1(
     Called when permute_l is True and p_indices is False, and returns a tuple of (perm, L, U), where perm an integer
     array of row swaps, such that L[perm] @ U = A.
     """
-    return typing_cast(
-        tuple[np.ndarray, np.ndarray, np.ndarray],
-        linalg.lu(
-            a,
-            permute_l=permute_l,
-            check_finite=check_finite,
-            p_indices=p_indices,
-            overwrite_a=overwrite_a,
-        ),
+    return linalg.lu(
+        a,
+        permute_l=permute_l,
+        check_finite=check_finite,
+        p_indices=p_indices,
+        overwrite_a=overwrite_a,
     )
 
 
 def _lu_2(
     a: np.ndarray,
-    permute_l: bool,
+    permute_l: Literal[False],
     check_finite: bool,
-    p_indices: bool,
+    p_indices: Literal[True],
     overwrite_a: bool,
 ) -> tuple[np.ndarray, np.ndarray]:
     """
@@ -73,23 +70,20 @@ def _lu_2(
     Called when permute_l is False and p_indices is True, and returns a tuple of (PL, U), where PL is the
     permuted L matrix, PL = P @ L.
     """
-    return typing_cast(
-        tuple[np.ndarray, np.ndarray],
-        linalg.lu(
-            a,
-            permute_l=permute_l,
-            check_finite=check_finite,
-            p_indices=p_indices,
-            overwrite_a=overwrite_a,
-        ),
+    return linalg.lu(
+        a,
+        permute_l=permute_l,
+        check_finite=check_finite,
+        p_indices=p_indices,
+        overwrite_a=overwrite_a,
     )
 
 
 def _lu_3(
     a: np.ndarray,
-    permute_l: bool,
+    permute_l: Literal[False],
     check_finite: bool,
-    p_indices: bool,
+    p_indices: Literal[False],
     overwrite_a: bool,
 ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
     """
@@ -98,15 +92,12 @@ def _lu_3(
     Called when permute_l is False and p_indices is False, and returns a tuple of (P, L, U), where P is the permutation
     matrix, P @ L @ U = A.
     """
-    return typing_cast(
-        tuple[np.ndarray, np.ndarray, np.ndarray],
-        linalg.lu(
-            a,
-            permute_l=permute_l,
-            check_finite=check_finite,
-            p_indices=p_indices,
-            overwrite_a=overwrite_a,
-        ),
+    return linalg.lu(
+        a,
+        permute_l=permute_l,
+        check_finite=check_finite,
+        p_indices=p_indices,
+        overwrite_a=overwrite_a,
     )
 
 

pytensor/link/numba/dispatch/linalg/decomposition/lu_factor.py

@@ -1,4 +1,5 @@
 from collections.abc import Callable
+from typing import cast as typing_cast
 
 import numpy as np
 from numba.core.extending import overload
@@ -21,8 +22,13 @@ def _getrf(A, overwrite_a=False) -> tuple[np.ndarray, np.ndarray, int]:
     Underlying LAPACK function used for LU factorization. Compared to scipy.linalg.lu_factorize, this function also
     returns an info code with diagnostic information.
     """
-    (getrf,) = linalg.get_lapack_funcs("getrf", (A,))
-    A_copy, ipiv, info = getrf(A, overwrite_a=overwrite_a)
+    funcs = linalg.get_lapack_funcs("getrf", (A,))
+    assert isinstance(funcs, list)  # narrows `funcs: list[F] | F` to `funcs: list[F]`
+    getrf = funcs[0]
+
+    A_copy, ipiv, info = typing_cast(
+        tuple[np.ndarray, np.ndarray, int], getrf(A, overwrite_a=overwrite_a)
+    )
 
     return A_copy, ipiv, info
 

pytensor/link/numba/dispatch/linalg/decomposition/qr.py

@@ -1,3 +1,5 @@
+from typing import Literal
+
 import numpy as np
 from numba.core.extending import overload
 from numba.np.linalg import _copy_to_fortran_order, ensure_lapack
@@ -13,7 +15,13 @@
 
 def _xgeqrf(A: np.ndarray, overwrite_a: bool, lwork: int):
     """LAPACK geqrf: Computes a QR factorization of a general M-by-N matrix A."""
-    (geqrf,) = get_lapack_funcs(("geqrf",), (A,))
+    # (geqrf,) = typing_cast(
+    #     list[Callable[..., np.ndarray]], get_lapack_funcs(("geqrf",), (A,))
+    # )
+    funcs = get_lapack_funcs(("geqrf",), (A,))
+    assert isinstance(funcs, list)  # narrows `funcs: list[F] | F` to `funcs: list[F]`
+    geqrf = funcs[0]
+
     return geqrf(A, overwrite_a=overwrite_a, lwork=lwork)
 
