Don't convert vmin, vmax to floats.

They may be float128's in which case precision would be lost; this can result in `Normalize` returning values (barely) outside of `[0, 1]`. (The cast to `float` was introduced in 28e1d2, referring to bug 2997687 on SF; it may be worth checking what it was about.)
matplotlib · tacaswell · Jul 12, 2016 · Jul 6, 2016 · Jul 8, 2016 · Jul 7, 2016
commit 16f9778c6727531b4c06e407648c0c16e1a6ae3c
diff --git a/lib/matplotlib/colors.py b/lib/matplotlib/colors.py
@@ -918,8 +918,6 @@ def __call__(self, value, clip=None):
        elif vmin > vmax:
            raise ValueError("minvalue must be less than or equal to maxvalue")
        else:
-            vmin = float(vmin)
-            vmax = float(vmax)
            if clip:
                mask = np.ma.getmask(result)
                result = np.ma.array(np.clip(result.filled(vmax), vmin, vmax),
@@ -938,8 +936,7 @@ def __call__(self, value, clip=None):
    def inverse(self, value):
        if not self.scaled():
            raise ValueError("Not invertible until scaled")
-        vmin = float(self.vmin)
-        vmax = float(self.vmax)
+        vmin, vmax = self.vmin, self.vmax

        if cbook.iterable(value):
            val = np.ma.asarray(value)

diff --git a/lib/matplotlib/tests/test_colors.py b/lib/matplotlib/tests/test_colors.py
@@ -194,6 +194,11 @@ def test_Normalize():
    _scalar_tester(norm, vals)
    _mask_tester(norm, vals)

+    # Don't lose precision on longdoubles (float128 on Linux).
+    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])
+

 def test_SymLogNorm():
    """