Merge pull request #359 from jorenham/bump-dev-deps

update the development dependencies (and `optype`)

.pre-commit-config.yaml

@@ -72,7 +72,7 @@ repos:
       - id: codespell
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.8.0
+    rev: v0.8.1
     hooks:
       - id: ruff
         args: [--fix, --show-fixes]

lmo/_lm.py

@@ -1283,7 +1283,7 @@ def l_moment_cov(
         - Use the direct (Jacobi) method from Hosking (2015).
     """
     _r_max = clean_order(r_max, "r_max")
-    _trim = cast(tuple[int, int], clean_trim(trim))
+    _trim = cast("tuple[int, int]", clean_trim(trim))
 
     if any(int(t) != t for t in _trim):
         msg = "l_moment_cov does not support fractional trimming (yet)"
@@ -1585,7 +1585,7 @@ def l_moment_influence(
     n = len(x_k)
 
     w_k: onp.Array1D[np.float64] = l_weights(_r, n, (s, t))[-1]
-    l_r = cast(np.float64, w_k @ x_k)
+    l_r = cast("np.float64", w_k @ x_k)
 
     def influence_function(x: _T_x, /) -> _T_x:
         _x = np.asanyarray(x)
@@ -1604,7 +1604,7 @@ def influence_function(x: _T_x, /) -> _T_x:
 
         if _x.ndim == 0 and np.isscalar(x):
             return out.item()
-        return cast(_T_x, out)
+        return cast("_T_x", out)
 
     influence_function.__doc__ = (
         f"Empirical L-moment influence function given "
@@ -1663,7 +1663,7 @@ def l_ratio_influence(
     eif_k = l_moment_influence(_x, _s, trim, sort=False, tol=0)
 
     l_r, l_k = cast(
-        tuple[float, float],
+        "tuple[float, float]",
         (eif_r.l, eif_k.l),  # pyright: ignore[reportFunctionMemberAccess]
     )
     if abs(l_k) <= tol * abs(l_r):
@@ -1677,7 +1677,7 @@ def influence_function(x: _T_x, /) -> _T_x:
         # cheat a bit to avoid `inf - inf = nan` situations
         psi_k = np.where(np.isinf(psi_r), 0, eif_k(x))
 
-        return cast(_T_x, round0((psi_r - t_r * psi_k) / l_k, tol=tol)[()])
+        return cast("_T_x", round0((psi_r - t_r * psi_k) / l_k, tol=tol)[()])
 
     influence_function.__doc__ = (
         f"Theoretical influence function for L-moment ratio with "

lmo/_poly.py

@@ -297,7 +297,7 @@ def extrema_jacobi(n: int, a: float, b: float) -> tuple[float, float]:
     """
     x = peaks_jacobi(n, a, b)
     p = eval_sh_jacobi(n, a, b, (x + 1) / 2)
-    return cast(float, np.min(p)), cast(float, np.max(p))
+    return cast("float", np.min(p)), cast("float", np.max(p))
 
 
 def jacobi(
@@ -388,7 +388,7 @@ def roots(p: PolySeries, /, outside: op.CanBool = False) -> onp.Array1D[np.float
     """
     z = p.roots()
     x = cast(
-        onp.Array1D[np.float64],
+        "onp.Array1D[np.float64]",
         z[np.isreal(z)].real if np.isrealobj(p.domain) and not np.isrealobj(z) else z,
     )
 

lmo/contrib/pandas.py

@@ -158,10 +158,10 @@ def l_moment(
         """
         out = _l_moment(self, r, trim=trim, **kwargs)
         if np.isscalar(out):
-            return cast(float, out)
+            return cast("float", out)
 
         return pd.Series(
-            cast(npt.NDArray[np.float64], out),
+            cast("npt.NDArray[np.float64]", out),
             index=pd.Index(np.asarray(r), name="r"),
             dtype=float,
             copy=False,
@@ -185,10 +185,10 @@ def l_ratio(
         rk = np.stack(np.broadcast_arrays(np.asarray(r), np.asarray(k)))
         out = moments_to_ratio(rk, _l_moment(self, rk, trim=trim, **kwargs))
         if rk.ndim == 1:
-            return cast(float, out)
+            return cast("float", out)
 
