diff --git a/GPy/plotting/matplot_dep/defaults.py b/GPy/plotting/matplot_dep/defaults.py
index 5effddec..00c1b839 100644
--- a/GPy/plotting/matplot_dep/defaults.py
+++ b/GPy/plotting/matplot_dep/defaults.py
@@ -27,6 +27,11 @@
 # OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
 # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 #===============================================================================
+
+from matplotlib.colors import LinearSegmentedColormap
+from matplotlib import cm
+from . import Tango
+
 '''
 This file is for defaults for the gpy plot, specific to the plotting library.
 
@@ -38,9 +43,6 @@ In the code, always ise plotting.gpy_plots.defaults to get the defaults, as
 it gives back an empty default, when defaults are not defined.
 '''
 
-from matplotlib import cm
-from . import Tango
-
 # Data:
 data_1d = dict(lw=1.5, marker='x', edgecolor='k')
 data_2d = dict(s=35, edgecolors='none', linewidth=0., cmap=cm.get_cmap('hot'), alpha=.5)
@@ -53,5 +55,5 @@ yerrorbar = dict(ecolor=Tango.colorsHex['darkRed'], fmt='none', elinewidth=.5, a
 meanplot_1d = dict(color=Tango.colorsHex['mediumBlue'], linewidth=2)
 meanplot_2d = dict(cmap='hot', linewidth=.5)
 samples_1d = dict(color=Tango.colorsHex['mediumBlue'], linewidth=.3)
-confidence_interval = dict(edgecolor=Tango.colorsHex['darkBlue'],linewidth=.5,facecolor=Tango.colorsHex['lightBlue'],alpha=.3)
-density = dict(facecolor=Tango.colorsHex['mediumBlue'],edgecolors='none')
\ No newline at end of file
+confidence_interval = dict(edgecolor=Tango.colorsHex['darkBlue'],linewidth=.5,facecolor=Tango.colorsHex['lightBlue'],alpha=.2)
+density = dict(alpha=.5, facecolor=Tango.colorsHex['mediumBlue'], edgecolors='none')
\ No newline at end of file
diff --git a/GPy/plotting/matplot_dep/plot_definitions.py b/GPy/plotting/matplot_dep/plot_definitions.py
index 9b510a97..919f26e4 100644
--- a/GPy/plotting/matplot_dep/plot_definitions.py
+++ b/GPy/plotting/matplot_dep/plot_definitions.py
@@ -31,6 +31,7 @@ import numpy as np
 from matplotlib import pyplot as plt
 from ..abstract_plotting_library import AbstractPlottingLibrary
 from . import defaults
+from matplotlib.colors import LinearSegmentedColormap
 
 class MatplotlibPlots(AbstractPlottingLibrary):
     def __init__(self):
@@ -99,19 +100,37 @@ class MatplotlibPlots(AbstractPlottingLibrary):
     def fill_gradient(self, canvas, X, percentiles, **kwargs):
         ax = canvas
         plots = []
-        if not 'alpha' in kwargs.keys():
-            kwargs['alpha'] = 3./max(4, (len(percentiles)))
-    
+        
+        if 'facecolors' in kwargs:
+            kwargs['facecolor'] = kwargs.pop('facecolors')
+            
+        if 'cmap' not in kwargs:
+            kwargs['cmap'] = LinearSegmentedColormap.from_list('WhToColor', ((1., 1., 1.), kwargs['facecolor']), N=len(percentiles)-1)
+            kwargs['cmap']._init()
+            
+        if 'alpha' in kwargs:
+            kwargs['cmap']._lut[:, -1] = kwargs['alpha']
+        
+        if 'array' not in kwargs:
+            if (len(percentiles)%2) == 0:
+                up = np.linspace(0, 1, len(percentiles)/2)
+                kwargs['array'] = np.r_[up, up[::-1][1:]]
+            else:
+                up = np.linspace(0, 1, len(percentiles)/2)
+                kwargs['array'] = np.r_[up, up[::-1]]
+
         # pop where from kwargs
         where = kwargs.pop('where') if 'where' in kwargs else None
         # pop interpolate, which we actually do not do here!
         if 'interpolate' in kwargs: kwargs.pop('interpolate')
         
-        def pairwise(inlist):
-            l = len(inlist)
-            for i in range(int(np.ceil(l/2.))):
-                yield inlist[:][i], inlist[:][(l-1)-i]
-        
+        from itertools import tee, izip
+        def pairwise(iterable):
+            "s -> (s0,s1), (s1,s2), (s2, s3), ..."
+            a, b = tee(iterable)
+            next(b, None)
+            return izip(a, b)            
+            
         polycol = []
         for y1, y2 in pairwise(percentiles):
             import matplotlib.mlab as mlab