matplotlib
diff --git a/‎lib/matplotlib/colors.py
Copy file name to clipboardExpand all lines: lib/matplotlib/colors.py
+54Lines changed: 54 additions & 0 deletions b/‎lib/matplotlib/colors.py
Copy file name to clipboardExpand all lines: lib/matplotlib/colors.py
+54Lines changed: 54 additions & 0 deletions
diff --git a/‎lib/matplotlib/tests/test_colors.py
Copy file name to clipboardExpand all lines: lib/matplotlib/tests/test_colors.py
+43Lines changed: 43 additions & 0 deletions b/‎lib/matplotlib/tests/test_colors.py
Copy file name to clipboardExpand all lines: lib/matplotlib/tests/test_colors.py
+43Lines changed: 43 additions & 0 deletions
@@ -1349,6 +1349,60 @@ def __call__(self, value, clip=None):
     def inverse(self, value):
         return value
 
+class Norm2d:
+    """
+    Normalize a list of two values corresponding to two 1D normalizers
+    """
+    def __init__(self, norm_instances=None):
+        """
+        Parameters
+        ----------
+        norm_instances :
+            A list of length two having instances of 1D normalizers
+        """
+        if norm_instances is None:
+            self.norm_instances = [Normalize(), Normalize()]
+        else:
+            self.norm_instances = norm_instances
+
+    def __call__(self, values, clip=None):
+        """
+        Parameters
+        ----------
+        values : array-like
+            A list of two values to be normalized
+        clip : list of bools, None, optional
+            A list of two bools corresponding to value in values.
+            If clip is None then clip is set according to corresponding
+            normalizers.
+
+        Returns
+        -------
+        A list of two normalized values according to corresponding 1D
+        normalizers.
+        """
+        norm_one, norm_two = self.norm_instances
+
+        if clip is None:
+            clip = [norm_one.clip, norm_two.clip]
+
+        return [norm_one(values[0], clip=clip[0]),
+                norm_two(values[1], clip=clip[1])]
+
+    def inverse(self, values):
+        """
+        Parameters
+        ----------
+        values : array-like
+            A list of two values to be inverted
+
+        Returns
+        -------
+        A list of two unnormalized values
+        """
+        norm_one, norm_two = self.norm_instances
+
+        return [norm_one.inverse(values[0]), norm_two.inverse(values[1])]
 
 def rgb_to_hsv(arr):
     """
 
@@ -272,6 +272,49 @@ def _mask_tester(norm_instance, vals):
     masked_array[0] = np.ma.masked
     assert_array_equal(masked_array.mask, norm_instance(masked_array).mask)
 
+@pytest.mark.parametrize(
+    'norm_instances, values, expected, clip', [
+        (
+            None,
+            [[0.2, 0.4, 0.5], [5, 7, 9, 10]],
+            [[0, 0.666667, 1], [0, 0.4, 0.8, 1]],
+            None
+        ),
+        (
+            [mcolors.LogNorm(clip=True, vmax=5), mcolors.Normalize()],
+            [[1, 6], [5, 7, 9, 10]],
+            [[0, 1.0], [0, 0.4, 0.8, 1]],
+            None
+        ),
+        (
+            [mcolors.PowerNorm(2, vmin=0, vmax=8, clip=None),
+                mcolors.PowerNorm(2, vmin=2, vmax=8, clip=True)],
+            [np.array([-0.5, 0, 2, 4, 8], dtype=float),
+                np.array([-0.5, 0, 1, 8, 16], dtype=float)],
+            [[0, 0, 1/16, 1/4, 1], [0, 0, 0, 1, 1]],
+            None
+        ),
+        (
+            [mcolors.SymLogNorm(3, vmin=-30, vmax=5, linscale=1.2),
+                mcolors.PowerNorm(2, vmin=2, vmax=8, clip=False)],
+            [np.array([-30, -1, 2, 6], dtype=float),
+                np.array([-0.5, 0, 1, 8, 16], dtype=float)],
+            [[0., 0.53980074, 0.826991, 1.02758204], [0, 0, 0, 1, 1]],
+            [False, True]
+        )
+    ], ids=[
+            'norm_is_None',
+            'LogNorm_and_LinearNorm',
+            'PowerNorm_clip_None_and_True',
+            'SymLogNorm_and_PowerNorm_clip_in_call'
+    ]
+)
+def test_norm2d(norm_instances, values, expected, clip):
+    norm = mcolors.Norm2d(norm_instances=norm_instances)
+    ans1, ans2 = norm(values=values, clip=clip)
+    assert_array_almost_equal(ans1, expected[0])
+    assert_array_almost_equal(ans2, expected[1])
+
 
 @image_comparison(baseline_images=['levels_and_colors'],
                   extensions=['png'])