Testing ipython on rtd

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: ::