         return pd.Series(
-            cast(npt.NDArray[np.float64], out),
+            cast("npt.NDArray[np.float64]", out),
             index=_ratio_index(rk),
             dtype=float,
             copy=False,
@@ -224,7 +224,7 @@ def l_loc(
         Returns:
             out: A scalar.
         """
-        return cast(float, _l_moment(self, 1, trim, **kwargs))
+        return cast("float", _l_moment(self, 1, trim, **kwargs))
 
     def l_scale(
         self,
@@ -237,7 +237,7 @@ def l_scale(
         Returns:
             out: A scalar.
         """
-        return cast(float, _l_moment(self, 2, trim, **kwargs))
+        return cast("float", _l_moment(self, 2, trim, **kwargs))
 
     def l_variation(
         self,
@@ -250,7 +250,7 @@ def l_variation(
         Returns:
             out: A scalar.
         """
-        return cast(float, _l_ratio(self, 2, 1, trim, **kwargs))
+        return cast("float", _l_ratio(self, 2, 1, trim, **kwargs))
 
     def l_skew(
         self,
@@ -263,7 +263,7 @@ def l_skew(
         Returns:
             out: A scalar.
         """
-        return cast(float, _l_ratio(self, 3, 2, trim, **kwargs))
+        return cast("float", _l_ratio(self, 3, 2, trim, **kwargs))
 
     def l_kurtosis(
         self,
@@ -276,7 +276,7 @@ def l_kurtosis(
         Returns:
             out: A scalar.
         """
-        return cast(float, _l_ratio(self, 4, 2, trim, **kwargs))
+        return cast("float", _l_ratio(self, 4, 2, trim, **kwargs))
 
     l_kurt = l_kurtosis
 

lmo/contrib/scipy_stats.py

@@ -991,7 +991,7 @@ def _l_gmm_error(
             raise ValueError(msg)
 
         err = l_data - l_dist
-        return cast(float, err @ weights @ err)
+        return cast("float", err @ weights @ err)
 
     @overload
     def l_fit(
@@ -1473,5 +1473,5 @@ def install() -> None:
     to the `scipy.stats.rv_generic` and `scipy.stats.lmt.rv_frozen`
     types, respectively.
     """
-    l_rv_generic.patch(cast(type[object], lmt.rv_continuous.__base__))
-    l_rv_frozen.patch(cast(type[object], lmt.rv_frozen))
+    l_rv_generic.patch(cast("type[object]", lmt.rv_continuous.__base__))
+    l_rv_frozen.patch(cast("type[object]", lmt.rv_frozen))

lmo/diagnostic.py

@@ -5,7 +5,6 @@
 import math
 import sys
 import warnings
-from collections.abc import Callable
 from math import lgamma
 from typing import (
     TYPE_CHECKING,
@@ -35,6 +34,8 @@
 from .special import fpow
 
 if TYPE_CHECKING:
+    from collections.abc import Callable
+
     from .contrib.scipy_stats import l_rv_generic
 
 __all__ = (
@@ -366,7 +367,7 @@ def _lm2_bounds_single(r: int, trim: _Tuple2[float]) -> float:
 
 
 _lm2_bounds: Final = cast(
-    Callable[[lmt.ToOrderND, _Tuple2[float]], _FloatND],
+    "Callable[[lmt.ToOrderND, _Tuple2[float]], _FloatND]",
     np.vectorize(_lm2_bounds_single, otypes=[float], excluded={1}, signature="()->()"),
 )
 
@@ -614,7 +615,7 @@ def l_ratio_bounds(
 
     _cache: dict[int, _Tuple2[float]] = {}
     for i, ri in np.ndenumerate(_r):
-        _ri = cast(int, ri)
+        _ri = cast("int", ri)
         if _ri in _cache:
             t_min[i], t_max[i] = _cache[_ri]
 
@@ -747,7 +748,7 @@ def obj(r: _FloatND) -> float:
 
     res = minimize(obj, bounds=[(rho_min, rho_max)], x0=[rho_min], method="COBYLA")
 
