restructured sources

7 files changed, 0 insertions, 3008 deletions

diff --git a/Wrappers/Python/demos/demo_cpu_inpainters.py b/Wrappers/Python/demos/demo_cpu_inpainters.py
deleted file mode 100644
index 3b4191b..0000000
--- a/Wrappers/Python/demos/demo_cpu_inpainters.py
+++ /dev/null
@@ -1,192 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Demonstration of CPU inpainters
-@authors: Daniil Kazantsev, Edoardo Pasca
-"""
-
-import matplotlib.pyplot as plt
-import numpy as np
-import os
-import timeit
-from scipy import io
-from ccpi.filters.regularisers import NDF_INP, NVM_INP
-from qualitymetrics import rmse
-###############################################################################
-def printParametersToString(pars):
-        txt = r''
-        for key, value in pars.items():
-            if key== 'algorithm' :
-                txt += "{0} = {1}".format(key, value.__name__)
-            elif key == 'input':
-                txt += "{0} = {1}".format(key, np.shape(value))
-            elif key == 'maskData':
-                txt += "{0} = {1}".format(key, np.shape(value))
-            else:
-                txt += "{0} = {1}".format(key, value)
-            txt += '\n'
-        return txt
-###############################################################################
-
-# read sinogram and the mask
-filename = os.path.join(".." , ".." , ".." , "data" ,"SinoInpaint.mat")
-sino = io.loadmat(filename)
-sino_full = sino.get('Sinogram')
-Mask = sino.get('Mask')
-[angles_dim,detectors_dim] = sino_full.shape
-sino_full = sino_full/np.max(sino_full)
-#apply mask to sinogram
-sino_cut = sino_full*(1-Mask)
-#sino_cut_new = np.zeros((angles_dim,detectors_dim),'float32')
-#sino_cut_new = sino_cut.copy(order='c')
-#sino_cut_new[:] = sino_cut[:]
-sino_cut_new = np.ascontiguousarray(sino_cut, dtype=np.float32);
-#mask = np.zeros((angles_dim,detectors_dim),'uint8')
-#mask =Mask.copy(order='c')
-#mask[:] = Mask[:]
-mask = np.ascontiguousarray(Mask, dtype=np.uint8);
-
-plt.figure(1)
-plt.subplot(121)
-plt.imshow(sino_cut_new,vmin=0.0, vmax=1)
-plt.title('Missing Data sinogram')
-plt.subplot(122)
-plt.imshow(mask)
-plt.title('Mask')
-plt.show()
-#%%
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-print ("___Inpainting using linear diffusion (2D)__")
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-
-## plot 
-fig = plt.figure(2)
-plt.suptitle('Performance of linear inpainting using the CPU')
-a=fig.add_subplot(1,2,1)
-a.set_title('Missing data sinogram')
-imgplot = plt.imshow(sino_cut_new,cmap="gray")
-
-# set parameters
-pars = {'algorithm' : NDF_INP, \
-        'input' : sino_cut_new,\
-        'maskData' : mask,\
-        'regularisation_parameter':5000,\
-        'edge_parameter':0,\
-        'number_of_iterations' :5000 ,\
-        'time_marching_parameter':0.000075,\
-        'penalty_type':0
-        }
-        
-start_time = timeit.default_timer()
-ndf_inp_linear = NDF_INP(pars['input'],
-              pars['maskData'],
-              pars['regularisation_parameter'],
-              pars['edge_parameter'], 
-              pars['number_of_iterations'],
-              pars['time_marching_parameter'], 
-              pars['penalty_type'])
-             
-rms = rmse(sino_full, ndf_inp_linear)
-pars['rmse'] = rms
-
-txtstr = printParametersToString(pars)
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-a=fig.add_subplot(1,2,2)
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
-# place a text box in upper left in axes coords
-a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
-         verticalalignment='top', bbox=props)
-imgplot = plt.imshow(ndf_inp_linear, cmap="gray")
-plt.title('{}'.format('Linear diffusion inpainting results'))
-#%%
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-print ("_Inpainting using nonlinear diffusion (2D)_")
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-
-## plot 
-fig = plt.figure(3)
-plt.suptitle('Performance of nonlinear diffusion inpainting using the CPU')
-a=fig.add_subplot(1,2,1)
-a.set_title('Missing data sinogram')
-imgplot = plt.imshow(sino_cut_new,cmap="gray")
-
-# set parameters
-pars = {'algorithm' : NDF_INP, \
-        'input' : sino_cut_new,\
-        'maskData' : mask,\
-        'regularisation_parameter':80,\
-        'edge_parameter':0.00009,\
-        'number_of_iterations' :1500 ,\
-        'time_marching_parameter':0.000008,\
-        'penalty_type':1
-        }
-        
-start_time = timeit.default_timer()
-ndf_inp_nonlinear = NDF_INP(pars['input'],
-              pars['maskData'],
-              pars['regularisation_parameter'],
-              pars['edge_parameter'], 
-              pars['number_of_iterations'],
-              pars['time_marching_parameter'], 
-              pars['penalty_type'])
-             
-rms = rmse(sino_full, ndf_inp_nonlinear)
-pars['rmse'] = rms
-
-txtstr = printParametersToString(pars)
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-a=fig.add_subplot(1,2,2)
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
-# place a text box in upper left in axes coords
-a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
-         verticalalignment='top', bbox=props)
-imgplot = plt.imshow(ndf_inp_nonlinear, cmap="gray")
-plt.title('{}'.format('Nonlinear diffusion inpainting results'))
-#%%
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-print ("Inpainting using nonlocal vertical marching")
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-
-## plot 
-fig = plt.figure(4)
-plt.suptitle('Performance of NVM inpainting using the CPU')
-a=fig.add_subplot(1,2,1)
-a.set_title('Missing data sinogram')
-imgplot = plt.imshow(sino_cut,cmap="gray")
-
-# set parameters
-pars = {'algorithm' : NVM_INP, \
-        'input' : sino_cut_new,\
-        'maskData' : mask,\
-        'SW_increment': 1,\
-        'number_of_iterations' : 150
-        }
-        
-start_time = timeit.default_timer()
-(nvm_inp, mask_upd) = NVM_INP(pars['input'],
-              pars['maskData'],
-              pars['SW_increment'],
-              pars['number_of_iterations'])
-             
-rms = rmse(sino_full, nvm_inp)
-pars['rmse'] = rms
-
-txtstr = printParametersToString(pars)
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-a=fig.add_subplot(1,2,2)
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
-# place a text box in upper left in axes coords
-a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
-         verticalalignment='top', bbox=props)
-imgplot = plt.imshow(nvm_inp, cmap="gray")
-plt.title('{}'.format('Nonlocal Vertical Marching inpainting results'))
-#%%
diff --git a/Wrappers/Python/demos/demo_cpu_regularisers.py b/Wrappers/Python/demos/demo_cpu_regularisers.py
deleted file mode 100644
index e6befa9..0000000
--- a/Wrappers/Python/demos/demo_cpu_regularisers.py
+++ /dev/null
@@ -1,572 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Thu Feb 22 11:39:43 2018
-
-Demonstration of CPU regularisers 
-
-@authors: Daniil Kazantsev, Edoardo Pasca
-"""
-
-import matplotlib.pyplot as plt
-import numpy as np
-import os
-import timeit
-from ccpi.filters.regularisers import ROF_TV, FGP_TV, SB_TV, TGV, LLT_ROF, FGP_dTV, TNV, NDF, Diff4th
-from ccpi.filters.regularisers import PatchSelect, NLTV
-from qualitymetrics import rmse
-###############################################################################
-def printParametersToString(pars):
-        txt = r''
-        for key, value in pars.items():
-            if key== 'algorithm' :
-                txt += "{0} = {1}".format(key, value.__name__)
-            elif key == 'input':
-                txt += "{0} = {1}".format(key, np.shape(value))
-            elif key == 'refdata':
-                txt += "{0} = {1}".format(key, np.shape(value))
-            else:
-                txt += "{0} = {1}".format(key, value)
-            txt += '\n'
-        return txt
-###############################################################################
-#%%
-filename = os.path.join(".." , ".." , ".." , "data" ,"lena_gray_512.tif")
-
-# read image
-Im = plt.imread(filename)
-Im = np.asarray(Im, dtype='float32')
-
-Im = Im/255.0
-perc = 0.05
-u0 = Im + np.random.normal(loc = 0 ,
-                                  scale = perc * Im , 
-                                  size = np.shape(Im))
-u_ref = Im + np.random.normal(loc = 0 ,
-                                  scale = 0.01 * Im , 
-                                  size = np.shape(Im))
-(N,M) = np.shape(u0)
-# map the u0 u0->u0>0
-# f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1)
-u0 = u0.astype('float32')
-u_ref = u_ref.astype('float32')
-
-# change dims to check that modules work with non-squared images
-"""
-M = M-100
-u_ref2 = np.zeros([N,M],dtype='float32')
-u_ref2[:,0:M] = u_ref[:,0:M]
-u_ref = u_ref2
-del u_ref2
-
-u02 = np.zeros([N,M],dtype='float32')
-u02[:,0:M] = u0[:,0:M]
-u0 = u02
-del u02
-
-Im2 = np.zeros([N,M],dtype='float32')
-Im2[:,0:M] = Im[:,0:M]
-Im = Im2
-del Im2
-"""
-#%%
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-print ("_______________ROF-TV (2D)_________________")
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-
-## plot 
-fig = plt.figure()
-plt.suptitle('Performance of ROF-TV regulariser using the CPU')
-a=fig.add_subplot(1,2,1)
-a.set_title('Noisy Image')
-imgplot = plt.imshow(u0,cmap="gray")
-
-# set parameters
-pars = {'algorithm': ROF_TV, \
-        'input' : u0,\
-        'regularisation_parameter':0.04,\
-        'number_of_iterations': 1200,\
-        'time_marching_parameter': 0.0025        
-        }
-print ("#############ROF TV CPU####################")
-start_time = timeit.default_timer()
-rof_cpu = ROF_TV(pars['input'],
-             pars['regularisation_parameter'],
-             pars['number_of_iterations'],
-             pars['time_marching_parameter'],'cpu')
-rms = rmse(Im, rof_cpu)
-pars['rmse'] = rms
-
-txtstr = printParametersToString(pars)
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-a=fig.add_subplot(1,2,2)
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
-# place a text box in upper left in axes coords
-a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
-         verticalalignment='top', bbox=props)
-imgplot = plt.imshow(rof_cpu, cmap="gray")
-plt.title('{}'.format('CPU results'))
-
-#%%
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-print ("_______________FGP-TV (2D)__________________")
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-
-## plot 
-fig = plt.figure()
-plt.suptitle('Performance of FGP-TV regulariser using the CPU')
-a=fig.add_subplot(1,2,1)
-a.set_title('Noisy Image')
-imgplot = plt.imshow(u0,cmap="gray")
-
-# set parameters
-pars = {'algorithm' : FGP_TV, \
-        'input' : u0,\
-        'regularisation_parameter':0.04, \
-        'number_of_iterations' :2000 ,\
-        'tolerance_constant':1e-06,\
-        'methodTV': 0 ,\
-        'nonneg': 0 ,\
-        'printingOut': 0 
-        }
-        
-print ("#############FGP TV CPU####################")
-start_time = timeit.default_timer()
-fgp_cpu = FGP_TV(pars['input'], 
-              pars['regularisation_parameter'],
-              pars['number_of_iterations'],
-              pars['tolerance_constant'], 
-              pars['methodTV'],
-              pars['nonneg'],
-              pars['printingOut'],'cpu')  
-             
-             
-rms = rmse(Im, fgp_cpu)
-pars['rmse'] = rms
-
-txtstr = printParametersToString(pars)
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-a=fig.add_subplot(1,2,2)
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
-# place a text box in upper left in axes coords
-a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
-         verticalalignment='top', bbox=props)
-imgplot = plt.imshow(fgp_cpu, cmap="gray")
-plt.title('{}'.format('CPU results'))
-
-#%%
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-print ("_______________SB-TV (2D)__________________")
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-
-## plot 
-fig = plt.figure()
-plt.suptitle('Performance of SB-TV regulariser using the CPU')
-a=fig.add_subplot(1,2,1)
-a.set_title('Noisy Image')
-imgplot = plt.imshow(u0,cmap="gray")
-
-# set parameters
-pars = {'algorithm' : SB_TV, \
-        'input' : u0,\
-        'regularisation_parameter':0.04, \
-        'number_of_iterations' :150 ,\
-        'tolerance_constant':1e-06,\
-        'methodTV': 0 ,\
