FM / lib

/opt/imh-python/lib/python3.9/site-packages/numpy/lib/

/opt/imh-python/lib/python3.9/site-packages/numpy/lib

Name	Size	Mode	Actions
tests/	-	0755	rm
__pycache__/	-	0755	rm
arraypad.py	31803	0644	edit dl rm
arraypad.pyi	1728	0644	edit dl rm
arraysetops.py	33655	0644	edit dl rm
arraysetops.pyi	8372	0644	edit dl rm
arrayterator.py	7063	0644	edit dl rm
arrayterator.pyi	1537	0644	edit dl rm
format.py	34769	0644	edit dl rm
format.pyi	748	0644	edit dl rm
function_base.py	189103	0644	edit dl rm
function_base.pyi	16585	0644	edit dl rm
histograms.py	37778	0644	edit dl rm
histograms.pyi	995	0644	edit dl rm
index_tricks.py	31346	0644	edit dl rm
index_tricks.pyi	4251	0644	edit dl rm
mixins.py	7071	0644	edit dl rm
mixins.pyi	3117	0644	edit dl rm
nanfunctions.py	65775	0644	edit dl rm
nanfunctions.pyi	606	0644	edit dl rm
npyio.py	97316	0644	edit dl rm
npyio.pyi	9728	0644	edit dl rm
polynomial.py	44133	0644	edit dl rm
polynomial.pyi	6958	0644	edit dl rm
recfunctions.py	59423	0644	edit dl rm
scimath.py	15037	0644	edit dl rm
scimath.pyi	2883	0644	edit dl rm
setup.py	405	0644	edit dl rm
shape_base.py	38947	0644	edit dl rm
shape_base.pyi	5341	0644	edit dl rm
stride_tricks.py	17911	0644	edit dl rm
stride_tricks.pyi	1747	0644	edit dl rm
twodim_base.py	32947	0644	edit dl rm
twodim_base.pyi	5370	0644	edit dl rm
type_check.py	19954	0644	edit dl rm
type_check.pyi	5571	0644	edit dl rm
ufunclike.py	6325	0644	edit dl rm
ufunclike.pyi	1293	0644	edit dl rm
user_array.py	7721	0644	edit dl rm
utils.py	37804	0644	edit dl rm
utils.pyi	2360	0644	edit dl rm
_datasource.py	22631	0644	edit dl rm
_iotools.py	30868	0644	edit dl rm
_version.py	4855	0644	edit dl rm
_version.pyi	633	0644	edit dl rm
__init__.py	2713	0644	edit dl rm
__init__.pyi	5596	0644	edit dl rm

Edit: /opt/imh-python/lib/python3.9/site-packages/numpy/lib/function_base.pyi (16585B)

import sys
from collections.abc import Sequence, Iterator, Callable, Iterable
from typing import (
    Literal as L,
    Any,
    TypeVar,
    overload,
    Protocol,
    SupportsIndex,
    SupportsInt,
)

if sys.version_info >= (3, 10):
    from typing import TypeGuard
else:
    from typing_extensions import TypeGuard

from numpy import (
    vectorize as vectorize,
    ufunc,
    generic,
    floating,
    complexfloating,
    intp,
    float64,
    complex128,
    timedelta64,
    datetime64,
    object_,
    _OrderKACF,
)

from numpy._typing import (
    NDArray,
    ArrayLike,
    DTypeLike,
    _ShapeLike,
    _ScalarLike_co,
    _DTypeLike,
    _ArrayLike,
    _ArrayLikeInt_co,
    _ArrayLikeFloat_co,
    _ArrayLikeComplex_co,
    _ArrayLikeTD64_co,
    _ArrayLikeDT64_co,
    _ArrayLikeObject_co,
    _FloatLike_co,
    _ComplexLike_co,
)

from numpy.core.function_base import (
    add_newdoc as add_newdoc,
)

from numpy.core.multiarray import (
    add_docstring as add_docstring,
    bincount as bincount,
)

from numpy.core.umath import _add_newdoc_ufunc

_T = TypeVar("_T")
_T_co = TypeVar("_T_co", covariant=True)
_SCT = TypeVar("_SCT", bound=generic)
_ArrayType = TypeVar("_ArrayType", bound=NDArray[Any])

_2Tuple = tuple[_T, _T]

class _TrimZerosSequence(Protocol[_T_co]):
    def __len__(self) -> int: ...
    def __getitem__(self, key: slice, /) -> _T_co: ...
    def __iter__(self) -> Iterator[Any]: ...

class _SupportsWriteFlush(Protocol):
    def write(self, s: str, /) -> object: ...
    def flush(self) -> object: ...