-    rho = cast(float, res.x[0])
+    rho = cast("float", res.x[0])
     if rho <= _MIN_RHO or influence_fn(-rho) or influence_fn(rho):
         return np.nan
 

lmo/distributions/__init__.py

@@ -32,7 +32,7 @@
     [Distributions - Wakeby](../distributions.md#wakeby).
     """
 else:
-    wakeby: Final = cast(lmt.rv_continuous, wakeby_gen(a=0.0, name="wakeby"))
+    wakeby: Final = cast("lmt.rv_continuous", wakeby_gen(a=0.0, name="wakeby"))
 
 # mkdocstring workaround
 if not TYPE_CHECKING:
@@ -57,7 +57,7 @@
     """
 else:
     kumaraswamy: Final = cast(
-        lmt.rv_continuous,
+        "lmt.rv_continuous",
         kumaraswamy_gen(a=0.0, b=1.0, name="kumaraswamy"),
     )
 
@@ -77,4 +77,4 @@
     [Distributions - GLD](../distributions.md#gld).
     """
 else:
-    genlambda: Final = cast(lmt.rv_continuous, genlambda_gen(name="genlambda"))
+    genlambda: Final = cast("lmt.rv_continuous", genlambda_gen(name="genlambda"))

lmo/inference/_l_gmm.py

@@ -141,7 +141,7 @@ def AIC(self) -> float:  # noqa: N802
             identification](https://doi.org/10.1109%2FTAC.1974.1100705)
 
         """
-        return 2 * (self.n_arg - np.log(cast(float, self.j_test.pvalue)))
+        return 2 * (self.n_arg - np.log(cast("float", self.j_test.pvalue)))
 
     @property
     def AICc(self) -> float:  # noqa: N802

lmo/theoretical/_f_to_lm_eif.py

@@ -145,7 +145,7 @@ def influence(x: _T_x, /) -> _T_x:
         # cheat a bit and replace 0 * inf by 0, ensuring convergence if s or t
         alpha = w * eval_sh_jacobi(_r - 1, t, s, q) * np.where(w, _x, 0)
 
-        return cast(_T_x, round0(alpha - lm, tol)[()])
+        return cast("_T_x", round0(alpha - lm, tol)[()])
 
     influence.__doc__ = (
         f"Theoretical influence function for L-moment with {r=} and {trim=}."

pyproject.toml

@@ -38,7 +38,7 @@ dependencies = [
     "numpy>=1.24",
     "scipy>=1.10",
     "scipy-stubs>=1.14.1.4",
-    "optype[numpy]>=0.7.1",
+    "optype[numpy]>=0.7.2",
 ]
 
 [project.optional-dependencies]
@@ -53,7 +53,7 @@ Funding = "https://github.com/sponsors/jorenham"
 
 [dependency-groups]
 debug = [
-    "ipython>=8.29.0",
+    "ipython>=8.30.0",
     "ipykernel>=6.29.5",
     "line_profiler>=4.1.3; python_version<'3.13'",
     "matplotlib>=3.9.2",
@@ -65,7 +65,7 @@ docs = [
     "mkdocs-git-revision-date-localized-plugin>=1.3.0",
     "mkdocs-include-markdown-plugin>=7.1.1",
     "mkdocs-jupyter>=0.25.1",
-    "mkdocs-material>=9.5.45",
+    "mkdocs-material>=9.5.46",
     "mkdocs-minify-plugin>=0.8.0",
     "mkdocstrings[python]>=0.27.0",
 ]
@@ -76,12 +76,12 @@ pandas = [
 dev = [
     "blacken-docs>=1.19.1",
     "codespell>=2.3.0",
-    "hypothesis[numpy]>=6.119.3",
+    "hypothesis[numpy]>=6.121.2",
     "pre-commit>=4.0.1",
-    "basedpyright>=1.21.1",
+    "basedpyright>=1.22.0",
     "pytest>=8.3.3",
-    "pytest-doctestplus>=1.2.1",
-    "ruff>=0.7.4",
+    "pytest-doctestplus>=1.3.0",
+    "ruff>=0.8.1",
     "sp-repo-review[cli]>=2024.8.19",
     "tox>=4.23.2",
 ]
@@ -184,8 +184,6 @@ ignore = [
     "D205",     # blank-line-after-summary
     "D212",     # multi-line-summary-first-line
     # flake8-annotations
-    "ANN101",   # missing-type-self (deprecated)
-    "ANN102",   # missing-type-cls (deprecated)
     "ANN401",   # any-type
     # flake8-bandit
     "S101",     # assert

tests/test_distributions.py

@@ -24,7 +24,7 @@
 def test_l_poly_eq_uniform(trim: lmt.ToTrim):
     p0 = x0 = np.linspace(0, 1)
 
