Proper norm'ing of float128 scalars too.

matplotlib · tacaswell · Jul 12, 2016 · Jul 6, 2016 · Jul 8, 2016 · Jul 8, 2016
commit ff78a069bc1ad261ce4001d8347934e3995579b0
diff --git a/lib/matplotlib/colors.py b/lib/matplotlib/colors.py
@@ -880,22 +880,13 @@ def process_value(value):
        Experimental; we may want to add an option to force the
        use of float32.
        """
-        if cbook.iterable(value):
-            is_scalar = False
-            result = np.ma.asarray(value)
-            if result.dtype.kind == 'f':
-                # this is overkill for lists of floats, but required
-                # to support pd.Series as input until we can reliable
-                # determine if result and value share memory in all cases
-                # (list, tuple, deque, ndarray, Series, ...)
-                result = result.copy()
-            elif result.dtype.itemsize > 2:
-                result = result.astype(float)
-            else:
-                result = result.astype(np.float32)
-        else:
-            is_scalar = True
-            result = np.ma.array([value]).astype(float)
+        is_scalar = not cbook.iterable(value)
+        if is_scalar:
+            value = [value]
+        dtype = np.min_scalar_type(value)
+        dtype = (np.float32 if dtype.itemsize <= 2
+                 else np.promote_types(dtype, float))
+        result = np.ma.array(value, dtype=dtype, copy=True)
        return result, is_scalar

    def __call__(self, value, clip=None):

diff --git a/lib/matplotlib/tests/test_colors.py b/lib/matplotlib/tests/test_colors.py
@@ -194,10 +194,15 @@ def test_Normalize():
    _scalar_tester(norm, vals)
    _mask_tester(norm, vals)

-    # Don't lose precision on longdoubles (float128 on Linux).
+    # Don't lose precision on longdoubles (float128 on Linux):
+    # for array inputs...
    vals = np.array([1.2345678901, 9.8765432109], dtype=np.longdouble)
    norm = mcolors.Normalize(vals.min(), vals.max())
    assert_array_equal(np.asarray(norm(vals)), [0, 1])
+    # and for scalar ones.
+    eps = np.finfo(np.longdouble).resolution
+    norm = plt.Normalize(1, 1 + 100 * eps)
+    assert_equal(norm(1 + 50 * eps), .5)


 def test_SymLogNorm():