-        'printingOut': 0 
-        }
-        
-print ("#############SB TV CPU####################")
-start_time = timeit.default_timer()
-sb_cpu = SB_TV(pars['input'], 
-              pars['regularisation_parameter'],
-              pars['number_of_iterations'],
-              pars['tolerance_constant'], 
-              pars['methodTV'],
-              pars['printingOut'],'cpu')  
-             
-             
-rms = rmse(Im, sb_cpu)
-pars['rmse'] = rms
-
-txtstr = printParametersToString(pars)
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-a=fig.add_subplot(1,2,2)
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
-# place a text box in upper left in axes coords
-a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
-         verticalalignment='top', bbox=props)
-imgplot = plt.imshow(sb_cpu, cmap="gray")
-plt.title('{}'.format('CPU results'))
-#%%
-
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-print ("_____Total Generalised Variation (2D)______")
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-
-## plot 
-fig = plt.figure()
-plt.suptitle('Performance of TGV regulariser using the CPU')
-a=fig.add_subplot(1,2,1)
-a.set_title('Noisy Image')
-imgplot = plt.imshow(u0,cmap="gray")
-
-# set parameters
-pars = {'algorithm' : TGV, \
-        'input' : u0,\
-        'regularisation_parameter':0.04, \
-        'alpha1':1.0,\
-        'alpha0':2.0,\
-        'number_of_iterations' :1350 ,\
-        'LipshitzConstant' :12 ,\
-        }
-        
-print ("#############TGV CPU####################")
-start_time = timeit.default_timer()
-tgv_cpu = TGV(pars['input'], 
-              pars['regularisation_parameter'],
-              pars['alpha1'],
-              pars['alpha0'],
-              pars['number_of_iterations'],
-              pars['LipshitzConstant'],'cpu')
-             
-             
-rms = rmse(Im, tgv_cpu)
-pars['rmse'] = rms
-
-txtstr = printParametersToString(pars)
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-a=fig.add_subplot(1,2,2)
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
-# place a text box in upper left in axes coords
-a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
-         verticalalignment='top', bbox=props)
-imgplot = plt.imshow(tgv_cpu, cmap="gray")
-plt.title('{}'.format('CPU results'))
-
-#%%
-
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-print ("______________LLT- ROF (2D)________________")
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-
-## plot 
-fig = plt.figure()
-plt.suptitle('Performance of LLT-ROF regulariser using the CPU')
-a=fig.add_subplot(1,2,1)
-a.set_title('Noisy Image')
-imgplot = plt.imshow(u0,cmap="gray")
-
-# set parameters
-pars = {'algorithm' : LLT_ROF, \
-        'input' : u0,\
-        'regularisation_parameterROF':0.04, \
-        'regularisation_parameterLLT':0.01, \
-        'number_of_iterations' :500 ,\
-        'time_marching_parameter' :0.0025 ,\
-        }
-        
-print ("#############LLT- ROF CPU####################")
-start_time = timeit.default_timer()
-lltrof_cpu = LLT_ROF(pars['input'], 
-              pars['regularisation_parameterROF'],
-              pars['regularisation_parameterLLT'],
-              pars['number_of_iterations'],
-              pars['time_marching_parameter'],'cpu')
-
-rms = rmse(Im, lltrof_cpu)
-pars['rmse'] = rms
-
-txtstr = printParametersToString(pars)
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-a=fig.add_subplot(1,2,2)
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
-# place a text box in upper left in axes coords
-a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
-         verticalalignment='top', bbox=props)
-imgplot = plt.imshow(lltrof_cpu, cmap="gray")
-plt.title('{}'.format('CPU results'))
-
-#%%
-
-
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-print ("________________NDF (2D)___________________")
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-
-## plot 
-fig = plt.figure()
-plt.suptitle('Performance of NDF regulariser using the CPU')
-a=fig.add_subplot(1,2,1)
-a.set_title('Noisy Image')
-imgplot = plt.imshow(u0,cmap="gray")
-
-# set parameters
-pars = {'algorithm' : NDF, \
-        'input' : u0,\
-        'regularisation_parameter':0.025, \
-        'edge_parameter':0.015,\
-        'number_of_iterations' :500 ,\
-        'time_marching_parameter':0.025,\
-        'penalty_type':1
-        }
-        
-print ("#############NDF CPU################")
-start_time = timeit.default_timer()
-ndf_cpu = NDF(pars['input'], 
-              pars['regularisation_parameter'],
-              pars['edge_parameter'], 
-              pars['number_of_iterations'],
-              pars['time_marching_parameter'], 
-              pars['penalty_type'],'cpu')  
-             
-rms = rmse(Im, ndf_cpu)
-pars['rmse'] = rms
-
-txtstr = printParametersToString(pars)
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-a=fig.add_subplot(1,2,2)
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
-# place a text box in upper left in axes coords
-a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
-         verticalalignment='top', bbox=props)
-imgplot = plt.imshow(ndf_cpu, cmap="gray")
-plt.title('{}'.format('CPU results'))
-
-#%%
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-print ("___Anisotropic Diffusion 4th Order (2D)____")
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-
-## plot 
-fig = plt.figure()
-plt.suptitle('Performance of Diff4th regulariser using the CPU')
-a=fig.add_subplot(1,2,1)
-a.set_title('Noisy Image')
-imgplot = plt.imshow(u0,cmap="gray")
-
-# set parameters
-pars = {'algorithm' : Diff4th, \
-        'input' : u0,\
-        'regularisation_parameter':3.5, \
-        'edge_parameter':0.02,\
-        'number_of_iterations' :500 ,\
-        'time_marching_parameter':0.0015
-        }
-        
-print ("#############Diff4th CPU################")
-start_time = timeit.default_timer()
-diff4_cpu = Diff4th(pars['input'], 
-              pars['regularisation_parameter'],
-              pars['edge_parameter'], 
-              pars['number_of_iterations'],
-              pars['time_marching_parameter'],'cpu')
-             
-rms = rmse(Im, diff4_cpu)
-pars['rmse'] = rms
-
-txtstr = printParametersToString(pars)
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-a=fig.add_subplot(1,2,2)
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
-# place a text box in upper left in axes coords
-a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
-         verticalalignment='top', bbox=props)
-imgplot = plt.imshow(diff4_cpu, cmap="gray")
-plt.title('{}'.format('CPU results'))
-
-#%%
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-print ("___Nonlocal patches pre-calculation____")
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-start_time = timeit.default_timer()
-# set parameters
-pars = {'algorithm' : PatchSelect, \
-        'input' : u0,\
-        'searchwindow': 7, \
-        'patchwindow': 2,\
-        'neighbours' : 15 ,\
-        'edge_parameter':0.18}
-
-H_i, H_j, Weights = PatchSelect(pars['input'], 
-              pars['searchwindow'],
-              pars['patchwindow'], 
-              pars['neighbours'],
-              pars['edge_parameter'],'cpu')
-              
-txtstr = printParametersToString(pars)
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-"""
-plt.figure()
-plt.imshow(Weights[0,:,:],cmap="gray",interpolation="nearest",vmin=0, vmax=1)
-plt.show()
-"""
-#%%
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-print ("___Nonlocal Total Variation penalty____")
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-## plot 
-fig = plt.figure()
-plt.suptitle('Performance of NLTV regulariser using the CPU')
-a=fig.add_subplot(1,2,1)
-a.set_title('Noisy Image')
-imgplot = plt.imshow(u0,cmap="gray")
-
-pars2 = {'algorithm' : NLTV, \
-        'input' : u0,\
-        'H_i': H_i, \
-        'H_j': H_j,\
-        'H_k' : 0,\
-        'Weights' : Weights,\
-        'regularisation_parameter': 0.04,\
-        'iterations': 3
-        }
-start_time = timeit.default_timer()
-nltv_cpu = NLTV(pars2['input'], 
-              pars2['H_i'],
-              pars2['H_j'], 
-              pars2['H_k'],
-              pars2['Weights'],
-              pars2['regularisation_parameter'],
-              pars2['iterations'])
-
-rms = rmse(Im, nltv_cpu)
-pars['rmse'] = rms
-
-txtstr = printParametersToString(pars)
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-a=fig.add_subplot(1,2,2)
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
-# place a text box in upper left in axes coords
-a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
-         verticalalignment='top', bbox=props)
-imgplot = plt.imshow(nltv_cpu, cmap="gray")
-plt.title('{}'.format('CPU results'))
-
-#%%
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-print ("_____________FGP-dTV (2D)__________________")
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-
-## plot 
-fig = plt.figure()
-plt.suptitle('Performance of FGP-dTV regulariser using the CPU')
-a=fig.add_subplot(1,2,1)
-a.set_title('Noisy Image')
-imgplot = plt.imshow(u0,cmap="gray")
-
-# set parameters
-pars = {'algorithm' : FGP_dTV, \
-        'input' : u0,\
-        'refdata' : u_ref,\
-        'regularisation_parameter':0.04, \
-        'number_of_iterations' :2000 ,\
-        'tolerance_constant':1e-06,\
-        'eta_const':0.2,\
-        'methodTV': 0 ,\
-        'nonneg': 0 ,\
-        'printingOut': 0 
-        }
-        
-print ("#############FGP dTV CPU####################")
-start_time = timeit.default_timer()
-fgp_dtv_cpu = FGP_dTV(pars['input'], 
-              pars['refdata'], 
-              pars['regularisation_parameter'],
-              pars['number_of_iterations'],
-              pars['tolerance_constant'], 
-              pars['eta_const'], 
-              pars['methodTV'],
-              pars['nonneg'],
-              pars['printingOut'],'cpu')
-             
-rms = rmse(Im, fgp_dtv_cpu)
-pars['rmse'] = rms
-
-txtstr = printParametersToString(pars)
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-a=fig.add_subplot(1,2,2)
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
-# place a text box in upper left in axes coords
-a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
-         verticalalignment='top', bbox=props)
-imgplot = plt.imshow(fgp_dtv_cpu, cmap="gray")
-plt.title('{}'.format('CPU results'))
-#%%
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-print ("__________Total nuclear Variation__________")
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-
-## plot 
-fig = plt.figure()
-plt.suptitle('Performance of TNV regulariser using the CPU')
-a=fig.add_subplot(1,2,1)
-a.set_title('Noisy Image')
-imgplot = plt.imshow(u0,cmap="gray")
-
-channelsNo = 5
-noisyVol = np.zeros((channelsNo,N,M),dtype='float32')
-idealVol = np.zeros((channelsNo,N,M),dtype='float32')
-
-for i in range (channelsNo):
-    noisyVol[i,:,:] = Im + np.random.normal(loc = 0 , scale = perc * Im , size = np.shape(Im))
-    idealVol[i,:,:] = Im
-
-# set parameters
-pars = {'algorithm' : TNV, \
-        'input' : noisyVol,\
-        'regularisation_parameter': 0.04, \
-        'number_of_iterations' : 200 ,\
-        'tolerance_constant':1e-05
-        }
-        
-print ("#############TNV CPU#################")
-start_time = timeit.default_timer()
-tnv_cpu = TNV(pars['input'],           
-              pars['regularisation_parameter'],
-              pars['number_of_iterations'],
-              pars['tolerance_constant'])
-             
-rms = rmse(idealVol, tnv_cpu)
-pars['rmse'] = rms
-
-txtstr = printParametersToString(pars)
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-a=fig.add_subplot(1,2,2)
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
-# place a text box in upper left in axes coords
-a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
-         verticalalignment='top', bbox=props)
-imgplot = plt.imshow(tnv_cpu[3,:,:], cmap="gray")
-plt.title('{}'.format('CPU results'))
diff --git a/Wrappers/Python/demos/demo_cpu_regularisers3D.py b/Wrappers/Python/demos/demo_cpu_regularisers3D.py
deleted file mode 100644
index 2d2fc22..0000000
--- a/Wrappers/Python/demos/demo_cpu_regularisers3D.py
+++ /dev/null
@@ -1,458 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Thu Feb 22 11:39:43 2018
-
-Demonstration of 3D CPU regularisers 
-
-@authors: Daniil Kazantsev, Edoardo Pasca