-    X = cast(Any, uniform())
+    X = cast("Any", uniform())
     X_hat = l_poly(X.l_moment([1, 2], trim=trim), trim=trim)
 
     t4 = X.l_stats(trim=trim)
@@ -70,7 +70,7 @@ def test_l_poly_eq_uniform(trim: lmt.ToTrim):
     ],
 )
 def test_wakeby(b: float, d: float, f: float):
-    X = cast(Any, wakeby(b, d, f))
+    X = cast("Any", wakeby(b, d, f))
 
     assert X.cdf(X.support()[0]) == 0
     assert X.ppf(0) == X.support()[0]
@@ -100,8 +100,8 @@ def test_wakeby(b: float, d: float, f: float):
 
 @pytest.mark.parametrize("lam", [0, 0.14, 1, -1])
 def test_genlambda_tukeylamba(lam: float):
-    X0 = cast(Any, tukeylambda(lam))
-    X = cast(Any, genlambda(lam, lam, 0))
+    X0 = cast("Any", tukeylambda(lam))
+    X = cast("Any", genlambda(lam, lam, 0))
 
     x0 = X0.ppf(Q)
     x = X.ppf(Q)
@@ -110,7 +110,7 @@ def test_genlambda_tukeylamba(lam: float):
     assert_allclose(x, x0)
 
     _pp = cast(
-        npt.NDArray[np.float64],
+        "npt.NDArray[np.float64]",
         np.linspace(X0.ppf(0.05), X0.ppf(0.95), 100),
     )
     u0 = X0.cdf(_pp)
@@ -141,7 +141,7 @@ def test_genlambda_tukeylamba(lam: float):
 @pytest.mark.parametrize("d", [-1.95, 0, 1.95], ids="d={}".format)
 @pytest.mark.parametrize("b", [-1.95, 0, 1.95], ids="b={}".format)
 def test_genlambda(b: float, d: float, f: float):
-    X = cast(Any, genlambda(b, d, f))
+    X = cast("Any", genlambda(b, d, f))
 
     assert X.cdf(X.support()[0]) == 0
     assert X.ppf(0) == X.support()[0]

tests/test_theoretical.py

@@ -1,6 +1,5 @@
 import functools
-from collections.abc import Callable
-from typing import cast
+from typing import TYPE_CHECKING, cast
 
 import numpy as np
 import pytest
@@ -23,10 +22,13 @@
     qdf_from_l_moments,
 )
 
+if TYPE_CHECKING:
+    from collections.abc import Callable
+
 assert_allclose = functools.partial(_assert_allclose, rtol=1e-6, atol=1e-8)
 
-norm_cdf = cast(Callable[[float], float], ndtr)
-norm_ppf = cast(Callable[[float], float], ndtri)
+norm_cdf = cast("Callable[[float], float]", ndtr)
+norm_ppf = cast("Callable[[float], float]", ndtri)
 
 
 @np.errstate(over="ignore", under="ignore")
@@ -267,7 +269,7 @@ def test_ls_cov_uniform():
     trim=st.tuples(st.integers(0, 1), st.integers(0, 3)),
 )
 def test_ppf_from_l_moments_identity(
-    ppf: Callable[[float], float],
+    ppf: "Callable[[float], float]",
     trim: tuple[int, int] | int,
 ):
     rmax = 8
@@ -291,7 +293,7 @@ def test_ppf_from_l_moments_identity(
     trim=st.tuples(st.integers(0, 1), st.integers(0, 3)),
 )
 def test_qdf_from_l_moments_identity(
-    qdf: Callable[[float], float],
+    qdf: "Callable[[float], float]",
     trim: tuple[int, int] | int,
 ):
     rmax = 8