🏷️ stub numpy.linalg.lapack_lite

Author: jorenham

.mypyignore-todo

@@ -8,17 +8,24 @@ numpy(\..+)?\.floating.as_integer_ratio
 numpy(\..+)?\.complexfloating.__hash__
 numpy(\..+)?\.complexfloating.__complex__
 
-numpy(\.lib\._polynomial_impl|\.matlib)?\.poly1d\.integ
-
 numpy._globals._CopyMode.IF_NEEDED
 numpy._globals._CopyMode.__bool__
 
-numpy._pyinstaller.hook-numpy
-
 numpy.compat
 numpy.compat.py3k
 
+numpy._pyinstaller.hook-numpy
 numpy.ctypeslib._ctypeslib
+numpy.fft.helper
+
+numpy(\.lib\._polynomial_impl|\.matlib)?\.poly1d\.integ
+numpy.lib(._arrayterator_impl)?.Arrayterator.__array__
+numpy.lib.(_array_utils_impl|array_utils).normalize_axis_tuple
+numpy.lib._iotools.NameValidator.defaultdeletechars
+numpy.lib.format.open_memmap
+numpy.lib.format.read_array(_header_(1|2)_0)?
+numpy.lib.mixins.NDArrayOperatorsMixin.__array_ufunc__
+numpy.lib.recfunctions.unstructured_to_structured
 
 numpy.distutils
 
@@ -34,18 +41,6 @@ numpy.f2py.crackfortran
 numpy.f2py.symbolic
 numpy.f2py.((cb|common|f90mod|use)_)?rules
 
-numpy.fft.helper
-
-numpy.lib(._arrayterator_impl)?.Arrayterator.__array__
-numpy.lib.(_array_utils_impl|array_utils).normalize_axis_tuple
-numpy.lib._iotools.NameValidator.defaultdeletechars
-numpy.lib.format.open_memmap
-numpy.lib.format.read_array(_header_(1|2)_0)?
-numpy.lib.mixins.NDArrayOperatorsMixin.__array_ufunc__
-numpy.lib.recfunctions.unstructured_to_structured
-
-numpy.linalg.lapack_lite
-
 numpy.ma.testutils
 numpy.ma.timer_comparison
 numpy.ma.core._convert2ma.__doc__

src/numpy-stubs/linalg/_linalg.pyi

@@ -227,6 +227,8 @@ _LstSqResult: TypeAlias = tuple[Array[_ScalarT], Array[_FloatingT], np.int32, Ar
 
 ###
 
+fortran_int = np.intc
+
 class EigResult(NamedTuple, Generic[_InexactT_co]):
     eigenvalues: Array[_InexactT_co]
     eigenvectors: _Array_2nd[_InexactT_co]

src/numpy-stubs/linalg/lapack_lite.pyi

@@ -0,0 +1,141 @@
+from typing import Any, Final, TypedDict, type_check_only
+
+import numpy as np
+from numpy._typing import NDArray
+
+from ._linalg import fortran_int
+
+###
+
+@type_check_only
+class _GELSD(TypedDict):
+    m: int
+    n: int
+    nrhs: int
+    lda: int
+    ldb: int
+    rank: int
+    lwork: int
+    info: int
+
+@type_check_only
+class _DGELSD(_GELSD):
+    dgelsd_: int
+    rcond: float
+
+@type_check_only
+class _ZGELSD(_GELSD):
+    zgelsd_: int
+
+@type_check_only
+class _GEQRF(TypedDict):
+    m: int
+    n: int
+    lda: int
+    lwork: int
+    info: int
+
+@type_check_only
+class _DGEQRF(_GEQRF):
+    dgeqrf_: int
+
+@type_check_only
+class _ZGEQRF(_GEQRF):
+    zgeqrf_: int
+
+@type_check_only
+class _DORGQR(TypedDict):
+    dorgqr_: int
+    info: int
+
+@type_check_only
+class _ZUNGQR(TypedDict):
+    zungqr_: int
+    info: int
+
+###
+
+_ilp64: Final[bool] = ...
+
+def dgelsd(
+    m: int,
+    n: int,
+    nrhs: int,
+    a: NDArray[np.float64],
+    lda: int,
+    b: NDArray[np.float64],
+    ldb: int,
+    s: NDArray[np.float64],
+    rcond: float,
+    rank: int,
+    work: NDArray[np.float64],
+    lwork: int,
+    iwork: NDArray[fortran_int],
+    info: int,
+) -> _DGELSD: ...
+def zgelsd(
+    m: int,
+    n: int,
+    nrhs: int,
+    a: NDArray[np.complex128],
+    lda: int,
+    b: NDArray[np.complex128],
+    ldb: int,
+    s: NDArray[np.float64],
+    rcond: float,
+    rank: int,
+    work: NDArray[np.complex128],
+    lwork: int,
+    rwork: NDArray[np.float64],
+    iwork: NDArray[fortran_int],
+    info: int,
+) -> _ZGELSD: ...
+
+#
+def dgeqrf(
+    m: int,
+    n: int,
+    a: NDArray[np.float64],  # in/out, shape: (lda, n)
+    lda: int,
+    tau: NDArray[np.float64],  # out, shape: (min(m, n),)
+    work: NDArray[np.float64],  # out, shape: (max(1, lwork),)
+    lwork: int,
+    info: int,  # out
+) -> _DGEQRF: ...
+def zgeqrf(
+    m: int,
+    n: int,
+    a: NDArray[np.complex128],  # in/out, shape: (lda, n)
+    lda: int,
+    tau: NDArray[np.complex128],  # out, shape: (min(m, n),)
+    work: NDArray[np.complex128],  # out, shape: (max(1, lwork),)
+    lwork: int,
+    info: int,  # out
+) -> _ZGEQRF: ...
+
+#
+def dorgqr(
+    m: int,  # >=0
+    n: int,  # m >= n >= 0
+    k: int,  # n >= k >= 0
+    a: NDArray[np.float64],  # in/out, shape: (lda, n)
+    lda: int,  # >= max(1, m)
+    tau: NDArray[np.float64],  # in, shape: (k,)
+    work: NDArray[np.float64],  # out, shape: (max(1, lwork),)
+    lwork: int,
+    info: int,  # out
+) -> _DORGQR: ...
+def zungqr(
+    m: int,
+    n: int,
+    k: int,
+    a: NDArray[np.complex128],
+    lda: int,
+    tau: NDArray[np.complex128],
+    work: NDArray[np.complex128],
+    lwork: int,
+    info: int,
+) -> _ZUNGQR: ...
+
+#
+def xerbla(srname: Any, info: int) -> None: ...