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diff --git a/Wrappers/Python/demos/PDHG_examples/GatherAll/PDHG_TV_Tomo2D.py b/Wrappers/Python/demos/PDHG_examples/GatherAll/PDHG_TV_Tomo2D.py
deleted file mode 100644
index 4f7639e..0000000
--- a/Wrappers/Python/demos/PDHG_examples/GatherAll/PDHG_TV_Tomo2D.py
+++ /dev/null
@@ -1,173 +0,0 @@
-#========================================================================
-# Copyright 2019 Science Technology Facilities Council
-# Copyright 2019 University of Manchester
-#
-# This work is part of the Core Imaging Library developed by Science Technology
-# Facilities Council and University of Manchester
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#         http://www.apache.org/licenses/LICENSE-2.0.txt
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-#=========================================================================
-
-""" 
-
-Total Variation 2D Tomography Reconstruction using PDHG algorithm:
-
-
-Problem:     min_u  \alpha * ||\nabla u||_{2,1} + \frac{1}{2}||Au - g||^{2}
-             min_u, u>0  \alpha * ||\nabla u||_{2,1} + \int A u  - g log (Au + \eta)
-
-             \nabla: Gradient operator              
-             A: System Matrix
-             g: Noisy sinogram 
-             \eta: Background noise
-             
-             \alpha: Regularization parameter
- 
-"""
-
-from ccpi.framework import ImageData, ImageGeometry, AcquisitionGeometry, AcquisitionData
-
-import numpy as np 
-import numpy                          
-import matplotlib.pyplot as plt
-
-from ccpi.optimisation.algorithms import PDHG
-
-from ccpi.optimisation.operators import BlockOperator, Gradient
-from ccpi.optimisation.functions import ZeroFunction, L2NormSquared, \
-                      MixedL21Norm, BlockFunction, KullbackLeibler, IndicatorBox
-                      
-from ccpi.astra.ops import AstraProjectorSimple
-from ccpi.framework import TestData
-from PIL import Image
-import os, sys
-#if int(numpy.version.version.split('.')[1]) > 12:
-from skimage.util import random_noise
-#else:
-#    from demoutil import random_noise
-
-#import scipy.io
-
-# user supplied input
-if len(sys.argv) > 1:
-    which_noise = int(sys.argv[1])
-else:
-    which_noise = 1
-
-# Load 256 shepp-logan
-data256 = scipy.io.loadmat('phantom.mat')['phantom256']
-data = ImageData(numpy.array(Image.fromarray(data256).resize((256,256))))
-N, M = data.shape
-ig = ImageGeometry(voxel_num_x=N, voxel_num_y=M)
-
-# Add it to testdata or use tomophantom
-#loader = TestData(data_dir=os.path.join(sys.prefix, 'share','ccpi'))
-#data = loader.load(TestData.SIMPLE_PHANTOM_2D, size=(50, 50))
-#ig = data.geometry
-
-# Create acquisition data and geometry
-detectors = N
-angles = np.linspace(0, np.pi, 180)
-ag = AcquisitionGeometry('parallel','2D',angles, detectors)
-
-# Select device
-device = '0'
-#device = input('Available device: GPU==1 / CPU==0 ')
-if device=='1':
-    dev = 'gpu'
-else:
-    dev = 'cpu'
-    
-Aop = AstraProjectorSimple(ig, ag, dev)
-sin = Aop.direct(data)
-
-# Create noisy data. Apply Gaussian noise
-noises = ['gaussian', 'poisson']
-noise = noises[which_noise]
-
-if noise == 'poisson':
-    scale = 5
-    eta = 0
-    noisy_data = AcquisitionData(np.random.poisson( scale * (eta + sin.as_array()))/scale, ag)
-elif noise == 'gaussian':
-    n1 = np.random.normal(0, 1, size = ag.shape)
-    noisy_data = AcquisitionData(n1 + sin.as_array(), ag)
-else:
-    raise ValueError('Unsupported Noise ', noise)
-
-# Show Ground Truth and Noisy Data
-plt.figure(figsize=(10,10))
-plt.subplot(1,2,2)
-plt.imshow(data.as_array())
-plt.title('Ground Truth')
-plt.colorbar()
-plt.subplot(1,2,1)
-plt.imshow(noisy_data.as_array())
-plt.title('Noisy Data')
-plt.colorbar()
-plt.show()
-
-# Create operators
-op1 = Gradient(ig)
-op2 = Aop
-
-# Create BlockOperator
-operator = BlockOperator(op1, op2, shape=(2,1) ) 
-
-# Compute operator Norm
-normK = operator.norm()
-
-# Create functions
-if noise == 'poisson':
-    alpha = 3
-    f2 = KullbackLeibler(noisy_data)  
-    g =  IndicatorBox(lower=0) 
-    sigma = 1
-    tau = 1/(sigma*normK**2)    
-    
-elif noise == 'gaussian':   
-    alpha = 20
-    f2 = 0.5 * L2NormSquared(b=noisy_data)                                         
-    g = ZeroFunction()
-    sigma = 10
-    tau = 1/(sigma*normK**2)     
-    
-f1 = alpha * MixedL21Norm() 
-f = BlockFunction(f1, f2)    
-
-# Setup and run the PDHG algorithm
-pdhg = PDHG(f=f,g=g,operator=operator, tau=tau, sigma=sigma)
-pdhg.max_iteration = 2000
-pdhg.update_objective_interval = 200
-pdhg.run(2000)
-
-plt.figure(figsize=(15,15))
-plt.subplot(3,1,1)
-plt.imshow(data.as_array())
-plt.title('Ground Truth')
-plt.colorbar()
-plt.subplot(3,1,2)
-plt.imshow(noisy_data.as_array())
-plt.title('Noisy Data')
-plt.colorbar()
-plt.subplot(3,1,3)
-plt.imshow(pdhg.get_output().as_array())
-plt.title('TV Reconstruction')
-plt.colorbar()
-plt.show()
-plt.plot(np.linspace(0,ig.shape[1],ig.shape[1]), data.as_array()[int(N/2),:], label = 'GTruth')
-plt.plot(np.linspace(0,ig.shape[1],ig.shape[1]), pdhg.get_output().as_array()[int(N/2),:], label = 'TV reconstruction')
-plt.legend()
-plt.title('Middle Line Profiles')
-plt.show()