 
@@ -61,7 +69,10 @@ def impl(A, overwrite_a, lwork):
 
 def _xgeqp3(A: np.ndarray, overwrite_a: bool, lwork: int):
     """LAPACK geqp3: Computes a QR factorization with column pivoting of a general M-by-N matrix A."""
-    (geqp3,) = get_lapack_funcs(("geqp3",), (A,))
+    funcs = get_lapack_funcs(("geqp3",), (A,))
+    assert isinstance(funcs, list)  # narrows `funcs: list[F] | F` to `funcs: list[F]`
+    geqp3 = funcs[0]
+
     return geqp3(A, overwrite_a=overwrite_a, lwork=lwork)
 
 
@@ -111,7 +122,10 @@ def impl(A, overwrite_a, lwork):
 
 def _xorgqr(A: np.ndarray, tau: np.ndarray, overwrite_a: bool, lwork: int):
     """LAPACK orgqr: Generates the M-by-N matrix Q with orthonormal columns from a QR factorization (real types)."""
-    (orgqr,) = get_lapack_funcs(("orgqr",), (A,))
+    funcs = get_lapack_funcs(("orgqr",), (A,))
+    assert isinstance(funcs, list)  # narrows `funcs: list[F] | F` to `funcs: list[F]`
+    orgqr = funcs[0]
+
     return orgqr(A, tau, overwrite_a=overwrite_a, lwork=lwork)
 
 
@@ -160,7 +174,10 @@ def impl(A, tau, overwrite_a, lwork):
 
 def _xungqr(A: np.ndarray, tau: np.ndarray, overwrite_a: bool, lwork: int):
     """LAPACK ungqr: Generates the M-by-N matrix Q with orthonormal columns from a QR factorization (complex types)."""
-    (ungqr,) = get_lapack_funcs(("ungqr",), (A,))
+    funcs = get_lapack_funcs(("ungqr",), (A,))
+    assert isinstance(funcs, list)  # narrows `funcs: list[F] | F` to `funcs: list[F]`
+    ungqr = funcs[0]
+
     return ungqr(A, tau, overwrite_a=overwrite_a, lwork=lwork)
 
 
@@ -209,8 +226,8 @@ def impl(A, tau, overwrite_a, lwork):
 
 def _qr_full_pivot(
     x: np.ndarray,
-    mode: str = "full",
-    pivoting: bool = True,
+    mode: Literal["full", "economic"] = "full",
+    pivoting: Literal[True] = True,
     overwrite_a: bool = False,
     check_finite: bool = False,
     lwork: int | None = None,
@@ -234,8 +251,8 @@ def _qr_full_pivot(
 
 def _qr_full_no_pivot(
     x: np.ndarray,
-    mode: str = "full",
-    pivoting: bool = False,
+    mode: Literal["full", "economic"] = "full",
+    pivoting: Literal[False] = False,
     overwrite_a: bool = False,
     check_finite: bool = False,
     lwork: int | None = None,
@@ -258,8 +275,8 @@ def _qr_full_no_pivot(
 
 def _qr_r_pivot(
     x: np.ndarray,
-    mode: str = "r",
-    pivoting: bool = True,
+    mode: Literal["r", "raw"] = "r",
+    pivoting: Literal[True] = True,
     overwrite_a: bool = False,
     check_finite: bool = False,
     lwork: int | None = None,
@@ -282,8 +299,8 @@ def _qr_r_pivot(
 
 def _qr_r_no_pivot(
     x: np.ndarray,
-    mode: str = "r",
-    pivoting: bool = False,
+    mode: Literal["r", "raw"] = "r",
+    pivoting: Literal[False] = False,
     overwrite_a: bool = False,
     check_finite: bool = False,
     lwork: int | None = None,
@@ -306,8 +323,8 @@ def _qr_r_no_pivot(
 
 def _qr_raw_no_pivot(
     x: np.ndarray,
-    mode: str = "raw",
-    pivoting: bool = False,
+    mode: Literal["raw"] = "raw",
+    pivoting: Literal[False] = False,
     overwrite_a: bool = False,
     check_finite: bool = False,
     lwork: int | None = None,
@@ -332,8 +349,8 @@ def _qr_raw_no_pivot(
 
 def _qr_raw_pivot(
     x: np.ndarray,
-    mode: str = "raw",
-    pivoting: bool = True,
+    mode: Literal["raw"] = "raw",
+    pivoting: Literal[True] = True,
     overwrite_a: bool = False,
     check_finite: bool = False,
     lwork: int | None = None,

pytensor/link/numba/dispatch/linalg/solve/lu_solve.py

@@ -1,4 +1,5 @@
 from collections.abc import Callable
+from typing import Literal, TypeAlias
 
 import numpy as np
 from numba.core.extending import overload
@@ -20,8 +21,15 @@
 )
 
