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diff --git a/Wrappers/Python/wip/demo_simple_RGLTK.md b/Wrappers/Python/wip/demo_simple_RGLTK.md
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+
+from ccpi.framework import ImageData , ImageGeometry, AcquisitionGeometry
+from ccpi.optimisation.algs import FISTA, FBPD, CGLS
+from ccpi.optimisation.funcs import Norm2sq, Norm1, TV2D
+from ccpi.astra.ops import AstraProjectorSimple
+from ccpi.plugins.regularisers import _ROF_TV_, _FGP_TV_
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+test_case = 1   # 1=parallel2D, 2=cone2D
+
+# Set up phantom
+N = 128
+
+
+vg = ImageGeometry(voxel_num_x=N,voxel_num_y=N)
+Phantom = ImageData(geometry=vg)
+
+x = Phantom.as_array()
+x[round(N/4):round(3*N/4),round(N/4):round(3*N/4)] = 0.5
+x[round(N/8):round(7*N/8),round(3*N/8):round(5*N/8)] = 1
+
+#plt.imshow(x)
+#plt.show()
+
+# Set up measurement geometry
+angles_num = 20; # angles number
+
+if test_case==1:
+    angles = np.linspace(0,np.pi,angles_num,endpoint=False)
+elif test_case==2:
+    angles = np.linspace(0,2*np.pi,angles_num,endpoint=False)
+else:
+    NotImplemented
+
+det_w = 1.0
+det_num = N
+SourceOrig = 200
+OrigDetec = 0
+
+# Parallelbeam geometry test
+if test_case==1:
+    pg = AcquisitionGeometry('parallel',
+                          '2D',
+                          angles,
+                          det_num,det_w)
+elif test_case==2:
+    pg = AcquisitionGeometry('cone',
+                          '2D',
+                          angles,
+                          det_num,
+                          det_w,
+                          dist_source_center=SourceOrig, 
+                          dist_center_detector=OrigDetec)
+
+# ASTRA operator using volume and sinogram geometries
+Aop = AstraProjectorSimple(vg, pg, 'cpu')
+
+# Unused old astra projector without geometry
+# Aop_old = AstraProjector(det_w, det_num, SourceOrig, 
+#                      OrigDetec, angles, 
+#                      N,'fanbeam','gpu') 
+
+# Try forward and backprojection
+b = Aop.direct(Phantom)
+out2 = Aop.adjoint(b)
+
+#plt.imshow(b.array)
+#plt.show()
+
+#plt.imshow(out2.array)
+#plt.show()
+
+# Create least squares object instance with projector and data.
+f = Norm2sq(Aop,b,c=0.5)
+
+# Initial guess
+x_init = ImageData(np.zeros(x.shape),geometry=vg)
+#%%
+# FISTA with ROF-TV regularisation
+g_rof = _ROF_TV_(lambdaReg = 10.0,iterationsTV=50,tolerance=1e-5,time_marchstep=0.01,device='cpu')
+
+opt = {'tol': 1e-4, 'iter': 100}
+
+x_fista_rof, it1, timing1, criter_rof = FISTA(x_init, f, g_rof,opt)
+
+plt.figure()
+plt.subplot(121)
+plt.imshow(x_fista_rof.array,cmap="BuPu")
+plt.title('FISTA-ROF-TV')
+plt.subplot(122)
+plt.semilogy(criter_rof)
+plt.show()
+#%%
+# FISTA with FGP-TV regularisation
+g_fgp = _FGP_TV_(lambdaReg = 10.0,iterationsTV=50,tolerance=1e-5,methodTV=0,nonnegativity=0,printing=0,device='cpu')
+
+x_fista_fgp, it1, timing1, criter_fgp = FISTA(x_init, f, g_fgp,opt)
+
+plt.figure()
+plt.subplot(121)
+plt.imshow(x_fista_fgp.array,cmap="BuPu")
+plt.title('FISTA-FGP-TV')
+plt.subplot(122)
+plt.semilogy(criter_fgp)
+plt.show()
+#%%
+# Run FISTA for least squares without regularization
+x_fista0, it0, timing0, criter0 = FISTA(x_init, f, None, opt)
+
+plt.imshow(x_fista0.array)
+plt.title('FISTA0')
+plt.show()
+#%%
+# Now least squares plus 1-norm regularization
+lam = 0.1
+g0 = Norm1(lam)
+
+# Run FISTA for least squares plus 1-norm function.
