Apply black formatting to fix linting issues

Author: rhowardstone

.gitignore

@@ -52,3 +52,4 @@ benchmarks/results
 .idea
 .vscode
 *.lock
+venv/

package/MDAnalysis/analysis/rdf.py

@@ -322,9 +322,11 @@ def _single_frame(self):
 
         # Use optimized histogram if available, otherwise fall back to numpy
         if HAS_NUMBA and optimized_histogram is not None:
-            count, _ = optimized_histogram(dist, 
-                                          bins=self.rdf_settings['bins'],
-                                          range=self.rdf_settings['range'])
+            count, _ = optimized_histogram(
+                dist,
+                bins=self.rdf_settings["bins"],
+                range=self.rdf_settings["range"],
+            )
         else:
             count, _ = np.histogram(dist, **self.rdf_settings)
         self.results.count += count

package/MDAnalysis/lib/histogram_opt.py

@@ -33,19 +33,21 @@
 try:
     import numba as nb
     from numba import prange
+
     HAS_NUMBA = True
 except ImportError:
     HAS_NUMBA = False
-    
-__all__ = ['optimized_histogram', 'HAS_NUMBA']
+
+__all__ = ["optimized_histogram", "HAS_NUMBA"]
 
 
 if HAS_NUMBA:
+
     @nb.jit(nopython=True, parallel=True, fastmath=True)
     def _histogram_distances_parallel(distances, bins, bin_edges):
         """
         Parallel histogram calculation using Numba with efficient parallelization.
-        
+
         Parameters
         ----------
         distances : numpy.ndarray
@@ -54,7 +56,7 @@ def _histogram_distances_parallel(distances, bins, bin_edges):
             Number of histogram bins
         bin_edges : numpy.ndarray
             Pre-computed bin edges
-        
+
         Returns
         -------
         numpy.ndarray
@@ -64,19 +66,19 @@ def _histogram_distances_parallel(distances, bins, bin_edges):
         bin_width = (bin_edges[-1] - bin_edges[0]) / bins
         min_val = bin_edges[0]
         max_val = bin_edges[-1]
-        
+
         # Use chunks to avoid false sharing and improve cache performance
         chunk_size = max(1024, n // (nb.config.NUMBA_NUM_THREADS * 4))
         n_chunks = (n + chunk_size - 1) // chunk_size
-        
+
         # Pre-allocate result array
         partial_hists = np.zeros((n_chunks, bins), dtype=np.int64)
-        
+
         # Process chunks in parallel
         for chunk_id in prange(n_chunks):
             start = chunk_id * chunk_size
             end = min(start + chunk_size, n)
-            
+
             # Local histogram for this chunk
             for i in range(start, end):
                 dist = distances[i]
@@ -85,27 +87,26 @@ def _histogram_distances_parallel(distances, bins, bin_edges):
                     if bin_idx >= bins:
                         bin_idx = bins - 1
                     partial_hists[chunk_id, bin_idx] += 1
-        
+
         # Sum up partial histograms
         hist = np.sum(partial_hists, axis=0)
-        
-        return hist
 
+        return hist
 
     @nb.jit(nopython=True, cache=True, fastmath=True)
     def _histogram_distances_serial(distances, bins, bin_edges):
         """
         Serial histogram calculation using Numba with optimizations.
-        
+
         Parameters
         ----------
         distances : numpy.ndarray
             1D array of distances to histogram
         bins : int
-            Number of histogram bins  
+            Number of histogram bins
         bin_edges : numpy.ndarray
             Pre-computed bin edges
-        
+
         Returns
         -------
         numpy.ndarray
@@ -115,26 +116,28 @@ def _histogram_distances_serial(distances, bins, bin_edges):
         hist = np.zeros(bins, dtype=np.int64)
         bin_width = (bin_edges[-1] - bin_edges[0]) / bins
         min_val = bin_edges[0]
-        
+
         for i in range(n):
             dist = distances[i]
             if dist >= min_val and dist <= bin_edges[-1]:
                 bin_idx = int((dist - min_val) / bin_width)
                 if bin_idx >= bins:
                     bin_idx = bins - 1
                 hist[bin_idx] += 1
-        
+
         return hist
 
 
-def optimized_histogram(distances, bins=75, range=(0.0, 15.0), use_parallel=None):
+def optimized_histogram(
+    distances, bins=75, range=(0.0, 15.0), use_parallel=None
+):
     """
     Optimized histogram function for distance calculations.
-    
+
     This function provides a significant performance improvement over numpy.histogram
     for distance histogram calculations, particularly useful for RDF analysis.
     Performance improvements of 10-15x are typical for large datasets.
-    
+
     Parameters
     ----------
     distances : numpy.ndarray
@@ -147,56 +150,60 @@ def optimized_histogram(distances, bins=75, range=(0.0, 15.0), use_parallel=None
         Whether to use parallel execution. If None (default), automatically
         decides based on array size (parallel for >1000 elements).
         Requires Numba to be installed for acceleration.
-    
+
     Returns
     -------
     counts : numpy.ndarray
         The histogram counts
     edges : numpy.ndarray
         The bin edges
-        
+
     Notes
     -----
     This function requires Numba for acceleration. If Numba is not installed,
     it falls back to numpy.histogram with a warning.
-    
+
     The parallel version provides best performance for large arrays (>10000 elements)
     and when multiple CPU cores are available. For small arrays, the serial version
     may be faster due to lower overhead.
-    
+
     Examples
     --------
     >>> import numpy as np
     >>> from MDAnalysis.lib.histogram_opt import optimized_histogram
     >>> distances = np.random.random(10000) * 15.0
     >>> hist, edges = optimized_histogram(distances, bins=75, range=(0, 15))
-    
+
     .. versionadded:: 2.10.0
     """
     if not HAS_NUMBA:
         import warnings
-        warnings.warn("Numba not available, falling back to numpy.histogram. "
-                      "Install numba for 10-15x performance improvement.",
-                      RuntimeWarning, stacklevel=2)
+
+        warnings.warn(
+            "Numba not available, falling back to numpy.histogram. "
+            "Install numba for 10-15x performance improvement.",
+            RuntimeWarning,
+            stacklevel=2,
+        )
         return np.histogram(distances, bins=bins, range=range)
-    
+
     # Create bin edges
     edges = np.linspace(range[0], range[1], bins + 1)
-    
+
     # Ensure distances is contiguous for optimal performance
-    if not distances.flags['C_CONTIGUOUS']:
+    if not distances.flags["C_CONTIGUOUS"]:
         distances = np.ascontiguousarray(distances)
-    
+
     # Auto-decide parallel vs serial if not specified
     if use_parallel is None:
         use_parallel = len(distances) > 1000
-    
+
     # Choose implementation based on size and parallelization setting
     if use_parallel:
         counts = _histogram_distances_parallel(distances, bins, edges)
     else:
         counts = _histogram_distances_serial(distances, bins, edges)
-    
+
     return counts.astype(np.float64), edges
 
 
@@ -211,4 +218,4 @@ def optimized_histogram(distances, bins=75, range=(0.0, 15.0), use_parallel=None
         del _test_data, _test_edges
     except:
         # Silently fail if precompilation doesn't work
-        pass
+        pass