 
+_Trans: TypeAlias = Literal[0, 1, 2]
+
+
 def _getrs(
-    LU: np.ndarray, B: np.ndarray, IPIV: np.ndarray, trans: int, overwrite_b: bool
+    LU: np.ndarray,
+    B: np.ndarray,
+    IPIV: np.ndarray,
+    trans: _Trans | bool,  # mypy does not realize that `bool <: Literal[0, 1]`
+    overwrite_b: bool,
 ) -> tuple[np.ndarray, int]:
     """
     Placeholder for solving a linear system with a matrix that has been LU-factored. Used by linalg.lu_solve.
@@ -31,8 +39,10 @@ def _getrs(
 
 @overload(_getrs)
 def getrs_impl(
-    LU: np.ndarray, B: np.ndarray, IPIV: np.ndarray, trans: int, overwrite_b: bool
-) -> Callable[[np.ndarray, np.ndarray, np.ndarray, int, bool], tuple[np.ndarray, int]]:
+    LU: np.ndarray, B: np.ndarray, IPIV: np.ndarray, trans: _Trans, overwrite_b: bool
+) -> Callable[
+    [np.ndarray, np.ndarray, np.ndarray, _Trans, bool], tuple[np.ndarray, int]
+]:
     ensure_lapack()
     _check_scipy_linalg_matrix(LU, "getrs")
     _check_scipy_linalg_matrix(B, "getrs")
@@ -41,7 +51,11 @@ def getrs_impl(
     numba_getrs = _LAPACK().numba_xgetrs(dtype)
 
     def impl(
-        LU: np.ndarray, B: np.ndarray, IPIV: np.ndarray, trans: int, overwrite_b: bool
+        LU: np.ndarray,
+        B: np.ndarray,
+        IPIV: np.ndarray,
+        trans: _Trans,
+        overwrite_b: bool,
     ) -> tuple[np.ndarray, int]:
         _N = np.int32(LU.shape[-1])
         _solve_check_input_shapes(LU, B)
@@ -89,7 +103,7 @@ def impl(
 def _lu_solve(
     lu_and_piv: tuple[np.ndarray, np.ndarray],
     b: np.ndarray,
-    trans: int,
+    trans: _Trans,
     overwrite_b: bool,
     check_finite: bool,
 ):
@@ -105,10 +119,10 @@ def _lu_solve(
 def lu_solve_impl(
     lu_and_piv: tuple[np.ndarray, np.ndarray],
     b: np.ndarray,
-    trans: int,
+    trans: _Trans,
     overwrite_b: bool,
     check_finite: bool,
-) -> Callable[[np.ndarray, np.ndarray, np.ndarray, bool, bool, bool], np.ndarray]:
+) -> Callable[[np.ndarray, np.ndarray, np.ndarray, _Trans, bool, bool], np.ndarray]:
     ensure_lapack()
     _check_scipy_linalg_matrix(lu_and_piv[0], "lu_solve")
     _check_scipy_linalg_matrix(b, "lu_solve")
@@ -117,7 +131,7 @@ def impl(
         lu: np.ndarray,
         piv: np.ndarray,
         b: np.ndarray,
-        trans: int,
+        trans: _Trans,
         overwrite_b: bool,
         check_finite: bool,
     ) -> np.ndarray:

pytensor/tensor/conv/abstract_conv.py

@@ -6,17 +6,27 @@
 import sys
 import warnings
 from math import gcd
+from typing import TYPE_CHECKING
 
 import numpy as np
 from numpy.exceptions import ComplexWarning
 
 
-try:
-    from scipy.signal.signaltools import _bvalfromboundary, _valfrommode, convolve
-    from scipy.signal.sigtools import _convolve2d
-except ImportError:
-    from scipy.signal._signaltools import _bvalfromboundary, _valfrommode, convolve
+if TYPE_CHECKING:
+    # https://github.com/scipy/scipy-stubs/issues/851
+    from scipy.signal._signaltools import (  # type: ignore[attr-defined]
+        _bvalfromboundary,
+        _valfrommode,
+        convolve,
+    )
     from scipy.signal._sigtools import _convolve2d
+else:
+    try:
+        from scipy.signal.signaltools import _bvalfromboundary, _valfrommode, convolve
+        from scipy.signal.sigtools import _convolve2d
+    except ImportError:
+        from scipy.signal._signaltools import _bvalfromboundary, _valfrommode, convolve
+        from scipy.signal._sigtools import _convolve2d
 
 import pytensor
 from pytensor import tensor as pt