merge

6 files changed, 436 insertions, 46 deletions

diff --git a/Wrappers/Python/demos/CGLS_examples/CGLS_Tikhonov.py b/Wrappers/Python/demos/CGLS_examples/CGLS_Tikhonov.py
index d1cbe20..653e191 100644
--- a/Wrappers/Python/demos/CGLS_examples/CGLS_Tikhonov.py
+++ b/Wrappers/Python/demos/CGLS_examples/CGLS_Tikhonov.py
@@ -82,18 +82,18 @@ plt.colorbar()
 plt.show()
 
 # Setup and run the CGLS algorithm  
-alpha = 50
-Grad = Gradient(ig)
-
-# Form Tikhonov as a Block CGLS structure
-op_CGLS = BlockOperator( Aop, alpha * Grad, shape=(2,1))
-block_data = BlockDataContainer(noisy_data, Grad.range_geometry().allocate())
-
-x_init = ig.allocate()      
-cgls = CGLS(x_init=x_init, operator=op_CGLS, data=block_data)
-cgls.max_iteration = 1000
-cgls.update_objective_interval = 200
-cgls.run(1000,verbose=False)
+#alpha = 50
+#Grad = Gradient(ig)
+#
+## Form Tikhonov as a Block CGLS structure
+#op_CGLS = BlockOperator( Aop, alpha * Grad, shape=(2,1))
+#block_data = BlockDataContainer(noisy_data, Grad.range_geometry().allocate())
+#
+#x_init = ig.allocate()      
+#cgls = CGLS(x_init=x_init, operator=op_CGLS, data=block_data)
+#cgls.max_iteration = 1000
+#cgls.update_objective_interval = 200
+#cgls.run(1000,verbose=False)
 
 #%%
 # Show results
diff --git a/Wrappers/Python/demos/PDHG_examples/ColorbayDemo.py b/Wrappers/Python/demos/PDHG_examples/ColorbayDemo.py
index a735323..e69060f 100644
--- a/Wrappers/Python/demos/PDHG_examples/ColorbayDemo.py
+++ b/Wrappers/Python/demos/PDHG_examples/ColorbayDemo.py
@@ -57,9 +57,8 @@ elif phantom == 'powder':
         arrays[k] = numpy.array(v)
     XX = arrays['S']    
     X = numpy.transpose(XX,(0,2,1,3))
-    X = X[0:20]
+    X = X[100:120]
     
-        
     
 #%% Setup Geometry of Colorbay
 
@@ -125,11 +124,16 @@ plt.show()
 
 #%% CGLS
 
+def callback(iteration, objective, x):
+    plt.imshow(x.as_array()[5])
+    plt.colorbar()
+    plt.show()
+
 x_init = ig2d.allocate()      
 cgls1 = CGLS(x_init=x_init, operator=Aall, data=data2d)
 cgls1.max_iteration = 100
 cgls1.update_objective_interval = 1
-cgls1.run(5,verbose=True)
+cgls1.run(5,verbose=True, callback = callback)
 
 plt.imshow(cgls1.get_output().subset(channel=5).array)
 plt.title('CGLS')
@@ -148,7 +152,7 @@ cgls2 = CGLS(x_init=x_init, operator=op_CGLS, data=block_data)
 cgls2.max_iteration = 100
 cgls2.update_objective_interval = 1
 
-cgls2.run(10,verbose=True)
+cgls2.run(10,verbose=True, callback=callback)
 
 plt.imshow(cgls2.get_output().subset(channel=5).array)
 plt.title('Tikhonov')
@@ -174,8 +178,9 @@ f = BlockFunction(f1, f2)
 g = ZeroFunction()
 
