Update hist bin centers algorithm

Signed-off-by: Tomas Pereira de Vasconcelos <tomasvasconcelos1@gmail.com>

Author: tpvasconcelos

docs/_static/charts/basic_hist.webp

@@ -7,7 +7,7 @@ Unreleased changes
 
 ### Bug fixes
 
-- Fix the way histogram bin midpoints are computed ({gh-pr}`364`)
+- Fix the way histogram bin centers are computed ({gh-pr}`364`)
 
 ### Documentation
 

docs/reference/changelog.md

@@ -34,9 +34,9 @@ def bin_trace_samples(
             raise ValueError("The weights array should have the same length as the samples array.")
         if not np.isfinite(weights).all():
             raise ValueError("The weights array should not contain any infs or NaNs.")
-    hist, bins = np.histogram(trace_samples, bins=nbins, weights=weights)
-    bin_midpoints = np.linspace(bins[0], bins[-1], nbins)
-    return [(float(x), float(y)) for x, y in zip(bin_midpoints, hist, strict=True)]
+    hist_counts, hist_edges = np.histogram(trace_samples, bins=nbins, weights=weights)
+    bin_centers = 0.5 * (hist_edges[:-1] + hist_edges[1:])
+    return [(float(x), float(y)) for x, y in zip(bin_centers, hist_counts, strict=True)]
 
 
 def bin_samples(

src/ridgeplot/_hist.py

@@ -12,7 +12,11 @@
 
 SAMPLES_IN = [1, 2, 2, 3, 4]
 NBINS = 4
-DENSITIES_OUT = [(1, 1), (2, 2), (3, 1), (4, 1)]
+# NOTE: The x values in DENSITIES_OUT correspond to the centers of
+#       equally spaced bins over the range [1, 4].
+#       This can be counterintuitive for count data, as the bins
+#       do not align with the integer sample values.
+DENSITIES_OUT = [(1.375, 1), (2.125, 2), (2.875, 1), (3.625, 1)]
 X_OUT, Y_OUT = zip(*DENSITIES_OUT, strict=True)
 
 WEIGHTS = [1, 1, 1, 1, 9]