[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

src/resample/__init__.py

@@ -11,6 +11,7 @@
 - Permutation-based variants of traditional statistical tests (t-test, K-S test, etc.)
 - Tools for working with empirical distributions (CDF, quantile, etc.)
 """
+
 from importlib.metadata import version
 
 from resample import bootstrap, empirical, jackknife, permutation

src/resample/bootstrap.py

@@ -143,6 +143,7 @@ def resample(
     replicated sample. This is a stricter constraint than that offered by the
     balanced bootstrap, which only guarantees that classes have the original
     proportions over all replicates, but not within each one replicate.
+
     """
     sample_np = np.atleast_1d(sample)
     n_sample = len(sample_np)
@@ -278,6 +279,7 @@ def bootstrap(
     >>> fb = bootstrap(np.mean, x, size=10000, random_state=1)
     >>> print(f"f(x) = {fx:.1f} +/- {np.std(fb):.1f}")
     f(x) = 4.5 +/- 0.9
+
     """
     gen = resample(sample, *args, **kwargs)
     if args:
@@ -331,6 +333,7 @@ def bias(
     This function has special space requirements, it needs to hold `size` replicates of
     the original sample in memory at once. The balanced bootstrap is recommended over
     the ordinary bootstrap for bias estimation, it tends to converge faster.
+
     """
     thetas = []
     if args:
@@ -390,6 +393,7 @@ def bias_corrected(
     8.2
     >>> round(bias_corrected(np.var, x, size=10000, random_state=1), 1)
     9.1
+
     """
     return fn(sample, *args) - bias(fn, sample, *args, **kwargs)
 
@@ -429,6 +433,7 @@ def variance(
     >>> x = np.arange(10)
     >>> round(variance(np.mean, x, size=10000, random_state=1), 1)
     0.8
+
     """
     thetas = bootstrap(fn, sample, *args, **kwargs)
     return np.var(thetas, ddof=1, axis=0)
@@ -488,6 +493,7 @@ def confidence_interval(
     increases the number of function evaluations in a direct comparison to
     'percentile'. However the increase in accuracy should compensate for this, with the
     result that less bootstrap replicas are needed overall to achieve the same accuracy.
+
     """
     if args and not isinstance(args[0], Collection):
         import warnings

src/resample/empirical.py

@@ -28,6 +28,7 @@ def cdf_gen(sample: "ArrayLike") -> Callable[[np.ndarray], np.ndarray]:
     -------
     callable
         Empirical distribution function.
+
     """
     sample_np = np.sort(sample)
     n = len(sample_np)
@@ -49,6 +50,7 @@ def quantile_function_gen(
     -------
     callable
         Empirical quantile function.
+
     """
 
     class QuantileFn:
@@ -89,6 +91,7 @@ def influence(
     -------
     ndarray
         Empirical influence values.
+
     """
     sample = np.atleast_1d(sample)
     n = len(sample)

src/resample/jackknife.py

@@ -75,6 +75,7 @@ def resample(
     ...     r2.append(x) # x is now a view into the same array in memory
     >>> print(r2)
     [array([1, 2]), array([1, 2]), array([1, 2])]
+
     """
     sample_np = np.atleast_1d(sample)
     n_sample = len(sample_np)
@@ -174,6 +175,7 @@ def jackknife(
     >>> fb = jackknife(np.mean, x)
     >>> print(f"f(x) = {fx:.1f} +/- {np.std(fb):.1f}")
     f(x) = 4.5 +/- 0.3
+
     """
     gen = resample(sample, *args, copy=False)
     if args:
@@ -219,6 +221,7 @@ def bias(
     -0.9
     >>> round(bias(lambda x: np.var(x, ddof=1), x), 1)
     0.0
+
     """
     sample = np.atleast_1d(sample)
     n = len(sample)
@@ -266,6 +269,7 @@ def bias_corrected(
     8.2
     >>> round(bias_corrected(np.var, x), 1)
     9.2
+
     """
     sample = np.atleast_1d(sample)
     n = len(sample)
@@ -307,6 +311,7 @@ def variance(
     >>> x = np.arange(10)
     >>> round(variance(np.mean, x), 1)
     0.9
+
     """
     # formula is (n - 1) / n * sum((fj - mean(fj)) ** 2)
     #   = np.var(fj, ddof=0) * (n - 1)

src/resample/permutation.py

@@ -65,6 +65,7 @@ class TestResult:
         hypothesis. See https://en.wikipedia.org/wiki/P-value for details.
     samples: array
         Values of the test statistic from the permutated samples.
+
     """
 
     statistic: float
@@ -142,6 +143,7 @@ def usp(
     Returns
     -------
     TestResult
+
     """
     if size <= 0:
         raise ValueError("size must be positive")
@@ -238,6 +240,7 @@ def same_population(
     Returns
     -------
     TestResult
+
     """
     if size <= 0:
         raise ValueError("max_size must be positive")
@@ -317,6 +320,7 @@ def anova(
     -----
     https://en.wikipedia.org/wiki/One-way_analysis_of_variance
     https://en.wikipedia.org/wiki/F-test
+
     """
     kwargs["transform"] = None
     return same_population(_ANOVA(), x, y, *args, **kwargs)
@@ -350,6 +354,7 @@ def kruskal(
     Notes
     -----
     https://en.wikipedia.org/wiki/Kruskal%E2%80%93Wallis_one-way_analysis_of_variance
+
     """
     kwargs["transform"] = None
     return same_population(_kruskal, x, y, *args, **kwargs)
@@ -377,6 +382,7 @@ def pearsonr(x: "ArrayLike", y: "ArrayLike", **kwargs: Any) -> TestResult:
     Returns
     -------
     TestResult
+
     """
     if len(x) != len(y):
         raise ValueError("x and y must have have the same length")
@@ -403,6 +409,7 @@ def spearmanr(x: "ArrayLike", y: "ArrayLike", **kwargs: Any) -> TestResult:
     Returns
     -------
     TestResult
+
     """
     if len(x) != len(y):
         raise ValueError("x and y must have have the same length")
@@ -432,6 +439,7 @@ def ttest(x: "ArrayLike", y: "ArrayLike", **kwargs: Any) -> TestResult:
     Returns
     -------
     TestResult
+
     """
     kwargs["transform"] = np.abs
     return same_population(_ttest, x, y, **kwargs)