-"""
-
-import matplotlib.pyplot as plt
-import numpy as np
-import os
-import timeit
-from ccpi.filters.regularisers import ROF_TV, FGP_TV, SB_TV, TGV, LLT_ROF, FGP_dTV, NDF, Diff4th
-from qualitymetrics import rmse
-###############################################################################
-def printParametersToString(pars):
-        txt = r''
-        for key, value in pars.items():
-            if key== 'algorithm' :
-                txt += "{0} = {1}".format(key, value.__name__)
-            elif key == 'input':
-                txt += "{0} = {1}".format(key, np.shape(value))
-            elif key == 'refdata':
-                txt += "{0} = {1}".format(key, np.shape(value))
-            else:
-                txt += "{0} = {1}".format(key, value)
-            txt += '\n'
-        return txt
-###############################################################################
-#%%
-filename = os.path.join(".." , ".." , ".." , "data" ,"lena_gray_512.tif")
-
-# read image
-Im = plt.imread(filename)
-Im = np.asarray(Im, dtype='float32')
-
-Im = Im/255
-perc = 0.05
-u0 = Im + np.random.normal(loc = 0 ,
-                                  scale = perc * Im , 
-                                  size = np.shape(Im))
-u_ref = Im + np.random.normal(loc = 0 ,
-                                  scale = 0.01 * Im , 
-                                  size = np.shape(Im))
-(N,M) = np.shape(u0)
-# map the u0 u0->u0>0
-# f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1)
-u0 = u0.astype('float32')
-u_ref = u_ref.astype('float32')
-
-# change dims to check that modules work with non-squared images
-"""
-M = M-100
-u_ref2 = np.zeros([N,M],dtype='float32')
-u_ref2[:,0:M] = u_ref[:,0:M]
-u_ref = u_ref2
-del u_ref2
-
-u02 = np.zeros([N,M],dtype='float32')
-u02[:,0:M] = u0[:,0:M]
-u0 = u02
-del u02
-
-Im2 = np.zeros([N,M],dtype='float32')
-Im2[:,0:M] = Im[:,0:M]
-Im = Im2
-del Im2
-"""
-slices = 15
-
-noisyVol = np.zeros((slices,N,M),dtype='float32')
-noisyRef = np.zeros((slices,N,M),dtype='float32')
-idealVol = np.zeros((slices,N,M),dtype='float32')
-
-for i in range (slices):
-    noisyVol[i,:,:] = Im + np.random.normal(loc = 0 , scale = perc * Im , size = np.shape(Im))
-    noisyRef[i,:,:] = Im + np.random.normal(loc = 0 , scale = 0.01 * Im , size = np.shape(Im))
-    idealVol[i,:,:] = Im
-
-#%%
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-print ("_______________ROF-TV (3D)_________________")
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-
-## plot 
-fig = plt.figure()
-plt.suptitle('Performance of ROF-TV regulariser using the CPU')
-a=fig.add_subplot(1,2,1)
-a.set_title('Noisy 15th slice of a volume')
-imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray")
-
-# set parameters
-pars = {'algorithm': ROF_TV, \
-        'input' : noisyVol,\
-        'regularisation_parameter':0.04,\
-        'number_of_iterations': 500,\
-        'time_marching_parameter': 0.0025
-        }
-print ("#############ROF TV CPU####################")
-start_time = timeit.default_timer()
-rof_cpu3D = ROF_TV(pars['input'],
-             pars['regularisation_parameter'],
-             pars['number_of_iterations'],
-             pars['time_marching_parameter'],'cpu')
-rms = rmse(idealVol, rof_cpu3D)
-pars['rmse'] = rms
-
-txtstr = printParametersToString(pars)
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-a=fig.add_subplot(1,2,2)
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
-# place a text box in upper left in axes coords
-a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
-         verticalalignment='top', bbox=props)
-imgplot = plt.imshow(rof_cpu3D[10,:,:], cmap="gray")
-plt.title('{}'.format('Recovered volume on the CPU using ROF-TV'))
-
-#%%
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-print ("_______________FGP-TV (3D)__________________")
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-
-## plot 
-fig = plt.figure()
-plt.suptitle('Performance of FGP-TV regulariser using the CPU')
-a=fig.add_subplot(1,2,1)
-a.set_title('Noisy Image')
-imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray")
-
-# set parameters
-pars = {'algorithm' : FGP_TV, \
-        'input' : noisyVol,\
-        'regularisation_parameter':0.04, \
-        'number_of_iterations' :300 ,\
-        'tolerance_constant':0.00001,\
-        'methodTV': 0 ,\
-        'nonneg': 0 ,\
-        'printingOut': 0 
-        }
-        
-print ("#############FGP TV CPU####################")
-start_time = timeit.default_timer()
-fgp_cpu3D = FGP_TV(pars['input'], 
-              pars['regularisation_parameter'],
-              pars['number_of_iterations'],
-              pars['tolerance_constant'], 
-              pars['methodTV'],
-              pars['nonneg'],
-              pars['printingOut'],'cpu')  
-             
-             
-rms = rmse(idealVol, fgp_cpu3D)
-pars['rmse'] = rms
-
-txtstr = printParametersToString(pars)
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-a=fig.add_subplot(1,2,2)
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
-# place a text box in upper left in axes coords
-a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
-         verticalalignment='top', bbox=props)
-imgplot = plt.imshow(fgp_cpu3D[10,:,:], cmap="gray")
-plt.title('{}'.format('Recovered volume on the CPU using FGP-TV'))
-
-#%%
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-print ("_______________SB-TV (3D)_________________")
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-
-## plot 
-fig = plt.figure()
-plt.suptitle('Performance of SB-TV regulariser using the CPU')
-a=fig.add_subplot(1,2,1)
-a.set_title('Noisy Image')
-imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray")
-
-# set parameters
-pars = {'algorithm' : SB_TV, \
-        'input' : noisyVol,\
-        'regularisation_parameter':0.04, \
-        'number_of_iterations' :150 ,\
-        'tolerance_constant':0.00001,\
-        'methodTV': 0 ,\
-        'printingOut': 0 
-        }
-        
-print ("#############SB TV CPU####################")
-start_time = timeit.default_timer()
-sb_cpu3D = SB_TV(pars['input'], 
-              pars['regularisation_parameter'],
-              pars['number_of_iterations'],
-              pars['tolerance_constant'], 
-              pars['methodTV'],
-              pars['printingOut'],'cpu')
-             
-rms = rmse(idealVol, sb_cpu3D)
-pars['rmse'] = rms
-
-txtstr = printParametersToString(pars)
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-a=fig.add_subplot(1,2,2)
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
-# place a text box in upper left in axes coords
-a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
-         verticalalignment='top', bbox=props)
-imgplot = plt.imshow(sb_cpu3D[10,:,:], cmap="gray")
-plt.title('{}'.format('Recovered volume on the CPU using SB-TV'))
-
-#%%
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-print ("_______________LLT-ROF (3D)_________________")
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-
-## plot 
-fig = plt.figure()
-plt.suptitle('Performance of LLT-ROF regulariser using the CPU')
-a=fig.add_subplot(1,2,1)
-a.set_title('Noisy Image')
-imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray")
-
-# set parameters
-pars = {'algorithm' : LLT_ROF, \
-        'input' : noisyVol,\
-        'regularisation_parameterROF':0.04, \
-        'regularisation_parameterLLT':0.015, \
-        'number_of_iterations' :300 ,\
-        'time_marching_parameter' :0.0025 ,\
-        }
-
-print ("#############LLT ROF CPU####################")
-start_time = timeit.default_timer()
-lltrof_cpu3D = LLT_ROF(pars['input'], 
-              pars['regularisation_parameterROF'],
-              pars['regularisation_parameterLLT'],
-              pars['number_of_iterations'],
-              pars['time_marching_parameter'],'cpu')
-
-rms = rmse(idealVol, lltrof_cpu3D)
-pars['rmse'] = rms
-
-txtstr = printParametersToString(pars)
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-a=fig.add_subplot(1,2,2)
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
-# place a text box in upper left in axes coords
-a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
-         verticalalignment='top', bbox=props)
-imgplot = plt.imshow(lltrof_cpu3D[10,:,:], cmap="gray")
-plt.title('{}'.format('Recovered volume on the CPU using LLT-ROF'))
-
-#%%
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-print ("_______________TGV (3D)_________________")
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-
-## plot 
-fig = plt.figure()
-plt.suptitle('Performance of TGV regulariser using the CPU')
-a=fig.add_subplot(1,2,1)
-a.set_title('Noisy Image')
-imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray")
-
-# set parameters
-pars = {'algorithm' : TGV, \
-        'input' : noisyVol,\
-        'regularisation_parameter':0.04, \
-        'alpha1':1.0,\
-        'alpha0':2.0,\
-        'number_of_iterations' :250 ,\
-        'LipshitzConstant' :12 ,\
-        }
-
-print ("#############TGV CPU####################")
-start_time = timeit.default_timer()
-tgv_cpu3D = TGV(pars['input'], 
-              pars['regularisation_parameter'],
-              pars['alpha1'],
-              pars['alpha0'],
-              pars['number_of_iterations'],
-              pars['LipshitzConstant'],'cpu')
-             
-
-rms = rmse(idealVol, tgv_cpu3D)
-pars['rmse'] = rms
-
-txtstr = printParametersToString(pars)
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-a=fig.add_subplot(1,2,2)
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
-# place a text box in upper left in axes coords
-a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
-         verticalalignment='top', bbox=props)
-imgplot = plt.imshow(tgv_cpu3D[10,:,:], cmap="gray")
-plt.title('{}'.format('Recovered volume on the CPU using TGV'))
-
-#%%
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-print ("________________NDF (3D)___________________")
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-
-## plot 
-fig = plt.figure()
-plt.suptitle('Performance of NDF regulariser using the CPU')
-a=fig.add_subplot(1,2,1)
-a.set_title('Noisy volume')
-imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray")
-
-# set parameters
-pars = {'algorithm' : NDF, \
-        'input' : noisyVol,\
-        'regularisation_parameter':0.025, \
-        'edge_parameter':0.015,\
-        'number_of_iterations' :500 ,\
-        'time_marching_parameter':0.025,\
-        'penalty_type':  1
-        }
-        
-print ("#############NDF CPU################")
-start_time = timeit.default_timer()
-ndf_cpu3D = NDF(pars['input'], 
-              pars['regularisation_parameter'],
-              pars['edge_parameter'], 
-              pars['number_of_iterations'],
-              pars['time_marching_parameter'], 
-              pars['penalty_type'])  
-             
-rms = rmse(idealVol, ndf_cpu3D)
-pars['rmse'] = rms
-
-txtstr = printParametersToString(pars)
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-a=fig.add_subplot(1,2,2)
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
-# place a text box in upper left in axes coords
-a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
-         verticalalignment='top', bbox=props)
-imgplot = plt.imshow(ndf_cpu3D[10,:,:], cmap="gray")
-plt.title('{}'.format('Recovered volume on the CPU using NDF iterations'))
-
-#%%
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-print ("___Anisotropic Diffusion 4th Order (2D)____")
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-
-## plot 
-fig = plt.figure()
-plt.suptitle('Performance of Diff4th regulariser using the CPU')
-a=fig.add_subplot(1,2,1)
-a.set_title('Noisy volume')
-imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray")
-
-# set parameters
-pars = {'algorithm' : Diff4th, \
-        'input' : noisyVol,\
-        'regularisation_parameter':3.5, \
-        'edge_parameter':0.02,\
-        'number_of_iterations' :300 ,\
-        'time_marching_parameter':0.0015
-        }
-        
-print ("#############Diff4th CPU################")
-start_time = timeit.default_timer()
-diff4th_cpu3D = Diff4th(pars['input'], 
-              pars['regularisation_parameter'],
-              pars['edge_parameter'], 
-              pars['number_of_iterations'],
-              pars['time_marching_parameter'])  
-             
-rms = rmse(idealVol, diff4th_cpu3D)
