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🏷️ sparse: stub the remaining private modules (#724)
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‎scipy-stubs/sparse/_csparsetools.pyi

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from collections.abc import Iterable
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from typing import TypeAlias, TypeVar
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import numpy as np
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import optype.numpy as onp
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import optype.numpy.compat as npc
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_Index: TypeAlias = int | np.intp
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_Scalar: TypeAlias = npc.number | np.bool_
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_FusedScalarT = TypeVar(
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"_FusedScalarT",
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np.bool_,
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np.uint8,
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np.int8,
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np.uint16,
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np.int16,
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np.uint32,
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np.int32,
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np.uint64,
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np.int64,
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np.float32,
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np.float64,
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np.longdouble,
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np.complex64,
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np.complex128,
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np.clongdouble,
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)
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###
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#
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def lil_get1(
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M: _Index, N: _Index, rows: list[list[_FusedScalarT]], datas: list[list[_FusedScalarT]], i: int, j: int
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) -> _FusedScalarT: ...
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def lil_insert(
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M: _Index, N: _Index, rows: list[list[_FusedScalarT]], datas: list[list[_FusedScalarT]], i: int, j: int, x: _FusedScalarT
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) -> None: ...
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def lil_get_lengths(input: list[list[_FusedScalarT]], output: onp.Array1D[npc.integer]) -> None: ...
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def lil_flatten_to_array(input: onp.Array1D[np.object_ | np.float64], output: onp.Array1D[_Scalar]) -> None: ...
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def lil_fancy_get(
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M: _Index,
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N: _Index,
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rows: list[list[_FusedScalarT]],
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datas: list[list[_FusedScalarT]],
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new_rows: list[list[_FusedScalarT]],
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new_datas: list[list[_FusedScalarT]],
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i_idx: onp.Array1D[np.intp],
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j_idx: onp.Array1D[np.intp],
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) -> None: ...
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def lil_fancy_set(
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M: _Index,
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N: _Index,
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rows: list[list[_FusedScalarT]],
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data: list[list[_FusedScalarT]],
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i_idx: onp.Array1D[np.intp],
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j_idx: onp.Array1D[np.intp],
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values: onp.Array2D[_Scalar],
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) -> None: ...
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def lil_get_row_ranges(
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M: _Index,
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N: _Index,
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rows: list[list[_FusedScalarT]],
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datas: list[list[_FusedScalarT]],
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new_rows: list[list[_FusedScalarT]],
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new_datas: list[list[_FusedScalarT]],
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irows: Iterable[_Index],
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j_start: _Index,
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j_stop: _Index,
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j_stride: _Index,
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nj: _Index,
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) -> None: ...

‎scipy-stubs/sparse/_sparsetools.pyi

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# NOTE: The scipy source is generated in `scipy/sparse/_generate_sparsetools.py`
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# Type codes used:
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#
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# - i: integer scalar
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# - I: integer array
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# - T: data array
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# - V: std::vector<integer>*
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# - W: std::vector<data>*
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# - *: indicates that the next argument is an output argument
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# - v: void
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# - l: 64-bit integer scalar
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#
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# See `scipy/sparse/sparsetools/sparsetools.cxx` for more details.
