Merge remote-tracking branch 'remotes/origin/master' into newdirstructure

8 files changed, 844 insertions, 0 deletions

diff --git a/Wrappers/Python/demos/SoftwareX_supp/Demo_RealData_Recon_SX.py b/Wrappers/Python/demos/SoftwareX_supp/Demo_RealData_Recon_SX.py
new file mode 100644
index 0000000..01491d9
--- /dev/null
+++ b/Wrappers/Python/demos/SoftwareX_supp/Demo_RealData_Recon_SX.py
@@ -0,0 +1,231 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+This demo scripts support the following publication: 
+"CCPi-Regularisation Toolkit for computed tomographic image reconstruction with 
+proximal splitting algorithms" by Daniil Kazantsev, Edoardo Pasca, Martin J. Turner,
+ Philip J. Withers; Software X, 2019
+____________________________________________________________________________
+* Reads real tomographic data (stored at Zenodo)
+--- https://doi.org/10.5281/zenodo.2578893
+* Reconstructs using TomoRec software
+* Saves reconstructed images 
+____________________________________________________________________________
+>>>>> Dependencies: <<<<<
+1. ASTRA toolbox: conda install -c astra-toolbox astra-toolbox
+2. TomoRec: conda install -c dkazanc tomorec
+or install from https://github.com/dkazanc/TomoRec
+3. libtiff if one needs to save tiff images:
+    install pip install libtiff
+
+@author: Daniil Kazantsev, e:mail daniil.kazantsev@diamond.ac.uk
+GPLv3 license (ASTRA toolbox)
+"""
+import numpy as np
+import matplotlib.pyplot as plt
+import h5py
+from tomorec.supp.suppTools import normaliser
+import time
+
+# load dendritic projection data
+h5f = h5py.File('data/DendrData_3D.h5','r')
+dataRaw = h5f['dataRaw'][:]
+flats = h5f['flats'][:]
+darks = h5f['darks'][:]
+angles_rad = h5f['angles_rad'][:]
+h5f.close()
+#%%
+# normalise the data [detectorsVert, Projections, detectorsHoriz]
+data_norm = normaliser(dataRaw, flats, darks, log='log')
+del dataRaw, darks, flats
+
+intens_max = 2.3
+plt.figure() 
+plt.subplot(131)
+plt.imshow(data_norm[:,150,:],vmin=0, vmax=intens_max)
+plt.title('2D Projection (analytical)')
+plt.subplot(132)
+plt.imshow(data_norm[300,:,:],vmin=0, vmax=intens_max)
+plt.title('Sinogram view')
+plt.subplot(133)
+plt.imshow(data_norm[:,:,600],vmin=0, vmax=intens_max)
+plt.title('Tangentogram view')
+plt.show()
+
+detectorHoriz = np.size(data_norm,2)
+det_y_crop = [i for i in range(0,detectorHoriz-22)]
+N_size = 950 # reconstruction domain
+time_label = int(time.time())
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("%%%%%%%%%%%%Reconstructing with FBP method %%%%%%%%%%%%%%%%%")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+from tomorec.methodsDIR import RecToolsDIR
+
+RectoolsDIR = RecToolsDIR(DetectorsDimH = np.size(det_y_crop),  # DetectorsDimH # detector dimension (horizontal)
+                    DetectorsDimV = 100,  # DetectorsDimV # detector dimension (vertical) for 3D case only
+                    AnglesVec = angles_rad, # array of angles in radians
+                    ObjSize = N_size, # a scalar to define reconstructed object dimensions
+                    device='gpu')
+
+FBPrec = RectoolsDIR.FBP(data_norm[0:100,:,det_y_crop])
+
+sliceSel = 50
+max_val = 0.003
+plt.figure() 
+plt.subplot(131)
+plt.imshow(FBPrec[sliceSel,:,:],vmin=0, vmax=max_val, cmap="gray")
+plt.title('FBP Reconstruction, axial view')
+
+plt.subplot(132)
+plt.imshow(FBPrec[:,sliceSel,:],vmin=0, vmax=max_val, cmap="gray")
+plt.title('FBP Reconstruction, coronal view')
+
+plt.subplot(133)
+plt.imshow(FBPrec[:,:,sliceSel],vmin=0, vmax=max_val, cmap="gray")
+plt.title('FBP Reconstruction, sagittal view')
+plt.show()
+
+# saving to tiffs (16bit)
+"""
+from libtiff import TIFF
+FBPrec += np.abs(np.min(FBPrec))
+multiplier = (int)(65535/(np.max(FBPrec)))
+
+# saving to tiffs (16bit)
+for i in range(0,np.size(FBPrec,0)):
+    tiff = TIFF.open('Dendr_FBP'+'_'+str(i)+'.tiff', mode='w')
+    tiff.write_image(np.uint16(FBPrec[i,:,:]*multiplier))
+    tiff.close()
+"""
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("Reconstructing with ADMM method using TomoRec software")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+# initialise TomoRec ITERATIVE reconstruction class ONCE
+from tomorec.methodsIR import RecToolsIR
+RectoolsIR = RecToolsIR(DetectorsDimH =  np.size(det_y_crop),  # DetectorsDimH # detector dimension (horizontal)
+                    DetectorsDimV = 100,  # DetectorsDimV # detector dimension (vertical) for 3D case only
+                    AnglesVec = angles_rad, # array of angles in radians
+                    ObjSize = N_size, # a scalar to define reconstructed object dimensions
+                    datafidelity='LS',# data fidelity, choose LS, PWLS (wip), GH (wip), Student (wip)
+                    nonnegativity='ENABLE', # enable nonnegativity constraint (set to 'ENABLE')
+                    OS_number = None, # the number of subsets, NONE/(or > 1) ~ classical / ordered subsets