-pars['rmse'] = rms
-
-txtstr = printParametersToString(pars)
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-a=fig.add_subplot(1,2,2)
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
-# place a text box in upper left in axes coords
-a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
-         verticalalignment='top', bbox=props)
-imgplot = plt.imshow(diff4th_cpu3D[10,:,:], cmap="gray")
-plt.title('{}'.format('Recovered volume on the CPU using DIFF4th iterations'))
-
-#%%
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-print ("_______________FGP-dTV (3D)__________________")
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-
-## plot 
-fig = plt.figure()
-plt.suptitle('Performance of FGP-dTV regulariser using the CPU')
-a=fig.add_subplot(1,2,1)
-a.set_title('Noisy Image')
-imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray")
-
-# set parameters
-pars = {'algorithm' : FGP_dTV,\
-        'input' : noisyVol,\
-        'refdata' : noisyRef,\
-        'regularisation_parameter':0.04, \
-        'number_of_iterations' :300 ,\
-        'tolerance_constant':0.00001,\
-        'eta_const':0.2,\
-        'methodTV': 0 ,\
-        'nonneg': 0 ,\
-        'printingOut': 0 
-        }
-        
-print ("#############FGP dTV CPU####################")
-start_time = timeit.default_timer()
-fgp_dTV_cpu3D = FGP_dTV(pars['input'],
-              pars['refdata'], 
-              pars['regularisation_parameter'],
-              pars['number_of_iterations'],
-              pars['tolerance_constant'], 
-              pars['eta_const'],
-              pars['methodTV'],
-              pars['nonneg'],
-              pars['printingOut'],'cpu')
-             
-             
-rms = rmse(idealVol, fgp_dTV_cpu3D)
-pars['rmse'] = rms
-
-txtstr = printParametersToString(pars)
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-a=fig.add_subplot(1,2,2)
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
-# place a text box in upper left in axes coords
-a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
-         verticalalignment='top', bbox=props)
-imgplot = plt.imshow(fgp_dTV_cpu3D[10,:,:], cmap="gray")
-plt.title('{}'.format('Recovered volume on the CPU using FGP-dTV'))
-#%%
diff --git a/Wrappers/Python/demos/demo_cpu_vs_gpu_regularisers.py b/Wrappers/Python/demos/demo_cpu_vs_gpu_regularisers.py
deleted file mode 100644
index 230a761..0000000
--- a/Wrappers/Python/demos/demo_cpu_vs_gpu_regularisers.py
+++ /dev/null
@@ -1,790 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Thu Feb 22 11:39:43 2018
-
-Demonstration of CPU implementation against the GPU one
-
-@authors: Daniil Kazantsev, Edoardo Pasca
-"""
-
-import matplotlib.pyplot as plt
-import numpy as np
-import os
-import timeit
-from ccpi.filters.regularisers import ROF_TV, FGP_TV, SB_TV, TGV, LLT_ROF, FGP_dTV, NDF, Diff4th
-from ccpi.filters.regularisers import PatchSelect
-from qualitymetrics import rmse
-###############################################################################
-def printParametersToString(pars):
-        txt = r''
-        for key, value in pars.items():
-            if key== 'algorithm' :
-                txt += "{0} = {1}".format(key, value.__name__)
-            elif key == 'input':
-                txt += "{0} = {1}".format(key, np.shape(value))
-            elif key == 'refdata':
-                txt += "{0} = {1}".format(key, np.shape(value))
-            else:
-                txt += "{0} = {1}".format(key, value)
-            txt += '\n'
-        return txt
-###############################################################################
-
-filename = os.path.join(".." , ".." , ".." , "data" ,"lena_gray_512.tif")
-
-# read image
-Im = plt.imread(filename)                     
-Im = np.asarray(Im, dtype='float32')
-
-Im = Im/255
-perc = 0.05
-u0 = Im + np.random.normal(loc = 0 ,
-                                  scale = perc * Im , 
-                                  size = np.shape(Im))
-u_ref = Im + np.random.normal(loc = 0 ,
-                                  scale = 0.01 * Im , 
-                                  size = np.shape(Im))
-
-# map the u0 u0->u0>0
-# f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1)
-u0 = u0.astype('float32')
-u_ref = u_ref.astype('float32')
-
-#%%
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-print ("____________ROF-TV bench___________________")
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-
-## plot 
-fig = plt.figure()
-plt.suptitle('Comparison of ROF-TV regulariser using CPU and GPU implementations')
-a=fig.add_subplot(1,4,1)
-a.set_title('Noisy Image')
-imgplot = plt.imshow(u0,cmap="gray")
-
-# set parameters
-pars = {'algorithm': ROF_TV, \
-        'input' : u0,\
-        'regularisation_parameter':0.04,\
-        'number_of_iterations': 4500,\
-        'time_marching_parameter': 0.00002
-        }
-print ("#############ROF TV CPU####################")
-start_time = timeit.default_timer()
-rof_cpu = ROF_TV(pars['input'],
-             pars['regularisation_parameter'],
-             pars['number_of_iterations'],
-             pars['time_marching_parameter'],'cpu')
-rms = rmse(Im, rof_cpu)
-pars['rmse'] = rms
-
-txtstr = printParametersToString(pars)
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-a=fig.add_subplot(1,4,2)
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
-# place a text box in upper left in axes coords
-a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
-         verticalalignment='top', bbox=props)
-imgplot = plt.imshow(rof_cpu, cmap="gray")
-plt.title('{}'.format('CPU results'))
-
-print ("##############ROF TV GPU##################")
-start_time = timeit.default_timer()
-rof_gpu = ROF_TV(pars['input'], 
-                     pars['regularisation_parameter'],
-                     pars['number_of_iterations'], 
-                     pars['time_marching_parameter'],'gpu')
-                     
-rms = rmse(Im, rof_gpu)
-pars['rmse'] = rms
-pars['algorithm'] = ROF_TV
-txtstr = printParametersToString(pars)
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-a=fig.add_subplot(1,4,3)
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
-# place a text box in upper left in axes coords
-a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
-         verticalalignment='top', bbox=props)
-imgplot = plt.imshow(rof_gpu, cmap="gray")
-plt.title('{}'.format('GPU results'))
-
-
-print ("--------Compare the results--------")
-tolerance = 1e-05
-diff_im = np.zeros(np.shape(rof_cpu))
-diff_im = abs(rof_cpu - rof_gpu)
-diff_im[diff_im > tolerance] = 1
-a=fig.add_subplot(1,4,4)
-imgplot = plt.imshow(diff_im, vmin=0, vmax=1, cmap="gray")
-plt.title('{}'.format('Pixels larger threshold difference'))
-if (diff_im.sum() > 1):
-    print ("Arrays do not match!")
-else:
-    print ("Arrays match")
-
-#%%
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-print ("____________FGP-TV bench___________________")
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-
-## plot 
-fig = plt.figure()
-plt.suptitle('Comparison of FGP-TV regulariser using CPU and GPU implementations')
-a=fig.add_subplot(1,4,1)
-a.set_title('Noisy Image')
-imgplot = plt.imshow(u0,cmap="gray")
-
-# set parameters
-pars = {'algorithm' : FGP_TV, \
-        'input' : u0,\
-        'regularisation_parameter':0.04, \
-        'number_of_iterations' :1200 ,\
-        'tolerance_constant':0.00001,\
-        'methodTV': 0 ,\
-        'nonneg': 0 ,\
-        'printingOut': 0 
-        }
-        
-print ("#############FGP TV CPU####################")
-start_time = timeit.default_timer()
-fgp_cpu = FGP_TV(pars['input'], 
-              pars['regularisation_parameter'],
-              pars['number_of_iterations'],
-              pars['tolerance_constant'], 
-              pars['methodTV'],
-              pars['nonneg'],
-              pars['printingOut'],'cpu')  
-             
-             
-rms = rmse(Im, fgp_cpu)
-pars['rmse'] = rms
-
-txtstr = printParametersToString(pars)
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-a=fig.add_subplot(1,4,2)
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
-# place a text box in upper left in axes coords
-a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
-         verticalalignment='top', bbox=props)
-imgplot = plt.imshow(fgp_cpu, cmap="gray")
-plt.title('{}'.format('CPU results'))
-
-
-print ("##############FGP TV GPU##################")
-start_time = timeit.default_timer()
-fgp_gpu = FGP_TV(pars['input'], 
-              pars['regularisation_parameter'],
-              pars['number_of_iterations'],
-              pars['tolerance_constant'], 
-              pars['methodTV'],
-              pars['nonneg'],
-              pars['printingOut'],'gpu')
-                                   
-rms = rmse(Im, fgp_gpu)
-pars['rmse'] = rms
-pars['algorithm'] = FGP_TV
-txtstr = printParametersToString(pars)
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-a=fig.add_subplot(1,4,3)
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
-# place a text box in upper left in axes coords
-a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
-         verticalalignment='top', bbox=props)
-imgplot = plt.imshow(fgp_gpu, cmap="gray")
-plt.title('{}'.format('GPU results'))
-
-
-print ("--------Compare the results--------")
-tolerance = 1e-05
-diff_im = np.zeros(np.shape(fgp_cpu))
-diff_im = abs(fgp_cpu - fgp_gpu)
-diff_im[diff_im > tolerance] = 1
-a=fig.add_subplot(1,4,4)
-imgplot = plt.imshow(diff_im, vmin=0, vmax=1, cmap="gray")
-plt.title('{}'.format('Pixels larger threshold difference'))
-if (diff_im.sum() > 1):
-    print ("Arrays do not match!")
-else:
-    print ("Arrays match")
-
-#%%
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-print ("____________SB-TV bench___________________")
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-
-## plot 
-fig = plt.figure()
-plt.suptitle('Comparison of SB-TV regulariser using CPU and GPU implementations')
-a=fig.add_subplot(1,4,1)
-a.set_title('Noisy Image')
-imgplot = plt.imshow(u0,cmap="gray")
-
-# set parameters
-pars = {'algorithm' : SB_TV, \
-        'input' : u0,\
-        'regularisation_parameter':0.04, \
-        'number_of_iterations' :150 ,\
-        'tolerance_constant':1e-05,\
-        'methodTV': 0 ,\
-        'printingOut': 0 
-        }
-        
-print ("#############SB-TV CPU####################")
-start_time = timeit.default_timer()
-sb_cpu = SB_TV(pars['input'], 
-              pars['regularisation_parameter'],
-              pars['number_of_iterations'],
-              pars['tolerance_constant'], 
-              pars['methodTV'],
-              pars['printingOut'],'cpu')  
-             
-             
-rms = rmse(Im, sb_cpu)
-pars['rmse'] = rms
-
-txtstr = printParametersToString(pars)
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-a=fig.add_subplot(1,4,2)
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
-# place a text box in upper left in axes coords
-a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
-         verticalalignment='top', bbox=props)
-imgplot = plt.imshow(sb_cpu, cmap="gray")
-plt.title('{}'.format('CPU results'))
-
-
-print ("##############SB TV GPU##################")
-start_time = timeit.default_timer()
-sb_gpu = SB_TV(pars['input'], 
-              pars['regularisation_parameter'],
-              pars['number_of_iterations'],
-              pars['tolerance_constant'], 
-              pars['methodTV'],
-              pars['printingOut'],'gpu')
-                                   
-rms = rmse(Im, sb_gpu)
-pars['rmse'] = rms
-pars['algorithm'] = SB_TV
-txtstr = printParametersToString(pars)
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-a=fig.add_subplot(1,4,3)
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
-# place a text box in upper left in axes coords
-a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
-         verticalalignment='top', bbox=props)
-imgplot = plt.imshow(sb_gpu, cmap="gray")
-plt.title('{}'.format('GPU results'))
-
-print ("--------Compare the results--------")
-tolerance = 1e-05
-diff_im = np.zeros(np.shape(sb_cpu))
-diff_im = abs(sb_cpu - sb_gpu)
-diff_im[diff_im > tolerance] = 1
-a=fig.add_subplot(1,4,4)
-imgplot = plt.imshow(diff_im, vmin=0, vmax=1, cmap="gray")
-plt.title('{}'.format('Pixels larger threshold difference'))
-if (diff_im.sum() > 1):
-    print ("Arrays do not match!")
