diff --git a/grid_parameters.py b/grid_parameters.py
new file mode 100644
index 00000000..8c4c1e0a
--- /dev/null
+++ b/grid_parameters.py
@@ -0,0 +1,51 @@
+import numpy as np
+import pylab as pb
+pb.ion()
+import sys
+import GPy
+
+pb.close('all')
+
+N = 1000
+M = 10
+resolution=5
+
+X = np.linspace(0,12,N)[:,None]
+Z = np.linspace(0,12,M)[:,None] # inducing points (fixed for now)
+Y = np.sin(X) + np.random.randn(*X.shape)/np.sqrt(50.)
+k = GPy.kern.rbf(1)
+
+
+m = GPy.models.sparse_GP_regression(X,Y,Z=Z,kernel=k)
+m.constrain_fixed('iip')
+#m.constrain_fixed('white',1e-6)
+m.constrain_fixed('precision',50)
+m.ensure_default_constraints()
+
+
+xx,yy = np.mgrid[1.5:4:0+resolution*1j,-2:2:0+resolution*1j]
+
+lls = []
+cgs = []
+for l,v in zip(xx.flatten(),yy.flatten()):
+    m.set('lengthscale',l)
+    m.set('rbf_variance',10.**v)
+    lls.append(m.log_likelihood())
+    cgs.append(m.checkgrad())
+    #m.plot()
+
+lls = np.array(lls).reshape(resolution,resolution)
+cgs = np.array(cgs,dtype=np.float64).reshape(resolution,resolution)
+
+pb.contourf(xx,yy,lls,np.linspace(-500,560,100),linewidths=2,cmap=pb.cm.jet)
+pb.colorbar()
+pb.scatter(xx.flatten(),yy.flatten(),10,cgs.flatten(),linewidth=0,cmap=pb.cm.gray)
+pb.figure()
+#pb.imshow(lls,origin='upper',cmap=pb.cm.jet,extent=[xx[0,0],xx[-1,0],yy[0].min(),yy[0].max()],vmin=-500)
+pb.scatter(xx.flatten(),yy.flatten(),10,lls.flatten(),linewidth=0,cmap=pb.cm.jet)
+pb.colorbar()
+pb.figure()
+#pb.imshow(cgs,origin='upper',cmap=pb.cm.jet,extent=[xx[0,0],xx[-1,0],yy[0].min(),yy[0].max()])
+pb.scatter(xx.flatten(),yy.flatten(),10,cgs.flatten(),linewidth=0,cmap=pb.cm.jet)
+pb.colorbar()
+