+                    tolerance = 1e-08, # tolerance to stop outer iterations earlier
+                    device='gpu')
+#%%
+print ("Reconstructing with ADMM method using SB-TV penalty")
+RecADMM_reg_sbtv = RectoolsIR.ADMM(data_norm[0:100,:,det_y_crop],
+                              rho_const = 2000.0, \
+                              iterationsADMM = 15, \
+                              regularisation = 'SB_TV', \
+                              regularisation_parameter = 0.00085,\
+                              regularisation_iterations = 50)
+
+sliceSel = 50
+max_val = 0.003
+plt.figure() 
+plt.subplot(131)
+plt.imshow(RecADMM_reg_sbtv[sliceSel,:,:],vmin=0, vmax=max_val, cmap="gray")
+plt.title('3D ADMM-SB-TV Reconstruction, axial view')
+
+plt.subplot(132)
+plt.imshow(RecADMM_reg_sbtv[:,sliceSel,:],vmin=0, vmax=max_val, cmap="gray")
+plt.title('3D ADMM-SB-TV Reconstruction, coronal view')
+
+plt.subplot(133)
+plt.imshow(RecADMM_reg_sbtv[:,:,sliceSel],vmin=0, vmax=max_val, cmap="gray")
+plt.title('3D ADMM-SB-TV Reconstruction, sagittal view')
+plt.show()
+
+
+# saving to tiffs (16bit)
+"""
+from libtiff import TIFF
+multiplier = (int)(65535/(np.max(RecADMM_reg_sbtv)))
+for i in range(0,np.size(RecADMM_reg_sbtv,0)):
+    tiff = TIFF.open('Dendr_ADMM_SBTV'+'_'+str(i)+'.tiff', mode='w')
+    tiff.write_image(np.uint16(RecADMM_reg_sbtv[i,:,:]*multiplier))
+    tiff.close()
+"""
+# Saving recpnstructed data with a unique time label
+np.save('Dendr_ADMM_SBTV'+str(time_label)+'.npy', RecADMM_reg_sbtv)
+del RecADMM_reg_sbtv
+#%%
+print ("Reconstructing with ADMM method using ROF-LLT penalty")
+RecADMM_reg_rofllt = RectoolsIR.ADMM(data_norm[0:100,:,det_y_crop],
+                              rho_const = 2000.0, \
+                              iterationsADMM = 15, \
+                              regularisation = 'LLT_ROF', \
+                              regularisation_parameter = 0.0009,\
+                              regularisation_parameter2 = 0.0007,\
+                              time_marching_parameter = 0.001,\
+                              regularisation_iterations = 550)
+
+sliceSel = 50
+max_val = 0.003
+plt.figure() 
+plt.subplot(131)
+plt.imshow(RecADMM_reg_rofllt[sliceSel,:,:],vmin=0, vmax=max_val)
+plt.title('3D ADMM-ROFLLT Reconstruction, axial view')
+
+plt.subplot(132)
+plt.imshow(RecADMM_reg_rofllt[:,sliceSel,:],vmin=0, vmax=max_val)
+plt.title('3D ADMM-ROFLLT Reconstruction, coronal view')
+
+plt.subplot(133)
+plt.imshow(RecADMM_reg_rofllt[:,:,sliceSel],vmin=0, vmax=max_val)
+plt.title('3D ADMM-ROFLLT Reconstruction, sagittal view')
+plt.show()
+
+# saving to tiffs (16bit)
+"""
+from libtiff import TIFF
+multiplier = (int)(65535/(np.max(RecADMM_reg_rofllt)))
+for i in range(0,np.size(RecADMM_reg_rofllt,0)):
+    tiff = TIFF.open('Dendr_ADMM_ROFLLT'+'_'+str(i)+'.tiff', mode='w')
+    tiff.write_image(np.uint16(RecADMM_reg_rofllt[i,:,:]*multiplier))
+    tiff.close()
+"""
+
+# Saving recpnstructed data with a unique time label
+np.save('Dendr_ADMM_ROFLLT'+str(time_label)+'.npy', RecADMM_reg_rofllt)
+del RecADMM_reg_rofllt
+#%%
+print ("Reconstructing with ADMM method using TGV penalty")
+RecADMM_reg_tgv = RectoolsIR.ADMM(data_norm[0:100,:,det_y_crop],
+                              rho_const = 2000.0, \
+                              iterationsADMM = 15, \
+                              regularisation = 'TGV', \
+                              regularisation_parameter = 0.01,\
+                              regularisation_iterations = 500)
+
+sliceSel = 50
+max_val = 0.003
+plt.figure() 
+plt.subplot(131)
+plt.imshow(RecADMM_reg_tgv[sliceSel,:,:],vmin=0, vmax=max_val)
+plt.title('3D ADMM-TGV Reconstruction, axial view')
+
+plt.subplot(132)
+plt.imshow(RecADMM_reg_tgv[:,sliceSel,:],vmin=0, vmax=max_val)
+plt.title('3D ADMM-TGV Reconstruction, coronal view')
+
+plt.subplot(133)
+plt.imshow(RecADMM_reg_tgv[:,:,sliceSel],vmin=0, vmax=max_val)
+plt.title('3D ADMM-TGV Reconstruction, sagittal view')
+plt.show()
+
+# saving to tiffs (16bit)
+"""
+from libtiff import TIFF
+multiplier = (int)(65535/(np.max(RecADMM_reg_tgv)))
+for i in range(0,np.size(RecADMM_reg_tgv,0)):
+    tiff = TIFF.open('Dendr_ADMM_TGV'+'_'+str(i)+'.tiff', mode='w')
+    tiff.write_image(np.uint16(RecADMM_reg_tgv[i,:,:]*multiplier))
+    tiff.close()
+"""
+# Saving recpnstructed data with a unique time label
+np.save('Dendr_ADMM_TGV'+str(time_label)+'.npy', RecADMM_reg_tgv)
+del RecADMM_reg_tgv
+#%%
\ No newline at end of file
diff --git a/Wrappers/Python/demos/SoftwareX_supp/Demo_SimulData_ParOptimis_SX.py b/Wrappers/Python/demos/SoftwareX_supp/Demo_SimulData_ParOptimis_SX.py
new file mode 100644
index 0000000..59ffc0e
--- /dev/null
+++ b/Wrappers/Python/demos/SoftwareX_supp/Demo_SimulData_ParOptimis_SX.py
@@ -0,0 +1,161 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+This demo scripts support the following publication: 
+"CCPi-Regularisation Toolkit for computed tomographic image reconstruction with 
