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96c9abb058
* Always use `_typeshed.Self`, where applicable * Revert changes to `google-cloud-ndb` (ambiguous) * Remove empty line added by script * Revert changes to `stubs/python-dateutil/dateutil/relativedelta.pyi` * Manually add a few more that the script missed * Improve `filelock` annotation Source code here:79ec7b2826/src/filelock/_api.py (L207)* Improve `opentracing/scope` annotation Source code here:3e1d357a34/opentracing/scope.py (L71)* Improve `redis/client` stub Source code here:15f315a496/redis/client.py (L1217)* Improve `redis/lock` annotation Source code here:15f315a496/redis/lock.py (L155)* Improve `requests/models` annotation Source code here:d718e75383/requests/models.py (L653)
87 lines
3.7 KiB
Python
87 lines
3.7 KiB
Python
import sys
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from _typeshed import Self, SupportsRichComparisonT
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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, NamedTuple, Sequence, SupportsFloat, TypeVar, Union
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# Most functions in this module accept homogeneous collections of one of these types
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_Number = Union[float, Decimal, Fraction]
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_NumberT = TypeVar("_NumberT", float, Decimal, Fraction)
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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[_NumberT]) -> _NumberT: ...
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if sys.version_info >= (3, 10):
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def harmonic_mean(data: Iterable[_NumberT], weights: Iterable[_Number] | None = ...) -> _NumberT: ...
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else:
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def harmonic_mean(data: Iterable[_NumberT]) -> _NumberT: ...
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def median(data: Iterable[_NumberT]) -> _NumberT: ...
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def median_low(data: Iterable[SupportsRichComparisonT]) -> SupportsRichComparisonT: ...
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def median_high(data: Iterable[SupportsRichComparisonT]) -> SupportsRichComparisonT: ...
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def median_grouped(data: Iterable[_NumberT], interval: _NumberT = ...) -> _NumberT: ...
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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[_NumberT], mu: _NumberT | None = ...) -> _NumberT: ...
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def pvariance(data: Iterable[_NumberT], mu: _NumberT | None = ...) -> _NumberT: ...
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if sys.version_info >= (3, 8):
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def quantiles(data: Iterable[_NumberT], *, n: int = ..., method: str = ...) -> list[_NumberT]: ...
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def stdev(data: Iterable[_NumberT], xbar: _NumberT | None = ...) -> _NumberT: ...
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def variance(data: Iterable[_NumberT], xbar: _NumberT | None = ...) -> _NumberT: ...
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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[Self], data: Iterable[SupportsFloat]) -> Self: ...
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def samples(self, n: int, *, seed: Any | None = ...) -> 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: float | NormalDist) -> NormalDist: ...
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def __sub__(self, x2: 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: float | NormalDist) -> NormalDist: ...
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__rmul__ = __mul__
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def __hash__(self) -> int: ...
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if sys.version_info >= (3, 10):
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def correlation(__x: Sequence[_Number], __y: Sequence[_Number]) -> float: ...
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def covariance(__x: Sequence[_Number], __y: Sequence[_Number]) -> float: ...
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class LinearRegression(NamedTuple):
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slope: float
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intercept: float
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def linear_regression(__regressor: Sequence[_Number], __dependent_variable: Sequence[_Number]) -> LinearRegression: ...
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