-else:
-    print ("Arrays match")
-#%%
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-print ("____________TGV bench___________________")
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-
-## plot 
-fig = plt.figure()
-plt.suptitle('Comparison of TGV regulariser using CPU and GPU implementations')
-a=fig.add_subplot(1,4,1)
-a.set_title('Noisy Image')
-imgplot = plt.imshow(u0,cmap="gray")
-
-# set parameters
-pars = {'algorithm' : TGV, \
-        'input' : u0,\
-        'regularisation_parameter':0.04, \
-        'alpha1':1.0,\
-        'alpha0':2.0,\
-        'number_of_iterations' :400 ,\
-        'LipshitzConstant' :12 ,\
-        }
-        
-print ("#############TGV CPU####################")
-start_time = timeit.default_timer()
-tgv_cpu = TGV(pars['input'], 
-              pars['regularisation_parameter'],
-              pars['alpha1'],
-              pars['alpha0'],
-              pars['number_of_iterations'],
-              pars['LipshitzConstant'],'cpu')
-             
-rms = rmse(Im, tgv_cpu)
-pars['rmse'] = rms
-
-txtstr = printParametersToString(pars)
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-a=fig.add_subplot(1,4,2)
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
-# place a text box in upper left in axes coords
-a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
-         verticalalignment='top', bbox=props)
-imgplot = plt.imshow(tgv_cpu, cmap="gray")
-plt.title('{}'.format('CPU results'))
-
-print ("##############TGV GPU##################")
-start_time = timeit.default_timer()
-tgv_gpu = TGV(pars['input'], 
-              pars['regularisation_parameter'],
-              pars['alpha1'],
-              pars['alpha0'],
-              pars['number_of_iterations'],
-              pars['LipshitzConstant'],'gpu')
-                                   
-rms = rmse(Im, tgv_gpu)
-pars['rmse'] = rms
-pars['algorithm'] = TGV
-txtstr = printParametersToString(pars)
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-a=fig.add_subplot(1,4,3)
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
-# place a text box in upper left in axes coords
-a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
-         verticalalignment='top', bbox=props)
-imgplot = plt.imshow(tgv_gpu, cmap="gray")
-plt.title('{}'.format('GPU results'))
-
-print ("--------Compare the results--------")
-tolerance = 1e-05
-diff_im = np.zeros(np.shape(tgv_gpu))
-diff_im = abs(tgv_cpu - tgv_gpu)
-diff_im[diff_im > tolerance] = 1
-a=fig.add_subplot(1,4,4)
-imgplot = plt.imshow(diff_im, vmin=0, vmax=1, cmap="gray")
-plt.title('{}'.format('Pixels larger threshold difference'))
-if (diff_im.sum() > 1):
-    print ("Arrays do not match!")
-else:
-    print ("Arrays match")
-#%%
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-print ("____________LLT-ROF bench___________________")
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-
-## plot 
-fig = plt.figure()
-plt.suptitle('Comparison of LLT-ROF regulariser using CPU and GPU implementations')
-a=fig.add_subplot(1,4,1)
-a.set_title('Noisy Image')
-imgplot = plt.imshow(u0,cmap="gray")
-
-# set parameters
-pars = {'algorithm' : LLT_ROF, \
-        'input' : u0,\
-        'regularisation_parameterROF':0.04, \
-        'regularisation_parameterLLT':0.01, \
-        'number_of_iterations' :4500 ,\
-        'time_marching_parameter' :0.00002 ,\
-        }
-        
-print ("#############LLT- ROF CPU####################")
-start_time = timeit.default_timer()
-lltrof_cpu = LLT_ROF(pars['input'], 
-              pars['regularisation_parameterROF'],
-              pars['regularisation_parameterLLT'],
-              pars['number_of_iterations'],
-              pars['time_marching_parameter'],'cpu')
-
-rms = rmse(Im, lltrof_cpu)
-pars['rmse'] = rms
-
-txtstr = printParametersToString(pars)
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-a=fig.add_subplot(1,4,2)
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
-# place a text box in upper left in axes coords
-a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
-         verticalalignment='top', bbox=props)
-imgplot = plt.imshow(lltrof_cpu, cmap="gray")
-plt.title('{}'.format('CPU results'))
-
-print ("#############LLT- ROF GPU####################")
-start_time = timeit.default_timer()
-lltrof_gpu = LLT_ROF(pars['input'], 
-              pars['regularisation_parameterROF'],
-              pars['regularisation_parameterLLT'],
-              pars['number_of_iterations'],
-              pars['time_marching_parameter'],'gpu')
-
-rms = rmse(Im, lltrof_gpu)
-pars['rmse'] = rms
-pars['algorithm'] = LLT_ROF
-txtstr = printParametersToString(pars)
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-a=fig.add_subplot(1,4,3)
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
-# place a text box in upper left in axes coords
-a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
-         verticalalignment='top', bbox=props)
-imgplot = plt.imshow(lltrof_gpu, cmap="gray")
-plt.title('{}'.format('GPU results'))
-
-print ("--------Compare the results--------")
-tolerance = 1e-05
-diff_im = np.zeros(np.shape(lltrof_gpu))
-diff_im = abs(lltrof_cpu - lltrof_gpu)
-diff_im[diff_im > tolerance] = 1
-a=fig.add_subplot(1,4,4)
-imgplot = plt.imshow(diff_im, vmin=0, vmax=1, cmap="gray")
-plt.title('{}'.format('Pixels larger threshold difference'))
-if (diff_im.sum() > 1):
-    print ("Arrays do not match!")
-else:
-    print ("Arrays match")
-#%%
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-print ("_______________NDF bench___________________")
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-
-## plot 
-fig = plt.figure()
-plt.suptitle('Comparison of NDF regulariser using CPU and GPU implementations')
-a=fig.add_subplot(1,4,1)
-a.set_title('Noisy Image')
-imgplot = plt.imshow(u0,cmap="gray")
-
-# set parameters
-pars = {'algorithm' : NDF, \
-        'input' : u0,\
-        'regularisation_parameter':0.06, \
-        'edge_parameter':0.04,\
-        'number_of_iterations' :1000 ,\
-        'time_marching_parameter':0.025,\
-        'penalty_type':  1
-        }
-        
-print ("#############NDF CPU####################")
-start_time = timeit.default_timer()
-ndf_cpu = NDF(pars['input'], 
-              pars['regularisation_parameter'],
-              pars['edge_parameter'], 
-              pars['number_of_iterations'],
-              pars['time_marching_parameter'], 
-              pars['penalty_type'],'cpu')
-             
-rms = rmse(Im, ndf_cpu)
-pars['rmse'] = rms
-
-txtstr = printParametersToString(pars)
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-a=fig.add_subplot(1,4,2)
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
-# place a text box in upper left in axes coords
-a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
-         verticalalignment='top', bbox=props)
-imgplot = plt.imshow(ndf_cpu, cmap="gray")
-plt.title('{}'.format('CPU results'))
-
-
-print ("##############NDF GPU##################")
-start_time = timeit.default_timer()
-ndf_gpu = NDF(pars['input'], 
-              pars['regularisation_parameter'],
-              pars['edge_parameter'], 
-              pars['number_of_iterations'],
-              pars['time_marching_parameter'], 
-              pars['penalty_type'],'gpu')
-             
-rms = rmse(Im, ndf_gpu)
-pars['rmse'] = rms
-pars['algorithm'] = NDF
-txtstr = printParametersToString(pars)
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-a=fig.add_subplot(1,4,3)
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
-# place a text box in upper left in axes coords
-a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
-         verticalalignment='top', bbox=props)
-imgplot = plt.imshow(ndf_gpu, cmap="gray")
-plt.title('{}'.format('GPU results'))
-
-print ("--------Compare the results--------")
-tolerance = 1e-05
-diff_im = np.zeros(np.shape(ndf_cpu))
-diff_im = abs(ndf_cpu - ndf_gpu)
-diff_im[diff_im > tolerance] = 1
-a=fig.add_subplot(1,4,4)
-imgplot = plt.imshow(diff_im, vmin=0, vmax=1, cmap="gray")
-plt.title('{}'.format('Pixels larger threshold difference'))
-if (diff_im.sum() > 1):
-    print ("Arrays do not match!")
-else:
-    print ("Arrays match")
-
-#%%
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-print ("___Anisotropic Diffusion 4th Order (2D)____")
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-
-## plot 
-fig = plt.figure()
-plt.suptitle('Comparison of Diff4th regulariser using CPU and GPU implementations')
-a=fig.add_subplot(1,4,1)
-a.set_title('Noisy Image')
-imgplot = plt.imshow(u0,cmap="gray")
-
-# set parameters
-pars = {'algorithm' : Diff4th, \
-        'input' : u0,\
-        'regularisation_parameter':3.5, \
-        'edge_parameter':0.02,\
-        'number_of_iterations' :500 ,\
-        'time_marching_parameter':0.001
-        }
-
-print ("#############Diff4th CPU####################")
-start_time = timeit.default_timer()
-diff4th_cpu = Diff4th(pars['input'], 
-              pars['regularisation_parameter'],
-              pars['edge_parameter'], 
-              pars['number_of_iterations'],
-              pars['time_marching_parameter'],'cpu')
-             
-rms = rmse(Im, diff4th_cpu)
-pars['rmse'] = rms
-
-txtstr = printParametersToString(pars)
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-a=fig.add_subplot(1,4,2)
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
-# place a text box in upper left in axes coords
-a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
-         verticalalignment='top', bbox=props)
-imgplot = plt.imshow(diff4th_cpu, cmap="gray")
-plt.title('{}'.format('CPU results'))
-
-print ("##############Diff4th GPU##################")
-start_time = timeit.default_timer()
-diff4th_gpu = Diff4th(pars['input'], 
-              pars['regularisation_parameter'],
-              pars['edge_parameter'], 
-              pars['number_of_iterations'],
-              pars['time_marching_parameter'], 'gpu')
-             
-rms = rmse(Im, diff4th_gpu)
-pars['rmse'] = rms
-pars['algorithm'] = Diff4th
-txtstr = printParametersToString(pars)
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-a=fig.add_subplot(1,4,3)
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
-# place a text box in upper left in axes coords
-a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
-         verticalalignment='top', bbox=props)
-imgplot = plt.imshow(diff4th_gpu, cmap="gray")
-plt.title('{}'.format('GPU results'))
-
-print ("--------Compare the results--------")
-tolerance = 1e-05
-diff_im = np.zeros(np.shape(diff4th_cpu))
-diff_im = abs(diff4th_cpu - diff4th_gpu)
-diff_im[diff_im > tolerance] = 1
-a=fig.add_subplot(1,4,4)
-imgplot = plt.imshow(diff_im, vmin=0, vmax=1, cmap="gray")
-plt.title('{}'.format('Pixels larger threshold difference'))
-if (diff_im.sum() > 1):
-    print ("Arrays do not match!")