+proximal splitting algorithms" by Daniil Kazantsev, Edoardo Pasca, Martin J. Turner,
+ Philip J. Withers; Software X, 2019
+____________________________________________________________________________
+* Reads data which is previosly generated by TomoPhantom software (Zenodo link)
+--- https://doi.org/10.5281/zenodo.2578893
+* Optimises for the regularisation parameters which later used in the script:
+Demo_SimulData_Recon_SX.py
+____________________________________________________________________________
+>>>>> Dependencies: <<<<<
+>>>>> Dependencies: <<<<<
+1. ASTRA toolbox: conda install -c astra-toolbox astra-toolbox
+2. TomoRec: conda install -c dkazanc tomorec
+or install from https://github.com/dkazanc/TomoRec
+
+@author: Daniil Kazantsev, e:mail daniil.kazantsev@diamond.ac.uk
+GPLv3 license (ASTRA toolbox)
+"""
+#import timeit
+import matplotlib.pyplot as plt
+import numpy as np
+import h5py
+from ccpi.supp.qualitymetrics import QualityTools
+
+# loading the data 
+h5f = h5py.File('data/TomoSim_data1550671417.h5','r')
+phantom = h5f['phantom'][:]
+projdata_norm = h5f['projdata_norm'][:]
+proj_angles = h5f['proj_angles'][:]
+h5f.close()
+
+[Vert_det, AnglesNum, Horiz_det] = np.shape(projdata_norm)
+N_size = Vert_det
+
+sliceSel = 128
+#plt.gray()
+plt.figure() 
+plt.subplot(131)
+plt.imshow(phantom[sliceSel,:,:],vmin=0, vmax=1)
+plt.title('3D Phantom, axial view')
+
+plt.subplot(132)
+plt.imshow(phantom[:,sliceSel,:],vmin=0, vmax=1)
+plt.title('3D Phantom, coronal view')
+
+plt.subplot(133)
+plt.imshow(phantom[:,:,sliceSel],vmin=0, vmax=1)
+plt.title('3D Phantom, sagittal view')
+plt.show()
+
+intens_max = 240
+plt.figure() 
+plt.subplot(131)
+plt.imshow(projdata_norm[:,sliceSel,:],vmin=0, vmax=intens_max)
+plt.title('2D Projection (erroneous)')
+plt.subplot(132)
+plt.imshow(projdata_norm[sliceSel,:,:],vmin=0, vmax=intens_max)
+plt.title('Sinogram view')
+plt.subplot(133)
+plt.imshow(projdata_norm[:,:,sliceSel],vmin=0, vmax=intens_max)
+plt.title('Tangentogram view')
+plt.show()
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("Reconstructing with ADMM method using TomoRec software")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+# initialise TomoRec ITERATIVE reconstruction class ONCE
+from tomorec.methodsIR import RecToolsIR
+RectoolsIR = RecToolsIR(DetectorsDimH = Horiz_det,  # DetectorsDimH # detector dimension (horizontal)
+                    DetectorsDimV = Vert_det,  # DetectorsDimV # detector dimension (vertical) for 3D case only
+                    AnglesVec = proj_angles, # array of angles in radians
+                    ObjSize = N_size, # a scalar to define reconstructed object dimensions
+                    datafidelity='LS',# data fidelity, choose LS, PWLS (wip), GH (wip), Student (wip)
+                    nonnegativity='ENABLE', # enable nonnegativity constraint (set to 'ENABLE')
+                    OS_number = None, # the number of subsets, NONE/(or > 1) ~ classical / ordered subsets
+                    tolerance = 1e-08, # tolerance to stop outer iterations earlier
+                    device='gpu')
+#%%
+param_space = 30
+reg_param_sb_vec = np.linspace(0.03,0.15,param_space,dtype='float32') # a vector of parameters
+erros_vec_sbtv = np.zeros((param_space)) # a vector of errors
+
+print ("Reconstructing with ADMM method using SB-TV penalty")
+for i in range(0,param_space):
+    RecADMM_reg_sbtv = RectoolsIR.ADMM(projdata_norm,
+                                  rho_const = 2000.0, \
+                                  iterationsADMM = 15, \
+                                  regularisation = 'SB_TV', \
+                                  regularisation_parameter = reg_param_sb_vec[i],\
+                                  regularisation_iterations = 50)
+    # calculate errors 
+    Qtools = QualityTools(phantom, RecADMM_reg_sbtv)
+    erros_vec_sbtv[i] = Qtools.rmse()
+    print("RMSE for regularisation parameter {} for ADMM-SB-TV is {}".format(reg_param_sb_vec[i],erros_vec_sbtv[i]))
+
+plt.figure() 
+plt.plot(erros_vec_sbtv)
+
+# Saving generated data with a unique time label
+h5f = h5py.File('Optim_admm_sbtv.h5', 'w')
+h5f.create_dataset('reg_param_sb_vec', data=reg_param_sb_vec)
+h5f.create_dataset('erros_vec_sbtv', data=erros_vec_sbtv)
+h5f.close()
+#%%
+param_space = 30
+reg_param_rofllt_vec = np.linspace(0.03,0.15,param_space,dtype='float32') # a vector of parameters
+erros_vec_rofllt = np.zeros((param_space)) # a vector of errors
+
+print ("Reconstructing with ADMM method using ROF-LLT penalty")
+for i in range(0,param_space):
+    RecADMM_reg_rofllt = RectoolsIR.ADMM(projdata_norm,
+                                  rho_const = 2000.0, \
+                                  iterationsADMM = 15, \
+                                  regularisation = 'LLT_ROF', \
+                                  regularisation_parameter = reg_param_rofllt_vec[i],\
+                                  regularisation_parameter2 = 0.005,\
