Correctly convert units for a stacked histogram

dstansby · dstansby · commit 00cb61689a50 · 2017-11-01T17:33:38.000Z
diff --git a/lib/matplotlib/axes/_axes.py b/lib/matplotlib/axes/_axes.py
@@ -5879,7 +5879,7 @@ def hist(self, x, bins=None, range=None, density=None, weights=None,
         Parameters
         ----------
         x : (n,) array or sequence of (n,) arrays
-            Input values, this takes either a single array or a sequency of
+            Input values, this takes either a single array or a sequence of
             arrays which are not required to be of the same length
 
         bins : integer or sequence or 'auto', optional
@@ -6091,9 +6091,17 @@ def hist(self, x, bins=None, range=None, density=None, weights=None,
                              "Please only use 'density', since 'normed'"
                              "will be deprecated.")
 
-        # process the unit information
-        self._process_unit_info(xdata=x, kwargs=kwargs)
-        x = self.convert_xunits(x)
+        # Process unit information
+        # If doing a stacked histogram, the input is a list of datasets, so
+        # we need to do the unit conversion individually on eaach dataset
+        if stacked:
+            self._process_unit_info(xdata=x[0], kwargs=kwargs)
+            for i, xi in enumerate(x):
+                x[i] = self.convert_xunits(xi)
+        else:
+            self._process_unit_info(xdata=x, kwargs=kwargs)
+            x = self.convert_xunits(x)
+
         if bin_range is not None:
             bin_range = self.convert_xunits(bin_range)
 
diff --git a/lib/matplotlib/tests/test_axes.py b/lib/matplotlib/tests/test_axes.py
@@ -1576,6 +1576,13 @@ def test_hist_unequal_bins_density():
     assert_allclose(mpl_heights, np_heights)
 
 
+def test_hist_stacked_datetime():
+    data = [[datetime.datetime(2017, 1, 1), datetime.datetime(2017, 1, 1)],
+            [datetime.datetime(2017, 1, 1), datetime.datetime(2017, 1, 2)]]
+    fig, ax = plt.subplots()
+    ax.hist(data, stacked=True)
+
+
 def contour_dat():
     x = np.linspace(-3, 5, 150)
     y = np.linspace(-3, 5, 120)