__all__: list[str]

# NOTE: This is in reality a re-export of `np.core.umath._add_newdoc_ufunc`
def add_newdoc_ufunc(ufunc: ufunc, new_docstring: str, /) -> None: ...

@overload
def rot90(
    m: _ArrayLike[_SCT],
    k: int = ...,
    axes: tuple[int, int] = ...,
) -> NDArray[_SCT]: ...
@overload
def rot90(
    m: ArrayLike,
    k: int = ...,
    axes: tuple[int, int] = ...,
) -> NDArray[Any]: ...

@overload
def flip(m: _SCT, axis: None = ...) -> _SCT: ...
@overload
def flip(m: _ScalarLike_co, axis: None = ...) -> Any: ...
@overload
def flip(m: _ArrayLike[_SCT], axis: None | _ShapeLike = ...) -> NDArray[_SCT]: ...
@overload
def flip(m: ArrayLike, axis: None | _ShapeLike = ...) -> NDArray[Any]: ...

def iterable(y: object) -> TypeGuard[Iterable[Any]]: ...

@overload
def average(
    a: _ArrayLikeFloat_co,
    axis: None = ...,
    weights: None | _ArrayLikeFloat_co= ...,
    returned: L[False] = ...,
    keepdims: L[False] = ...,
) -> floating[Any]: ...
@overload
def average(
    a: _ArrayLikeComplex_co,
    axis: None = ...,
    weights: None | _ArrayLikeComplex_co = ...,
    returned: L[False] = ...,
    keepdims: L[False] = ...,
) -> complexfloating[Any, Any]: ...
@overload
def average(
    a: _ArrayLikeObject_co,
    axis: None = ...,
    weights: None | Any = ...,
    returned: L[False] = ...,
    keepdims: L[False] = ...,
) -> Any: ...
@overload
def average(
    a: _ArrayLikeFloat_co,
    axis: None = ...,
    weights: None | _ArrayLikeFloat_co= ...,
    returned: L[True] = ...,
    keepdims: L[False] = ...,
) -> _2Tuple[floating[Any]]: ...
@overload
def average(
    a: _ArrayLikeComplex_co,
    axis: None = ...,
    weights: None | _ArrayLikeComplex_co = ...,
    returned: L[True] = ...,
    keepdims: L[False] = ...,
) -> _2Tuple[complexfloating[Any, Any]]: ...
@overload
def average(
    a: _ArrayLikeObject_co,
    axis: None = ...,
    weights: None | Any = ...,
    returned: L[True] = ...,
    keepdims: L[False] = ...,
) -> _2Tuple[Any]: ...
@overload
def average(
    a: _ArrayLikeComplex_co | _ArrayLikeObject_co,
    axis: None | _ShapeLike = ...,
    weights: None | Any = ...,
    returned: L[False] = ...,
    keepdims: bool = ...,
) -> Any: ...
@overload
def average(
    a: _ArrayLikeComplex_co | _ArrayLikeObject_co,
    axis: None | _ShapeLike = ...,
    weights: None | Any = ...,
    returned: L[True] = ...,
    keepdims: bool = ...,
) -> _2Tuple[Any]: ...

@overload
def asarray_chkfinite(
    a: _ArrayLike[_SCT],
    dtype: None = ...,
    order: _OrderKACF = ...,
) -> NDArray[_SCT]: ...
@overload
def asarray_chkfinite(
    a: object,
    dtype: None = ...,
    order: _OrderKACF = ...,
) -> NDArray[Any]: ...
@overload
def asarray_chkfinite(
    a: Any,
    dtype: _DTypeLike[_SCT],
    order: _OrderKACF = ...,
) -> NDArray[_SCT]: ...
@overload
def asarray_chkfinite(
    a: Any,
    dtype: DTypeLike,
    order: _OrderKACF = ...,
) -> NDArray[Any]: ...

