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diff --git a/Wrappers/Python/demos/PDHG_examples/TGV_Denoising/PDHG_TGV_Denoising_SaltPepper.py b/Wrappers/Python/demos/PDHG_examples/TGV_Denoising/PDHG_TGV_Denoising_SaltPepper.py
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index 15c0a05..0000000
--- a/Wrappers/Python/demos/PDHG_examples/TGV_Denoising/PDHG_TGV_Denoising_SaltPepper.py
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@@ -1,245 +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 Generalised Variation (TGV) Denoising using PDHG algorithm:
-
-
-Problem:     min_{x} \alpha * ||\nabla x - w||_{2,1} +
-                     \beta *  || E w ||_{2,1} +
-                     \frac{1}{2} * || x - g ||_{2}^{2}
-
-             \alpha: Regularization parameter
-             \beta: Regularization parameter
-             
-             \nabla: Gradient operator 
-              E: Symmetrized Gradient operator
-             
-             g: Noisy Data with Salt & Pepper Noise
-                          
-             Method = 0 ( PDHG - split ) :  K = [ \nabla, - Identity
-                                                  ZeroOperator, E 
-                                                  Identity, ZeroOperator]
-                          
-                                                                    
-             Method = 1 (PDHG - explicit ):  K = [ \nabla, - Identity
-                                                  ZeroOperator, E ] 
-                                                                
-"""
-
-from ccpi.framework import ImageData, ImageGeometry
-
-import numpy as np 
-import numpy                          
-import matplotlib.pyplot as plt
-
-from ccpi.optimisation.algorithms import PDHG
-
-from ccpi.optimisation.operators import BlockOperator, Identity, \
-                        Gradient, SymmetrizedGradient, ZeroOperator
-from ccpi.optimisation.functions import ZeroFunction, L1Norm, \
-                      MixedL21Norm, BlockFunction
-
-from skimage.util import random_noise
-
-# Create phantom for TGV SaltPepper denoising
-
-N = 100
-
-data = np.zeros((N,N))
-
-x1 = np.linspace(0, int(N/2), N)
-x2 = np.linspace(int(N/2), 0., N)
-xv, yv = np.meshgrid(x1, x2)
-
-xv[int(N/4):int(3*N/4)-1, int(N/4):int(3*N/4)-1] = yv[int(N/4):int(3*N/4)-1, int(N/4):int(3*N/4)-1].T
-
-data = xv
-data = ImageData(data/data.max())
-
-ig = ImageGeometry(voxel_num_x = N, voxel_num_y = N)
-ag = ig
-
-# Create noisy data. Add Gaussian noise
-n1 = random_noise(data.as_array(), mode = 's&p', salt_vs_pepper = 0.9, amount=0.2)
-noisy_data = ImageData(n1)
-
-# Show Ground Truth and Noisy Data
-plt.figure(figsize=(15,15))
-plt.subplot(2,1,1)
-plt.imshow(data.as_array())
-plt.title('Ground Truth')
-plt.colorbar()
-plt.subplot(2,1,2)
-plt.imshow(noisy_data.as_array())
-plt.title('Noisy Data')
-plt.colorbar()
-plt.show()
-
-# Regularisation Parameters
-alpha = 0.8
-beta = numpy.sqrt(2)* alpha
-
-method = '1'
-
-if method == '0':
-
-    # Create operators
-    op11 = Gradient(ig)
-    op12 = Identity(op11.range_geometry())
-    
-    op22 = SymmetrizedGradient(op11.domain_geometry())    
-    op21 = ZeroOperator(ig, op22.range_geometry())
-        
-    op31 = Identity(ig, ag)
-    op32 = ZeroOperator(op22.domain_geometry(), ag)
-    
-    operator = BlockOperator(op11, -1*op12, op21, op22, op31, op32, shape=(3,2) ) 
-        
-    f1 = alpha * MixedL21Norm()
