feat: add StepPatch baseline array to the demo

andrzejnovak · andrzejnovak · commit 378ac3d960ce · 2020-09-18T11:10:03.000+02:00
diff --git a/examples/lines_bars_and_markers/stairs_demo.py b/examples/lines_bars_and_markers/stairs_demo.py
@@ -46,6 +46,15 @@
     ax.legend()
 plt.show()
 
+#############################################################################
+# ``baseline`` can take an array to allow for stacked histogram plots
+
+A = np.tile([1,2,3], (3,1)) * np.arange(1, 4).reshape(-1, 1)
+A = np.vstack([np.zeros(3), A])
+
+for i in range(3):
+    plt.stairs(A[i+1], baseline=A[i], fill=True)
+
 #############################################################################
 # Comparison of `.pyplot.step` and `.pyplot.stairs`.
 
diff --git a/lib/matplotlib/patches.py b/lib/matplotlib/patches.py
@@ -1031,15 +1031,20 @@ def _update_path(self):
                              f"`len(values) = {self._values.size}` and "
                              f"`len(edges) = {self._edges.size}`.")
         verts, codes = [], []
-        for idx0, idx1 in cbook.contiguous_regions(~np.isnan(self._values)):
+
+        _nan_vals = ~np.isnan(self._values)
+        if self.baseline is not None:
+            _nan_vals = _nan_vals & ~np.isnan(self.baseline)
+        for idx0, idx1 in cbook.contiguous_regions(_nan_vals):
             x = np.repeat(self._edges[idx0:idx1+1], 2)
             y = np.repeat(self._values[idx0:idx1], 2)     
             if np.isscalar(self.baseline):  # baseline values
                 y = np.hstack((self.baseline, y, self.baseline))
             elif hasattr(self.baseline, '__len__'):  # baseline array
                 base = np.repeat(self.baseline[idx0:idx1], 2)[::-1]
                 x = np.concatenate([x, x[::-1]])
-                y = np.concatenate([np.hstack((base[-1], y, base[0], base[0], base, base[-1]))])
+                y = np.concatenate([np.hstack((base[-1], y, base[0],
+                                               base[0], base, base[-1]))])
             else:  # no baseline
                 y = np.hstack((y[0], y, y[-1]))
             if self.orientation == 'vertical':