# TODO: Use PEP 612 `ParamSpec` once mypy supports `Concatenate`
# xref python/mypy#8645
@overload
def piecewise(
    x: _ArrayLike[_SCT],
    condlist: ArrayLike,
    funclist: Sequence[Any | Callable[..., Any]],
    *args: Any,
    **kw: Any,
) -> NDArray[_SCT]: ...
@overload
def piecewise(
    x: ArrayLike,
    condlist: ArrayLike,
    funclist: Sequence[Any | Callable[..., Any]],
    *args: Any,
    **kw: Any,
) -> NDArray[Any]: ...

def select(
    condlist: Sequence[ArrayLike],
    choicelist: Sequence[ArrayLike],
    default: ArrayLike = ...,
) -> NDArray[Any]: ...

@overload
def copy(
    a: _ArrayType,
    order: _OrderKACF,
    subok: L[True],
) -> _ArrayType: ...
@overload
def copy(
    a: _ArrayType,
    order: _OrderKACF = ...,
    *,
    subok: L[True],
) -> _ArrayType: ...
@overload
def copy(
    a: _ArrayLike[_SCT],
    order: _OrderKACF = ...,
    subok: L[False] = ...,
) -> NDArray[_SCT]: ...
@overload
def copy(
    a: ArrayLike,
    order: _OrderKACF = ...,
    subok: L[False] = ...,
) -> NDArray[Any]: ...

def gradient(
    f: ArrayLike,
    *varargs: ArrayLike,
    axis: None | _ShapeLike = ...,
    edge_order: L[1, 2] = ...,
) -> Any: ...

@overload
def diff(
    a: _T,
    n: L[0],
    axis: SupportsIndex = ...,
    prepend: ArrayLike = ...,
    append: ArrayLike = ...,
) -> _T: ...
@overload
def diff(
    a: ArrayLike,
    n: int = ...,
    axis: SupportsIndex = ...,
    prepend: ArrayLike = ...,
    append: ArrayLike = ...,
) -> NDArray[Any]: ...

@overload
def interp(
    x: _ArrayLikeFloat_co,
    xp: _ArrayLikeFloat_co,
    fp: _ArrayLikeFloat_co,
    left: None | _FloatLike_co = ...,
    right: None | _FloatLike_co = ...,
    period: None | _FloatLike_co = ...,
) -> NDArray[float64]: ...
@overload
def interp(
    x: _ArrayLikeFloat_co,
    xp: _ArrayLikeFloat_co,
    fp: _ArrayLikeComplex_co,
    left: None | _ComplexLike_co = ...,
    right: None | _ComplexLike_co = ...,
    period: None | _FloatLike_co = ...,
) -> NDArray[complex128]: ...

@overload
def angle(z: _ComplexLike_co, deg: bool = ...) -> floating[Any]: ...
@overload
def angle(z: object_, deg: bool = ...) -> Any: ...
@overload
def angle(z: _ArrayLikeComplex_co, deg: bool = ...) -> NDArray[floating[Any]]: ...
@overload
def angle(z: _ArrayLikeObject_co, deg: bool = ...) -> NDArray[object_]: ...

@overload
def unwrap(
    p: _ArrayLikeFloat_co,
    discont: None | float = ...,
    axis: int = ...,
    *,
    period: float = ...,
) -> NDArray[floating[Any]]: ...
@overload
def unwrap(
    p: _ArrayLikeObject_co,
    discont: None | float = ...,
    axis: int = ...,
    *,
    period: float = ...,
) -> NDArray[object_]: ...

def sort_complex(a: ArrayLike) -> NDArray[complexfloating[Any, Any]]: ...

def trim_zeros(
    filt: _TrimZerosSequence[_T],
    trim: L["f", "b", "fb", "bf"] = ...,
) -> _T: ...

