Merge pull request #6494 from Carreau/stepfilled

tacaswell · tacaswell · commit 8fcc26ee3607 · 2016-05-29T21:45:57.000-04:00
FIX: Make edgecolors and facecolors the same when using stepfilled histogram.
diff --git a/lib/matplotlib/axes/_axes.py b/lib/matplotlib/axes/_axes.py
@@ -6281,24 +6281,19 @@ def _normalize_input(inp, ename='input'):
                     xvals.append(x.copy())
                     yvals.append(y.copy())
 
-            if fill:
-                # add patches in reverse order so that when stacking,
-                # items lower in the stack are plottted on top of
-                # items higher in the stack
-                for x, y, c in reversed(list(zip(xvals, yvals, color))):
-                    patches.append(self.fill(
-                        x, y,
-                        closed=True,
-                        facecolor=c,
-                        margins=margins))
-            else:
-                for x, y, c in reversed(list(zip(xvals, yvals, color))):
-                    split = 2 * len(bins)
-                    patches.append(self.fill(
-                        x[:split], y[:split],
-                        closed=False, edgecolor=c,
-                        fill=False,
-                        margins=margins))
+            #stepfill is closed, step is not
+            split = -1 if fill else 2 * len(bins)
+            # add patches in reverse order so that when stacking,
+            # items lower in the stack are plottted on top of
+            # items higher in the stack
+            for x, y, c in reversed(list(zip(xvals, yvals, color))):
+                patches.append(self.fill(
+                    x[:split], y[:split],
+                    closed=True if fill else None,
+                    facecolor=c,
+                    edgecolor=None if fill else c,
+                    fill=fill if fill else None,
+                    margins=margins))
 
             # we return patches, so put it back in the expected order
             patches.reverse()
diff --git a/lib/matplotlib/tests/baseline_images/test_axes/hist_step_filled.png b/lib/matplotlib/tests/baseline_images/test_axes/hist_step_filled.png
diff --git a/lib/matplotlib/tests/test_axes.py b/lib/matplotlib/tests/test_axes.py
@@ -27,7 +27,6 @@
 import matplotlib.markers as mmarkers
 import matplotlib.patches as mpatches
 from numpy.testing import assert_allclose, assert_array_equal
-import warnings
 from matplotlib.cbook import IgnoredKeywordWarning
 import matplotlib.colors as mcolors
 
@@ -36,6 +35,7 @@
 #       different baseline images to prevent race conditions when nose runs
 #       the tests with multiple threads.
 
+
 @image_comparison(baseline_images=['formatter_ticker_001',
                                    'formatter_ticker_002',
                                    'formatter_ticker_003',
@@ -1151,6 +1151,27 @@ def test_hist_steplog():
     plt.hist(data_big, 100, histtype='stepfilled', log=True, orientation='horizontal')
 
 
+@image_comparison(baseline_images=['hist_step_filled'], remove_text=True,
+                  extensions=['png'])
+def test_hist_step_filled():
+    np.random.seed(0)
+    x = np.random.randn(1000, 3)
+    n_bins = 10
+
+    kwargs = [{'fill': True}, {'fill': False}, {'fill': None}, {}]*2
+    types = ['step']*4+['stepfilled']*4
+    fig, axes = plt.subplots(nrows=2, ncols=4)
+    axes = axes.flatten()
+
+    for kg, _type, ax in zip(kwargs, types, axes):
+        ax.hist(x, n_bins, histtype=_type, stacked=True, **kg)
+        ax.set_title('%s/%s' % (kg, _type))
+        ax.set_ylim(ymin=-50)
+
+    patches = axes[0].patches
+    assert all([p.get_facecolor() == p.get_edgecolor() for p in patches])
+
+
 @image_comparison(baseline_images=['hist_step_log_bottom'],
                   remove_text=True, extensions=['png'])
 def test_hist_step_log_bottom():
@@ -1765,7 +1786,7 @@ def test_boxplot():
     ax.set_ylim((-30, 30))
 
     # Reuse testcase from above for a labeled data test
-    data={"x": [x, x]}
+    data = {"x": [x, x]}
     fig, ax = plt.subplots()
     ax.boxplot("x", bootstrap=10000, notch=1, data=data)
     ax.set_ylim((-30, 30))
