diff --git a/doc/conf.py b/doc/conf.py
index 078279d4..8a05f386 100644
--- a/doc/conf.py
+++ b/doc/conf.py
@@ -103,7 +103,7 @@ class Mock(object):
 #import mock
 
 print "Mocking"
-MOCK_MODULES = ['pylab', 'sympy', 'sympy.utilities', 'sympy.utilities.codegen', 'sympy.core.cache', 'sympy.core', 'sympy.parsing', 'sympy.parsing.sympy_parser']
+MOCK_MODULES = ['pylab', 'sympy', 'sympy.utilities', 'sympy.utilities.codegen', 'sympy.core.cache', 'sympy.core', 'sympy.parsing', 'sympy.parsing.sympy_parser', 'matplotlib']
 #'matplotlib', 'matplotlib.color', 'matplotlib.pyplot', 'pylab' ]
 for mod_name in MOCK_MODULES:
     sys.modules[mod_name] = Mock()
diff --git a/doc/tuto_GP_regression.rst b/doc/tuto_GP_regression.rst
index 92b25bc0..a78929b3 100644
--- a/doc/tuto_GP_regression.rst
+++ b/doc/tuto_GP_regression.rst
@@ -2,6 +2,28 @@
 Gaussian process regression tutorial
 *************************************
 
+
+.. ipython:: python
+
+    import numpy as np
+    import GPy as gpy
+
+    """run a simple demonstration of a standard gaussian process fitting it to data sampled from an rbf covariance."""
+    data = gpy.util.datasets.toy_rbf_1d()
+
+    # create simple gp model
+    m = gpy.models.GP_regression(data['X'],data['Y'])
+
+    # optimize
+    m.ensure_default_constraints()
+    m.optimize()
+
+    print(m)
+
+    # plot
+    m.plot()
+
+
 We will see in this tutorial the basics for building a 1 dimensional and a 2 dimensional Gaussian process regression model, also known as a kriging model.
 
 We first import the libraries we will need: ::