diff --git a/cython/gtsam/tests/experiments.py b/cython/gtsam/tests/experiments.py
new file mode 100644
index 000000000..db3127a83
--- /dev/null
+++ b/cython/gtsam/tests/experiments.py
@@ -0,0 +1,113 @@
+"""
+This file is not a real python unittest. It contains small experiments
+to test the wrapper with gtsam_test, a short version of gtsam.h.
+Its name convention is different from other tests so it won't be discovered.
+"""
+from __future__ import print_function
+import gtsam
+import numpy as np
+
+r = gtsam.Rot3()
+print(r)
+print(r.pitch())
+r2 = gtsam.Rot3()
+r3 = r.compose(r2)
+print("r3 pitch:", r3.pitch())
+
+v = np.array([1, 1, 1]) 
+print("v = ", v)
+r4 = r3.retract(v)
+print("r4 pitch:", r4.pitch())
+r4.print_(b'r4: ')
+r3.print_(b"r3: ")
+
+v = r3.localCoordinates(r4)
+print("localCoordinates:", v)
+
+Rmat = np.array([
+    [0.990074, -0.0942928, 0.104218], 
+    [0.104218,  0.990074, -0.0942928], 
+    [-0.0942928, 0.104218, 0.990074]
+    ])
+r5 = gtsam.Rot3(Rmat)
+r5.print_(b"r5: ")
+
+l = gtsam.Rot3.Logmap(r5)
+print("l = ", l)
+
+
+noise = gtsam.noiseModel_Gaussian.Covariance(Rmat)
+noise.print_(b"noise:")
+
+D = np.array([1.,2.,3.])
+diag = gtsam.noiseModel_Diagonal.Variances(D)
+print("diag:", diag)
+diag.print_(b"diag:")
+print("diag R:", diag.R())
+
+p = gtsam.Point3()
+p.print_("p:")
+factor = gtsam.BetweenFactorPoint3(1,2,p, noise)
+factor.print_(b"factor:")
+
+vv = gtsam.VectorValues()
+vv.print_(b"vv:")
+vv.insert(1, np.array([1.,2.,3.]))
+vv.insert(2, np.array([3.,4.]))
+vv.insert(3, np.array([5.,6.,7.,8.]))
+vv.print_(b"vv:")
+
+vv2 = gtsam.VectorValues(vv)
+vv2.insert(4, np.array([4.,2.,1]))
+vv2.print_(b"vv2:")
+vv.print_(b"vv:")
+
+vv.insert(4, np.array([1.,2.,4.]))
+vv.print_(b"vv:")
+vv3 = vv.add(vv2)
+
+vv3.print_(b"vv3:")
+
+values = gtsam.Values()
+values.insert(1, gtsam.Point3())
+values.insert(2, gtsam.Rot3())
+values.print_(b"values:")
+
+factor = gtsam.PriorFactorVector(1, np.array([1.,2.,3.]), diag)
+print("Prior factor vector: ", factor)
+
+
+
+keys = gtsam.KeyVector()
+
+keys.push_back(1)
+keys.push_back(2)
+print('size: ', keys.size())
+print(keys.at(0))
+print(keys.at(1))
+
+noise = gtsam.noiseModel_Isotropic.Precision(2, 3.0)
+noise.print_('noise:')
+print('noise print:', noise)
+f = gtsam.JacobianFactor(7, np.ones([2,2]), model=noise,  b=np.ones(2))
+print('JacobianFactor(7):\n', f)
+print("A = ", f.getA())
+print("b = ", f.getb())
+
+f = gtsam.JacobianFactor(np.ones(2))
+f.print_('jacoboian b_in:')
+
+
+print("JacobianFactor initalized with b_in:", f)
+
+diag = gtsam.noiseModel_Diagonal.Sigmas(np.array([1.,2.,3.]))
+fv = gtsam.PriorFactorVector(1, np.array([4.,5.,6.]), diag)
+print("priorfactorvector: ", fv)
+
+print("base noise: ", fv.get_noiseModel())
+print("casted to gaussian2: ", gtsam.dynamic_cast_noiseModel_Diagonal_noiseModel_Base(fv.get_noiseModel()))
+
+X = gtsam.symbol(65, 19)
+print(X)
+print(gtsam.symbolChr(X))
+print(gtsam.symbolIndex(X))