+                                  regularisation_iterations = 600)
+    # calculate errors 
+    Qtools = QualityTools(phantom, RecADMM_reg_rofllt)
+    erros_vec_rofllt[i] = Qtools.rmse()
+    print("RMSE for regularisation parameter {} for ADMM-ROF-LLT is {}".format(reg_param_rofllt_vec[i],erros_vec_rofllt[i]))
+
+plt.figure() 
+plt.plot(erros_vec_rofllt)
+
+# Saving generated data with a unique time label
+h5f = h5py.File('Optim_admm_rofllt.h5', 'w')
+h5f.create_dataset('reg_param_rofllt_vec', data=reg_param_rofllt_vec)
+h5f.create_dataset('erros_vec_rofllt', data=erros_vec_rofllt)
+h5f.close()
+#%%
+param_space = 30
+reg_param_tgv_vec = np.linspace(0.03,0.15,param_space,dtype='float32') # a vector of parameters
+erros_vec_tgv = np.zeros((param_space)) # a vector of errors
+
+print ("Reconstructing with ADMM method using TGV penalty")
+for i in range(0,param_space):
+    RecADMM_reg_tgv = RectoolsIR.ADMM(projdata_norm,
+                                  rho_const = 2000.0, \
+                                  iterationsADMM = 15, \
+                                  regularisation = 'TGV', \
+                                  regularisation_parameter = reg_param_tgv_vec[i],\
+                                  regularisation_iterations = 600)
+    # calculate errors 
+    Qtools = QualityTools(phantom, RecADMM_reg_tgv)
+    erros_vec_tgv[i] = Qtools.rmse()
+    print("RMSE for regularisation parameter {} for ADMM-TGV is {}".format(reg_param_tgv_vec[i],erros_vec_tgv[i]))
+
+plt.figure() 
+plt.plot(erros_vec_tgv)
+
+# Saving generated data with a unique time label
+h5f = h5py.File('Optim_admm_tgv.h5', 'w')
+h5f.create_dataset('reg_param_tgv_vec', data=reg_param_tgv_vec)
+h5f.create_dataset('erros_vec_tgv', data=erros_vec_tgv)
+h5f.close()
+#%%
\ No newline at end of file
diff --git a/Wrappers/Python/demos/SoftwareX_supp/Demo_SimulData_Recon_SX.py b/Wrappers/Python/demos/SoftwareX_supp/Demo_SimulData_Recon_SX.py
new file mode 100644
index 0000000..93b0cef
--- /dev/null
+++ b/Wrappers/Python/demos/SoftwareX_supp/Demo_SimulData_Recon_SX.py
@@ -0,0 +1,309 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+This demo scripts support the following publication: 
+"CCPi-Regularisation Toolkit for computed tomographic image reconstruction with 
+proximal splitting algorithms" by Daniil Kazantsev, Edoardo Pasca, Martin J. Turner,
+ Philip J. Withers; Software X, 2019
+____________________________________________________________________________
+* Reads data which is previously generated by TomoPhantom software (Zenodo link)
+--- https://doi.org/10.5281/zenodo.2578893
+* Reconstruct using optimised regularisation parameters (see Demo_SimulData_ParOptimis_SX.py)
+____________________________________________________________________________
+>>>>> Dependencies: <<<<<
+1. ASTRA toolbox: conda install -c astra-toolbox astra-toolbox
+2. TomoRec: conda install -c dkazanc tomorec
+or install from https://github.com/dkazanc/TomoRec
+
+@author: Daniil Kazantsev, e:mail daniil.kazantsev@diamond.ac.uk
+GPLv3 license (ASTRA toolbox)
+"""
+#import timeit
+import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+import numpy as np
+import h5py
+from ccpi.supp.qualitymetrics import QualityTools
+from scipy.signal import gaussian
+
+# loading the data 
+h5f = h5py.File('data/TomoSim_data1550671417.h5','r')
+phantom = h5f['phantom'][:]
+projdata_norm = h5f['projdata_norm'][:]
+proj_angles = h5f['proj_angles'][:]
+h5f.close()
+
+[Vert_det, AnglesNum, Horiz_det] = np.shape(projdata_norm)
+N_size = Vert_det
+
+# loading optmisation parameters (the result of running Demo_SimulData_ParOptimis_SX)
+h5f = h5py.File('optim_param/Optim_admm_sbtv.h5','r')
+reg_param_sb_vec = h5f['reg_param_sb_vec'][:]
+erros_vec_sbtv = h5f['erros_vec_sbtv'][:]
+h5f.close()
+
+h5f = h5py.File('optim_param/Optim_admm_rofllt.h5','r')
+reg_param_rofllt_vec = h5f['reg_param_rofllt_vec'][:]
+erros_vec_rofllt = h5f['erros_vec_rofllt'][:]
+h5f.close()
+
+h5f = h5py.File('optim_param/Optim_admm_tgv.h5','r')
+reg_param_tgv_vec = h5f['reg_param_tgv_vec'][:]
+erros_vec_tgv = h5f['erros_vec_tgv'][:]
+h5f.close()
+
+index_minSBTV = min(xrange(len(erros_vec_sbtv)), key=erros_vec_sbtv.__getitem__)
+index_minROFLLT = min(xrange(len(erros_vec_rofllt)), key=erros_vec_rofllt.__getitem__)
+index_minTGV = min(xrange(len(erros_vec_tgv)), key=erros_vec_tgv.__getitem__)
+# assign optimal regularisation parameters:
+optimReg_sbtv = reg_param_sb_vec[index_minSBTV]
+optimReg_rofllt = reg_param_rofllt_vec[index_minROFLLT]
+optimReg_tgv = reg_param_tgv_vec[index_minTGV]
+#%%
+# plot loaded data
+sliceSel = 128
+#plt.figure() 
+fig, (ax1, ax2) = plt.subplots(figsize=(15, 5), ncols=2)
+plt.rcParams.update({'xtick.labelsize': 'x-small'})
+plt.rcParams.update({'ytick.labelsize':'x-small'})
+plt.subplot(121)
+one = plt.imshow(phantom[sliceSel,:,:],vmin=0, vmax=1, interpolation='none', cmap="PuOr")
+fig.colorbar(one, ax=ax1)