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from collections.abc import Sequence
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from typing import Never, TypeAlias
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import numpy as np
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import optype.numpy as onp
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import optype.numpy.compat as npc
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_i: TypeAlias = int | np.int32 | np.int64 # noqa: PYI042
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_l: TypeAlias = int | np.int64 # noqa: PYI042
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_I: TypeAlias = onp.ArrayND[np.int32 | np.int64]
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_T: TypeAlias = onp.ArrayND[npc.number | np.bool_]
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_V: TypeAlias = Sequence[_i]
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_W: TypeAlias = Sequence[complex | npc.number | np.bool_]
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###
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# bsr.h
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def bsr_diagonal(a0: _i, a1: _i, a2: _i, a3: _i, a4: _i, a5: _I, a6: _I, a7: _T, a8: _T, /) -> None: ... # v iiiiiIIT*T
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def bsr_tocsr(a0: _i, a1: _i, a2: _i, a3: _i, a4: _I, a5: _I, a6: _T, a7: _I, a8: _I, a9: _T, /) -> None: ... # v iiiiIIT*I*I*T
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def bsr_scale_columns(a0: _i, a1: _i, a2: _i, a3: _i, a4: _I, a5: _I, a6: _T, a7: _T, /) -> None: ... # v iiiiII*TT
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def bsr_scale_rows(a0: _i, a1: _i, a2: _i, a3: _i, a4: _I, a5: _I, a6: _T, a7: _T, /) -> None: ... # v iiiiII*TT
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def bsr_sort_indices(a0: _i, a1: _i, a2: _i, a3: _i, a4: _I, a5: _I, a6: _T, /) -> None: ... # v iiii*I*I*T
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def bsr_transpose(
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a0: _i, a1: _i, a2: _i, a3: _i, a4: _I, a5: _I, a6: _T, a7: _I, a8: _I, a9: _T, /
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) -> None: ... # v iiiiIIT*I*I*T
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def bsr_matmat(
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a0: _i, a1: _i, a2: _i, a3: _i, a4: _i, a5: _i, a6: _I, a7: _I, a8: _T, a9: _I, a10: _I, a11: _T, a12: _I, a13: _I, a14: _T, /
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) -> None: ... # v iiiiiiIITIIT*I*I*T
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def bsr_matvec(a0: _i, a1: _i, a2: _i, a3: _i, a4: _I, a5: _I, a6: _T, a7: _T, a8: _T, /) -> None: ... # v iiiiIITT*T
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def bsr_matvecs(a0: _i, a1: _i, a2: _i, a3: _i, a4: _i, a5: _I, a6: _I, a7: _T, a8: _T, a9: _T, /) -> None: ... # v iiiiiIITT*T
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def bsr_elmul_bsr(
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a0: _i, a1: _i, a2: _i, a3: _i, a4: _I, a5: _I, a6: _T, a7: _I, a8: _I, a9: _T, a10: _I, a11: _I, a12: _T, /
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) -> None: ... # v iiiiIITIIT*I*I*T
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def bsr_eldiv_bsr(
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a0: _i, a1: _i, a2: _i, a3: _i, a4: _I, a5: _I, a6: _T, a7: _I, a8: _I, a9: _T, a10: _I, a11: _I, a12: _T, /
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) -> None: ... # v iiiiIITIIT*I*I*T
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def bsr_plus_bsr(
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a0: _i, a1: _i, a2: _i, a3: _i, a4: _I, a5: _I, a6: _T, a7: _I, a8: _I, a9: _T, a10: _I, a11: _I, a12: _T, /
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) -> None: ... # v iiiiIITIIT*I*I*T
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def bsr_minus_bsr(
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a0: _i, a1: _i, a2: _i, a3: _i, a4: _I, a5: _I, a6: _T, a7: _I, a8: _I, a9: _T, a10: _I, a11: _I, a12: _T, /
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) -> None: ... # v iiiiIITIIT*I*I*T
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def bsr_maximum_bsr(
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a0: _i, a1: _i, a2: _i, a3: _i, a4: _I, a5: _I, a6: _T, a7: _I, a8: _I, a9: _T, a10: _I, a11: _I, a12: _T, /
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) -> None: ... # v iiiiIITIIT*I*I*T
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def bsr_minimum_bsr(
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a0: _i, a1: _i, a2: _i, a3: _i, a4: _I, a5: _I, a6: _T, a7: _I, a8: _I, a9: _T, a10: _I, a11: _I, a12: _T, /
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) -> None: ... # v iiiiIITIIT*I*I*T
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def bsr_ne_bsr(
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a0: _i, a1: _i, a2: _i, a3: _i, a4: _I, a5: _I, a6: _T, a7: _I, a8: _I, a9: _T, a10: _I, a11: _I, a12: _T, /
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) -> None: ... # v iiiiIITIIT*I*I*T
