Fix ruff lint and format errors from Andrews-Ploberger commit

Sort imports and __all__ entries to satisfy ruff I001 and RUF022 rules
across 4 source files. Apply ruff format to 3 files with formatting drift.
All 718 tests pass.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

Author: Morten Tabor

src/regimes/__init__.py

@@ -40,14 +40,14 @@
     ARResults,
     BaiPerronResults,
     BaiPerronTest,
+    BreakResultsBase,
+    ChowTest,
+    ChowTestResults,
+    CovType,
     CUSUMResults,
     CUSUMSQResults,
     CUSUMSQTest,
     CUSUMTest,
-    ChowTest,
-    ChowTestResults,
-    BreakResultsBase,
-    CovType,
     DiagnosticsResults,
     DiagnosticTestResult,
     OLSResults,
@@ -77,8 +77,8 @@
     plot_breaks,
     plot_cusum,
     plot_cusum_sq,
-    plot_f_sequence,
     plot_diagnostics,
+    plot_f_sequence,
     plot_params_over_time,
     plot_regime_means,
     plot_residual_acf,
@@ -93,18 +93,18 @@
     "AR",
     "OLS",
     "ADLResults",
+    "ARResults",
     "AndrewsPlobergerResults",
     "AndrewsPlobergerTest",
-    "ARResults",
     "BaiPerronResults",
     "BaiPerronTest",
+    "BreakResultsBase",
     "CUSUMResults",
     "CUSUMSQResults",
     "CUSUMSQTest",
     "CUSUMTest",
     "ChowTest",
     "ChowTestResults",
-    "BreakResultsBase",
     "CovType",
     "DiagnosticTestResult",
     "DiagnosticsResults",

src/regimes/api.py

@@ -56,12 +56,12 @@
     AndrewsPlobergerTest,
     BaiPerronResults,
     BaiPerronTest,
+    ChowTest,
+    ChowTestResults,
     CUSUMResults,
     CUSUMSQResults,
     CUSUMSQTest,
     CUSUMTest,
-    ChowTest,
-    ChowTestResults,
 )
 
 # Visualization
@@ -71,8 +71,8 @@
     plot_breaks,
     plot_cusum,
     plot_cusum_sq,
-    plot_f_sequence,
     plot_diagnostics,
+    plot_f_sequence,
     plot_params_over_time,
     plot_regime_means,
     plot_residual_acf,
@@ -84,24 +84,21 @@
 __all__ = [
     "ADL",
     "AR",
-    # Models
     "OLS",
     "ADLResults",
+    "ARResults",
     "AndrewsPlobergerResults",
     "AndrewsPlobergerTest",
-    "ARResults",
     "BaiPerronResults",
-    # Tests
     "BaiPerronTest",
+    "BreakResultsBase",
     "CUSUMResults",
     "CUSUMSQResults",
     "CUSUMSQTest",
     "CUSUMTest",
     "ChowTest",
     "ChowTestResults",
-    "BreakResultsBase",
     "CovType",
-    # Diagnostics
     "DiagnosticTestResult",
     "DiagnosticsResults",
     "OLSResults",
@@ -111,7 +108,6 @@
     "RecursiveARResults",
     "RecursiveOLS",
     "RecursiveOLSResults",
-    # Base classes
     "RegimesModelBase",
     "RegimesResultsBase",
     "RegressionResultsBase",
@@ -121,7 +117,6 @@
     "RollingARResults",
     "RollingCovType",
     "RollingEstimatorBase",
-    # Rolling/Recursive estimation
     "RollingOLS",
     "RollingOLSResults",
     "RollingResultsBase",
@@ -130,12 +125,11 @@
     "ar_summary_by_regime",
     "plot_actual_fitted",
     "plot_break_confidence",
-    # Visualization
     "plot_breaks",
-    "plot_f_sequence",
     "plot_cusum",
     "plot_cusum_sq",
     "plot_diagnostics",
+    "plot_f_sequence",
     "plot_params_over_time",
     "plot_regime_means",
     "plot_residual_acf",

src/regimes/tests/__init__.py

@@ -1,6 +1,9 @@
 """Structural break tests."""
 