+x_fista1, it1, timing1, criter1 = FISTA(x_init, f, g0)
+
+plt.imshow(x_fista1.array)
+plt.title('FISTA1')
+plt.show()
+
+plt.semilogy(criter1)
+plt.show()
+#%%
+# Run FBPD=Forward Backward Primal Dual method on least squares plus 1-norm
+opt = {'tol': 1e-4, 'iter': 100}
+x_fbpd1, it_fbpd1, timing_fbpd1, criter_fbpd1 = FBPD(x_init,None,f,g0,opt=opt)
+
+plt.imshow(x_fbpd1.array)
+plt.title('FBPD1')
+plt.show()
+
+plt.semilogy(criter_fbpd1)
+plt.show()
+#%%
+opt_FBPD = {'tol': 1e-4, 'iter': 10000}
+# Now FBPD for least squares plus TV
+lamtv = 10.0
+gtv = TV2D(lamtv)
+
+x_fbpdtv, it_fbpdtv, timing_fbpdtv, criter_fbpdtv = FBPD(x_init,None,f,gtv,opt=opt_FBPD)
+
+plt.imshow(x_fbpdtv.array)
+plt.show()
+
+plt.semilogy(criter_fbpdtv)
+plt.show()
+
+
+# Run CGLS, which should agree with the FISTA0
+x_CGLS, it_CGLS, timing_CGLS, criter_CGLS = CGLS(x_init, Aop, b, opt )
+
+plt.imshow(x_CGLS.array)
+plt.title('CGLS')
+#plt.title('CGLS recon, compare FISTA0')
+plt.show()
+
+plt.semilogy(criter_CGLS)
+plt.title('CGLS criterion')
+plt.show()
+#%%
+
+clims = (0,1)
+cols = 3
+rows = 2
+current = 1
+fig = plt.figure()
+# projections row
+a=fig.add_subplot(rows,cols,current)
+a.set_title('phantom {0}'.format(np.shape(Phantom.as_array())))
+
+imgplot = plt.imshow(Phantom.as_array(),vmin=clims[0],vmax=clims[1])
+
+current = current + 1
+a=fig.add_subplot(rows,cols,current)
+a.set_title('FISTA0')
+imgplot = plt.imshow(x_fista0.as_array(),vmin=clims[0],vmax=clims[1])
+
+current = current + 1
+a=fig.add_subplot(rows,cols,current)
+a.set_title('FISTA1')
+imgplot = plt.imshow(x_fista1.as_array(),vmin=clims[0],vmax=clims[1])
+
+current = current + 1
+a=fig.add_subplot(rows,cols,current)
+a.set_title('FBPD1')
+imgplot = plt.imshow(x_fbpd1.as_array(),vmin=clims[0],vmax=clims[1])
+
+current = current + 1
+a=fig.add_subplot(rows,cols,current)
+a.set_title('CGLS')
+imgplot = plt.imshow(x_CGLS.as_array(),vmin=clims[0],vmax=clims[1])
+
+#current = current + 1
+#a=fig.add_subplot(rows,cols,current)
+#a.set_title('FBPD TV')
+#imgplot = plt.imshow(x_fbpdtv.as_array(),vmin=clims[0],vmax=clims[1])
+
+fig = plt.figure()
+# projections row
+b=fig.add_subplot(1,1,1)
+b.set_title('criteria')
+imgplot = plt.loglog(criter0 , label='FISTA0')
+imgplot = plt.loglog(criter1 , label='FISTA1')
+imgplot = plt.loglog(criter_fbpd1, label='FBPD1')
+imgplot = plt.loglog(criter_CGLS, label='CGLS')
+b.legend(loc='right')
+plt.show()
+#%%