Apply Black formatting to test_histogram_opt.py

Fixes CI linting failure by applying Black code formatter to the test file.

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <[email protected]>

Author: rhowardstone

testsuite/MDAnalysisTests/lib/test_histogram_opt.py

@@ -29,151 +29,202 @@
 @pytest.mark.skipif(not HAS_NUMBA, reason="Numba not available")
 class TestOptimizedHistogram:
     """Test the optimized histogram implementation."""
-    
+
     def test_correctness_uniform(self):
         """Test correctness with uniform distribution."""
         np.random.seed(42)
         data = np.random.uniform(0, 15, 10000).astype(np.float64)
-        
+
         np_hist, np_edges = np.histogram(data, bins=75, range=(0.0, 15.0))
-        opt_hist, opt_edges = optimized_histogram(data, bins=75, range=(0.0, 15.0))
-        
+        opt_hist, opt_edges = optimized_histogram(
+            data, bins=75, range=(0.0, 15.0)
+        )
+
         assert_allclose(np_hist, opt_hist, rtol=1e-14, atol=1)
         assert_allclose(np_edges, opt_edges, rtol=1e-14)
-    
+
     def test_correctness_normal(self):
         """Test correctness with normal distribution."""
         np.random.seed(42)
         data = np.random.normal(7.5, 2, 10000).clip(0, 15).astype(np.float64)
-        
+
         np_hist, np_edges = np.histogram(data, bins=75, range=(0.0, 15.0))
-        opt_hist, opt_edges = optimized_histogram(data, bins=75, range=(0.0, 15.0))
-        
+        opt_hist, opt_edges = optimized_histogram(
+            data, bins=75, range=(0.0, 15.0)
+        )
+
         assert_allclose(np_hist, opt_hist, rtol=1e-14, atol=1)
         assert_allclose(np_edges, opt_edges, rtol=1e-14)
-    
+
     def test_edge_cases_zeros(self):
         """Test with all zeros."""
         data = np.zeros(1000, dtype=np.float64)
-        
+
         np_hist, np_edges = np.histogram(data, bins=75, range=(0.0, 15.0))
-        opt_hist, opt_edges = optimized_histogram(data, bins=75, range=(0.0, 15.0))
-        
+        opt_hist, opt_edges = optimized_histogram(
+            data, bins=75, range=(0.0, 15.0)
+        )
+
         assert_allclose(np_hist, opt_hist, rtol=1e-14, atol=1)
         assert_allclose(np_edges, opt_edges, rtol=1e-14)
-    
+
     def test_edge_cases_max_values(self):
         """Test with values at maximum range."""
         data = np.ones(1000, dtype=np.float64) * 14.999
-        
+
         np_hist, np_edges = np.histogram(data, bins=75, range=(0.0, 15.0))
-        opt_hist, opt_edges = optimized_histogram(data, bins=75, range=(0.0, 15.0))
-        
+        opt_hist, opt_edges = optimized_histogram(
+            data, bins=75, range=(0.0, 15.0)
+        )
+
         assert_allclose(np_hist, opt_hist, rtol=1e-14, atol=1)
         assert_allclose(np_edges, opt_edges, rtol=1e-14)
-    
+
     def test_boundary_values(self):
         """Test with boundary values."""
         data = np.array([0.0, 14.999, 15.0, 7.5] * 250, dtype=np.float64)
-        
+
         np_hist, np_edges = np.histogram(data, bins=75, range=(0.0, 15.0))
-        opt_hist, opt_edges = optimized_histogram(data, bins=75, range=(0.0, 15.0))
-        
+        opt_hist, opt_edges = optimized_histogram(
+            data, bins=75, range=(0.0, 15.0)
+        )
+
         # Allow for small differences at boundaries due to floating point precision
         assert_allclose(np_hist, opt_hist, rtol=1e-14, atol=1)
         assert_allclose(np_edges, opt_edges, rtol=1e-14)
-    
+
     def test_bin_edges_values(self):
         """Test with values exactly at bin edges."""
         data = np.linspace(0, 15, 1001, dtype=np.float64)
-        
+
         np_hist, np_edges = np.histogram(data, bins=75, range=(0.0, 15.0))
-        opt_hist, opt_edges = optimized_histogram(data, bins=75, range=(0.0, 15.0))
-        
+        opt_hist, opt_edges = optimized_histogram(
+            data, bins=75, range=(0.0, 15.0)
+        )
+
         # Allow for small differences at boundaries due to floating point precision
         assert_allclose(np_hist, opt_hist, rtol=1e-14, atol=1)
         assert_allclose(np_edges, opt_edges, rtol=1e-14)
-    
+
     def test_serial_parallel_consistency(self):
         """Test that serial and parallel versions produce identical results."""
         np.random.seed(42)
         data = np.random.random(100000).astype(np.float64) * 15.0
-        
+
         hist_serial, edges_serial = optimized_histogram(
             data, bins=75, range=(0.0, 15.0), use_parallel=False
         )
         hist_parallel, edges_parallel = optimized_histogram(
             data, bins=75, range=(0.0, 15.0), use_parallel=True
         )
-        
+
         assert_allclose(hist_serial, hist_parallel, rtol=1e-14, atol=1)
         assert_array_equal(edges_serial, edges_parallel)
-    
+
     def test_different_bin_counts(self):
         """Test with different bin counts."""
         np.random.seed(42)
         data = np.random.random(10000).astype(np.float64) * 15.0
-        
+
         for bins in [10, 50, 100, 200]:
-            np_hist, np_edges = np.histogram(data, bins=bins, range=(0.0, 15.0))
-            opt_hist, opt_edges = optimized_histogram(data, bins=bins, range=(0.0, 15.0))
-            
-            assert_allclose(np_hist, opt_hist, rtol=1e-14, atol=1,
-                          err_msg=f"Failed for bins={bins}")
-            assert_allclose(np_edges, opt_edges, rtol=1e-14,
-                          err_msg=f"Failed for bins={bins}")
-    
+            np_hist, np_edges = np.histogram(
+                data, bins=bins, range=(0.0, 15.0)
+            )
+            opt_hist, opt_edges = optimized_histogram(
+                data, bins=bins, range=(0.0, 15.0)
+            )
+
+            assert_allclose(
+                np_hist,
+                opt_hist,
+                rtol=1e-14,
+                atol=1,
+                err_msg=f"Failed for bins={bins}",
+            )
+            assert_allclose(
+                np_edges,
+                opt_edges,
+                rtol=1e-14,
+                err_msg=f"Failed for bins={bins}",
+            )
+
     def test_different_ranges(self):
         """Test with different histogram ranges."""
         np.random.seed(42)
         data = np.random.random(10000).astype(np.float64) * 20.0
-        
+
         for range_val in [(0.0, 10.0), (0.0, 20.0), (5.0, 15.0)]:
             np_hist, np_edges = np.histogram(data, bins=75, range=range_val)
-            opt_hist, opt_edges = optimized_histogram(data, bins=75, range=range_val)
-            
-            assert_allclose(np_hist, opt_hist, rtol=1e-14, atol=1,
-                          err_msg=f"Failed for range={range_val}")
-            assert_allclose(np_edges, opt_edges, rtol=1e-14,
-                          err_msg=f"Failed for range={range_val}")
-    
+            opt_hist, opt_edges = optimized_histogram(
+                data, bins=75, range=range_val
+            )
+
+            assert_allclose(
+                np_hist,
+                opt_hist,
+                rtol=1e-14,
+                atol=1,
+                err_msg=f"Failed for range={range_val}",
+            )
+            assert_allclose(
+                np_edges,
+                opt_edges,
+                rtol=1e-14,
+                err_msg=f"Failed for range={range_val}",
+            )
+
     def test_non_contiguous_array(self):
         """Test with non-contiguous array."""
         np.random.seed(42)
         # Create a non-contiguous array by slicing
         data = np.random.random(20000).astype(np.float64) * 15.0
         data_non_contig = data[::2]  # Every other element
-        
-        assert not data_non_contig.flags['C_CONTIGUOUS']
-        
-        np_hist, np_edges = np.histogram(data_non_contig, bins=75, range=(0.0, 15.0))
-        opt_hist, opt_edges = optimized_histogram(data_non_contig, bins=75, range=(0.0, 15.0))
-        
+
+        assert not data_non_contig.flags["C_CONTIGUOUS"]
+
+        np_hist, np_edges = np.histogram(
+            data_non_contig, bins=75, range=(0.0, 15.0)
+        )
+        opt_hist, opt_edges = optimized_histogram(
+            data_non_contig, bins=75, range=(0.0, 15.0)
+        )
+
         assert_allclose(np_hist, opt_hist, rtol=1e-14, atol=1)
         assert_allclose(np_edges, opt_edges, rtol=1e-14)
-    
+
     @pytest.mark.parametrize("size", [100, 1000, 10000, 100000])
     def test_scaling(self, size):
         """Test correctness at different scales."""
         np.random.seed(42)
         data = np.random.random(size).astype(np.float64) * 15.0
-        
+
         np_hist, np_edges = np.histogram(data, bins=75, range=(0.0, 15.0))
-        opt_hist, opt_edges = optimized_histogram(data, bins=75, range=(0.0, 15.0))
-        
-        assert_allclose(np_hist, opt_hist, rtol=1e-14, atol=1,
-                      err_msg=f"Failed for size={size}")
-        assert_allclose(np_edges, opt_edges, rtol=1e-14,
-                      err_msg=f"Failed for size={size}")
+        opt_hist, opt_edges = optimized_histogram(
+            data, bins=75, range=(0.0, 15.0)
+        )
+
+        assert_allclose(
+            np_hist,
+            opt_hist,
+            rtol=1e-14,
+            atol=1,
+            err_msg=f"Failed for size={size}",
+        )
+        assert_allclose(
+            np_edges, opt_edges, rtol=1e-14, err_msg=f"Failed for size={size}"
+        )
 
 
-@pytest.mark.skipif(HAS_NUMBA, reason="Testing fallback when Numba not available")
+@pytest.mark.skipif(
+    HAS_NUMBA, reason="Testing fallback when Numba not available"
+)
 class TestHistogramFallback:
     """Test that the module falls back gracefully when Numba is not available."""
-    
+
     def test_import_without_numba(self):
         """Test that import works without Numba."""
         # This test will only run if Numba is not installed
         # The import should still work but HAS_NUMBA should be False
         from MDAnalysis.lib import histogram_opt
-        assert not histogram_opt.HAS_NUMBA
+
+        assert not histogram_opt.HAS_NUMBA