Merge pull request matplotlib#9195 from anntzer/empty-lognorm

Don't fail on empty autoscale_None.

Author: dstansby

lib/matplotlib/colors.py

@@ -961,15 +961,17 @@ def autoscale(self, A):
         """
         Set *vmin*, *vmax* to min, max of *A*.
         """
-        self.vmin = np.ma.min(A)
-        self.vmax = np.ma.max(A)
+        A = np.asanyarray(A)
+        self.vmin = A.min()
+        self.vmax = A.max()
 
     def autoscale_None(self, A):
-        ' autoscale only None-valued vmin or vmax'
-        if self.vmin is None and np.size(A) > 0:
-            self.vmin = np.ma.min(A)
-        if self.vmax is None and np.size(A) > 0:
-            self.vmax = np.ma.max(A)
+        """autoscale only None-valued vmin or vmax."""
+        A = np.asanyarray(A)
+        if self.vmin is None and A.size:
+            self.vmin = A.min()
+        if self.vmax is None and A.size:
+            self.vmax = A.max()
 
     def scaled(self):
         'return true if vmin and vmax set'
@@ -1037,14 +1039,14 @@ def autoscale(self, A):
         self.vmax = np.ma.max(A)
 
     def autoscale_None(self, A):
-        ' autoscale only None-valued vmin or vmax'
+        """autoscale only None-valued vmin or vmax."""
         if self.vmin is not None and self.vmax is not None:
             return
         A = np.ma.masked_less_equal(A, 0, copy=False)
-        if self.vmin is None:
-            self.vmin = np.ma.min(A)
-        if self.vmax is None:
-            self.vmax = np.ma.max(A)
+        if self.vmin is None and A.size:
+            self.vmin = A.min()
+        if self.vmax is None and A.size:
+            self.vmax = A.max()
 
 
 class SymLogNorm(Normalize):
@@ -1153,13 +1155,14 @@ def autoscale(self, A):
         self._transform_vmin_vmax()
 
     def autoscale_None(self, A):
-        """ autoscale only None-valued vmin or vmax """
+        """autoscale only None-valued vmin or vmax."""
         if self.vmin is not None and self.vmax is not None:
             pass
-        if self.vmin is None:
-            self.vmin = np.ma.min(A)
-        if self.vmax is None:
-            self.vmax = np.ma.max(A)
+        A = np.asanyarray(A)
+        if self.vmin is None and A.size:
+            self.vmin = A.min()
+        if self.vmax is None and A.size:
+            self.vmax = A.max()
         self._transform_vmin_vmax()
 
 
@@ -1223,20 +1226,19 @@ def autoscale(self, A):
             self.vmin = 0
             warnings.warn("Power-law scaling on negative values is "
                           "ill-defined, clamping to 0.")
-
         self.vmax = np.ma.max(A)
 
     def autoscale_None(self, A):
-        ' autoscale only None-valued vmin or vmax'
-        if self.vmin is None and np.size(A) > 0:
-            self.vmin = np.ma.min(A)
+        """autoscale only None-valued vmin or vmax."""
+        A = np.asanyarray(A)
+        if self.vmin is None and A.size:
+            self.vmin = A.min()
             if self.vmin < 0:
                 self.vmin = 0
                 warnings.warn("Power-law scaling on negative values is "
                               "ill-defined, clamping to 0.")
-
-        if self.vmax is None and np.size(A) > 0:
-            self.vmax = np.ma.max(A)
+        if self.vmax is None and A.size:
+            self.vmax = A.max()
 
 
 class BoundaryNorm(Normalize):

lib/matplotlib/tests/test_image.py

@@ -2,29 +2,26 @@
                         unicode_literals)
 
 import six
+
+from copy import copy
 import io
 import os
 import warnings
 
 import numpy as np
+from numpy import ma
 from numpy.testing import assert_array_equal
 
-from matplotlib.testing.decorators import image_comparison
+from matplotlib import (
+    colors, image as mimage, mlab, patches, pyplot as plt,
+    rc_context, rcParams)
 from matplotlib.image import (AxesImage, BboxImage, FigureImage,
                               NonUniformImage, PcolorImage)
+from matplotlib.testing.decorators import image_comparison
 from matplotlib.transforms import Bbox, Affine2D, TransformedBbox
-from matplotlib import rcParams, rc_context
-from matplotlib import patches
-import matplotlib.pyplot as plt
 
-from matplotlib import mlab
 import pytest
 
-from copy import copy
-from numpy import ma
-import matplotlib.image as mimage
-import matplotlib.colors as colors
-
 
 try:
     from PIL import Image
@@ -789,9 +786,16 @@ def test_imshow_flatfield():
     im.set_clim(.5, 1.5)
 
 
-def test_empty_imshow():
+@pytest.mark.parametrize(
+    "make_norm",
+    [colors.Normalize,
+     colors.LogNorm,
+     lambda: colors.SymLogNorm(1),
+     lambda: colors.PowerNorm(1)])
+@pytest.mark.filterwarnings("ignore:Attempting to set identical left==right")
+def test_empty_imshow(make_norm):
     fig, ax = plt.subplots()
-    im = ax.imshow([[]])
+    im = ax.imshow([[]], norm=make_norm())
     im.set_extent([-5, 5, -5, 5])
     fig.canvas.draw()