Change name of class to BivariateNorm, take normalizer instances sepa…

…rately
matplotlib · patniharshit · Jun 9, 2017 · Jun 9, 2017 · Jul 3, 2017 · Jul 9, 2017
commit 72175362833539a029cbc398f0d145f6695f8a9b
diff --git a/lib/matplotlib/colors.py b/lib/matplotlib/colors.py
@@ -1349,21 +1349,27 @@ def __call__(self, value, clip=None):
    def inverse(self, value):
        return value

-class Norm2d:
+class BivariateNorm:
    """
    Normalize a list of two values corresponding to two 1D normalizers
    """
-    def __init__(self, norm_instances=None):
+    def __init__(self, norm1=None, norm2=None):
        """
        Parameters
        ----------
-        norm_instances :
-            A list of length two having instances of 1D normalizers
+        norm1 :
+            An instance of 1D normalizers
+        norm2 :
+            An instance of 1D normalizers
        """
-        if norm_instances is None:
-            self.norm_instances = [Normalize(), Normalize()]
+        if norm1 is None:
+            self.norm1 = Normalize()
        else:
-            self.norm_instances = norm_instances
+            self.norm1 = norm1
+        if norm2 is None:
+            self.norm2 = Normalize()
+        else:
+            self.norm2 = norm2

    def __call__(self, values, clip=None):
        """
@@ -1381,13 +1387,11 @@ def __call__(self, values, clip=None):
        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]
+            clip = [self.norm1.clip, self.norm2.clip]

-        return [norm_one(values[0], clip=clip[0]),
-                norm_two(values[1], clip=clip[1])]
+        return [self.norm1(values[0], clip=clip[0]),
+                self.norm2(values[1], clip=clip[1])]

    def inverse(self, values):
        """
@@ -1400,9 +1404,7 @@ def inverse(self, values):
        -------
        A list of two unnormalized values
        """
-        norm_one, norm_two = self.norm_instances
-
-        return [norm_one.inverse(values[0]), norm_two.inverse(values[1])]
+        return [self.norm1.inverse(values[0]), self.norm.inverse(values[1])]

 def rgb_to_hsv(arr):
    """

diff --git a/lib/matplotlib/tests/test_colors.py b/lib/matplotlib/tests/test_colors.py
@@ -273,30 +273,32 @@ def _mask_tester(norm_instance, vals):
    assert_array_equal(masked_array.mask, norm_instance(masked_array).mask)

 @pytest.mark.parametrize(
-    'norm_instances, values, expected, clip', [
+    'norm1, norm2, values, expected, clip', [
        (
+            None,
            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()],
+            mcolors.LogNorm(clip=True, vmax=5),
+            None,
            [[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)],
+            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)],
+            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]],
@@ -309,8 +311,8 @@ def _mask_tester(norm_instance, vals):
            'SymLogNorm_and_PowerNorm_clip_in_call'
    ]
 )
-def test_norm2d(norm_instances, values, expected, clip):
-    norm = mcolors.Norm2d(norm_instances=norm_instances)
+def test_BivariateNorm(norm1, norm2, values, expected, clip):
+    norm = mcolors.BivariateNorm(norm1=norm1, norm2=norm2)
    ans1, ans2 = norm(values=values, clip=clip)
    assert_array_almost_equal(ans1, expected[0])
    assert_array_almost_equal(ans2, expected[1])