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def bsr_lt_bsr(
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a0: _i, a1: _i, a2: _i, a3: _i, a4: _I, a5: _I, a6: _T, a7: _I, a8: _I, a9: _T, a10: _I, a11: _I, a12: _T, /
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) -> None: ... # v iiiiIITIIT*I*I*T
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def bsr_gt_bsr(
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a0: _i, a1: _i, a2: _i, a3: _i, a4: _I, a5: _I, a6: _T, a7: _I, a8: _I, a9: _T, a10: _I, a11: _I, a12: _T, /
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) -> None: ... # v iiiiIITIIT*I*I*T
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def bsr_le_bsr(
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a0: _i, a1: _i, a2: _i, a3: _i, a4: _I, a5: _I, a6: _T, a7: _I, a8: _I, a9: _T, a10: _I, a11: _I, a12: _T, /
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) -> None: ... # v iiiiIITIIT*I*I*T
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def bsr_ge_bsr(
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a0: _i, a1: _i, a2: _i, a3: _i, a4: _I, a5: _I, a6: _T, a7: _I, a8: _I, a9: _T, a10: _I, a11: _I, a12: _T, /
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) -> None: ... # v iiiiIITIIT*I*I*T
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# csc.h
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def csc_matvec(a0: _i, a1: _i, a2: _I, a3: _I, a4: _T, a5: _T, a6: _T, /) -> None: ... # v iiIITT*T
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def csc_matvecs(a0: _i, a1: _i, a2: _i, a3: _I, a4: _I, a5: _T, a6: _T, a7: _T, /) -> None: ... # v iiiIITT*T
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# csr.h
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def csr_matmat_maxnnz(a0: _i, a1: _i, a2: _I, a3: _I, a4: _I, a5: _I, /) -> np.int64: ... # l iiIIII
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def csr_matmat(
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a0: _i, a1: _i, a2: _I, a3: _I, a4: _T, a5: _I, a6: _I, a7: _T, a8: _I, a9: _I, a10: _T, /
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) -> None: ... # v iiIITIIT*I*I*T
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def csr_diagonal(a0: _i, a1: _i, a2: _i, a3: _I, a4: _I, a5: _T, a6: _T, /) -> None: ... # v iiiIIT*T
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def csr_tocsc(a0: _i, a1: _i, a2: _I, a3: _I, a4: _T, a5: _I, a6: _I, a7: _T, /) -> None: ... # v iiIIT*I*I*T
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def csr_tobsr(a0: _i, a1: _i, a2: _i, a3: _i, a4: _I, a5: _I, a6: _T, a7: _I, a8: _I, a9: _T, /) -> None: ... # v iiiiIIT*I*I*T
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def csr_todense(a0: _i, a1: _i, a2: _I, a3: _I, a4: _I, a5: _T, /) -> None: ... # v iiIIT*T
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def csr_matvec(a0: _i, a1: _i, a2: _I, a3: _I, a4: _T, a5: _T, a6: _T, /) -> None: ... # v iiIITT*T
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def csr_matvecs(a0: _i, a1: _i, a2: _i, a3: _I, a4: _I, a5: _T, a6: _T, a7: _T, /) -> None: ... # v iiiIITT*T
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def csr_elmul_csr(
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a0: _i, a1: _i, a2: _I, a3: _I, a4: _T, a5: _I, a6: _I, a7: _T, a8: _I, a9: _I, a10: _T, /
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) -> None: ... # v iiIITIIT*I*I*T
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def csr_eldiv_csr(
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a0: _i, a1: _i, a2: _I, a3: _I, a4: _T, a5: _I, a6: _I, a7: _T, a8: _I, a9: _I, a10: _T, /
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) -> None: ... # v iiIITIIT*I*I*T
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def csr_plus_csr(
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a0: _i, a1: _i, a2: _I, a3: _I, a4: _T, a5: _I, a6: _I, a7: _T, a8: _I, a9: _I, a10: _T, /
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) -> None: ... # v iiIITIIT*I*I*T
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def csr_minus_csr(
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a0: _i, a1: _i, a2: _I, a3: _I, a4: _T, a5: _I, a6: _I, a7: _T, a8: _I, a9: _I, a10: _T, /
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) -> None: ... # v iiIITIIT*I*I*T
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def csr_maximum_csr(
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a0: _i, a1: _i, a2: _I, a3: _I, a4: _T, a5: _I, a6: _I, a7: _T, a8: _I, a9: _I, a10: _T, /
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) -> None: ... # v iiIITIIT*I*I*T
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def csr_minimum_csr(
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a0: _i, a1: _i, a2: _I, a3: _I, a4: _T, a5: _I, a6: _I, a7: _T, a8: _I, a9: _I, a10: _T, /
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) -> None: ... # v iiIITIIT*I*I*T
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def csr_ne_csr(
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a0: _i, a1: _i, a2: _I, a3: _I, a4: _T, a5: _I, a6: _I, a7: _T, a8: _I, a9: _I, a10: _T, /
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) -> None: ... # v iiIITIIT*I*I*T