-else:
-    print ("Arrays match")
-
-#%%
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-print ("____________FGP-dTV bench___________________")
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-
-## plot 
-fig = plt.figure()
-plt.suptitle('Comparison of FGP-dTV regulariser using CPU and GPU implementations')
-a=fig.add_subplot(1,4,1)
-a.set_title('Noisy Image')
-imgplot = plt.imshow(u0,cmap="gray")
-
-# set parameters
-pars = {'algorithm' : FGP_dTV, \
-        'input' : u0,\
-        'refdata' : u_ref,\
-        'regularisation_parameter':0.04, \
-        'number_of_iterations' :1000 ,\
-        'tolerance_constant':1e-07,\
-        'eta_const':0.2,\
-        'methodTV': 0 ,\
-        'nonneg': 0 ,\
-        'printingOut': 0 
-        }
-        
-print ("#############FGP dTV CPU####################")
-start_time = timeit.default_timer()
-fgp_dtv_cpu = FGP_dTV(pars['input'], 
-              pars['refdata'], 
-              pars['regularisation_parameter'],
-              pars['number_of_iterations'],
-              pars['tolerance_constant'], 
-              pars['eta_const'], 
-              pars['methodTV'],
-              pars['nonneg'],
-              pars['printingOut'],'cpu')
-             
-             
-rms = rmse(Im, fgp_dtv_cpu)
-pars['rmse'] = rms
-
-txtstr = printParametersToString(pars)
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-a=fig.add_subplot(1,4,2)
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
-# place a text box in upper left in axes coords
-a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
-         verticalalignment='top', bbox=props)
-imgplot = plt.imshow(fgp_dtv_cpu, cmap="gray")
-plt.title('{}'.format('CPU results'))
-
-print ("##############FGP dTV GPU##################")
-start_time = timeit.default_timer()
-fgp_dtv_gpu = FGP_dTV(pars['input'], 
-              pars['refdata'], 
-              pars['regularisation_parameter'],
-              pars['number_of_iterations'],
-              pars['tolerance_constant'], 
-              pars['eta_const'], 
-              pars['methodTV'],
-              pars['nonneg'],
-              pars['printingOut'],'gpu')
-rms = rmse(Im, fgp_dtv_gpu)
-pars['rmse'] = rms
-pars['algorithm'] = FGP_dTV
-txtstr = printParametersToString(pars)
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-a=fig.add_subplot(1,4,3)
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
-# place a text box in upper left in axes coords
-a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
-         verticalalignment='top', bbox=props)
-imgplot = plt.imshow(fgp_dtv_gpu, cmap="gray")
-plt.title('{}'.format('GPU results'))
-
-
-print ("--------Compare the results--------")
-tolerance = 1e-05
-diff_im = np.zeros(np.shape(fgp_dtv_cpu))
-diff_im = abs(fgp_dtv_cpu - fgp_dtv_gpu)
-diff_im[diff_im > tolerance] = 1
-a=fig.add_subplot(1,4,4)
-imgplot = plt.imshow(diff_im, vmin=0, vmax=1, cmap="gray")
-plt.title('{}'.format('Pixels larger threshold difference'))
-if (diff_im.sum() > 1):
-    print ("Arrays do not match!")
-else:
-    print ("Arrays match")
-#%%
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-print ("____Non-local regularisation bench_________")
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-
-## plot 
-fig = plt.figure()
-plt.suptitle('Comparison of Nonlocal TV regulariser using CPU and GPU implementations')
-a=fig.add_subplot(1,2,1)
-a.set_title('Noisy Image')
-imgplot = plt.imshow(u0,cmap="gray")
-
-pars = {'algorithm' : PatchSelect, \
-        'input' : u0,\
-        'searchwindow': 7, \
-        'patchwindow': 2,\
-        'neighbours' : 15 ,\
-        'edge_parameter':0.18}
-
-print ("############## Nonlocal Patches on CPU##################")
-start_time = timeit.default_timer()
-H_i, H_j, WeightsCPU = PatchSelect(pars['input'], 
-              pars['searchwindow'],
-              pars['patchwindow'], 
-              pars['neighbours'],
-              pars['edge_parameter'],'cpu')
-txtstr = printParametersToString(pars)
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-
-print ("############## Nonlocal Patches on GPU##################")
-start_time = timeit.default_timer()
-start_time = timeit.default_timer()
-H_i, H_j, WeightsGPU = PatchSelect(pars['input'], 
-              pars['searchwindow'],
-              pars['patchwindow'], 
-              pars['neighbours'],
-              pars['edge_parameter'],'gpu')
-txtstr = printParametersToString(pars)
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-
-print ("--------Compare the results--------")
-tolerance = 1e-05
-diff_im = np.zeros(np.shape(u0))
-diff_im = abs(WeightsCPU[0,:,:] - WeightsGPU[0,:,:])
-diff_im[diff_im > tolerance] = 1
-a=fig.add_subplot(1,2,2)
-imgplot = plt.imshow(diff_im, vmin=0, vmax=1, cmap="gray")
-plt.title('{}'.format('Pixels larger threshold difference'))
-if (diff_im.sum() > 1):
-    print ("Arrays do not match!")
-else:
-    print ("Arrays match")
-#%%
\ No newline at end of file
diff --git a/Wrappers/Python/demos/demo_gpu_regularisers.py b/Wrappers/Python/demos/demo_gpu_regularisers.py
deleted file mode 100644
index e1c6575..0000000
--- a/Wrappers/Python/demos/demo_gpu_regularisers.py
+++ /dev/null
@@ -1,518 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Thu Feb 22 11:39:43 2018
-
-Demonstration of GPU regularisers
-
-@authors: Daniil Kazantsev, Edoardo Pasca
-"""
-
-import matplotlib.pyplot as plt
-import numpy as np
-import os
-import timeit
-from ccpi.filters.regularisers import ROF_TV, FGP_TV, SB_TV, TGV, LLT_ROF, FGP_dTV, NDF, Diff4th
-from ccpi.filters.regularisers import PatchSelect, NLTV
-from qualitymetrics import rmse
-###############################################################################
-def printParametersToString(pars):
-        txt = r''
-        for key, value in pars.items():
-            if key== 'algorithm' :
-                txt += "{0} = {1}".format(key, value.__name__)
-            elif key == 'input':
-                txt += "{0} = {1}".format(key, np.shape(value))
-            elif key == 'refdata':
-                txt += "{0} = {1}".format(key, np.shape(value))
-            else:
-                txt += "{0} = {1}".format(key, value)
-            txt += '\n'
-        return txt
-###############################################################################
-#%%
-filename = os.path.join(".." , ".." , ".." , "data" ,"lena_gray_512.tif")
-
-# read image
-Im = plt.imread(filename)                     
-Im = np.asarray(Im, dtype='float32')
-
-Im = Im/255
-perc = 0.05
-u0 = Im + np.random.normal(loc = 0 ,
-                                  scale = perc * Im , 
-                                  size = np.shape(Im))
-u_ref = Im + np.random.normal(loc = 0 ,
-                                  scale = 0.01 * Im , 
-                                  size = np.shape(Im))
-(N,M) = np.shape(u0)
-# map the u0 u0->u0>0
-# f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1)
-u0 = u0.astype('float32')
-u_ref = u_ref.astype('float32')
-"""
-M = M-100
-u_ref2 = np.zeros([N,M],dtype='float32')
-u_ref2[:,0:M] = u_ref[:,0:M]
-u_ref = u_ref2
-del u_ref2
-
-u02 = np.zeros([N,M],dtype='float32')
-u02[:,0:M] = u0[:,0:M]
-u0 = u02
-del u02
-
-Im2 = np.zeros([N,M],dtype='float32')
-Im2[:,0:M] = Im[:,0:M]
-Im = Im2
-del Im2
-"""
-#%%
-
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-print ("____________ROF-TV regulariser_____________")
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-
-## plot 
-fig = plt.figure()
-plt.suptitle('Performance of the ROF-TV regulariser using the GPU')
-a=fig.add_subplot(1,2,1)
-a.set_title('Noisy Image')
-imgplot = plt.imshow(u0,cmap="gray")
-
-# set parameters
-pars = {'algorithm': ROF_TV, \
-        'input' : u0,\
-        'regularisation_parameter':0.04,\
-        'number_of_iterations': 1200,\
-        'time_marching_parameter': 0.0025
-        }
-print ("##############ROF TV GPU##################")
-start_time = timeit.default_timer()
-rof_gpu = ROF_TV(pars['input'], 
-                     pars['regularisation_parameter'],
-                     pars['number_of_iterations'], 
-                     pars['time_marching_parameter'],'gpu')
-                     
-rms = rmse(Im, rof_gpu)
-pars['rmse'] = rms
-pars['algorithm'] = ROF_TV
-txtstr = printParametersToString(pars)
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-a=fig.add_subplot(1,2,2)
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
-# place a text box in upper left in axes coords
-a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
-         verticalalignment='top', bbox=props)
-imgplot = plt.imshow(rof_gpu, cmap="gray")
-plt.title('{}'.format('GPU results'))
-
-#%%
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-print ("____________FGP-TV regulariser_____________")
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-
-## plot 
-fig = plt.figure()
-plt.suptitle('Performance of the FGP-TV regulariser using the GPU')
-a=fig.add_subplot(1,2,1)
-a.set_title('Noisy Image')
-imgplot = plt.imshow(u0,cmap="gray")
-
-# set parameters
-pars = {'algorithm' : FGP_TV, \
-        'input' : u0,\
-        'regularisation_parameter':0.04, \
-        'number_of_iterations' :1200 ,\
-        'tolerance_constant':1e-06,\
-        'methodTV': 0 ,\
-        'nonneg': 0 ,\
-        'printingOut': 0 
-        }
-
-print ("##############FGP TV GPU##################")
-start_time = timeit.default_timer()
-fgp_gpu = FGP_TV(pars['input'], 
-              pars['regularisation_parameter'],
-              pars['number_of_iterations'],
-              pars['tolerance_constant'], 
-              pars['methodTV'],
-              pars['nonneg'],
-              pars['printingOut'],'gpu')
-                                   
-rms = rmse(Im, fgp_gpu)
-pars['rmse'] = rms
-pars['algorithm'] = FGP_TV
-txtstr = printParametersToString(pars)
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-a=fig.add_subplot(1,2,2)
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
-# place a text box in upper left in axes coords
-a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
-         verticalalignment='top', bbox=props)
-imgplot = plt.imshow(fgp_gpu, cmap="gray")
-plt.title('{}'.format('GPU results'))
-
-#%%
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-print ("____________SB-TV regulariser______________")
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-
-## plot 
-fig = plt.figure()
-plt.suptitle('Performance of the SB-TV regulariser using the GPU')
-a=fig.add_subplot(1,2,1)
-a.set_title('Noisy Image')
-imgplot = plt.imshow(u0,cmap="gray")
-
-# set parameters
-pars = {'algorithm' : SB_TV, \
-        'input' : u0,\
-        'regularisation_parameter':0.04, \
-        'number_of_iterations' :150 ,\
-        'tolerance_constant':1e-06,\
-        'methodTV': 0 ,\
-        'printingOut': 0 
-        }
-
-print ("##############SB TV GPU##################")
-start_time = timeit.default_timer()
-sb_gpu = SB_TV(pars['input'], 
-              pars['regularisation_parameter'],
-              pars['number_of_iterations'],
-              pars['tolerance_constant'], 
-              pars['methodTV'],
-              pars['printingOut'],'gpu')
-                                   
-rms = rmse(Im, sb_gpu)
-pars['rmse'] = rms
-pars['algorithm'] = SB_TV
-txtstr = printParametersToString(pars)
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-a=fig.add_subplot(1,2,2)
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
-# place a text box in upper left in axes coords
-a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
-         verticalalignment='top', bbox=props)
-imgplot = plt.imshow(sb_gpu, cmap="gray")
-plt.title('{}'.format('GPU results'))
-#%%
-
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-print ("_____Total Generalised Variation (2D)______")
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-
-## plot 
-fig = plt.figure()
-plt.suptitle('Performance of TGV regulariser using the GPU')
-a=fig.add_subplot(1,2,1)
-a.set_title('Noisy Image')
-imgplot = plt.imshow(u0,cmap="gray")
-
-# set parameters
-pars = {'algorithm' : TGV, \
-        'input' : u0,\
-        'regularisation_parameter':0.04, \
-        'alpha1':1.0,\
-        'alpha0':2.0,\
-        'number_of_iterations' :1250 ,\
-        'LipshitzConstant' :12 ,\
-        }
-        
-print ("#############TGV CPU####################")