@overload
def extract(condition: ArrayLike, arr: _ArrayLike[_SCT]) -> NDArray[_SCT]: ...
@overload
def extract(condition: ArrayLike, arr: ArrayLike) -> NDArray[Any]: ...

def place(arr: NDArray[Any], mask: ArrayLike, vals: Any) -> None: ...

def disp(
    mesg: object,
    device: None | _SupportsWriteFlush = ...,
    linefeed: bool = ...,
) -> None: ...

@overload
def cov(
    m: _ArrayLikeFloat_co,
    y: None | _ArrayLikeFloat_co = ...,
    rowvar: bool = ...,
    bias: bool = ...,
    ddof: None | SupportsIndex | SupportsInt = ...,
    fweights: None | ArrayLike = ...,
    aweights: None | ArrayLike = ...,
    *,
    dtype: None = ...,
) -> NDArray[floating[Any]]: ...
@overload
def cov(
    m: _ArrayLikeComplex_co,
    y: None | _ArrayLikeComplex_co = ...,
    rowvar: bool = ...,
    bias: bool = ...,
    ddof: None | SupportsIndex | SupportsInt = ...,
    fweights: None | ArrayLike = ...,
    aweights: None | ArrayLike = ...,
    *,
    dtype: None = ...,
) -> NDArray[complexfloating[Any, Any]]: ...
@overload
def cov(
    m: _ArrayLikeComplex_co,
    y: None | _ArrayLikeComplex_co = ...,
    rowvar: bool = ...,
    bias: bool = ...,
    ddof: None | SupportsIndex | SupportsInt = ...,
    fweights: None | ArrayLike = ...,
    aweights: None | ArrayLike = ...,
    *,
    dtype: _DTypeLike[_SCT],
) -> NDArray[_SCT]: ...
@overload
def cov(
    m: _ArrayLikeComplex_co,
    y: None | _ArrayLikeComplex_co = ...,
    rowvar: bool = ...,
    bias: bool = ...,
    ddof: None | SupportsIndex | SupportsInt = ...,
    fweights: None | ArrayLike = ...,
    aweights: None | ArrayLike = ...,
    *,
    dtype: DTypeLike,
) -> NDArray[Any]: ...

# NOTE `bias` and `ddof` have been deprecated
@overload
def corrcoef(
    m: _ArrayLikeFloat_co,
    y: None | _ArrayLikeFloat_co = ...,
    rowvar: bool = ...,
    *,
    dtype: None = ...,
) -> NDArray[floating[Any]]: ...
@overload
def corrcoef(
    m: _ArrayLikeComplex_co,
    y: None | _ArrayLikeComplex_co = ...,
    rowvar: bool = ...,
    *,
    dtype: None = ...,
) -> NDArray[complexfloating[Any, Any]]: ...
@overload
def corrcoef(
    m: _ArrayLikeComplex_co,
    y: None | _ArrayLikeComplex_co = ...,
    rowvar: bool = ...,
    *,
    dtype: _DTypeLike[_SCT],
) -> NDArray[_SCT]: ...
@overload
def corrcoef(
    m: _ArrayLikeComplex_co,
    y: None | _ArrayLikeComplex_co = ...,
    rowvar: bool = ...,
    *,
    dtype: DTypeLike,
) -> NDArray[Any]: ...

def blackman(M: _FloatLike_co) -> NDArray[floating[Any]]: ...

def bartlett(M: _FloatLike_co) -> NDArray[floating[Any]]: ...

def hanning(M: _FloatLike_co) -> NDArray[floating[Any]]: ...

def hamming(M: _FloatLike_co) -> NDArray[floating[Any]]: ...

def i0(x: _ArrayLikeFloat_co) -> NDArray[floating[Any]]: ...

def kaiser(
    M: _FloatLike_co,
    beta: _FloatLike_co,
) -> NDArray[floating[Any]]: ...

@overload
def sinc(x: _FloatLike_co) -> floating[Any]: ...
@overload
def sinc(x: _ComplexLike_co) -> complexfloating[Any, Any]: ...
@overload
def sinc(x: _ArrayLikeFloat_co) -> NDArray[floating[Any]]: ...
@overload
def sinc(x: _ArrayLikeComplex_co) -> NDArray[complexfloating[Any, Any]]: ...