+plt.title('3D Phantom, axial (X-Y) view')
+plt.subplot(122)
+two = plt.imshow(phantom[:,sliceSel,:],vmin=0, vmax=1,interpolation='none', cmap="PuOr")
+fig.colorbar(two, ax=ax2)
+plt.title('3D Phantom, coronal (Y-Z) view')
+"""
+plt.subplot(133)
+plt.imshow(phantom[:,:,sliceSel],vmin=0, vmax=1, cmap="PuOr")
+plt.title('3D Phantom, sagittal view')
+
+"""
+plt.show()
+#%%
+intens_max = 220
+plt.figure() 
+plt.rcParams.update({'xtick.labelsize': 'x-small'})
+plt.rcParams.update({'ytick.labelsize':'x-small'})
+plt.subplot(131)
+plt.imshow(projdata_norm[:,sliceSel,:],vmin=0, vmax=intens_max, cmap="PuOr")
+plt.xlabel('X-detector', fontsize=16)
+plt.ylabel('Z-detector', fontsize=16)
+plt.title('2D Projection (X-Z) view', fontsize=19)
+plt.subplot(132)
+plt.imshow(projdata_norm[sliceSel,:,:],vmin=0, vmax=intens_max, cmap="PuOr")
+plt.xlabel('X-detector', fontsize=16)
+plt.ylabel('Projection angle', fontsize=16)
+plt.title('Sinogram (X-Y) view', fontsize=19)
+plt.subplot(133)
+plt.imshow(projdata_norm[:,:,sliceSel],vmin=0, vmax=intens_max, cmap="PuOr")
+plt.xlabel('Projection angle', fontsize=16)
+plt.ylabel('Z-detector', fontsize=16)
+plt.title('Vertical (Y-Z) view', fontsize=19)
+plt.show()
+#plt.savefig('projdata.pdf', format='pdf', dpi=1200)
+#%%
+# initialise TomoRec DIRECT reconstruction class ONCE
+from tomorec.methodsDIR import RecToolsDIR
+RectoolsDIR = RecToolsDIR(DetectorsDimH = Horiz_det,  # DetectorsDimH # detector dimension (horizontal)
+                    DetectorsDimV = Vert_det,  # DetectorsDimV # detector dimension (vertical) for 3D case only
+                    AnglesVec = proj_angles, # array of angles in radians
+                    ObjSize = N_size, # a scalar to define reconstructed object dimensions
+                    device = 'gpu')
+#%%
+print ("Reconstruction using FBP from TomoRec")
+recFBP= RectoolsDIR.FBP(projdata_norm) # FBP reconstruction
+#%%
+x0, y0 = 0, 127 # These are in _pixel_ coordinates!!
+x1, y1 = 255, 127
+
+sliceSel = int(0.5*N_size)
+max_val = 1
+plt.figure(figsize = (20,5))
+gs1 = gridspec.GridSpec(1, 3)
+gs1.update(wspace=0.1, hspace=0.05) # set the spacing between axes. 
+ax1 = plt.subplot(gs1[0])
+plt.imshow(recFBP[sliceSel,:,:],vmin=0, vmax=max_val, cmap="PuOr")
+ax1.plot([x0, x1], [y0, y1], 'ko-', linestyle='--')
+plt.colorbar(ax=ax1)
+plt.title('FBP Reconstruction, axial (X-Y) view', fontsize=19)
+ax1.set_aspect('equal')
+ax3 = plt.subplot(gs1[1])
+plt.plot(phantom[sliceSel,sliceSel,0:N_size],color='k',linewidth=2)
+plt.plot(recFBP[sliceSel,sliceSel,0:N_size],linestyle='--',color='g')
+plt.title('Profile', fontsize=19)
+ax2 = plt.subplot(gs1[2])
+plt.imshow(recFBP[:,sliceSel,:],vmin=0, vmax=max_val, cmap="PuOr")
+plt.title('FBP Reconstruction, coronal (Y-Z) view', fontsize=19)
+ax2.set_aspect('equal')
+plt.show()
+#plt.savefig('FBP_phantom.pdf', format='pdf', dpi=1600)
+
+# calculate errors 
+Qtools = QualityTools(phantom, recFBP)
+RMSE_fbp = Qtools.rmse()
+print("Root Mean Square Error for FBP is {}".format(RMSE_fbp))
+
+# SSIM measure
+Qtools = QualityTools(phantom[128,:,:]*255, recFBP[128,:,:]*235)
+win = np.array([gaussian(11, 1.5)])
+win2d = win * (win.T)
+ssim_fbp = Qtools.ssim(win2d)
+print("Mean SSIM for FBP is {}".format(ssim_fbp[0]))
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("Reconstructing with ADMM method using TomoRec software")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+# initialise TomoRec ITERATIVE reconstruction class ONCE
+from tomorec.methodsIR import RecToolsIR
+RectoolsIR = RecToolsIR(DetectorsDimH = Horiz_det,  # DetectorsDimH # detector dimension (horizontal)
+                    DetectorsDimV = Vert_det,  # DetectorsDimV # detector dimension (vertical) for 3D case only
+                    AnglesVec = proj_angles, # array of angles in radians
+                    ObjSize = N_size, # a scalar to define reconstructed object dimensions
+                    datafidelity='LS',# data fidelity, choose LS, PWLS (wip), GH (wip), Student (wip)
+                    nonnegativity='ENABLE', # enable nonnegativity constraint (set to 'ENABLE')
+                    OS_number = None, # the number of subsets, NONE/(or > 1) ~ classical / ordered subsets
+                    tolerance = 1e-08, # tolerance to stop outer iterations earlier
+                    device='gpu')
+#%%
+print ("Reconstructing with ADMM method using SB-TV penalty")
+RecADMM_reg_sbtv = RectoolsIR.ADMM(projdata_norm,
+                                  rho_const = 2000.0, \
+                                  iterationsADMM = 25, \
+                                  regularisation = 'SB_TV', \
+                                  regularisation_parameter = optimReg_sbtv,\
+                                  regularisation_iterations = 50)
+
+sliceSel = int(0.5*N_size)
+max_val = 1
+plt.figure(figsize = (20,3))
+gs1 = gridspec.GridSpec(1, 4)
+gs1.update(wspace=0.02, hspace=0.01) # set the spacing between axes. 