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def csr_lt_csr(
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a0: _i, a1: _i, a2: _I, a3: _I, a4: _T, a5: _I, a6: _I, a7: _T, a8: _I, a9: _I, a10: _T, /
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) -> None: ... # v iiIITIIT*I*I*T
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def csr_gt_csr(
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a0: _i, a1: _i, a2: _I, a3: _I, a4: _T, a5: _I, a6: _I, a7: _T, a8: _I, a9: _I, a10: _T, /
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) -> None: ... # v iiIITIIT*I*I*T
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def csr_le_csr(
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a0: _i, a1: _i, a2: _I, a3: _I, a4: _T, a5: _I, a6: _I, a7: _T, a8: _I, a9: _I, a10: _T, /
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) -> None: ... # v iiIITIIT*I*I*T
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def csr_ge_csr(
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a0: _i, a1: _i, a2: _I, a3: _I, a4: _T, a5: _I, a6: _I, a7: _T, a8: _I, a9: _I, a10: _T, /
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) -> None: ... # v iiIITIIT*I*I*T
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def csr_scale_rows(a0: _i, a1: _i, a2: _I, a3: _I, a4: _T, a5: _T, /) -> None: ... # v iiII*TT
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def csr_scale_columns(a0: _i, a1: _i, a2: _I, a3: _I, a4: _T, a5: _T, /) -> None: ... # v iiII*TT
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def csr_sort_indices(a0: _i, a1: _I, a2: _I, a3: _T, /) -> None: ... # v iI*I*T
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def csr_eliminate_zeros(a0: _i, a1: _i, a2: _I, a3: _I, a4: _T, /) -> None: ... # v ii*I*I*T
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def csr_sum_duplicates(a0: _i, a1: _i, a2: _I, a3: _I, a4: _T, /) -> None: ... # v ii*I*I*T
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def get_csr_submatrix(
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a0: _i, a1: _i, a2: _I, a3: _I, a4: _T, a5: _i, a6: _i, a7: _i, a8: _i, a9: _V, a10: _V, a11: _W, /
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) -> None: ... # v iiIITiiii*V*V*W
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def csr_row_index(a0: _i, a1: _I, a2: _I, a3: _I, a4: _T, a5: _I, a6: _T, /) -> None: ... # v iIIIT*I*T
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def csr_row_slice(a0: _i, a1: _i, a2: _i, a3: _I, a4: _I, a5: _T, a6: _I, a7: _T, /) -> None: ... # v iiiIIT*I*T
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def csr_column_index1(a0: _i, a1: _i, a2: _i, a3: _I, a4: _I, a5: _T, a6: _I, a7: _T, /) -> None: ... # v iiiIIT*I*T
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def csr_column_index2(a0: _I, a1: _I, a2: _i, a3: _I, a4: _T, a5: _I, a6: _T, /) -> None: ... # v IIiIT*I*T
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def csr_sample_values(a0: _i, a1: _i, a2: _I, a3: _I, a4: _T, a5: _i, a6: _I, a7: _I, a8: _T, /) -> None: ... # v iiIITiII*T
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def csr_count_blocks(a0: _i, a1: _i, a2: _i, a3: _i, a4: _I, a5: _I, /) -> int: ... # i iiiiII
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def csr_sample_offsets(a0: _i, a1: _i, a2: _I, a3: _I, a4: _i, a5: _I, a6: _I, a7: _I, /) -> int: ... # i iiIIiII*I
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def csr_hstack(a0: _i, a1: _i, a2: _I, a3: _I, a4: _I, a5: _T, a6: _I, a7: _I, a8: _T, /) -> None: ... # v iiIIIT*I*I*T
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def csr_has_sorted_indices(a0: _i, a1: _I, a2: _I, /) -> int: ... # i iII
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def csr_has_canonical_format(a0: _i, a1: _I, a2: _I, /) -> int: ... # i iII
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def expandptr(a0: _i, a1: _I, a2: _I, /) -> None: ... # v iI*I
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def test_throw_error() -> Never: ... # i # raises `MemoryError` if called
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# coo.h
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def coo_tocsr(a0: _i, a1: _i, a2: _i, a3: _I, a4: _I, a5: _T, a6: _I, a7: _I, a8: _T, /) -> None: ... # v iiiIIT*I*I*T
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def coo_todense(a0: _i, a1: _i, a2: _l, a3: _I, a4: _I, a5: _T, a6: _T, a7: _i, /) -> None: ... # v iilIIT*Ti
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def coo_todense_nd(a0: _I, a1: _l, a2: _l, a3: _I, a4: _T, a5: _T, a6: _i, /) -> None: ... # v IllIT*Ti
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def coo_matvec(a0: _l, a1: _I, a2: _I, a3: _T, a4: _T, a5: _T, /) -> None: ... # v lIITT*T
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def coo_matvec_nd(a0: _l, a1: _l, a2: _I, a3: _I, a4: _T, a5: _T, a6: _T, /) -> None: ... # v llIITT*T
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def coo_matmat_dense(a0: _l, a1: _l, a2: _I, a3: _I, a4: _T, a5: _T, a6: _T, /) -> None: ... # v llIITT*T
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def coo_matmat_dense_nd(a0: _l, a1: _l, a2: _l, a3: _I, a4: _I, a5: _I, a6: _T, a7: _T, a8: _T, /) -> None: ... # v lllIIITT*T
