Fix Windows dtype issue and apply black formatting

- Replace 'long' with 'cnp.int64_t' to fix cross-platform compatibility
- On Windows, 'long' is 32-bit while np.int64 is 64-bit causing mismatch
- Apply black formatting to Python files

Author: rhowardstone

package/MDAnalysis/analysis/rdf.py

@@ -272,8 +272,7 @@ def __init__(
 
         if self.norm not in ["rdf", "density", "none"]:
             raise ValueError(
-                f"'{self.norm}' is an invalid norm. "
-                "Use 'rdf', 'density' or 'none'."
+                f"'{self.norm}' is an invalid norm. " "Use 'rdf', 'density' or 'none'."
             )
 
         self.backend = backend
@@ -603,9 +602,7 @@ def __init__(
         backend="serial",
         **kwargs,
     ):
-        super(InterRDF_s, self).__init__(
-            ags[0][0].universe.trajectory, **kwargs
-        )
+        super(InterRDF_s, self).__init__(ags[0][0].universe.trajectory, **kwargs)
 
         warnings.warn(
             "The `u` attribute is superflous and will be removed "
@@ -619,8 +616,7 @@ def __init__(
 
         if self.norm not in ["rdf", "density", "none"]:
             raise ValueError(
-                f"'{self.norm}' is an invalid norm. "
-                "Use 'rdf', 'density' or 'none'."
+                f"'{self.norm}' is an invalid norm. " "Use 'rdf', 'density' or 'none'."
             )
 
         if density:

package/MDAnalysis/lib/c_histogram.pyx

@@ -64,7 +64,7 @@ __all__ = ['histogram', 'OPENMP_ENABLED']
 cdef void _histogram_serial(
     const double[::1] distances,
     uint64_t n,
-    long[::1] hist,
+    cnp.int64_t[::1] hist,
     int nbins,
     double bin_width,
     double min_val,
@@ -79,7 +79,7 @@ cdef void _histogram_serial(
         1D memory view of distances
     n : uint64_t
         Number of distances
-    hist : long[::1]
+    hist : cnp.int64_t[::1]
         Output histogram array
     nbins : int
         Number of bins
@@ -108,12 +108,12 @@ cdef void _histogram_serial(
 cdef void _histogram_parallel(
     const double[::1] distances,
     uint64_t n,
-    long[::1] hist,
+    cnp.int64_t[::1] hist,
     int nbins,
     double bin_width,
     double min_val,
     double max_val,
-    long[:, ::1] partial_hists,
+    cnp.int64_t[:, ::1] partial_hists,
     int num_threads
 ) noexcept nogil:
     """
@@ -128,7 +128,7 @@ cdef void _histogram_parallel(
         1D memory view of distances
     n : uint64_t
         Number of distances
-    hist : long[::1]
+    hist : cnp.int64_t[::1]
         Output histogram array
     nbins : int
         Number of bins
@@ -138,7 +138,7 @@ cdef void _histogram_parallel(
         Minimum value of range
     max_val : double
         Maximum value of range
-    partial_hists : long[:, ::1]
+    partial_hists : cnp.int64_t[:, ::1]
         Preallocated array for thread-local histograms
     num_threads : int
         Number of OpenMP threads to use
@@ -250,19 +250,19 @@ def histogram(
         distances = distances.astype(np.float64)
 
     # Create output arrays
-    cdef cnp.ndarray[long, ndim=1] hist = np.zeros(nbins, dtype=np.int64)
+    cdef cnp.ndarray[cnp.int64_t, ndim=1] hist = np.zeros(nbins, dtype=np.int64)
     cdef cnp.ndarray[double, ndim=1] edges = np.linspace(min_val, max_val, nbins + 1)
 
     # Create memory views for efficient access
     cdef const double[::1] dist_view = distances
-    cdef long[::1] hist_view = hist
+    cdef cnp.int64_t[::1] hist_view = hist
 