+ax1 = plt.subplot(gs1[0])
+plt.plot(reg_param_sb_vec, erros_vec_sbtv, color='k',linewidth=2)
+plt.xlabel('Regularisation parameter', fontsize=16)
+plt.ylabel('RMSE value', fontsize=16)
+plt.title('Regularisation selection', fontsize=19)
+ax2 = plt.subplot(gs1[1])
+plt.imshow(RecADMM_reg_sbtv[sliceSel,:,:],vmin=0, vmax=max_val, cmap="PuOr")
+ax2.plot([x0, x1], [y0, y1], 'ko-', linestyle='--')
+plt.title('ADMM-SBTV (X-Y) view', fontsize=19)
+#ax2.set_aspect('equal')
+ax3 = plt.subplot(gs1[2])
+plt.plot(phantom[sliceSel,sliceSel,0:N_size],color='k',linewidth=2)
+plt.plot(RecADMM_reg_sbtv[sliceSel,sliceSel,0:N_size],linestyle='--',color='g')
+plt.title('Profile', fontsize=19)
+ax4 = plt.subplot(gs1[3])
+plt.imshow(RecADMM_reg_sbtv[:,sliceSel,:],vmin=0, vmax=max_val, cmap="PuOr")
+plt.title('ADMM-SBTV (Y-Z) view', fontsize=19)
+plt.colorbar(ax=ax4)
+plt.show()
+plt.savefig('SBTV_phantom.pdf', format='pdf', dpi=1600)
+
+# calculate errors 
+Qtools = QualityTools(phantom, RecADMM_reg_sbtv)
+RMSE_admm_sbtv = Qtools.rmse()
+print("Root Mean Square Error for ADMM-SB-TV is {}".format(RMSE_admm_sbtv))
+
+# SSIM measure
+Qtools = QualityTools(phantom[128,:,:]*255, RecADMM_reg_sbtv[128,:,:]*235)
+win = np.array([gaussian(11, 1.5)])
+win2d = win * (win.T)
+ssim_admm_sbtv = Qtools.ssim(win2d)
+print("Mean SSIM ADMM-SBTV is {}".format(ssim_admm_sbtv[0]))
+#%%
+print ("Reconstructing with ADMM method using ROFLLT penalty")
+RecADMM_reg_rofllt = RectoolsIR.ADMM(projdata_norm,
+                                  rho_const = 2000.0, \
+                                  iterationsADMM = 25, \
+                                  regularisation = 'LLT_ROF', \
+                                  regularisation_parameter = optimReg_rofllt,\
+                                  regularisation_parameter2 = 0.0085,\
+                                  regularisation_iterations = 600)
+
+sliceSel = int(0.5*N_size)
+max_val = 1
+plt.figure(figsize = (20,3))
+gs1 = gridspec.GridSpec(1, 4)
+gs1.update(wspace=0.02, hspace=0.01) # set the spacing between axes. 
+ax1 = plt.subplot(gs1[0])
+plt.plot(reg_param_rofllt_vec, erros_vec_rofllt, color='k',linewidth=2)
+plt.xlabel('Regularisation parameter', fontsize=16)
+plt.ylabel('RMSE value', fontsize=16)
+plt.title('Regularisation selection', fontsize=19)
+ax2 = plt.subplot(gs1[1])
+plt.imshow(RecADMM_reg_rofllt[sliceSel,:,:],vmin=0, vmax=max_val, cmap="PuOr")
+ax2.plot([x0, x1], [y0, y1], 'ko-', linestyle='--')
+plt.title('ADMM-ROFLLT (X-Y) view', fontsize=19)
+#ax2.set_aspect('equal')
+ax3 = plt.subplot(gs1[2])
+plt.plot(phantom[sliceSel,sliceSel,0:N_size],color='k',linewidth=2)
+plt.plot(RecADMM_reg_rofllt[sliceSel,sliceSel,0:N_size],linestyle='--',color='g')
+plt.title('Profile', fontsize=19)
+ax4 = plt.subplot(gs1[3])
+plt.imshow(RecADMM_reg_rofllt[:,sliceSel,:],vmin=0, vmax=max_val, cmap="PuOr")
+plt.title('ADMM-ROFLLT (Y-Z) view', fontsize=19)
+plt.colorbar(ax=ax4)
+plt.show()
+#plt.savefig('ROFLLT_phantom.pdf', format='pdf', dpi=1600)
+
+# calculate errors 
+Qtools = QualityTools(phantom, RecADMM_reg_rofllt)
+RMSE_admm_rofllt = Qtools.rmse()
+print("Root Mean Square Error for ADMM-ROF-LLT is {}".format(RMSE_admm_rofllt))
+
+# SSIM measure
+Qtools = QualityTools(phantom[128,:,:]*255, RecADMM_reg_rofllt[128,:,:]*235)
+win = np.array([gaussian(11, 1.5)])
+win2d = win * (win.T)
+ssim_admm_rifllt = Qtools.ssim(win2d)
+print("Mean SSIM ADMM-ROFLLT is {}".format(ssim_admm_rifllt[0]))
+#%%
+print ("Reconstructing with ADMM method using TGV penalty")
+RecADMM_reg_tgv = RectoolsIR.ADMM(projdata_norm,
+                                  rho_const = 2000.0, \
+                                  iterationsADMM = 25, \
+                                  regularisation = 'TGV', \
+                                  regularisation_parameter = optimReg_tgv,\
+                                  regularisation_iterations = 600)
+#%%
+sliceSel = int(0.5*N_size)
+max_val = 1
+plt.figure(figsize = (20,3))
+gs1 = gridspec.GridSpec(1, 4)
+gs1.update(wspace=0.02, hspace=0.01) # set the spacing between axes. 