-start_time = timeit.default_timer()
-tgv_gpu = TGV(pars['input'], 
-              pars['regularisation_parameter'],
-              pars['alpha1'],
-              pars['alpha0'],
-              pars['number_of_iterations'],
-              pars['LipshitzConstant'],'gpu')  
-             
-             
-rms = rmse(Im, tgv_gpu)
-pars['rmse'] = rms
-
-txtstr = printParametersToString(pars)
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-a=fig.add_subplot(1,2,2)
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
-# place a text box in upper left in axes coords
-a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
-         verticalalignment='top', bbox=props)
-imgplot = plt.imshow(tgv_gpu, cmap="gray")
-plt.title('{}'.format('GPU results'))
-
-#%%
-
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-print ("______________LLT- ROF (2D)________________")
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-
-## plot 
-fig = plt.figure()
-plt.suptitle('Performance of LLT-ROF regulariser using the GPU')
-a=fig.add_subplot(1,2,1)
-a.set_title('Noisy Image')
-imgplot = plt.imshow(u0,cmap="gray")
-
-# set parameters
-pars = {'algorithm' : LLT_ROF, \
-        'input' : u0,\
-        'regularisation_parameterROF':0.04, \
-        'regularisation_parameterLLT':0.01, \
-        'number_of_iterations' :500 ,\
-        'time_marching_parameter' :0.0025 ,\
-        }
-        
-print ("#############LLT- ROF GPU####################")
-start_time = timeit.default_timer()
-lltrof_gpu = LLT_ROF(pars['input'], 
-              pars['regularisation_parameterROF'],
-              pars['regularisation_parameterLLT'],
-              pars['number_of_iterations'],
-              pars['time_marching_parameter'],'gpu')
-             
-             
-rms = rmse(Im, lltrof_gpu)
-pars['rmse'] = rms
-
-txtstr = printParametersToString(pars)
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-a=fig.add_subplot(1,2,2)
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
-# place a text box in upper left in axes coords
-a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
-         verticalalignment='top', bbox=props)
-imgplot = plt.imshow(lltrof_gpu, cmap="gray")
-plt.title('{}'.format('GPU results'))
-
-#%%
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-print ("_______________NDF regulariser_____________")
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-
-## plot 
-fig = plt.figure()
-plt.suptitle('Performance of the NDF regulariser using the GPU')
-a=fig.add_subplot(1,2,1)
-a.set_title('Noisy Image')
-imgplot = plt.imshow(u0,cmap="gray")
-
-# set parameters
-pars = {'algorithm' : NDF, \
-        'input' : u0,\
-        'regularisation_parameter':0.025, \
-        'edge_parameter':0.015,\
-        'number_of_iterations' :500 ,\
-        'time_marching_parameter':0.025,\
-        'penalty_type':  1
-        }
-
-print ("##############NDF GPU##################")
-start_time = timeit.default_timer()
-ndf_gpu = NDF(pars['input'], 
-              pars['regularisation_parameter'],
-              pars['edge_parameter'], 
-              pars['number_of_iterations'],
-              pars['time_marching_parameter'], 
-              pars['penalty_type'],'gpu')  
-             
-rms = rmse(Im, ndf_gpu)
-pars['rmse'] = rms
-pars['algorithm'] = NDF
-txtstr = printParametersToString(pars)
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-a=fig.add_subplot(1,2,2)
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
-# place a text box in upper left in axes coords
-a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
-         verticalalignment='top', bbox=props)
-imgplot = plt.imshow(ndf_gpu, cmap="gray")
-plt.title('{}'.format('GPU results'))
-
-#%%
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-print ("___Anisotropic Diffusion 4th Order (2D)____")
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-
-## plot 
-fig = plt.figure()
-plt.suptitle('Performance of Diff4th regulariser using the GPU')
-a=fig.add_subplot(1,2,1)
-a.set_title('Noisy Image')
-imgplot = plt.imshow(u0,cmap="gray")
-
-# set parameters
-pars = {'algorithm' : Diff4th, \
-        'input' : u0,\
-        'regularisation_parameter':3.5, \
-        'edge_parameter':0.02,\
-        'number_of_iterations' :500 ,\
-        'time_marching_parameter':0.0015
-        }
-        
-print ("#############DIFF4th CPU################")
-start_time = timeit.default_timer()
-diff4_gpu = Diff4th(pars['input'], 
-              pars['regularisation_parameter'],
-              pars['edge_parameter'], 
-              pars['number_of_iterations'],
-              pars['time_marching_parameter'],'gpu')
-             
-rms = rmse(Im, diff4_gpu)
-pars['rmse'] = rms
-
-txtstr = printParametersToString(pars)
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-a=fig.add_subplot(1,2,2)
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
-# place a text box in upper left in axes coords
-a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
-         verticalalignment='top', bbox=props)
-imgplot = plt.imshow(diff4_gpu, cmap="gray")
-plt.title('{}'.format('GPU results'))
-
-#%%
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-print ("___Nonlocal patches pre-calculation____")
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-start_time = timeit.default_timer()
-# set parameters
-pars = {'algorithm' : PatchSelect, \
-        'input' : u0,\
-        'searchwindow': 7, \
-        'patchwindow': 2,\
-        'neighbours' : 15 ,\
-        'edge_parameter':0.18}
-
-H_i, H_j, Weights = PatchSelect(pars['input'], 
-              pars['searchwindow'],
-              pars['patchwindow'], 
-              pars['neighbours'],
-              pars['edge_parameter'],'gpu')
-              
-txtstr = printParametersToString(pars)
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-"""
-plt.figure()
-plt.imshow(Weights[0,:,:],cmap="gray",interpolation="nearest",vmin=0, vmax=1)
-plt.show()
-"""
-#%%
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-print ("___Nonlocal Total Variation penalty____")
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-## plot 
-fig = plt.figure()
-plt.suptitle('Performance of NLTV regulariser using the CPU')
-a=fig.add_subplot(1,2,1)
-a.set_title('Noisy Image')
-imgplot = plt.imshow(u0,cmap="gray")
-
-pars2 = {'algorithm' : NLTV, \
-        'input' : u0,\
-        'H_i': H_i, \
-        'H_j': H_j,\
-        'H_k' : 0,\
-        'Weights' : Weights,\
-        'regularisation_parameter': 0.02,\
-        'iterations': 3
-        }
-start_time = timeit.default_timer()
-nltv_cpu = NLTV(pars2['input'], 
-              pars2['H_i'],
-              pars2['H_j'], 
-              pars2['H_k'],
-              pars2['Weights'],
-              pars2['regularisation_parameter'],
-              pars2['iterations'])
-
-rms = rmse(Im, nltv_cpu)
-pars['rmse'] = rms
-
-txtstr = printParametersToString(pars)
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-a=fig.add_subplot(1,2,2)
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
-# place a text box in upper left in axes coords
-a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
-         verticalalignment='top', bbox=props)
-imgplot = plt.imshow(nltv_cpu, cmap="gray")
-plt.title('{}'.format('CPU results'))
-#%%
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-print ("____________FGP-dTV bench___________________")
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-
-## plot 
-fig = plt.figure()
-plt.suptitle('Performance of the FGP-dTV regulariser using the GPU')
-a=fig.add_subplot(1,2,1)
-a.set_title('Noisy Image')
-imgplot = plt.imshow(u0,cmap="gray")
-
-# set parameters
-pars = {'algorithm' : FGP_dTV, \
-        'input' : u0,\
-        'refdata' : u_ref,\
-        'regularisation_parameter':0.04, \
-        'number_of_iterations' :2000 ,\
-        'tolerance_constant':1e-06,\
-        'eta_const':0.2,\
-        'methodTV': 0 ,\
-        'nonneg': 0 ,\
-        'printingOut': 0 
-        }
-
-print ("##############FGP dTV GPU##################")
-start_time = timeit.default_timer()
-fgp_dtv_gpu = FGP_dTV(pars['input'], 
-              pars['refdata'], 
-              pars['regularisation_parameter'],
-              pars['number_of_iterations'],
-              pars['tolerance_constant'], 
-              pars['eta_const'], 
-              pars['methodTV'],
-              pars['nonneg'],
-              pars['printingOut'],'gpu')
-                                   
-rms = rmse(Im, fgp_dtv_gpu)
-pars['rmse'] = rms
-pars['algorithm'] = FGP_dTV
-txtstr = printParametersToString(pars)
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-a=fig.add_subplot(1,2,2)
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
-# place a text box in upper left in axes coords
-a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
-         verticalalignment='top', bbox=props)
-imgplot = plt.imshow(fgp_dtv_gpu, cmap="gray")
-plt.title('{}'.format('GPU results'))
diff --git a/Wrappers/Python/demos/demo_gpu_regularisers3D.py b/Wrappers/Python/demos/demo_gpu_regularisers3D.py
deleted file mode 100644
index b6058d2..0000000
--- a/Wrappers/Python/demos/demo_gpu_regularisers3D.py
+++ /dev/null
@@ -1,460 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Thu Feb 22 11:39:43 2018
-
-Demonstration of GPU regularisers
-
-@authors: Daniil Kazantsev, Edoardo Pasca
-"""
-
-import matplotlib.pyplot as plt
-import numpy as np
-import os
-import timeit
-from ccpi.filters.regularisers import ROF_TV, FGP_TV, SB_TV, TGV, LLT_ROF, FGP_dTV, NDF, Diff4th
-from qualitymetrics import rmse
-###############################################################################
-def printParametersToString(pars):
-        txt = r''
-        for key, value in pars.items():
-            if key== 'algorithm' :
-                txt += "{0} = {1}".format(key, value.__name__)
-            elif key == 'input':
-                txt += "{0} = {1}".format(key, np.shape(value))
-            elif key == 'refdata':
-                txt += "{0} = {1}".format(key, np.shape(value))
-            else:
-                txt += "{0} = {1}".format(key, value)
-            txt += '\n'
-        return txt
-###############################################################################
-#%%
-filename = os.path.join(".." , ".." , ".." , "data" ,"lena_gray_512.tif")
-
-# read image
-Im = plt.imread(filename)                     
-Im = np.asarray(Im, dtype='float32')
-
-Im = Im/255
-perc = 0.05
-u0 = Im + np.random.normal(loc = 0 ,
-                                  scale = perc * Im , 
-                                  size = np.shape(Im))
-u_ref = Im + np.random.normal(loc = 0 ,
-                                  scale = 0.01 * Im , 
-                                  size = np.shape(Im))
-(N,M) = np.shape(u0)
-# map the u0 u0->u0>0
-# f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1)
-u0 = u0.astype('float32')
-u_ref = u_ref.astype('float32')
-"""
-M = M-100
-u_ref2 = np.zeros([N,M],dtype='float32')
-u_ref2[:,0:M] = u_ref[:,0:M]
-u_ref = u_ref2
-del u_ref2
-
-u02 = np.zeros([N,M],dtype='float32')
-u02[:,0:M] = u0[:,0:M]
-u0 = u02
-del u02
-
-Im2 = np.zeros([N,M],dtype='float32')
-Im2[:,0:M] = Im[:,0:M]
-Im = Im2
-del Im2
-"""
-
-
-slices = 20
-
-filename = os.path.join(".." , ".." , ".." , "data" ,"lena_gray_512.tif")
-Im = plt.imread(filename)
-Im = np.asarray(Im, dtype='float32')
-
-Im = Im/255
-perc = 0.05
-
-noisyVol = np.zeros((slices,N,N),dtype='float32')
-noisyRef = np.zeros((slices,N,N),dtype='float32')
-idealVol = np.zeros((slices,N,N),dtype='float32')
-
-for i in range (slices):
-    noisyVol[i,:,:] = Im + np.random.normal(loc = 0 , scale = perc * Im , size = np.shape(Im))
-    noisyRef[i,:,:] = Im + np.random.normal(loc = 0 , scale = 0.01 * Im , size = np.shape(Im))
-    idealVol[i,:,:] = Im
-
-#%%
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-print ("_______________ROF-TV (3D)_________________")
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-
-## plot 
-fig = plt.figure()
-plt.suptitle('Performance of ROF-TV regulariser using the GPU')
-a=fig.add_subplot(1,2,1)
-a.set_title('Noisy 15th slice of a volume')
-imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray")
-
-# set parameters
-pars = {'algorithm': ROF_TV, \
-        'input' : noisyVol,\
-        'regularisation_parameter':0.04,\
-        'number_of_iterations': 500,\
