Merge pull request matplotlib#9223 from dstansby/hexbin-log

Set norm to log if bins=='log' in hexbin

Author: efiring

doc/api/api_changes.rst

@@ -451,6 +451,12 @@ Deprecations
 `.Legend.draggable()` is drepecated in favor of `.Legend.set_draggable()`.
 ``Legend.draggable`` may be reintroduced as a property in future releases.
 
+Colorbar for log-scaled hexbin
+------------------------------
+
+When using `hexbin` and plotting with a logarithmic color scale, the colorbar
+ticks are now correctly log scaled. Previously the tick values were linear
+scaled log(number of counts).
 
 API Changes in 2.2.0
 ====================

lib/matplotlib/axes/_axes.py

@@ -4326,9 +4326,23 @@ def hexbin(self, x, y, C=None, gridsize=100, bins=None,
                 offset_position="data"
                 )
 
+        # Check for valid norm
+        if norm is not None and not isinstance(norm, mcolors.Normalize):
+            msg = "'norm' must be an instance of 'mcolors.Normalize'"
+            raise ValueError(msg)
+
+        # Set normalizer if bins is 'log'
+        if bins == 'log':
+            if norm is not None:
+                warnings.warn("Only one of 'bins' and 'norm' arguments can be "
+                              "supplied, ignoring bins={}".format(bins))
+            else:
+                norm = mcolors.LogNorm()
+            bins = None
+
         if isinstance(norm, mcolors.LogNorm):
             if (accum == 0).any():
-                # make sure we have not zeros
+                # make sure we have no zeros
                 accum += 1
 
         # autoscale the norm with curren accum values if it hasn't
@@ -4337,20 +4351,14 @@ def hexbin(self, x, y, C=None, gridsize=100, bins=None,
             if norm.vmin is None and norm.vmax is None:
                 norm.autoscale(accum)
 
-        # Transform accum if needed
-        if bins == 'log':
-            accum = np.log10(accum + 1)
-        elif bins is not None:
+        if bins is not None:
             if not iterable(bins):
                 minimum, maximum = min(accum), max(accum)
                 bins -= 1  # one less edge than bins
                 bins = minimum + (maximum - minimum) * np.arange(bins) / bins
             bins = np.sort(bins)
             accum = bins.searchsorted(accum)
 
-        if norm is not None and not isinstance(norm, mcolors.Normalize):
-            raise ValueError(
-                "'norm' must be an instance of 'mcolors.Normalize'")
         collection.set_array(accum)
         collection.set_cmap(cmap)
         collection.set_norm(norm)

lib/matplotlib/tests/baseline_images/test_axes/hexbin_log.png

@@ -858,18 +858,18 @@ def __init__(self, x, y):
 
 
 @image_comparison(baseline_images=['hexbin_log'],
-                  remove_text=True,
-                  extensions=['png'])
+                  extensions=['png'], style='mpl20')
 def test_hexbin_log():
-    # Issue #1636
-    np.random.seed(0)
+    # Issue #1636 (and also test log scaled colorbar)
+    np.random.seed(19680801)
     n = 100000
     x = np.random.standard_normal(n)
     y = 2.0 + 3.0 * x + 4.0 * np.random.standard_normal(n)
     y = np.power(2, y * 0.5)
 
     fig, ax = plt.subplots()
-    ax.hexbin(x, y, yscale='log')
+    h = ax.hexbin(x, y, yscale='log', bins='log')
+    plt.colorbar(h)
 
 
 def test_inverted_limits():