+ax1 = plt.subplot(gs1[0])
+plt.plot(reg_param_tgv_vec, erros_vec_tgv, color='k',linewidth=2)
+plt.xlabel('Regularisation parameter', fontsize=16)
+plt.ylabel('RMSE value', fontsize=16)
+plt.title('Regularisation selection', fontsize=19)
+ax2 = plt.subplot(gs1[1])
+plt.imshow(RecADMM_reg_tgv[sliceSel,:,:],vmin=0, vmax=max_val, cmap="PuOr")
+ax2.plot([x0, x1], [y0, y1], 'ko-', linestyle='--')
+plt.title('ADMM-TGV (X-Y) view', fontsize=19)
+#ax2.set_aspect('equal')
+ax3 = plt.subplot(gs1[2])
+plt.plot(phantom[sliceSel,sliceSel,0:N_size],color='k',linewidth=2)
+plt.plot(RecADMM_reg_tgv[sliceSel,sliceSel,0:N_size],linestyle='--',color='g')
+plt.title('Profile', fontsize=19)
+ax4 = plt.subplot(gs1[3])
+plt.imshow(RecADMM_reg_tgv[:,sliceSel,:],vmin=0, vmax=max_val, cmap="PuOr")
+plt.title('ADMM-TGV (Y-Z) view', fontsize=19)
+plt.colorbar(ax=ax4)
+plt.show()
+#plt.savefig('TGV_phantom.pdf', format='pdf', dpi=1600)
+
+# calculate errors 
+Qtools = QualityTools(phantom, RecADMM_reg_tgv)
+RMSE_admm_tgv = Qtools.rmse()
+print("Root Mean Square Error for ADMM-TGV is {}".format(RMSE_admm_tgv))
+
+# SSIM measure
+#Create a 2d gaussian for the window parameter
+Qtools = QualityTools(phantom[128,:,:]*255, RecADMM_reg_tgv[128,:,:]*235)
+win = np.array([gaussian(11, 1.5)])
+win2d = win * (win.T)
+ssim_admm_tgv = Qtools.ssim(win2d)
+print("Mean SSIM ADMM-TGV is {}".format(ssim_admm_tgv[0]))
+#%%
\ No newline at end of file
diff --git a/Wrappers/Python/demos/SoftwareX_supp/Demo_SimulData_SX.py b/Wrappers/Python/demos/SoftwareX_supp/Demo_SimulData_SX.py
new file mode 100644
index 0000000..cdf4325
--- /dev/null
+++ b/Wrappers/Python/demos/SoftwareX_supp/Demo_SimulData_SX.py
@@ -0,0 +1,117 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+This demo scripts support the following publication: 
+"CCPi-Regularisation Toolkit for computed tomographic image reconstruction with 
+proximal splitting algorithms" by Daniil Kazantsev, Edoardo Pasca, Martin J. Turner,
+ Philip J. Withers; Software X, 2019
+____________________________________________________________________________
+* Runs TomoPhantom software to simulate tomographic projection data with
+some imaging errors and noise
+* Saves the data into hdf file to be uploaded in reconstruction scripts
+__________________________________________________________________________
+
+>>>>> Dependencies: <<<<<
+1. TomoPhantom software for phantom and data generation
+
+@author: Daniil Kazantsev, e:mail daniil.kazantsev@diamond.ac.uk
+Apache 2.0 license
+"""
+import timeit
+import os
+import matplotlib.pyplot as plt
+import numpy as np
+import tomophantom
+from tomophantom import TomoP3D
+from tomophantom.supp.flatsgen import flats
+from tomophantom.supp.normraw import normaliser_sim
+
+print ("Building 3D phantom using TomoPhantom software")
+tic=timeit.default_timer()
+model = 16 # select a model number from the library
+N_size = 256 # Define phantom dimensions using a scalar value (cubic phantom)
+path = os.path.dirname(tomophantom.__file__)
+path_library3D = os.path.join(path, "Phantom3DLibrary.dat")
+#This will generate a N_size x N_size x N_size phantom (3D)
+phantom_tm = TomoP3D.Model(model, N_size, path_library3D)
+toc=timeit.default_timer()
+Run_time = toc - tic
+print("Phantom has been built in {} seconds".format(Run_time))
+
+sliceSel = int(0.5*N_size)
+#plt.gray()
+plt.figure() 
+plt.subplot(131)
+plt.imshow(phantom_tm[sliceSel,:,:],vmin=0, vmax=1)
+plt.title('3D Phantom, axial view')
+
+plt.subplot(132)
+plt.imshow(phantom_tm[:,sliceSel,:],vmin=0, vmax=1)
+plt.title('3D Phantom, coronal view')
+
+plt.subplot(133)
+plt.imshow(phantom_tm[:,:,sliceSel],vmin=0, vmax=1)
+plt.title('3D Phantom, sagittal view')
+plt.show()
+
+# Projection geometry related parameters:
+Horiz_det = int(np.sqrt(2)*N_size) # detector column count (horizontal)
+Vert_det = N_size # detector row count (vertical) (no reason for it to be > N)
+angles_num = int(0.35*np.pi*N_size); # angles number
+angles = np.linspace(0.0,179.9,angles_num,dtype='float32') # in degrees
+angles_rad = angles*(np.pi/180.0)
+#%%
+print ("Building 3D analytical projection data with TomoPhantom")
+projData3D_analyt= TomoP3D.ModelSino(model, N_size, Horiz_det, Vert_det, angles, path_library3D)
+
+intens_max = N_size
+sliceSel = int(0.5*N_size)
+plt.figure() 
+plt.subplot(131)
+plt.imshow(projData3D_analyt[:,sliceSel,:],vmin=0, vmax=intens_max)
+plt.title('2D Projection (analytical)')
+plt.subplot(132)
+plt.imshow(projData3D_analyt[sliceSel,:,:],vmin=0, vmax=intens_max)
+plt.title('Sinogram view')
+plt.subplot(133)
+plt.imshow(projData3D_analyt[:,:,sliceSel],vmin=0, vmax=intens_max)
+plt.title('Tangentogram view')
+plt.show()
+#%%
+print ("Simulate flat fields, add noise and normalise projections...")