-from regimes.tests.andrews_ploberger import AndrewsPlobergerResults, AndrewsPlobergerTest
+from regimes.tests.andrews_ploberger import (
+    AndrewsPlobergerResults,
+    AndrewsPlobergerTest,
+)
 from regimes.tests.bai_perron import BaiPerronResults, BaiPerronTest
 from regimes.tests.base import BreakTestBase, BreakTestResultsBase
 from regimes.tests.chow import ChowTest, ChowTestResults

src/regimes/tests/andrews_ploberger.py

@@ -338,7 +338,9 @@ class AndrewsPlobergerResults(BreakTestResultsBase):
     exp_f_pvalue: float = np.nan
     ave_f_pvalue: float = np.nan
     f_sequence: np.ndarray = field(default_factory=lambda: np.array([]))
-    candidate_indices: np.ndarray = field(default_factory=lambda: np.array([], dtype=int))
+    candidate_indices: np.ndarray = field(
+        default_factory=lambda: np.array([], dtype=int)
+    )
     sup_f_break_index: int = 0
     trimming: float = 0.15
     q: int = 0
@@ -451,8 +453,7 @@ def summary(self) -> str:
         lines.append("-" * 78)
         n_rejected = sum(1 for v in self.rejected.values() if v)
         lines.append(
-            f"\nRejected H0 for {n_rejected} of 3 statistics "
-            f"at the {alpha:.0%} level"
+            f"\nRejected H0 for {n_rejected} of 3 statistics at the {alpha:.0%} level"
         )
         lines.append("=" * 78)
         return "\n".join(lines)
@@ -834,9 +835,7 @@ def fit(
         # ExpF: use log-sum-exp trick for numerical stability
         half_f = 0.5 * f_sequence
         max_half_f = np.max(half_f)
-        exp_f = float(
-            max_half_f + np.log(np.mean(np.exp(half_f - max_half_f)))
-        )
+        exp_f = float(max_half_f + np.log(np.mean(np.exp(half_f - max_half_f))))
 
         # Break location
         sup_f_break_index = int(candidate_indices[np.argmax(f_sequence)])

src/regimes/visualization/__init__.py

@@ -32,7 +32,6 @@
 __all__ = [
     "REGIMES_COLORS",
     "REGIMES_COLOR_CYCLE",
-    "plot_f_sequence",
     "add_break_dates",
     "add_confidence_band",
     "add_source",
@@ -44,6 +43,7 @@
     "plot_cusum",
     "plot_cusum_sq",
     "plot_diagnostics",
+    "plot_f_sequence",
     "plot_params_over_time",
     "plot_regime_means",
     "plot_residual_acf",

src/regimes/visualization/andrews_ploberger.py

@@ -116,9 +116,7 @@ def plot_f_sequence(
             )
 
         # Zero line
-        ax.axhline(
-            y=0, color=REGIMES_COLORS["near_black"], linewidth=0.5, alpha=0.3
-        )
+        ax.axhline(y=0, color=REGIMES_COLORS["near_black"], linewidth=0.5, alpha=0.3)
 
         ax.set_title(title or "Andrews-Ploberger F-statistic sequence")
         ax.set_xlabel(xlabel)

tests/test_tests/test_andrews_ploberger.py

@@ -54,10 +54,12 @@ def test_break_location_near_truth(
 
     def test_strong_break_rejects(self, rng: np.random.Generator) -> None:
         """A very large mean shift should be detected by all three statistics."""
-        y = np.concatenate([
-            rng.standard_normal(100),
-            rng.standard_normal(100) + 10,
-        ])
+        y = np.concatenate(
+            [
+                rng.standard_normal(100),
+                rng.standard_normal(100) + 10,
+            ]
+        )
 
         test = AndrewsPlobergerTest(y)
         results = test.fit()
@@ -122,10 +124,12 @@ def test_sup_f_geq_ave_f(
 
     def test_large_for_strong_break(self, rng: np.random.Generator) -> None:
         """Statistics should be large for a strong break."""
-        y = np.concatenate([
-            rng.standard_normal(100),
-            rng.standard_normal(100) + 10,
-        ])
+        y = np.concatenate(
+            [
+                rng.standard_normal(100),
+                rng.standard_normal(100) + 10,
+            ]
+        )
 
         test = AndrewsPlobergerTest(y)
         results = test.fit()
@@ -243,9 +247,7 @@ def test_critical_values_increasing_with_alpha(
         ]:
             assert cv_dict[0.10] < cv_dict[0.05] < cv_dict[0.01]
 
