FIX: colormapping of integer normed images

examples/pylab_examples/image_masked.py

@@ -4,13 +4,14 @@
 The second subplot illustrates the use of BoundaryNorm to
 get a filled contour effect.
 """
-
+from copy import copy
 from numpy import ma
 import matplotlib.colors as colors
 import matplotlib.pyplot as plt
 import matplotlib.mlab as mlab
 import numpy as np
 
+# compute some interesting data
 delta = 0.025
 x = y = np.arange(-3.0, 3.0, delta)
 X, Y = np.meshgrid(x, y)
@@ -19,7 +20,8 @@
 Z = 10*(Z2 - Z1)  # difference of Gaussians
 
 # Set up a colormap:
-palette = plt.cm.gray
+# use copy so that we do not mutate the global colormap instance
+palette = copy(plt.cm.gray)
 palette.set_over('r', 1.0)
 palette.set_under('g', 1.0)
 palette.set_bad('b', 1.0)
@@ -35,22 +37,25 @@
 # range to which the regular palette color scale is applied.
 # Anything above that range is colored based on palette.set_over, etc.
 
-plt.subplot(1, 2, 1)
-im = plt.imshow(Zm, interpolation='bilinear',
+# set up the axes
+fig, (ax1, ax2) = plt.subplots(1, 2)
+
+# plot using 'continuous' color map
+im = ax1.imshow(Zm, interpolation='bilinear',
                 cmap=palette,
                 norm=colors.Normalize(vmin=-1.0, vmax=1.0, clip=False),
                 origin='lower', extent=[-3, 3, -3, 3])
-plt.title('Green=low, Red=high, Blue=bad')
-plt.colorbar(im, extend='both', orientation='horizontal', shrink=0.8)
+ax1.set_title('Green=low, Red=high, Blue=bad')
+fig.colorbar(im, extend='both', orientation='horizontal', shrink=0.8, ax=ax1)
 
-plt.subplot(1, 2, 2)
-im = plt.imshow(Zm, interpolation='nearest',
+# plot using 'discrete' color map
+im = ax2.imshow(Zm, interpolation='nearest',
                 cmap=palette,
                 norm=colors.BoundaryNorm([-1, -0.5, -0.2, 0, 0.2, 0.5, 1],
                                          ncolors=256, clip=False),
                 origin='lower', extent=[-3, 3, -3, 3])
-plt.title('With BoundaryNorm')
-plt.colorbar(im, extend='both', spacing='proportional',
-             orientation='horizontal', shrink=0.8)
+ax2.set_title('With BoundaryNorm')
+fig.colorbar(im, extend='both', spacing='proportional',
+             orientation='horizontal', shrink=0.8, ax=ax2)
 
 plt.show()

lib/matplotlib/image.py

@@ -356,21 +356,42 @@ def _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification=1.0,
         if not unsampled:
             created_rgba_mask = False
 
+            if A.ndim not in (2, 3):
+                raise ValueError("Invalid dimensions, got %s" % (A.shape,))
+
             if A.ndim == 2:
                 A = self.norm(A)
-                # If the image is greyscale, convert to RGBA with the
-                # correct alpha channel for resizing
-                rgba = np.empty((A.shape[0], A.shape[1], 4), dtype=A.dtype)
-                rgba[..., 0:3] = np.expand_dims(A, 2)
                 if A.dtype.kind == 'f':
-                    rgba[..., 3] = ~A.mask
+                    # If the image is greyscale, convert to RGBA and
+                    # use the extra channels for resizing the over,
+                    # under, and bad pixels.  This is needed because
+                    # Agg's resampler is very aggressive about
+                    # clipping to [0, 1] and we use out-of-bounds
+                    # values to carry the over/under/bad information
+                    rgba = np.empty((A.shape[0], A.shape[1], 4), dtype=A.dtype)
+                    rgba[..., 0] = A  # normalized data
+                    rgba[..., 1] = A < 0  # under data
+                    rgba[..., 2] = A > 1  # over data
+                    rgba[..., 3] = ~A.mask  # bad data
+                    A = rgba
+                    output = np.zeros((out_height, out_width, 4),
+                                      dtype=A.dtype)
+                    alpha = 1.0
+                    created_rgba_mask = True
                 else:
-                    rgba[..., 3] = np.where(A.mask, 0, np.iinfo(A.dtype).max)
-                A = rgba
-                output = np.zeros((out_height, out_width, 4), dtype=A.dtype)
-                alpha = 1.0
-                created_rgba_mask = True
-            elif A.ndim == 3:
+                    # colormap norms that output integers (ex NoNorm
+                    # and BoundaryNorm) to RGBA space before
+                    # interpolating.  This is needed due to the
+                    # Agg resampler only working on floats in the
+                    # range [0, 1] and because interpolating indexes
+                    # into an arbitrary LUT may be problematic.
+                    #
+                    # This falls back to interpolating in RGBA space which
+                    # can produce it's own artifacts of colors not in the map
+                    # showing up in the final image.
+                    A = self.cmap(A, alpha=self.get_alpha(), bytes=True)
+
+            if not created_rgba_mask:
                 # Always convert to RGBA, even if only RGB input
                 if A.shape[2] == 3:
                     A = _rgb_to_rgba(A)
@@ -382,8 +403,6 @@ def _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification=1.0,
                 alpha = self.get_alpha()
                 if alpha is None:
                     alpha = 1.0
-            else:
-                raise ValueError("Invalid dimensions, got %s" % (A.shape,))
 