+flatsnum = 20 # generate 20 flat fields
+flatsSIM = flats(Vert_det, Horiz_det, maxheight = 0.1, maxthickness = 3, sigma_noise = 0.2, sigmasmooth = 3, flatsnum=flatsnum)
+
+plt.figure() 
+plt.imshow(flatsSIM[0,:,:],vmin=0, vmax=1)
+plt.title('A selected simulated flat-field')
+#%%
+# Apply normalisation of data and add noise
+flux_intensity = 60000 # controls the level of noise 
+sigma_flats = 0.01 # contro the level of noise in flats (higher creates more ring artifacts)
+projData3D_norm = normaliser_sim(projData3D_analyt, flatsSIM, sigma_flats, flux_intensity)
+
+intens_max = N_size
+sliceSel = int(0.5*N_size)
+plt.figure() 
+plt.subplot(131)
+plt.imshow(projData3D_norm[:,sliceSel,:],vmin=0, vmax=intens_max)
+plt.title('2D Projection (erroneous)')
+plt.subplot(132)
+plt.imshow(projData3D_norm[sliceSel,:,:],vmin=0, vmax=intens_max)
+plt.title('Sinogram view')
+plt.subplot(133)
+plt.imshow(projData3D_norm[:,:,sliceSel],vmin=0, vmax=intens_max)
+plt.title('Tangentogram view')
+plt.show()
+#%%
+import h5py
+import time
+time_label = int(time.time())
+# Saving generated data with a unique time label
+h5f = h5py.File('TomoSim_data'+str(time_label)+'.h5', 'w')
+h5f.create_dataset('phantom', data=phantom_tm)
+h5f.create_dataset('projdata_norm', data=projData3D_norm)
+h5f.create_dataset('proj_angles', data=angles_rad)
+h5f.close()
+#%%
\ No newline at end of file
diff --git a/Wrappers/Python/demos/SoftwareX_supp/Readme.md b/Wrappers/Python/demos/SoftwareX_supp/Readme.md
new file mode 100644
index 0000000..54e83f1
--- /dev/null
+++ b/Wrappers/Python/demos/SoftwareX_supp/Readme.md
@@ -0,0 +1,26 @@
+
+# SoftwareX publication [1] supporting files
+
+## Decription:
+The scripts here support publication in SoftwareX journal [1] to ensure reproducibility of the research. The scripts linked with data shared at Zenodo. 
+
+## Data:
+Data is shared at Zenodo [here](https://doi.org/10.5281/zenodo.2578893)
+
+## Dependencies:
+1. [ASTRA toolbox](https://github.com/astra-toolbox/astra-toolbox): `conda install -c astra-toolbox astra-toolbox`
+2. [TomoRec](https://github.com/dkazanc/TomoRec): `conda install -c dkazanc tomorec`
+3. [Tomophantom](https://github.com/dkazanc/TomoPhantom): `conda install tomophantom -c ccpi`
+
+## Files description: 
+- `Demo_SimulData_SX.py` - simulates 3D projection data using [Tomophantom](https://github.com/dkazanc/TomoPhantom) software. One can skip this module if the data is taken from [Zenodo](https://doi.org/10.5281/zenodo.2578893)
+- `Demo_SimulData_ParOptimis_SX.py` - runs computationally extensive calculations for optimal regularisation parameters, the result are saved into directory `optim_param`. This script can be also skipped. 
+- `Demo_SimulData_Recon_SX.py` - using established regularisation parameters, one runs iterative reconstruction
+- `Demo_RealData_Recon_SX.py` - runs real data reconstructions. Can be quite intense on memory so reduce the size of the reconstructed volume if needed. 
+
+### References:
+[1] "CCPi-Regularisation Toolkit for computed tomographic image reconstruction with proximal splitting algorithms" by Daniil Kazantsev, Edoardo Pasca, Martin J. Turner and Philip J. Withers; SoftwareX, 2019. 
+
+### Acknowledgments:
+CCPi-RGL software is a product of the [CCPi](https://www.ccpi.ac.uk/) group, STFC SCD software developers and Diamond Light Source (DLS). Any relevant questions/comments can be e-mailed to Daniil Kazantsev at dkazanc@hotmail.com
+
diff --git a/Wrappers/Python/demos/SoftwareX_supp/optim_param/Optim_admm_rofllt.h5 b/Wrappers/Python/demos/SoftwareX_supp/optim_param/Optim_admm_rofllt.h5
new file mode 100644
index 0000000..63bc4fd
--- /dev/null
+++ b/Wrappers/Python/demos/SoftwareX_supp/optim_param/Optim_admm_rofllt.h5
diff --git a/Wrappers/Python/demos/SoftwareX_supp/optim_param/Optim_admm_sbtv.h5 b/Wrappers/Python/demos/SoftwareX_supp/optim_param/Optim_admm_sbtv.h5
new file mode 100644
index 0000000..03c0c14
--- /dev/null
+++ b/Wrappers/Python/demos/SoftwareX_supp/optim_param/Optim_admm_sbtv.h5
diff --git a/Wrappers/Python/demos/SoftwareX_supp/optim_param/Optim_admm_tgv.h5 b/Wrappers/Python/demos/SoftwareX_supp/optim_param/Optim_admm_tgv.h5
new file mode 100644
index 0000000..056d915
--- /dev/null
+++ b/Wrappers/Python/demos/SoftwareX_supp/optim_param/Optim_admm_tgv.h5