diff --git a/GPy/plotting/matplot_dep/models_plots.py b/GPy/plotting/matplot_dep/models_plots.py
index 3737f802..78c80cb5 100644
--- a/GPy/plotting/matplot_dep/models_plots.py
+++ b/GPy/plotting/matplot_dep/models_plots.py
@@ -1,17 +1,14 @@
 # Copyright (c) 2012-2015, GPy authors (see AUTHORS.txt).
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
-try:
-#    import Tango
-    import pylab as pb
-except:
-    pass
 import numpy as np
+from . import Tango
 from base_plots import gpplot, x_frame1D, x_frame2D
 from ...models.gp_coregionalized_regression import GPCoregionalizedRegression
 from ...models.sparse_gp_coregionalized_regression import SparseGPCoregionalizedRegression
 from scipy import sparse
 from ...core.parameterization.variational import VariationalPosterior
+from matplotlib import pyplot as plt
 
 def plot_fit(model, plot_limits=None, which_data_rows='all',
         which_data_ycols='all', fixed_inputs=[],
@@ -64,7 +61,7 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
     #if len(which_data_ycols)==0:
         #raise ValueError('No data selected for plotting')
     if ax is None:
-        fig = pb.figure(num=fignum)
+        fig = plt.figure(num=fignum)
         ax = fig.add_subplot(111)
 
     if hasattr(model, 'has_uncertain_inputs') and model.has_uncertain_inputs():
@@ -197,8 +194,8 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
             m, v = model.predict(Xgrid, full_cov=False, Y_metadata=Y_metadata, **predict_kw)
         for d in which_data_ycols:
             m_d = m[:,d].reshape(resolution, resolution).T
-            plots['contour'] = ax.contour(x, y, m_d, levels, vmin=m.min(), vmax=m.max(), cmap=pb.cm.jet)
-            if not plot_raw: plots['dataplot'] = ax.scatter(X[which_data_rows, free_dims[0]], X[which_data_rows, free_dims[1]], 40, Y[which_data_rows, d], cmap=pb.cm.jet, vmin=m.min(), vmax=m.max(), linewidth=0.)
+            plots['contour'] = ax.contour(x, y, m_d, levels, vmin=m.min(), vmax=m.max(), cmap=plt.cm.jet)
+            if not plot_raw: plots['dataplot'] = ax.scatter(X[which_data_rows, free_dims[0]], X[which_data_rows, free_dims[1]], 40, Y[which_data_rows, d], cmap=plt.cm.jet, vmin=m.min(), vmax=m.max(), linewidth=0.)
 
         #set the limits of the plot to some sensible values
         ax.set_xlim(xmin[0], xmax[0])