             _image.resample(
                 A, output, t, _interpd_[self.get_interpolation()],
@@ -393,8 +412,13 @@ def _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification=1.0,
             if created_rgba_mask:
                 # Convert back to a masked greyscale array so
                 # colormapping works correctly
+                hid_output = output
                 output = np.ma.masked_array(
-                    output[..., 0], output[..., 3] < 0.5)
+                    hid_output[..., 0], hid_output[..., 3] < 0.5)
+                # relabel under data
+                output[hid_output[..., 1] > .5] = -1
+                # relabel over data
+                output[hid_output[..., 2] > .5] = 2
 
             output = self.to_rgba(output, bytes=True, norm=False)
 

lib/matplotlib/tests/test_image.py

@@ -24,8 +24,12 @@
 from numpy.testing import (
     assert_array_equal, assert_array_almost_equal, assert_allclose)
 from matplotlib.testing.noseclasses import KnownFailureTest
-
-
+from copy import copy
+from numpy import ma
+import matplotlib.colors as colors
+import matplotlib.pyplot as plt
+import matplotlib.mlab as mlab
+import numpy as np
 
 import nose
 
@@ -658,6 +662,40 @@ def test_image_preserve_size2():
                        np.identity(n, bool)[::-1])
 
 
+@image_comparison(baseline_images=['mask_image_over_under'],
+                  remove_text=True, extensions=['png'])
+def test_mask_image_over_under():
+    delta = 0.025
+    x = y = np.arange(-3.0, 3.0, delta)
+    X, Y = np.meshgrid(x, y)
+    Z1 = mlab.bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
+    Z2 = mlab.bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
+    Z = 10*(Z2 - Z1)  # difference of Gaussians
+
+    palette = copy(plt.cm.gray)
+    palette.set_over('r', 1.0)
+    palette.set_under('g', 1.0)
+    palette.set_bad('b', 1.0)
+    Zm = ma.masked_where(Z > 1.2, Z)
+    fig, (ax1, ax2) = plt.subplots(1, 2)
+    im = ax1.imshow(Zm, interpolation='bilinear',
+                    cmap=palette,
+                    norm=colors.Normalize(vmin=-1.0, vmax=1.0, clip=False),
+                    origin='lower', extent=[-3, 3, -3, 3])
+    ax1.set_title('Green=low, Red=high, Blue=bad')
+    fig.colorbar(im, extend='both', orientation='horizontal',
+                 ax=ax1, aspect=10)
+
+    im = ax2.imshow(Zm, interpolation='nearest',
+                    cmap=palette,
+                    norm=colors.BoundaryNorm([-1, -0.5, -0.2, 0, 0.2, 0.5, 1],
+                                             ncolors=256, clip=False),
+                    origin='lower', extent=[-3, 3, -3, 3])
+    ax2.set_title('With BoundaryNorm')
+    fig.colorbar(im, extend='both', spacing='proportional',
+                 orientation='horizontal', ax=ax2, aspect=10)
+
+
 @image_comparison(baseline_images=['mask_image'],
                   remove_text=True)
 def test_mask_image():