-    def test_critical_values_vary_with_trimming(
-        self, rng: np.random.Generator
-    ) -> None:
+    def test_critical_values_vary_with_trimming(self, rng: np.random.Generator) -> None:
         """Critical values should differ for different trimming fractions."""
         y = rng.standard_normal(200)
 
@@ -281,9 +283,9 @@ def test_small_pvalues_for_break(
         results = test.fit()
 
         # At least one p-value should be small
-        assert min(
-            results.sup_f_pvalue, results.exp_f_pvalue, results.ave_f_pvalue
-        ) < 0.10
+        assert (
+            min(results.sup_f_pvalue, results.exp_f_pvalue, results.ave_f_pvalue) < 0.10
+        )
 
     def test_large_pvalues_for_no_break(self, rng: np.random.Generator) -> None:
         """P-values should be large when there is no break."""
@@ -297,10 +299,12 @@ def test_large_pvalues_for_no_break(self, rng: np.random.Generator) -> None:
 
     def test_pvalues_consistent_with_cvs(self, rng: np.random.Generator) -> None:
         """P-values should be consistent with critical value comparison."""
-        y = np.concatenate([
-            rng.standard_normal(100),
-            rng.standard_normal(100) + 5,
-        ])
+        y = np.concatenate(
+            [
+                rng.standard_normal(100),
+                rng.standard_normal(100) + 5,
+            ]
+        )
 
         test = AndrewsPlobergerTest(y)
         results = test.fit()
@@ -336,9 +340,7 @@ def test_various_trimming_values(
         assert results.trimming == trimming
         assert len(results.f_sequence) > 0
 
-    def test_narrower_trimming_more_candidates(
-        self, rng: np.random.Generator
-    ) -> None:
+    def test_narrower_trimming_more_candidates(self, rng: np.random.Generator) -> None:
         """Narrower trimming should produce more candidate dates."""
         y = rng.standard_normal(200)
 
@@ -578,10 +580,12 @@ class TestEdgeCases:
 
     def test_small_sample(self, rng: np.random.Generator) -> None:
         """Test with a small sample."""
-        y = np.concatenate([
-            rng.standard_normal(30),
-            rng.standard_normal(30) + 5,
-        ])
+        y = np.concatenate(
+            [
+                rng.standard_normal(30),
+                rng.standard_normal(30) + 5,
+            ]
+        )
 
         test = AndrewsPlobergerTest(y)
         results = test.fit(trimming=0.15)
@@ -608,10 +612,12 @@ def test_partial_break(self, rng: np.random.Generator) -> None:
 
     def test_strong_break_all_reject(self, rng: np.random.Generator) -> None:
         """Very strong break should cause all three statistics to reject."""
-        y = np.concatenate([
-            rng.standard_normal(100),
-            rng.standard_normal(100) + 10,
-        ])
+        y = np.concatenate(
+            [
+                rng.standard_normal(100),
+                rng.standard_normal(100) + 10,
+            ]
+        )
 
         test = AndrewsPlobergerTest(y)
         results = test.fit()
@@ -620,10 +626,12 @@ def test_strong_break_all_reject(self, rng: np.random.Generator) -> None:
 
     def test_constant_only_model(self, rng: np.random.Generator) -> None:
         """Test with default constant-only (mean-shift) model."""
-        y = np.concatenate([
-            rng.standard_normal(100),
-            rng.standard_normal(100) + 3,
-        ])
+        y = np.concatenate(
+            [
+                rng.standard_normal(100),
+                rng.standard_normal(100) + 3,
+            ]
+        )
 
         test = AndrewsPlobergerTest(y)
         results = test.fit()
@@ -669,10 +677,12 @@ def test_plot_f_sequence_function(self, rng: np.random.Generator) -> None:
         """Test the standalone plot_f_sequence function."""
         import matplotlib.pyplot as plt
 
-        y = np.concatenate([
-            rng.standard_normal(100),
-            rng.standard_normal(100) + 3,
-        ])
+        y = np.concatenate(
+            [
+                rng.standard_normal(100),
+                rng.standard_normal(100) + 3,
+            ]
+        )
 
         test = AndrewsPlobergerTest(y)
         results = test.fit()