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diff --git a/src/python/example/diagram_vectorizations_distances_kernels.py b/src/python/example/diagram_vectorizations_distances_kernels.py
new file mode 100755
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+++ b/src/python/example/diagram_vectorizations_distances_kernels.py
@@ -0,0 +1,116 @@
+#!/usr/bin/env python
+
+import matplotlib.pyplot as plt
+import numpy as np
+from sklearn.kernel_approximation import RBFSampler
+from sklearn.preprocessing import MinMaxScaler
+
+from gudhi.representations import (DiagramSelector, Clamping, Landscape, Silhouette, BettiCurve, ComplexPolynomial,\
+  TopologicalVector, DiagramScaler, BirthPersistenceTransform,\
+  PersistenceImage, PersistenceWeightedGaussianKernel, Entropy, \
+  PersistenceScaleSpaceKernel, SlicedWassersteinDistance,\
+  SlicedWassersteinKernel, PersistenceFisherKernel, WassersteinDistance)
+
+D1 = np.array([[0.,4.],[1.,2.],[3.,8.],[6.,8.], [0., np.inf], [5., np.inf]])
+
+proc1 = DiagramSelector(use=True, point_type="finite")
+proc2 = DiagramScaler(use=True, scalers=[([0,1], MinMaxScaler())])
+proc3 = DiagramScaler(use=True, scalers=[([1], Clamping(maximum=.9))])
+D1 = proc3(proc2(proc1(D1)))
+
+plt.scatter(D1[:,0], D1[:,1])
+plt.plot([0.,1.],[0.,1.])
+plt.title("Test Persistence Diagram for vector methods")
+plt.show()
+
+LS = Landscape(resolution=1000)
+L = LS(D1)
+plt.plot(L[:1000])
+plt.plot(L[1000:2000])
+plt.plot(L[2000:3000])
+plt.title("Landscape")
+plt.show()
+
+def pow(n):
+  return lambda x: np.power(x[1]-x[0],n)
+
+SH = Silhouette(resolution=1000, weight=pow(2))
+plt.plot(SH(D1))
+plt.title("Silhouette")
+plt.show()
+
+BC = BettiCurve(resolution=1000)
+plt.plot(BC(D1))
+plt.title("Betti Curve")
+plt.show()
+
+CP = ComplexPolynomial(threshold=-1, polynomial_type="T")
+print("Complex polynomial is " + str(CP(D1)))
+
+TV = TopologicalVector(threshold=-1)
+print("Topological vector is " + str(TV(D1)))
+
+PI = PersistenceImage(bandwidth=.1, weight=lambda x: x[1], im_range=[0,1,0,1], resolution=[100,100])
+plt.imshow(np.flip(np.reshape(PI(D1), [100,100]), 0))
+plt.title("Persistence Image")
+plt.show()
+
+ET = Entropy(mode="scalar")
+print("Entropy statistic is " + str(ET(D1)))
+
+ET = Entropy(mode="vector", normalized=False)
+plt.plot(ET(D1))
+plt.title("Entropy function")
+plt.show()
+
+D2 = np.array([[1.,5.],[3.,6.],[2.,7.]])
+D2 = proc3(proc2(proc1(D2)))
+
+plt.scatter(D1[:,0], D1[:,1])
+plt.scatter(D2[:,0], D2[:,1])
+plt.plot([0.,1.],[0.,1.])
+plt.title("Test Persistence Diagrams for kernel methods")
+plt.show()
+
+def arctan(C,p):
+  return lambda x: C*np.arctan(np.power(x[1], p))
+
+PWG = PersistenceWeightedGaussianKernel(bandwidth=1., kernel_approx=None, weight=arctan(1.,1.))
+print("PWG kernel is " + str(PWG(D1, D2)))
+
+PWG = PersistenceWeightedGaussianKernel(kernel_approx=RBFSampler(gamma=1./2, n_components=100000).fit(np.ones([1,2])), weight=arctan(1.,1.))
+print("Approximate PWG kernel is " + str(PWG(D1, D2)))
+
+PSS = PersistenceScaleSpaceKernel(bandwidth=1.)
+print("PSS kernel is " + str(PSS(D1, D2)))
+
+PSS = PersistenceScaleSpaceKernel(kernel_approx=RBFSampler(gamma=1./2, n_components=100000).fit(np.ones([1,2])))
+print("Approximate PSS kernel is " + str(PSS(D1, D2)))
+
+sW = SlicedWassersteinDistance(num_directions=100)
+print("SW distance is " + str(sW(D1, D2)))
+
+SW = SlicedWassersteinKernel(num_directions=100, bandwidth=1.)
+print("SW kernel is " + str(SW(D1, D2)))
+
+try:
+    W = WassersteinDistance(order=2, internal_p=2, mode="pot")
+    print("Wasserstein distance (POT) is " + str(W(D1, D2)))
+except ImportError:
+    print("WassersteinDistance (POT) is not available, you may be missing pot.")
+
+W = WassersteinDistance(order=2, internal_p=2, mode="hera", delta=0.0001)
+print("Wasserstein distance (hera) is " + str(W(D1, D2)))
+
+try:
+    from gudhi.representations import BottleneckDistance
+    W = BottleneckDistance(epsilon=.001)
+    print("Bottleneck distance is " + str(W(D1, D2)))
+except ImportError:
+    print("BottleneckDistance is not available, you may be missing CGAL.")
+
+PF = PersistenceFisherKernel(bandwidth_fisher=1., bandwidth=1.)
+print("PF kernel is " + str(PF(D1, D2)))
+
+PF = PersistenceFisherKernel(bandwidth_fisher=1., bandwidth=1., kernel_approx=RBFSampler(gamma=1./2, n_components=100000).fit(np.ones([1,2])))
+print("Approximate PF kernel is " + str(PF(D1, D2)))