fix scipy.stats._morestats stubtests

Author: jorenham

scipy-stubs/stats/_morestats.pyi

@@ -1,6 +1,6 @@
 from typing import NamedTuple
 
-from scipy._typing import Untyped
+from scipy._typing import Alternative, NanPolicy, Untyped
 
 __all__ = [
     "anderson",
@@ -35,6 +35,31 @@ __all__ = [
     "yeojohnson_normplot",
 ]
 
+class DirectionalStats:
+    mean_direction: Untyped
+    mean_resultant_length: Untyped
+    def __init__(self, mean_direction, mean_resultant_length) -> None: ...
+
+class ShapiroResult(NamedTuple):
+    statistic: Untyped
+    pvalue: Untyped
+
+class AnsariResult(NamedTuple):
+    statistic: Untyped
+    pvalue: Untyped
+
+class BartlettResult(NamedTuple):
+    statistic: Untyped
+    pvalue: Untyped
+
+class LeveneResult(NamedTuple):
+    statistic: Untyped
+    pvalue: Untyped
+
+class FlignerResult(NamedTuple):
+    statistic: Untyped
+    pvalue: Untyped
+
 class Mean(NamedTuple):
     statistic: Untyped
     minmax: Untyped
@@ -47,10 +72,30 @@ class Std_dev(NamedTuple):
     statistic: Untyped
     minmax: Untyped
 