# NOTE: Deprecated
# def msort(a: ArrayLike) -> NDArray[Any]: ...

@overload
def median(
    a: _ArrayLikeFloat_co,
    axis: None = ...,
    out: None = ...,
    overwrite_input: bool = ...,
    keepdims: L[False] = ...,
) -> floating[Any]: ...
@overload
def median(
    a: _ArrayLikeComplex_co,
    axis: None = ...,
    out: None = ...,
    overwrite_input: bool = ...,
    keepdims: L[False] = ...,
) -> complexfloating[Any, Any]: ...
@overload
def median(
    a: _ArrayLikeTD64_co,
    axis: None = ...,
    out: None = ...,
    overwrite_input: bool = ...,
    keepdims: L[False] = ...,
) -> timedelta64: ...
@overload
def median(
    a: _ArrayLikeObject_co,
    axis: None = ...,
    out: None = ...,
    overwrite_input: bool = ...,
    keepdims: L[False] = ...,
) -> Any: ...
@overload
def median(
    a: _ArrayLikeFloat_co | _ArrayLikeComplex_co | _ArrayLikeTD64_co | _ArrayLikeObject_co,
    axis: None | _ShapeLike = ...,
    out: None = ...,
    overwrite_input: bool = ...,
    keepdims: bool = ...,
) -> Any: ...
@overload
def median(
    a: _ArrayLikeFloat_co | _ArrayLikeComplex_co | _ArrayLikeTD64_co | _ArrayLikeObject_co,
    axis: None | _ShapeLike = ...,
    out: _ArrayType = ...,
    overwrite_input: bool = ...,
    keepdims: bool = ...,
) -> _ArrayType: ...

_MethodKind = L[
    "inverted_cdf",
    "averaged_inverted_cdf",
    "closest_observation",
    "interpolated_inverted_cdf",
    "hazen",
    "weibull",
    "linear",
    "median_unbiased",
    "normal_unbiased",
    "lower",
    "higher",
    "midpoint",
    "nearest",
]

