FIX: use left and right

Author: jklymak

lib/matplotlib/colors.py

@@ -1300,13 +1300,11 @@ def __call__(self, value, clip=None):
 
         if not self.vmin <= self.vcenter <= self.vmax:
             raise ValueError("vmin, vcenter, vmax must increase monotonically")
-        # must linearly extrapolate for ticks on colorbars that go
-        # beyond vmin and vmax:
-        min = self.vmin - (self.vcenter - self.vmin)
-        max = self.vmax + (self.vmax - self.vcenter)
+        # note that we must extrapolate for tick locators:
         result = np.ma.masked_array(
-            np.interp(result, [min, self.vcenter, max],
-                      [-0.5, 0.5, 1.5]), mask=np.ma.getmask(result))
+            np.interp(result, [self.vmin, self.vcenter, self.vmax],
+                      [0, 0.5, 1], left=-np.inf, right=np.inf),
+            mask=np.ma.getmask(result))
         if is_scalar:
             result = np.atleast_1d(result)[0]
         return result
@@ -1317,9 +1315,8 @@ def inverse(self, value):
         (vmin,), _ = self.process_value(self.vmin)
         (vmax,), _ = self.process_value(self.vmax)
         (vcenter,), _ = self.process_value(self.vcenter)
-        min = vmin - (vcenter - vmin)
-        max = vmax + (vmax - vcenter)
-        result = np.interp(value, [-0.5, 0.5, 1.5], [min, vcenter, max])
+        result = np.interp(value, [0, 0.5, 1], [vmin, vcenter, vmax],
+                           left=-np.inf, right=np.inf)
         return result
 
 

tutorials/colors/colormapnorms.py

@@ -325,17 +325,14 @@ def __init__(self, vmin=None, vmax=None, vcenter=None, clip=False):
     def __call__(self, value, clip=None):
         # I'm ignoring masked values and all kinds of edge cases to make a
         # simple example...
-        # Note also that we must extrapolate linearly beyond vmin/vmax
-        min = self.vmin - (self.vcenter - self.vmin)
-        max = self.vmax + (self.vmax - self.vcenter)
-        x, y = [min, self.vcenter, max], [-0.5, 0.5, 1.5]
-        return np.ma.masked_array(np.interp(value, x, y))
+        # Note also that we must extrapolate beyond vmin/vmax
+        x, y = [self.vmin, self.vcenter, self.vmax], [0, 0.5, 1.]
+        return np.ma.masked_array(np.interp(value, x, y,
+                                            left=-np.inf, right=np.inf))
 
     def inverse(self, value):
-        min = self.vmin - (self.vcenter - self.vmin)
-        max = self.vmax + (self.vmax - self.vcenter)
-        y, x = [min, self.vcenter, max], [-0.5, 0.5, 1.5]
-        return np.interp(value, x, y)
+        y, x = [self.vmin, self.vcenter, self.vmax], [0, 0.5, 1]
+        return np.interp(value, x, y, left=-np.inf, right=np.inf)
 
 
 fig, ax = plt.subplots()