r03ert0
diff --git a/‎lib/matplotlib/colors.py
Copy file name to clipboardExpand all lines: lib/matplotlib/colors.py
+9-6Lines changed: 9 additions & 6 deletions b/‎lib/matplotlib/colors.py
Copy file name to clipboardExpand all lines: lib/matplotlib/colors.py
+9-6Lines changed: 9 additions & 6 deletions
diff --git a/‎lib/matplotlib/tests/test_colors.py
Copy file name to clipboardExpand all lines: lib/matplotlib/tests/test_colors.py
+12Lines changed: 12 additions & 0 deletions b/‎lib/matplotlib/tests/test_colors.py
Copy file name to clipboardExpand all lines: lib/matplotlib/tests/test_colors.py
+12Lines changed: 12 additions & 0 deletions
@@ -66,7 +66,7 @@
 """
 
 import base64
-from collections.abc import Sized
+from collections.abc import Sized, Sequence
 import copy
 import functools
 import inspect
@@ -373,11 +373,14 @@ def to_rgba_array(c, alpha=None):
 
     # Quick path if the whole sequence can be directly converted to a numpy
     # array in one shot.
-    lens = {len(cc) if isinstance(cc, (list, tuple)) else -1 for cc in c}
-    if lens == {3}:
-        rgba = np.column_stack([c, np.ones(len(c))])
-    elif lens == {4}:
-        rgba = np.array(c)
+    if isinstance(c, Sequence):
+        lens = {len(cc) if isinstance(cc, (list, tuple)) else -1 for cc in c}
+        if lens == {3}:
+            rgba = np.column_stack([c, np.ones(len(c))])
+        elif lens == {4}:
+            rgba = np.array(c)
+        else:
+            rgba = np.array([to_rgba(cc) for cc in c])
     else:
         rgba = np.array([to_rgba(cc) for cc in c])
 
 
@@ -1346,6 +1346,18 @@ def test_2d_to_rgba():
     assert rgba_1d == rgba_2d
 
 
+def test_set_dict_to_rgba():
+    # downstream libraries do this...
+    # note we can't test this because it is not well-ordered
+    # so just smoketest:
+    colors = set([(0, .5, 1), (1, .2, .5), (.4, 1, .2)])
+    res = mcolors.to_rgba_array(colors)
+    palette = {"red": (1, 0, 0), "green": (0, 1, 0), "blue": (0, 0, 1)}
+    res = mcolors.to_rgba_array(palette.values())
+    exp = np.eye(3)
+    np.testing.assert_array_almost_equal(res[:, :-1], exp)
+
+
 def test_norm_deepcopy():
     norm = mcolors.LogNorm()
     norm.vmin = 0.0002