diff --git a/GPy/__init__.py b/GPy/__init__.py
index 320ebc7a..47232eb8 100644
--- a/GPy/__init__.py
+++ b/GPy/__init__.py
@@ -4,8 +4,11 @@ import warnings
 warnings.filterwarnings("ignore", category=DeprecationWarning)
 import os
 
-with open("version", 'r') as f:
-    __version__ = f.read() 
+
+def read(fname):
+    with open(os.path.join(os.path.dirname(__file__), fname)) as f:
+        return f.read()
+__version__ = read('version') 
 
 import core
 import models
diff --git a/GPy/kern/kern.py b/GPy/kern/kern.py
index 949df5ab..a6491fef 100644
--- a/GPy/kern/kern.py
+++ b/GPy/kern/kern.py
@@ -126,7 +126,7 @@ class kern(Parameterized):
                 xi = patch.get_x() + patch.get_width() / 2.
                 va = 'top'
                 c = 'w'
-                t = TextPath((0, 0), "${xi}$".format(xi=xi), rotation=0, usetex=True, ha='center')
+                t = TextPath((0, 0), "${xi}$".format(xi=xi), rotation=0, ha='center')
                 transform = transOffset
                 if patch.get_extents().height <= t.get_extents().height + 3:
                     va = 'bottom'
diff --git a/GPy/util/Tango.py b/GPy/util/Tango.py
index eeb2e075..06cf8368 100644
--- a/GPy/util/Tango.py
+++ b/GPy/util/Tango.py
@@ -2,9 +2,6 @@
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
 
-import matplotlib as mpl
-import pylab as pb
-import sys
 #sys.path.append('/home/james/mlprojects/sitran_cluster/')
 #from switch_pylab_backend import *
 
@@ -84,6 +81,7 @@ def reset():
         lightList.append(lightList.pop(0))
 
 def setLightFigures():
+    import matplotlib as mpl
     mpl.rcParams['axes.edgecolor']=colorsHex['Aluminium6']
     mpl.rcParams['axes.facecolor']=colorsHex['Aluminium2']
     mpl.rcParams['axes.labelcolor']=colorsHex['Aluminium6']
@@ -97,6 +95,7 @@ def setLightFigures():
     mpl.rcParams['ytick.color']=colorsHex['Aluminium6']
 
 def setDarkFigures():
+    import matplotlib as mpl
     mpl.rcParams['axes.edgecolor']=colorsHex['Aluminium2']
     mpl.rcParams['axes.facecolor']=colorsHex['Aluminium6']
     mpl.rcParams['axes.labelcolor']=colorsHex['Aluminium2']
@@ -157,10 +156,10 @@ cdict_Alu = {'red' :((0./5,colorsRGB['Aluminium1'][0]/256.,colorsRGB['Aluminium1
                      (5./5,colorsRGB['Aluminium6'][2]/256.,colorsRGB['Aluminium6'][2]/256.))}
 # cmap_Alu = mpl.colors.LinearSegmentedColormap('TangoAluminium',cdict_Alu,256)
 # cmap_BGR = mpl.colors.LinearSegmentedColormap('TangoRedBlue',cdict_BGR,256)
-# cmap_RB = mpl.colors.LinearSegmentedColormap('TangoRedBlue',cdict_RB,256)
 if __name__=='__main__':
-    import pylab as pb
+    import matplotlib.pyplot as pb, numpy as np
     pb.figure()
-    pb.pcolor(pb.rand(10,10),cmap=cmap_RB)
+    cmap_RB = mpl.colors.LinearSegmentedColormap('TangoRedBlue',cdict_RB,256)
+    pb.pcolor(np.random.rand(10,10),cmap=cmap_RB)
     pb.colorbar()
     pb.show()
diff --git a/GPy/util/plot_latent.py b/GPy/util/plot_latent.py
index 207a7974..997f3df2 100644
--- a/GPy/util/plot_latent.py
+++ b/GPy/util/plot_latent.py
@@ -1,7 +1,7 @@
 import pylab as pb
 import numpy as np
 from .. import util
-from GPy.util.latent_space_visualizations.controllers.imshow_controller import ImshowController
+from .latent_space_visualizations.controllers.imshow_controller import ImshowController
 import itertools
 
 def most_significant_input_dimensions(model, which_indices):
@@ -40,15 +40,20 @@ def plot_latent(model, labels=None, which_indices=None,
 
     # first, plot the output variance as a function of the latent space
     Xtest, xx, yy, xmin, xmax = util.plot.x_frame2D(model.X[:, [input_1, input_2]], resolution=resolution)
-    Xtest_full = np.zeros((Xtest.shape[0], model.X.shape[1]))
+    #Xtest_full = np.zeros((Xtest.shape[0], model.X.shape[1]))
 
     def plot_function(x):
+        Xtest_full = np.zeros((Xtest.shape[0], model.X.shape[1]))
         Xtest_full[:, [input_1, input_2]] = x
         mu, var, low, up = model.predict(Xtest_full)
         var = var[:, :1]
         return np.log(var)
+    
+    xmi, ymi = xmin
+    xma, yma = xmax
+    
     view = ImshowController(ax, plot_function,
-                            tuple(model.X[:, [input_1, input_2]].min(0)) + tuple(model.X[:, [input_1, input_2]].max(0)),
+                            (xmi, ymi, xma, yma),
                             resolution, aspect=aspect, interpolation='bilinear',
                             cmap=pb.cm.binary)