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# dia.h
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def dia_matmat(
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a0: _i, a1: _i, a2: _i, a3: _i, a4: _I, a5: _T, a6: _i, a7: _i, a8: _i, a9: _I, a10: _T, a11: _V, a12: _W, /
161+
) -> None: ... # v iiiiITiiiIT*V*W
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def dia_matvec(a0: _i, a1: _i, a2: _i, a3: _i, a4: _T, a5: _T, a6: _T, a7: _T, /) -> None: ... # v iiiiITT*T
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def dia_matvecs(a0: _i, a1: _i, a2: _i, a3: _i, a4: _I, a5: _T, a6: _i, a7: _T, a8: _T, /) -> None: ... # v iiiiITiT*T
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# csgraph.h
166+
def cs_graph_components(a0: _i, a1: _I, a2: _I, a3: _I, /) -> int: ... # i iII*I

‎scipy-stubs/sparse/linalg/_dsolve/_add_newdocs.pyi

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# nothing to see here

‎scipy-stubs/sparse/linalg/_svdp.pyi

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from _typeshed import Incomplete
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from typing import Any, Generic, Literal
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from typing_extensions import TypeVar
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import numpy as np
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import optype.numpy as onp
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import optype.numpy.compat as npc
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from ._interface import LinearOperator
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from scipy.sparse._base import _spbase
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__all__ = ["_svdp"]
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_NumberT = TypeVar("_NumberT", bound=npc.number)
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_NumberT_co = TypeVar("_NumberT_co", bound=npc.number, default=np.float64 | Any, covariant=True)
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class _AProd(Generic[_NumberT_co]):
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A: LinearOperator[_NumberT_co]
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def __init__(self, /, A: onp.ToArray2D[_NumberT_co] | _spbase[_NumberT_co, tuple[int, int]]) -> None: ...
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def __call__(
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self,
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/,
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transa: str,
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m: int,
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n: int,
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x: onp.Array1D[npc.number],
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y: onp.Array1D[npc.number],
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sparm: object, # unused
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iparm: object, # unused
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) -> None: ...
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@property
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def shape(self) -> tuple[int, int]: ...
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@property
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def dtype(self) -> np.dtype[_NumberT_co]: ...
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def _svdp(
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A: onp.ToArray2D[_NumberT] | _spbase[_NumberT, tuple[int, int]] | LinearOperator[_NumberT],
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k: int,
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which: Literal["LM", "SM"] = "LM",
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irl_mode: bool = True,
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kmax: int | None = None,
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compute_u: bool = True,
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compute_v: bool = True,
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v0: onp.ToFloatND | None = None,
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full_output: bool = False,
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tol: int = 0,
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delta: float | None = None,
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eta: float | None = None,
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anorm: int = 0,
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cgs: bool = False,
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elr: bool = True,
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min_relgap: float = 0.002,
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shifts: int | None = None,
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maxiter: int | None = None,
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rng: onp.random.ToRNG | None = None,
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) -> Incomplete: ... # complicated -- 8 different return types depending on parameters

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