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diff --git a/Wrappers/Python/wip/pdhg_TV_tomography2D.py b/Wrappers/Python/wip/pdhg_TV_tomography2D.py
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+++ b/Wrappers/Python/wip/pdhg_TV_tomography2D.py
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+# -*- coding: utf-8 -*-
+
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Fri Feb 22 14:53:03 2019
+
+@author: evangelos
+"""
+
+from ccpi.framework import ImageData, ImageGeometry, BlockDataContainer, AcquisitionGeometry, AcquisitionData
+
+import numpy as np                           
+import matplotlib.pyplot as plt
+
+from ccpi.optimisation.algorithms import PDHG
+
+from ccpi.optimisation.operators import BlockOperator, Identity, Gradient
+from ccpi.optimisation.functions import ZeroFun, L2NormSquared, \
+                      MixedL21Norm, BlockFunction, ScaledFunction
+
+from ccpi.astra.ops import AstraProjectorSimple
+from skimage.util import random_noise
+
+
+#%%###############################################################################
+# Create phantom for TV tomography
+
+N = 150
+x = np.zeros((N,N))
+x[round(N/4):round(3*N/4),round(N/4):round(3*N/4)] = 0.5
+x[round(N/8):round(7*N/8),round(3*N/8):round(5*N/8)] = 1
+
+data = ImageData(x)
+ig = ImageGeometry(voxel_num_x = N, voxel_num_y = N)
+
+detectors = 100
+angles = np.linspace(0,np.pi,100)
+
+ag = AcquisitionGeometry('parallel','2D',angles, detectors)
+Aop = AstraProjectorSimple(ig, ag, 'cpu')
+sin = Aop.direct(data)
+
+plt.imshow(sin.as_array())
+plt.title('Sinogram')
+plt.colorbar()
+plt.show()
+
+# Add Gaussian noise to the sinogram data
+np.random.seed(10)
+n1 = np.random.random(sin.shape)
+
+noisy_data = sin + ImageData(5*n1)
+
+plt.imshow(noisy_data.as_array())
+plt.title('Noisy Sinogram')
+plt.colorbar()
+plt.show()
+
+#%% Works only with Composite Operator Structure of PDHG
+
+ig = ImageGeometry(voxel_num_x = N, voxel_num_y = N)
+
+# Create operators
+op1 = Gradient(ig)
+op2 = Aop
+
+# Form Composite Operator
+operator = BlockOperator(op1, op2, shape=(2,1) ) 
+
+alpha = 50
+f = BlockFunction( alpha * MixedL21Norm(), \
+                   0.5 * L2NormSquared(b = noisy_data) )
+g = ZeroFun()
+
+# Compute operator Norm
+normK = operator.norm()
+
+## Primal & dual stepsizes
+
+sigma = 10
+tau = 1/(sigma*normK**2)
+
+pdhg = PDHG(f=f,g=g,operator=operator, tau=tau, sigma=sigma)
+pdhg.max_iteration = 5000
+pdhg.update_objective_interval = 500
+
+pdhg.run(5000)
+
+#%%
+sol = pdhg.get_output().as_array()
+fig = plt.figure()
+plt.subplot(1,2,1)
+plt.imshow(noisy_data.as_array())
+#plt.colorbar()
+plt.subplot(1,2,2)
+plt.imshow(sol)
+#plt.colorbar()
+plt.show()
+
+
+#%%
+plt.plot(np.linspace(0,N,N), data.as_array()[int(N/2),:], label = 'GTruth')
+plt.plot(np.linspace(0,N,N), sol[int(N/2),:], label = 'Recon')
+plt.legend()
+plt.show()
+
+