-        'time_marching_parameter': 0.0025        
-        }
-print ("#############ROF TV GPU####################")
-start_time = timeit.default_timer()
-rof_gpu3D = ROF_TV(pars['input'],
-             pars['regularisation_parameter'],
-             pars['number_of_iterations'],
-             pars['time_marching_parameter'],'gpu')
-rms = rmse(idealVol, rof_gpu3D)
-pars['rmse'] = rms
-
-txtstr = printParametersToString(pars)
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-a=fig.add_subplot(1,2,2)
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
-# place a text box in upper left in axes coords
-a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
-         verticalalignment='top', bbox=props)
-imgplot = plt.imshow(rof_gpu3D[10,:,:], cmap="gray")
-plt.title('{}'.format('Recovered volume on the GPU using ROF-TV'))
-#%%
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-print ("_______________FGP-TV (3D)__________________")
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-
-## plot 
-fig = plt.figure()
-plt.suptitle('Performance of FGP-TV regulariser using the GPU')
-a=fig.add_subplot(1,2,1)
-a.set_title('Noisy Image')
-imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray")
-
-# set parameters
-pars = {'algorithm' : FGP_TV, \
-        'input' : noisyVol,\
-        'regularisation_parameter':0.04, \
-        'number_of_iterations' :300 ,\
-        'tolerance_constant':0.00001,\
-        'methodTV': 0 ,\
-        'nonneg': 0 ,\
-        'printingOut': 0 
-        }
-
-print ("#############FGP TV GPU####################")
-start_time = timeit.default_timer()
-fgp_gpu3D = FGP_TV(pars['input'], 
-              pars['regularisation_parameter'],
-              pars['number_of_iterations'],
-              pars['tolerance_constant'], 
-              pars['methodTV'],
-              pars['nonneg'],
-              pars['printingOut'],'gpu')
-
-rms = rmse(idealVol, fgp_gpu3D)
-pars['rmse'] = rms
-
-txtstr = printParametersToString(pars)
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-a=fig.add_subplot(1,2,2)
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
-# place a text box in upper left in axes coords
-a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
-         verticalalignment='top', bbox=props)
-imgplot = plt.imshow(fgp_gpu3D[10,:,:], cmap="gray")
-plt.title('{}'.format('Recovered volume on the GPU using FGP-TV'))
-
-#%%
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-print ("_______________SB-TV (3D)__________________")
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-
-## plot 
-fig = plt.figure()
-plt.suptitle('Performance of SB-TV regulariser using the GPU')
-a=fig.add_subplot(1,2,1)
-a.set_title('Noisy Image')
-imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray")
-
-# set parameters
-pars = {'algorithm' : SB_TV, \
-        'input' : noisyVol,\
-        'regularisation_parameter':0.04, \
-        'number_of_iterations' :100 ,\
-        'tolerance_constant':1e-05,\
-        'methodTV': 0 ,\
-        'printingOut': 0 
-        }
-
-print ("#############SB TV GPU####################")
-start_time = timeit.default_timer()
-sb_gpu3D = SB_TV(pars['input'], 
-              pars['regularisation_parameter'],
-              pars['number_of_iterations'],
-              pars['tolerance_constant'], 
-              pars['methodTV'],
-              pars['printingOut'],'gpu')
-
-rms = rmse(idealVol, sb_gpu3D)
-pars['rmse'] = rms
-
-txtstr = printParametersToString(pars)
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-a=fig.add_subplot(1,2,2)
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
-# place a text box in upper left in axes coords
-a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
-         verticalalignment='top', bbox=props)
-imgplot = plt.imshow(sb_gpu3D[10,:,:], cmap="gray")
-plt.title('{}'.format('Recovered volume on the GPU using SB-TV'))
-#%%
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-print ("_______________LLT-ROF (3D)_________________")
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-
-## plot 
-fig = plt.figure()
-plt.suptitle('Performance of LLT-ROF regulariser using the GPU')
-a=fig.add_subplot(1,2,1)
-a.set_title('Noisy Image')
-imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray")
-
-# set parameters
-pars = {'algorithm' : LLT_ROF, \
-        'input' : noisyVol,\
-        'regularisation_parameterROF':0.04, \
-        'regularisation_parameterLLT':0.015, \
-        'number_of_iterations' :300 ,\
-        'time_marching_parameter' :0.0025 ,\
-        }
-
-print ("#############LLT ROF CPU####################")
-start_time = timeit.default_timer()
-lltrof_gpu3D = LLT_ROF(pars['input'], 
-              pars['regularisation_parameterROF'],
-              pars['regularisation_parameterLLT'],
-              pars['number_of_iterations'],
-              pars['time_marching_parameter'],'gpu')
-
-rms = rmse(idealVol, lltrof_gpu3D)
-pars['rmse'] = rms
-
-txtstr = printParametersToString(pars)
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-a=fig.add_subplot(1,2,2)
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
-# place a text box in upper left in axes coords
-a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
-         verticalalignment='top', bbox=props)
-imgplot = plt.imshow(lltrof_gpu3D[10,:,:], cmap="gray")
-plt.title('{}'.format('Recovered volume on the GPU using LLT-ROF'))
-
-#%%
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-print ("_______________TGV (3D)_________________")
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-
-## plot 
-fig = plt.figure()
-plt.suptitle('Performance of TGV regulariser using the GPU')
-a=fig.add_subplot(1,2,1)
-a.set_title('Noisy Image')
-imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray")
-
-# set parameters
-pars = {'algorithm' : TGV, \
-        'input' : noisyVol,\
-        'regularisation_parameter':0.04, \
-        'alpha1':1.0,\
-        'alpha0':2.0,\
-        'number_of_iterations' :600 ,\
-        'LipshitzConstant' :12 ,\
-        }
-
-print ("#############TGV GPU####################")
-start_time = timeit.default_timer()
-tgv_gpu3D = TGV(pars['input'], 
-              pars['regularisation_parameter'],
-              pars['alpha1'],
-              pars['alpha0'],
-              pars['number_of_iterations'],
-              pars['LipshitzConstant'],'gpu')
-             
-
-rms = rmse(idealVol, tgv_gpu3D)
-pars['rmse'] = rms
-
-txtstr = printParametersToString(pars)
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-a=fig.add_subplot(1,2,2)
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
-# place a text box in upper left in axes coords
-a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
-         verticalalignment='top', bbox=props)
-imgplot = plt.imshow(tgv_gpu3D[10,:,:], cmap="gray")
-plt.title('{}'.format('Recovered volume on the GPU using TGV'))
-#%%
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-print ("_______________NDF-TV (3D)_________________")
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-
-## plot 
-fig = plt.figure()
-plt.suptitle('Performance of NDF regulariser using the GPU')
-a=fig.add_subplot(1,2,1)
-a.set_title('Noisy Image')
-imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray")
-
-# set parameters
-pars = {'algorithm' : NDF, \
-        'input' : noisyVol,\
-        'regularisation_parameter':0.025, \
-        'edge_parameter':0.015,\
-        'number_of_iterations' :500 ,\
-        'time_marching_parameter':0.025,\
-        'penalty_type':  1
-        }
-
-print ("#############NDF GPU####################")
-start_time = timeit.default_timer()
-ndf_gpu3D = NDF(pars['input'], 
-              pars['regularisation_parameter'],
-              pars['edge_parameter'], 
-              pars['number_of_iterations'],
-              pars['time_marching_parameter'], 
-              pars['penalty_type'],'gpu')
-
-rms = rmse(idealVol, ndf_gpu3D)
-pars['rmse'] = rms
-
-txtstr = printParametersToString(pars)
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-a=fig.add_subplot(1,2,2)
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
-# place a text box in upper left in axes coords
-a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
-         verticalalignment='top', bbox=props)
-imgplot = plt.imshow(ndf_gpu3D[10,:,:], cmap="gray")
-plt.title('{}'.format('Recovered volume on the GPU using NDF'))
-
-#%%
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-print ("___Anisotropic Diffusion 4th Order (3D)____")
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-
-## plot 
-fig = plt.figure()
-plt.suptitle('Performance of DIFF4th regulariser using the GPU')
-a=fig.add_subplot(1,2,1)
-a.set_title('Noisy Image')
-imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray")
-
-# set parameters
-pars = {'algorithm' : Diff4th, \
-        'input' : noisyVol,\
-        'regularisation_parameter':3.5, \
-        'edge_parameter':0.02,\
-        'number_of_iterations' :300 ,\
-        'time_marching_parameter':0.0015
-        }
-        
-print ("#############DIFF4th CPU################")
-start_time = timeit.default_timer()
-diff4_gpu3D = Diff4th(pars['input'], 
-              pars['regularisation_parameter'],
-              pars['edge_parameter'], 
-              pars['number_of_iterations'],
-              pars['time_marching_parameter'],'gpu')
-             
-rms = rmse(idealVol, diff4_gpu3D)
-pars['rmse'] = rms
-
-txtstr = printParametersToString(pars)
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-a=fig.add_subplot(1,2,2)
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
-# place a text box in upper left in axes coords
-a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
-         verticalalignment='top', bbox=props)
-imgplot = plt.imshow(diff4_gpu3D[10,:,:], cmap="gray")
-plt.title('{}'.format('GPU results'))
-
-#%%
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-print ("_______________FGP-dTV (3D)________________")
-print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
-
-## plot 
-fig = plt.figure()
-plt.suptitle('Performance of FGP-dTV regulariser using the GPU')
-a=fig.add_subplot(1,2,1)
-a.set_title('Noisy Image')
-imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray")
-
-# set parameters
-pars = {'algorithm' : FGP_dTV, \
-        'input' : noisyVol,\
-        'refdata' : noisyRef,\
-        'regularisation_parameter':0.04, \
-        'number_of_iterations' :300 ,\
-        'tolerance_constant':0.00001,\
-        'eta_const':0.2,\
-        'methodTV': 0 ,\
-        'nonneg': 0 ,\
-        'printingOut': 0 
-        }
-
-print ("#############FGP TV GPU####################")
-start_time = timeit.default_timer()
-fgp_dTV_gpu3D = FGP_dTV(pars['input'],
-              pars['refdata'], 
-              pars['regularisation_parameter'],
-              pars['number_of_iterations'],
-              pars['tolerance_constant'], 
-              pars['eta_const'],
-              pars['methodTV'],
-              pars['nonneg'],
-              pars['printingOut'],'gpu')
-
-rms = rmse(idealVol, fgp_dTV_gpu3D)
-pars['rmse'] = rms
-
-txtstr = printParametersToString(pars)
-txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
-print (txtstr)
-a=fig.add_subplot(1,2,2)
-
-# these are matplotlib.patch.Patch properties
-props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
-# place a text box in upper left in axes coords
-a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
-         verticalalignment='top', bbox=props)
-imgplot = plt.imshow(fgp_dTV_gpu3D[10,:,:], cmap="gray")
-plt.title('{}'.format('Recovered volume on the GPU using FGP-dTV'))
-#%%
diff --git a/Wrappers/Python/demos/qualitymetrics.py b/Wrappers/Python/demos/qualitymetrics.py
deleted file mode 100644
index 850829e..0000000
--- a/Wrappers/Python/demos/qualitymetrics.py
+++ /dev/null
@@ -1,18 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Wed Feb 21 13:34:32 2018
-# quality metrics
-@authors: Daniil Kazantsev, Edoardo Pasca
-"""
-import numpy as np
-
-def nrmse(im1, im2):
-    rmse = np.sqrt(np.sum((im2 - im1) ** 2) / float(im1.size))
-    max_val = max(np.max(im1), np.max(im2))
-    min_val = min(np.min(im1), np.min(im2))
-    return 1 - (rmse / (max_val - min_val))
-    
-def rmse(im1, im2):
-    rmse = np.sqrt(np.sum((im1 - im2) ** 2) / float(im1.size))
-    return rmse