@overload
def percentile(
    a: _ArrayLikeFloat_co,
    q: _FloatLike_co,
    axis: None = ...,
    out: None = ...,
    overwrite_input: bool = ...,
    method: _MethodKind = ...,
    keepdims: L[False] = ...,
) -> floating[Any]: ...
@overload
def percentile(
    a: _ArrayLikeComplex_co,
    q: _FloatLike_co,
    axis: None = ...,
    out: None = ...,
    overwrite_input: bool = ...,
    method: _MethodKind = ...,
    keepdims: L[False] = ...,
) -> complexfloating[Any, Any]: ...
@overload
def percentile(
    a: _ArrayLikeTD64_co,
    q: _FloatLike_co,
    axis: None = ...,
    out: None = ...,
    overwrite_input: bool = ...,
    method: _MethodKind = ...,
    keepdims: L[False] = ...,
) -> timedelta64: ...
@overload
def percentile(
    a: _ArrayLikeDT64_co,
    q: _FloatLike_co,
    axis: None = ...,
    out: None = ...,
    overwrite_input: bool = ...,
    method: _MethodKind = ...,
    keepdims: L[False] = ...,
) -> datetime64: ...
@overload
def percentile(
    a: _ArrayLikeObject_co,
    q: _FloatLike_co,
    axis: None = ...,
    out: None = ...,
    overwrite_input: bool = ...,
    method: _MethodKind = ...,
    keepdims: L[False] = ...,
) -> Any: ...
@overload
def percentile(
    a: _ArrayLikeFloat_co,
    q: _ArrayLikeFloat_co,
    axis: None = ...,
    out: None = ...,
    overwrite_input: bool = ...,
    method: _MethodKind = ...,
    keepdims: L[False] = ...,
) -> NDArray[floating[Any]]: ...
@overload
def percentile(
    a: _ArrayLikeComplex_co,
    q: _ArrayLikeFloat_co,
    axis: None = ...,
    out: None = ...,
    overwrite_input: bool = ...,
    method: _MethodKind = ...,
    keepdims: L[False] = ...,
) -> NDArray[complexfloating[Any, Any]]: ...
@overload
def percentile(
    a: _ArrayLikeTD64_co,
    q: _ArrayLikeFloat_co,
    axis: None = ...,
    out: None = ...,
    overwrite_input: bool = ...,
    method: _MethodKind = ...,
    keepdims: L[False] = ...,
) -> NDArray[timedelta64]: ...
@overload
def percentile(
    a: _ArrayLikeDT64_co,
    q: _ArrayLikeFloat_co,
    axis: None = ...,
    out: None = ...,
    overwrite_input: bool = ...,
    method: _MethodKind = ...,
    keepdims: L[False] = ...,
) -> NDArray[datetime64]: ...
@overload
def percentile(
    a: _ArrayLikeObject_co,
    q: _ArrayLikeFloat_co,
    axis: None = ...,
    out: None = ...,
    overwrite_input: bool = ...,
    method: _MethodKind = ...,
    keepdims: L[False] = ...,
) -> NDArray[object_]: ...
@overload
def percentile(
    a: _ArrayLikeComplex_co | _ArrayLikeTD64_co | _ArrayLikeTD64_co | _ArrayLikeObject_co,
    q: _ArrayLikeFloat_co,
    axis: None | _ShapeLike = ...,
    out: None = ...,
    overwrite_input: bool = ...,
    method: _MethodKind = ...,
    keepdims: bool = ...,
) -> Any: ...
@overload
def percentile(
    a: _ArrayLikeComplex_co | _ArrayLikeTD64_co | _ArrayLikeTD64_co | _ArrayLikeObject_co,
    q: _ArrayLikeFloat_co,
    axis: None | _ShapeLike = ...,
    out: _ArrayType = ...,
    overwrite_input: bool = ...,
    method: _MethodKind = ...,
    keepdims: bool = ...,
) -> _ArrayType: ...

# NOTE: Not an alias, but they do have identical signatures
# (that we can reuse)
quantile = percentile

# TODO: Returns a scalar for <= 1D array-likes; returns an ndarray otherwise
def trapz(
    y: _ArrayLikeComplex_co | _ArrayLikeTD64_co | _ArrayLikeObject_co,
    x: None | _ArrayLikeComplex_co | _ArrayLikeTD64_co | _ArrayLikeObject_co = ...,
    dx: float = ...,
    axis: SupportsIndex = ...,
) -> Any: ...

def meshgrid(
    *xi: ArrayLike,
    copy: bool = ...,
    sparse: bool = ...,
    indexing: L["xy", "ij"] = ...,
) -> list[NDArray[Any]]: ...

@overload
def delete(
    arr: _ArrayLike[_SCT],
    obj: slice | _ArrayLikeInt_co,
    axis: None | SupportsIndex = ...,
) -> NDArray[_SCT]: ...
@overload
def delete(
    arr: ArrayLike,
    obj: slice | _ArrayLikeInt_co,
    axis: None | SupportsIndex = ...,
) -> NDArray[Any]: ...

@overload
def insert(
    arr: _ArrayLike[_SCT],
    obj: slice | _ArrayLikeInt_co,
    values: ArrayLike,
    axis: None | SupportsIndex = ...,
) -> NDArray[_SCT]: ...
@overload
def insert(
    arr: ArrayLike,
    obj: slice | _ArrayLikeInt_co,
    values: ArrayLike,
    axis: None | SupportsIndex = ...,
) -> NDArray[Any]: ...

def append(
    arr: ArrayLike,
    values: ArrayLike,
    axis: None | SupportsIndex = ...,
) -> NDArray[Any]: ...

@overload
def digitize(
    x: _FloatLike_co,
    bins: _ArrayLikeFloat_co,
    right: bool = ...,
) -> intp: ...
@overload
def digitize(
    x: _ArrayLikeFloat_co,
    bins: _ArrayLikeFloat_co,
    right: bool = ...,
) -> NDArray[intp]: ...

cmd: