massive update, probably broken

[dotfiles/.git] / .config / coc / extensions / node_modules / coc-pyright / node_modules / pyright / dist / typeshed-fallback / stdlib / statistics.pyi

diff --git a/.config/coc/extensions/node_modules/coc-pyright/node_modules/pyright/dist/typeshed-fallback/stdlib/statistics.pyi b/.config/coc/extensions/node_modules/coc-pyright/node_modules/pyright/dist/typeshed-fallback/stdlib/statistics.pyi
new file mode 100644 (file)
index 0000000..ec3574a
--- /dev/null
+++ b/.config/coc/extensions/node_modules/coc-pyright/node_modules/pyright/dist/typeshed-fallback/stdlib/statistics.pyi
@@ -0,0 +1,87 @@
+import sys
+from _typeshed import SupportsLessThanT
+from decimal import Decimal
+from fractions import Fraction
+from typing import Any, Hashable, Iterable, NamedTuple, Sequence, SupportsFloat, Type, TypeVar, Union
+
+_T = TypeVar("_T")
+# Most functions in this module accept homogeneous collections of one of these types
+_Number = Union[float, Decimal, Fraction]
+_NumberT = TypeVar("_NumberT", float, Decimal, Fraction)
+
+# Used in mode, multimode
+_HashableT = TypeVar("_HashableT", bound=Hashable)
+
+class StatisticsError(ValueError): ...
+
+if sys.version_info >= (3, 8):
+    def fmean(data: Iterable[SupportsFloat]) -> float: ...
+    def geometric_mean(data: Iterable[SupportsFloat]) -> float: ...
+
+def mean(data: Iterable[_NumberT]) -> _NumberT: ...
+
+if sys.version_info >= (3, 10):
+    def harmonic_mean(data: Iterable[_NumberT], weights: Iterable[_Number] | None = ...) -> _NumberT: ...
+
+else:
+    def harmonic_mean(data: Iterable[_NumberT]) -> _NumberT: ...
+
+def median(data: Iterable[_NumberT]) -> _NumberT: ...
+def median_low(data: Iterable[SupportsLessThanT]) -> SupportsLessThanT: ...
+def median_high(data: Iterable[SupportsLessThanT]) -> SupportsLessThanT: ...
+def median_grouped(data: Iterable[_NumberT], interval: _NumberT = ...) -> _NumberT: ...
+def mode(data: Iterable[_HashableT]) -> _HashableT: ...
+
+if sys.version_info >= (3, 8):
+    def multimode(data: Iterable[_HashableT]) -> list[_HashableT]: ...
+
+def pstdev(data: Iterable[_NumberT], mu: _NumberT | None = ...) -> _NumberT: ...
+def pvariance(data: Iterable[_NumberT], mu: _NumberT | None = ...) -> _NumberT: ...
+
+if sys.version_info >= (3, 8):
+    def quantiles(data: Iterable[_NumberT], *, n: int = ..., method: str = ...) -> list[_NumberT]: ...
+
+def stdev(data: Iterable[_NumberT], xbar: _NumberT | None = ...) -> _NumberT: ...
+def variance(data: Iterable[_NumberT], xbar: _NumberT | None = ...) -> _NumberT: ...
+
+if sys.version_info >= (3, 8):
+    class NormalDist:
+        def __init__(self, mu: float = ..., sigma: float = ...) -> None: ...
+        @property
+        def mean(self) -> float: ...
+        @property
+        def median(self) -> float: ...
+        @property
+        def mode(self) -> float: ...
+        @property
+        def stdev(self) -> float: ...
+        @property
+        def variance(self) -> float: ...
+        @classmethod
+        def from_samples(cls: Type[_T], data: Iterable[SupportsFloat]) -> _T: ...
+        def samples(self, n: int, *, seed: Any | None = ...) -> list[float]: ...
+        def pdf(self, x: float) -> float: ...
+        def cdf(self, x: float) -> float: ...
+        def inv_cdf(self, p: float) -> float: ...
+        def overlap(self, other: NormalDist) -> float: ...
+        def quantiles(self, n: int = ...) -> list[float]: ...
+        if sys.version_info >= (3, 9):
+            def zscore(self, x: float) -> float: ...
+        def __add__(self, x2: float | NormalDist) -> NormalDist: ...
+        def __sub__(self, x2: float | NormalDist) -> NormalDist: ...
+        def __mul__(self, x2: float) -> NormalDist: ...
+        def __truediv__(self, x2: float) -> NormalDist: ...
+        def __pos__(self) -> NormalDist: ...
+        def __neg__(self) -> NormalDist: ...
+        __radd__ = __add__
+        def __rsub__(self, x2: float | NormalDist) -> NormalDist: ...
+        __rmul__ = __mul__
+        def __hash__(self) -> int: ...
+
+if sys.version_info >= (3, 10):
+    def correlation(__x: Sequence[_Number], __y: Sequence[_Number]) -> float: ...
+    def covariance(__x: Sequence[_Number], __y: Sequence[_Number]) -> float: ...
+    class LinearRegression(NamedTuple):
+        slope: float
+        intercept: float
+    def linear_regression(__regressor: Sequence[_Number], __dependent_variable: Sequence[_Number]) -> LinearRegression: ...