+# TODO: bunches
+AndersonResult: Untyped
+Anderson_ksampResult: Untyped
+WilcoxonResult: Untyped
+MedianTestResult: Untyped
+
 def bayes_mvs(data, alpha: float = 0.9) -> Untyped: ...
 def mvsdist(data) -> Untyped: ...
-def kstat(data, n: int = 2, *, axis: Untyped | None = None) -> Untyped: ...
-def kstatvar(data, n: int = 2, *, axis: Untyped | None = None) -> Untyped: ...
+def kstat(
+    data,
+    n: int = 2,
+    *,
+    axis: int | None = None,
+    nan_policy: NanPolicy = "propagate",
+    keepdims: bool = False,
+) -> Untyped: ...
+def kstatvar(
+    data,
+    n: int = 2,
+    *,
+    axis: int | None = None,
+    nan_policy: NanPolicy = "propagate",
+    keepdims: bool = False,
+) -> Untyped: ...
 def probplot(
     x,
     sparams=(),
@@ -63,9 +108,6 @@ def ppcc_max(x, brack=(0.0, 1.0), dist: str = "tukeylambda") -> Untyped: ...
 def ppcc_plot(x, a, b, dist: str = "tukeylambda", plot: Untyped | None = None, N: int = 80) -> Untyped: ...
 def boxcox_llf(lmb, data) -> Untyped: ...
 def boxcox(x, lmbda: Untyped | None = None, alpha: Untyped | None = None, optimizer: Untyped | None = None) -> Untyped: ...
-
-class _BigFloat: ...
-
 def boxcox_normmax(
     x,
     brack: Untyped | None = None,
@@ -79,59 +121,44 @@ def yeojohnson(x, lmbda: Untyped | None = None) -> Untyped: ...
 def yeojohnson_llf(lmb, data) -> Untyped: ...
 def yeojohnson_normmax(x, brack: Untyped | None = None) -> Untyped: ...
 def yeojohnson_normplot(x, la, lb, plot: Untyped | None = None, N: int = 80) -> Untyped: ...
-
-class ShapiroResult(NamedTuple):
-    statistic: Untyped
-    pvalue: Untyped
-
-def shapiro(x) -> Untyped: ...
-
-AndersonResult: Untyped
-
+def shapiro(x, *, axis: int | None = None, nan_policy: NanPolicy = "propagate", keepdims: bool = False) -> Untyped: ...
 def anderson(x, dist: str = "norm") -> Untyped: ...
-
-Anderson_ksampResult: Untyped
-
 def anderson_ksamp(samples, midrank: bool = True, *, method: Untyped | None = None) -> Untyped: ...
-
-class AnsariResult(NamedTuple):
-    statistic: Untyped
-    pvalue: Untyped
-
-class _ABW:
-    m: Untyped
-    n: Untyped
-    astart: Untyped
-    total: Untyped
-    freqs: Untyped
-    def __init__(self) -> None: ...
-    def pmf(self, k, n, m) -> Untyped: ...
-    def cdf(self, k, n, m) -> Untyped: ...
-    def sf(self, k, n, m) -> Untyped: ...
-
-def ansari(x, y, alternative: str = "two-sided") -> Untyped: ...
-
-class BartlettResult(NamedTuple):
-    statistic: Untyped
-    pvalue: Untyped
-
-def bartlett(*samples, axis: int = 0) -> Untyped: ...
-
-class LeveneResult(NamedTuple):
-    statistic: Untyped
-    pvalue: Untyped
-
-def levene(*samples, center: str = "median", proportiontocut: float = 0.05) -> Untyped: ...
-
-class FlignerResult(NamedTuple):
-    statistic: Untyped
-    pvalue: Untyped
-
-def fligner(*samples, center: str = "median", proportiontocut: float = 0.05) -> Untyped: ...
-def mood(x, y, axis: int = 0, alternative: str = "two-sided") -> Untyped: ...
-
-WilcoxonResult: Untyped
-
+def ansari(
+    x,
+    y,
+    alternative: Alternative = "two-sided",
+    *,
+    axis: int | None = 0,
+    nan_policy: NanPolicy = "propagate",
+    keepdims: bool = False,
+) -> Untyped: ...
+def bartlett(*samples, axis: int = 0, nan_policy: NanPolicy = "propagate", keepdims: bool = False) -> Untyped: ...
+def levene(
+    *samples,
+    center: str = "median",
+    proportiontocut: float = 0.05,
+    axis: int | None = 0,
+    nan_policy: NanPolicy = "propagate",
+    keepdims: bool = False,
+) -> Untyped: ...
+def fligner(
+    *samples,
+    center: str = "median",
+    proportiontocut: float = 0.05,
+    axis: int | None = 0,
+    nan_policy: NanPolicy = "propagate",
+    keepdims: bool = False,
+) -> Untyped: ...
+def mood(
+    x,
+    y,
+    axis: int = 0,
+    alternative: Alternative = "two-sided",
+    *,
+    nan_policy: NanPolicy = "propagate",
+    keepdims: bool = False,
+) -> Untyped: ...
 def wilcoxon_result_unpacker(res) -> Untyped: ...
 def wilcoxon_result_object(statistic, pvalue, zstatistic: Untyped | None = None) -> Untyped: ...
 def wilcoxon_outputs(kwds) -> Untyped: ...
@@ -140,37 +167,47 @@ def wilcoxon(
     y: Untyped | None = None,
     zero_method: str = "wilcox",
     correction: bool = False,
-    alternative: str = "two-sided",
+    alternative: Alternative = "two-sided",
     method: str = "auto",
     *,
     axis: int = 0,
+    nan_policy: NanPolicy = "propagate",
+    keepdims: bool = False,
 ) -> Untyped: ...
-
-MedianTestResult: Untyped
-
 def median_test(
     *samples,
     ties: str = "below",
     correction: bool = True,
     lambda_: int = 1,
-    nan_policy: str = "propagate",
+    nan_policy: NanPolicy = "propagate",
+) -> Untyped: ...
+def circmean(
+    samples,
+    high=...,
+    low: int = 0,
+    axis: int | None = None,
+    nan_policy: NanPolicy = "propagate",
+    *,
+    keepdims: bool = False,
+) -> Untyped: ...
+def circvar(
+    samples,
+    high=...,
+    low: int = 0,
+    axis: int | None = None,
+    nan_policy: NanPolicy = "propagate",
+    *,
+    keepdims: bool = False,
 ) -> Untyped: ...
-def circmean(samples, high=..., low: int = 0, axis: Untyped | None = None, nan_policy: str = "propagate") -> Untyped: ...
-def circvar(samples, high=..., low: int = 0, axis: Untyped | None = None, nan_policy: str = "propagate") -> Untyped: ...
 def circstd(
     samples,
     high=...,
     low: int = 0,
-    axis: Untyped | None = None,
-    nan_policy: str = "propagate",
+    axis: int | None = None,
+    nan_policy: NanPolicy = "propagate",
     *,
     normalize: bool = False,
+    keepdims: bool = False,
 ) -> Untyped: ...
-
-class DirectionalStats:
-    mean_direction: Untyped
-    mean_resultant_length: Untyped
-    def __init__(self, mean_direction, mean_resultant_length) -> None: ...
-
 def directional_stats(samples, *, axis: int = 0, normalize: bool = True) -> Untyped: ...
 def false_discovery_control(ps, *, axis: int = 0, method: str = "bh") -> Untyped: ...