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83 lines
3.2 KiB
Python
83 lines
3.2 KiB
Python
# Stubs for statistics
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import sys
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from decimal import Decimal
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from fractions import Fraction
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from typing import Any, Hashable, Iterable, List, Optional, Protocol, SupportsFloat, Type, TypeVar, Union
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_T = TypeVar("_T")
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# Most functions in this module accept homogeneous collections of one of these types
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_Number = TypeVar("_Number", float, Decimal, Fraction)
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# Used in median_high, median_low
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class _Sortable(Protocol):
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def __lt__(self, other) -> bool: ...
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_SortableT = TypeVar("_SortableT", bound=_Sortable)
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# Used in mode, multimode
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_HashableT = TypeVar("_HashableT", bound=Hashable)
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class StatisticsError(ValueError): ...
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if sys.version_info >= (3, 8):
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def fmean(data: Iterable[SupportsFloat]) -> float: ...
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def geometric_mean(data: Iterable[SupportsFloat]) -> float: ...
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def mean(data: Iterable[_Number]) -> _Number: ...
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if sys.version_info >= (3, 6):
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def harmonic_mean(data: Iterable[_Number]) -> _Number: ...
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def median(data: Iterable[_Number]) -> _Number: ...
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def median_low(data: Iterable[_SortableT]) -> _SortableT: ...
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def median_high(data: Iterable[_SortableT]) -> _SortableT: ...
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def median_grouped(data: Iterable[_Number], interval: _Number = ...) -> _Number: ...
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def mode(data: Iterable[_HashableT]) -> _HashableT: ...
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if sys.version_info >= (3, 8):
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def multimode(data: Iterable[_HashableT]) -> List[_HashableT]: ...
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def pstdev(data: Iterable[_Number], mu: Optional[_Number] = ...) -> _Number: ...
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def pvariance(data: Iterable[_Number], mu: Optional[_Number] = ...) -> _Number: ...
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if sys.version_info >= (3, 8):
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def quantiles(data: Iterable[_Number], *, n: int = ..., method: str = ...) -> List[_Number]: ...
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def stdev(data: Iterable[_Number], xbar: Optional[_Number] = ...) -> _Number: ...
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def variance(data: Iterable[_Number], xbar: Optional[_Number] = ...) -> _Number: ...
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if sys.version_info >= (3, 8):
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class NormalDist:
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def __init__(self, mu: float = ..., sigma: float = ...) -> None: ...
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@property
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def mean(self) -> float: ...
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@property
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def median(self) -> float: ...
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@property
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def mode(self) -> float: ...
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@property
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def stdev(self) -> float: ...
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@property
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def variance(self) -> float: ...
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@classmethod
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def from_samples(cls: Type[_T], data: Iterable[SupportsFloat]) -> _T: ...
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def samples(self, n: int, *, seed: Optional[Any] = ...) -> List[float]: ...
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def pdf(self, x: float) -> float: ...
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def cdf(self, x: float) -> float: ...
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def inv_cdf(self, p: float) -> float: ...
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def overlap(self, other: NormalDist) -> float: ...
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def quantiles(self, n: int = ...) -> List[float]: ...
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if sys.version_info >= (3, 9):
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def zscore(self, x: float) -> float: ...
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def __add__(self, x2: Union[float, NormalDist]) -> NormalDist: ...
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def __sub__(self, x2: Union[float, NormalDist]) -> NormalDist: ...
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def __mul__(self, x2: float) -> NormalDist: ...
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def __truediv__(self, x2: float) -> NormalDist: ...
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def __pos__(self) -> NormalDist: ...
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def __neg__(self) -> NormalDist: ...
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__radd__ = __add__
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def __rsub__(self, x2: Union[float, NormalDist]) -> NormalDist: ...
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__rmul__ = __mul__
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def __hash__(self) -> int: ...
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