     # Declare variables for parallel execution
     cdef int num_threads = 0  # 0 means use OpenMP default
     cdef uint64_t chunk_size
     cdef uint64_t n_chunks
-    cdef cnp.ndarray[long, ndim=2] partial_hists_arr
-    cdef long[:, ::1] partial_hists_view
+    cdef cnp.ndarray[cnp.int64_t, ndim=2] partial_hists_arr
+    cdef cnp.int64_t[:, ::1] partial_hists_view
 
     if use_parallel:
         # Calculate number of chunks and allocate partial histograms

testsuite/MDAnalysisTests/lib/test_c_histogram.py

@@ -31,9 +31,7 @@ def test_correctness_uniform(self):
         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 = histogram(
-            data, bins=75, range_vals=(0.0, 15.0)
-        )
+        opt_hist, opt_edges = histogram(data, bins=75, range_vals=(0.0, 15.0))
 
         assert_allclose(np_hist, opt_hist, rtol=1e-14, atol=1)
         assert_allclose(np_edges, opt_edges, rtol=1e-14)
@@ -44,9 +42,7 @@ def test_correctness_normal(self):
         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 = histogram(
-            data, bins=75, range_vals=(0.0, 15.0)
-        )
+        opt_hist, opt_edges = histogram(data, bins=75, range_vals=(0.0, 15.0))
 
         assert_allclose(np_hist, opt_hist, rtol=1e-14, atol=1)
         assert_allclose(np_edges, opt_edges, rtol=1e-14)
@@ -56,9 +52,7 @@ def test_edge_cases_zeros(self):
         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 = histogram(
-            data, bins=75, range_vals=(0.0, 15.0)
-        )
+        opt_hist, opt_edges = histogram(data, bins=75, range_vals=(0.0, 15.0))
 
         assert_allclose(np_hist, opt_hist, rtol=1e-14, atol=1)
         assert_allclose(np_edges, opt_edges, rtol=1e-14)
@@ -68,9 +62,7 @@ def test_edge_cases_max_values(self):
         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 = histogram(
-            data, bins=75, range_vals=(0.0, 15.0)
-        )
+        opt_hist, opt_edges = histogram(data, bins=75, range_vals=(0.0, 15.0))
 
         assert_allclose(np_hist, opt_hist, rtol=1e-14, atol=1)
         assert_allclose(np_edges, opt_edges, rtol=1e-14)
@@ -80,9 +72,7 @@ def test_boundary_values(self):
         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 = histogram(
-            data, bins=75, range_vals=(0.0, 15.0)
-        )
+        opt_hist, opt_edges = histogram(data, bins=75, range_vals=(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)
@@ -93,9 +83,7 @@ def test_bin_edges_values(self):
         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 = histogram(
-            data, bins=75, range_vals=(0.0, 15.0)
-        )
+        opt_hist, opt_edges = histogram(data, bins=75, range_vals=(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)
@@ -122,12 +110,8 @@ def test_different_bin_counts(self):
         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 = histogram(
-                data, bins=bins, range_vals=(0.0, 15.0)
-            )
+            np_hist, np_edges = np.histogram(data, bins=bins, range=(0.0, 15.0))
+            opt_hist, opt_edges = histogram(data, bins=bins, range_vals=(0.0, 15.0))
 
             assert_allclose(
                 np_hist,
@@ -150,9 +134,7 @@ def test_different_ranges(self):
 
         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 = histogram(
-                data, bins=75, range_vals=range_val
-            )
+            opt_hist, opt_edges = histogram(data, bins=75, range_vals=range_val)
 
             assert_allclose(
                 np_hist,
@@ -177,9 +159,7 @@ def test_non_contiguous_array(self):
 
         assert not data_non_contig.flags["C_CONTIGUOUS"]
 
-        np_hist, np_edges = np.histogram(
-            data_non_contig, bins=75, range=(0.0, 15.0)
-        )
+        np_hist, np_edges = np.histogram(data_non_contig, bins=75, range=(0.0, 15.0))
         opt_hist, opt_edges = histogram(
             data_non_contig, bins=75, range_vals=(0.0, 15.0)
         )
@@ -194,9 +174,7 @@ def test_scaling(self, size):
         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 = histogram(
-            data, bins=75, range_vals=(0.0, 15.0)
-        )
+        opt_hist, opt_edges = histogram(data, bins=75, range_vals=(0.0, 15.0))
 
         assert_allclose(
             np_hist,