use codespell and pygrep hooks, replace isort with ruff, address bugb…

…ear issues

.github/dependabot.yml

@@ -9,8 +9,16 @@ updates:
     directory: /
     schedule:
       interval: monthly
+    groups:
+      actions:
+        patterns:
+          - "*"
 
   - package-ecosystem: pip
     directory: /
     schedule:
       interval: monthly
+    groups:
+      python:
+        patterns:
+          - "*"

.pre-commit-config.yaml

@@ -26,7 +26,15 @@ repos:
       - id: yamllint
         args: [ '--config-file=.yamllint.yaml' ]
         exclude: conda-recipe/meta.yaml
-  - repo: https://github.com/psf/black
+  - repo: https://github.com/pre-commit/pygrep-hooks
+    rev: v1.10.0
+    hooks:
+      - id: python-no-eval
+      - id: python-no-log-warn
+#      - id: rst-backticks
+      - id: rst-directive-colons
+      - id: rst-inline-touching-normal
+  - repo: https://github.com/psf/black-pre-commit-mirror
     rev: 24.8.0
     hooks:
       - id: black
@@ -35,22 +43,23 @@ repos:
     hooks:
       - id: flake8
         args: [ '--config=.flake8' ]
-  - repo: https://github.com/PyCQA/isort
-    rev: 5.13.2
-    hooks:
-      - id: isort
-        args: [ '--settings-file=pyproject.toml' ]
   - repo: https://github.com/astral-sh/ruff-pre-commit
     rev: v0.5.7
     hooks:
       - id: ruff
-        args: [ '--fix' ]
+        args: [ '--fix', '--show-fixes' ]
 #      - id: ruff-format
+  - repo: https://github.com/codespell-project/codespell
+    rev: v2.2.4
+    hooks:
+      - id: codespell
+        additional_dependencies: [ 'tomli' ]
   - repo: https://github.com/keewis/blackdoc
     rev: v0.3.9
     hooks:
       - id: blackdoc
         additional_dependencies: [ 'black==24.8.0' ]
+      - id: blackdoc-autoupdate-black
   - repo: meta
     hooks:
       - id: check-hooks-apply

docs/source/usage.rst

@@ -122,7 +122,7 @@ Example:  Calculate the Negative Log Likelihood of a Gamma distribution with par
 Other statistical methods
 -------------------------
 
-The :mod:`lmoments3.stats` module provides some additional statistical parametes to evaluate fitting of data to
+The :mod:`lmoments3.stats` module provides some additional statistical parameters to evaluate fitting of data to
 distribution function.
 
 :func:`AIC(data, distr_name, distr_paras)`

pyproject.toml

@@ -97,15 +97,9 @@ include = [
 ]
 exclude = [".*", "conda-recipe/**", "docs/build/**"]
 
-[tool.isort]
-profile = "black"
-py_version = 38
-append_only = true
-
 [tool.ruff]
 src = ["lmoments3"]
 line-length = 150
-target-version = "py38"
 exclude = [
   ".eggs",
   ".git",
@@ -128,12 +122,14 @@ ignore = [
 ]
 preview = true
 select = [
+  "B", # bugbear
   "BLE", # blind-except
   "C90", # mccabe-complexity
   "D", # docstrings
   "E", # pycodestyle errors
   "FLY002", # static-join-to-fstring
   "G", # logging-format
+  "I", # import-order
   "N", # naming conventions
   "PERF", # iterator performance
   "PTH", # pathlib

src/lmoments3/__init__.py

@@ -84,8 +84,8 @@ def _samlmularge(x, nmom=5):
         x = np.asarray(x, dtype=np.float64)
         n = len(x)
         x = np.sort(x)
-    except ValueError:
-        raise ValueError("Input data to estimate L-moments must be numeric.")
+    except ValueError as err:
+        raise ValueError("Input data to estimate L-moments must be numeric.") from err
 
     if nmom <= 0:
         raise ValueError("Invalid number of sample L-moments")
@@ -111,7 +111,7 @@ def _samlmularge(x, nmom=5):
         xtrans = []
         for i in range(0, n):
             coeftemp = []
-            for j in range(0, mom):
+            for _j in range(0, mom):
                 coeftemp.append(1)
 
             for j in range(0, mom - 1):
@@ -140,8 +140,8 @@ def _samlmusmall(x, nmom=5):
         x = np.asarray(x, dtype=np.float64)
         n = len(x)
         x = np.sort(x)
-    except ValueError:
-        raise ValueError("Input data to estimate L-moments must be numeric.")
+    except ValueError as err:
+        raise ValueError("Input data to estimate L-moments must be numeric.") from err
 
     if nmom <= 0 or nmom > 5:
         raise ValueError("Invalid number of sample L-moments")

src/lmoments3/distr.py

@@ -29,6 +29,7 @@
 
 import lmoments3 as lm
 
+
 try:
     # Scipy >= 1.9
     from scipy.stats._distn_infrastructure import rv_continuous_frozen
@@ -622,7 +623,7 @@ def _lmom_fit(self, lmom_ratios):
         # Newton-Raphson Iteration
         for it in range(1, MAXIT + 1):
             Success = 0
-            for i in range(1, MAXSR + 1):
+            for _ in range(1, MAXSR + 1):
                 if G > OFLGAM:
                     raise Exception("Failed to converge")
                 if H > 0:
@@ -959,7 +960,7 @@ def _cdfwak(self, x, para):  # noqa: C901
             if D > 0:
                 Z = math.log((x - XI) * D / C + 1) / D
 
-        for IT in range(1, MAXIT + 1):
+        for _IT in range(1, MAXIT + 1):
             EB = 0
             BZ = -B * Z
             if BZ >= UFL:
@@ -1321,7 +1322,7 @@ def _lmom_fit(self, lmom_ratios):
                 G = 1 - math.log(1 + T3) / DL2
 
             T0 = (T3 + 3) * 0.5
-            for IT in range(1, maxit):
+            for _IT in range(1, maxit):
                 X2 = 2**-G
                 X3 = 3**-G
                 XX2 = 1 - X2

src/lmoments3/tests/test_samlmu_speed.py

@@ -23,14 +23,14 @@ def tearDown(self):
 
     def test_n50_nmom3(self):
         start_i = 0
-        for i in range(10000):
+        for _i in range(10000):
             l1, l2, t3 = lm.lmom_ratios(self.record[start_i : start_i + self.n], nmom=3)
             assert l2 / l1
             start_i += self.n
 
     def test_n50_nmom4(self):
         start_i = 0
-        for i in range(10000):
+        for _i in range(10000):
             l1, l2, t3, t4 = lm.lmom_ratios(
                 self.record[start_i : start_i + self.n], nmom=4
             )
@@ -49,5 +49,5 @@ def tearDown(self):
     def test_n50(self):
         n = 50
         kappa_distr = distr.kap(loc=-0.7, scale=2.6, k=0.9, h=1.6)
-        for i in range(10000):
+        for _i in range(10000):
             kappa_distr.ppf(np.random.random(n))