-    f2 = beta * MixedL21Norm() 
-    f3 = L1Norm(b=noisy_data)    
-    f = BlockFunction(f1, f2, f3)         
-    g = ZeroFunction()
-        
-else:
-    
-    # Create operators
-    op11 = Gradient(ig)
-    op12 = Identity(op11.range_geometry())
-    op22 = SymmetrizedGradient(op11.domain_geometry())    
-    op21 = ZeroOperator(ig, op22.range_geometry())    
-    
-    operator = BlockOperator(op11, -1*op12, op21, op22, shape=(2,2) )      
-    
-    f1 = alpha * MixedL21Norm()
-    f2 = beta * MixedL21Norm()     
-    
-    f = BlockFunction(f1, f2)         
-    g = BlockFunction(L1Norm(b=noisy_data), ZeroFunction())
-     
-## Compute operator Norm
-normK = operator.norm()
-#
-# Primal & dual stepsizes
-sigma = 1
-tau = 1/(sigma*normK**2)
-
-
-# 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 = 50
-pdhg.run(2000, verbose = False)
-
-#%%
-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()[0].as_array())
-plt.title('TGV Reconstruction')
-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), pdhg.get_output()[0].as_array()[int(N/2),:], label = 'TV reconstruction')
-plt.legend()
-plt.title('Middle Line Profiles')
-plt.show()
-
-
-#%% Check with CVX solution
-
-from ccpi.optimisation.operators import SparseFiniteDiff
-
-try:
-    from cvxpy import *
-    cvx_not_installable = True
-except ImportError:
-    cvx_not_installable = False
-
-if cvx_not_installable:    
-    
-    u = Variable(ig.shape)
-    w1 = Variable((N, N))
-    w2 = Variable((N, N))
-    
-    # create TGV regulariser
-    DY = SparseFiniteDiff(ig, direction=0, bnd_cond='Neumann')
-    DX = SparseFiniteDiff(ig, direction=1, bnd_cond='Neumann')
-    
-    regulariser = alpha * sum(norm(vstack([DX.matrix() * vec(u) - vec(w1), \
-                                           DY.matrix() * vec(u) - vec(w2)]), 2, axis = 0)) + \
-                  beta * sum(norm(vstack([ DX.matrix().transpose() * vec(w1), DY.matrix().transpose() * vec(w2), \
-                                      0.5 * ( DX.matrix().transpose() * vec(w2) + DY.matrix().transpose() * vec(w1) ), \
-                                      0.5 * ( DX.matrix().transpose() * vec(w2) + DY.matrix().transpose() * vec(w1) ) ]), 2, axis = 0  ) )  
-    
-    constraints = []
-    fidelity = pnorm(u - noisy_data.as_array(),1)    
-    solver = MOSEK
-
-        # choose solver
-    if 'MOSEK' in installed_solvers():
-        solver = MOSEK
-    else:
-        solver = SCS  
-        
-    obj =  Minimize( regulariser +  fidelity)
-    prob = Problem(obj)
-    result = prob.solve(verbose = True, solver = solver)
-    
-    diff_cvx = numpy.abs( pdhg.get_output()[0].as_array() - u.value )
-        
-    plt.figure(figsize=(15,15))
-    plt.subplot(3,1,1)
-    plt.imshow(pdhg.get_output()[0].as_array())
-    plt.title('PDHG solution')
-    plt.colorbar()
-    plt.subplot(3,1,2)
-    plt.imshow(u.value)
-    plt.title('CVX solution')
-    plt.colorbar()
-    plt.subplot(3,1,3)
-    plt.imshow(diff_cvx)
-    plt.title('Difference')
-    plt.colorbar()
-    plt.show()    
-    
-    plt.plot(np.linspace(0,N,N), pdhg.get_output()[0].as_array()[int(N/2),:], label = 'PDHG')
-    plt.plot(np.linspace(0,N,N), u.value[int(N/2),:], label = 'CVX')
-    plt.legend()
-    plt.title('Middle Line Profiles')
-    plt.show()
-            
-    print('Primal Objective (CVX) {} '.format(obj.value))
-    print('Primal Objective (PDHG) {} '.format(pdhg.objective[-1][0]))
-
-
-
-
-