diff --git a/doc/api/next_api_changes/behavior/21038-DS.rst b/doc/api/next_api_changes/behavior/21038-DS.rst
new file mode 100644
index 000000000000..55ca3fb7597d
--- /dev/null
+++ b/doc/api/next_api_changes/behavior/21038-DS.rst
@@ -0,0 +1,4 @@
+hexbin with a log norm
+----------------------
+`~.axes.Axes.hexbin` no longer (incorrectly) adds 1 to every bin value if a
+log norm is being used.
diff --git a/lib/matplotlib/axes/_axes.py b/lib/matplotlib/axes/_axes.py
index 3856e45229fc..6855299689fc 100644
--- a/lib/matplotlib/axes/_axes.py
+++ b/lib/matplotlib/axes/_axes.py
@@ -4589,6 +4589,11 @@ def reduce_C_function(C: array) -> float
# Count the number of data in each hexagon
x = np.array(x, float)
y = np.array(y, float)
+
+ if marginals:
+ xorig = x.copy()
+ yorig = y.copy()
+
if xscale == 'log':
if np.any(x <= 0.0):
raise ValueError("x contains non-positive values, so can not"
@@ -4617,10 +4622,6 @@ def reduce_C_function(C: array) -> float
sx = (xmax - xmin) / nx
sy = (ymax - ymin) / ny
- if marginals:
- xorig = x.copy()
- yorig = y.copy()
-
x = (x - xmin) / sx
y = (y - ymin) / sy
ix1 = np.round(x).astype(int)
@@ -4746,11 +4747,6 @@ def reduce_C_function(C: array) -> float
vmin = vmax = None
bins = None
- if isinstance(norm, mcolors.LogNorm):
- if (accum == 0).any():
- # make sure we have no zeros
- accum += 1
-
# autoscale the norm with current accum values if it hasn't
# been set
if norm is not None:
@@ -4781,40 +4777,42 @@ def reduce_C_function(C: array) -> float
if not marginals:
return collection
+ # Process marginals
if C is None:
C = np.ones(len(x))
- def coarse_bin(x, y, coarse):
- ind = coarse.searchsorted(x).clip(0, len(coarse) - 1)
- mus = np.zeros(len(coarse))
- for i in range(len(coarse)):
- yi = y[ind == i]
+ def coarse_bin(x, y, bin_edges):
+ """
+ Sort x-values into bins defined by *bin_edges*, then for all the
+ corresponding y-values in each bin use *reduce_c_function* to
+ compute the bin value.
+ """
+ nbins = len(bin_edges) - 1
+ # Sort x-values into bins
+ bin_idxs = np.searchsorted(bin_edges, x) - 1
+ mus = np.zeros(nbins) * np.nan
+ for i in range(nbins):
+ # Get y-values for each bin
+ yi = y[bin_idxs == i]
if len(yi) > 0:
- mu = reduce_C_function(yi)
- else:
- mu = np.nan
- mus[i] = mu
+ mus[i] = reduce_C_function(yi)
return mus
- coarse = np.linspace(xmin, xmax, gridsize)
+ if xscale == 'log':
+ bin_edges = np.geomspace(xmin, xmax, nx + 1)
+ else:
+ bin_edges = np.linspace(xmin, xmax, nx + 1)
+ xcoarse = coarse_bin(xorig, C, bin_edges)
- xcoarse = coarse_bin(xorig, C, coarse)
- valid = ~np.isnan(xcoarse)
verts, values = [], []
- for i, val in enumerate(xcoarse):
- thismin = coarse[i]
- if i < len(coarse) - 1:
- thismax = coarse[i + 1]
- else:
- thismax = thismin + np.diff(coarse)[-1]
-
- if not valid[i]:
+ for bin_left, bin_right, val in zip(
+ bin_edges[:-1], bin_edges[1:], xcoarse):
+ if np.isnan(val):
continue
-
- verts.append([(thismin, 0),
- (thismin, 0.05),
- (thismax, 0.05),
- (thismax, 0)])
+ verts.append([(bin_left, 0),
+ (bin_left, 0.05),
+ (bin_right, 0.05),
+ (bin_right, 0)])
values.append(val)
values = np.array(values)
@@ -4829,20 +4827,21 @@ def coarse_bin(x, y, coarse):
hbar.update(kwargs)
self.add_collection(hbar, autolim=False)
- coarse = np.linspace(ymin, ymax, gridsize)
- ycoarse = coarse_bin(yorig, C, coarse)
- valid = ~np.isnan(ycoarse)
+ if yscale == 'log':
+ bin_edges = np.geomspace(ymin, ymax, 2 * ny + 1)
+ else:
+ bin_edges = np.linspace(ymin, ymax, 2 * ny + 1)
+ ycoarse = coarse_bin(yorig, C, bin_edges)
+
verts, values = [], []
- for i, val in enumerate(ycoarse):
- thismin = coarse[i]
- if i < len(coarse) - 1:
- thismax = coarse[i + 1]
- else:
- thismax = thismin + np.diff(coarse)[-1]
- if not valid[i]:
+ for bin_bottom, bin_top, val in zip(
+ bin_edges[:-1], bin_edges[1:], ycoarse):
+ if np.isnan(val):
continue
- verts.append([(0, thismin), (0.0, thismax),
- (0.05, thismax), (0.05, thismin)])
+ verts.append([(0, bin_bottom),
+ (0, bin_top),
+ (0.05, bin_top),
+ (0.05, bin_bottom)])
values.append(val)
values = np.array(values)
diff --git a/lib/matplotlib/tests/baseline_images/test_axes/hexbin_log.png b/lib/matplotlib/tests/baseline_images/test_axes/hexbin_log.png
index febefb870918..466519461aac 100644
Binary files a/lib/matplotlib/tests/baseline_images/test_axes/hexbin_log.png and b/lib/matplotlib/tests/baseline_images/test_axes/hexbin_log.png differ
diff --git a/lib/matplotlib/tests/test_axes.py b/lib/matplotlib/tests/test_axes.py
index 59ebc2c2f53d..b4dbd9fdb4e1 100644
--- a/lib/matplotlib/tests/test_axes.py
+++ b/lib/matplotlib/tests/test_axes.py
@@ -781,7 +781,8 @@ def test_hexbin_log():
y = np.power(2, y * 0.5)
fig, ax = plt.subplots()
- h = ax.hexbin(x, y, yscale='log', bins='log')
+ h = ax.hexbin(x, y, yscale='log', bins='log',
+ marginals=True, reduce_C_function=np.sum)
plt.colorbar(h)
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