 # Compute operator Norm
-normK = 8.70320267279591 # Run one time no need to compute again takes time
-    
+#normK = 8.70320267279591 # For powder Run one time no need to compute again takes time
+normK = 14.60320657253632 # for carbon
+
 # Primal & dual stepsizes
 sigma = 1
 tau = 1/(sigma*normK**2)
@@ -184,11 +189,11 @@ tau = 1/(sigma*normK**2)
 pdhg = PDHG(f=f,g=g,operator=operator, tau=tau, sigma=sigma)
 pdhg.max_iteration = 2000
 pdhg.update_objective_interval = 100
-pdhg.run(1000, verbose =True)
+pdhg.run(1000, verbose =True, callback=callback)
 
 
 #%% Show sinograms
-channel_ind = [10,15,15]
+channel_ind = [10,15,19]
 
 plt.figure(figsize=(15,15))
 
diff --git a/Wrappers/Python/demos/PDHG_examples/GatherAll/PDHG_TV_Denoising_2D_time.py b/Wrappers/Python/demos/PDHG_examples/GatherAll/PDHG_TV_Denoising_2D_time.py
index 14608db..febe76d 100644
--- a/Wrappers/Python/demos/PDHG_examples/GatherAll/PDHG_TV_Denoising_2D_time.py
+++ b/Wrappers/Python/demos/PDHG_examples/GatherAll/PDHG_TV_Denoising_2D_time.py
@@ -18,8 +18,24 @@
 # limitations under the License.
 #
 #=========================================================================
+""" 
 
-from ccpi.framework import ImageData, ImageGeometry, AcquisitionGeometry, AcquisitionData
+Total Variation (Dynamic) Denoising using PDHG algorithm and Tomophantom:
+
+
+Problem:     min_{x} \alpha * ||\nabla x||_{2,1} + \frac{1}{2} * || x - g ||_{2}^{2}
+
+             \alpha: Regularization parameter
+             
+             \nabla: Gradient operator 
+             
+             g: 2D Dynamic noisy data with Gaussian Noise
+                          
+             K = \nabla  
+                                                                
+"""
+
+from ccpi.framework import ImageData, ImageGeometry
 
 import numpy as np 
 import numpy                          
@@ -28,7 +44,7 @@ import matplotlib.pyplot as plt
 from ccpi.optimisation.algorithms import PDHG
 
 from ccpi.optimisation.operators import BlockOperator, Gradient, Identity
-from ccpi.optimisation.functions import ZeroFunction, L2NormSquared, \
+from ccpi.optimisation.functions import ZeroFunction, L2NormSquared,  \
                       MixedL21Norm, BlockFunction
 
 from ccpi.astra.ops import AstraProjectorMC
@@ -41,11 +57,9 @@ from tomophantom import TomoP2D
 
 model = 102  # note that the selected model is temporal (2D + time)
 N = 128 # set dimension of the phantom
-# one can specify an exact path to the parameters file
-# path_library2D = '../../../PhantomLibrary/models/Phantom2DLibrary.dat'
+
 path = os.path.dirname(tomophantom.__file__)
 path_library2D = os.path.join(path, "Phantom2DLibrary.dat")
-#This will generate a N_size x N_size x Time frames phantom (2D + time)
 phantom_2Dt = TomoP2D.ModelTemporal(model, N, path_library2D)
 
 plt.close('all')
@@ -58,16 +72,17 @@ for sl in range(0,np.shape(phantom_2Dt)[0]):
 #    plt.pause(.1)
 #    plt.draw
 
-    
+# Setup geometries    
 ig = ImageGeometry(voxel_num_x = N, voxel_num_y = N, channels = np.shape(phantom_2Dt)[0])
 data = ImageData(phantom_2Dt, geometry=ig)
 ag = ig
                       
-# Create Noisy data. Add Gaussian noise
+# Create noisy data. Apply Gaussian noise
 np.random.seed(10)
 noisy_data = ImageData( data.as_array() + np.random.normal(0, 0.25, size=ig.shape) )
 
-tindex = [3, 6, 10]
+# time-frames index
+tindex = [8, 16, 24]
 
 fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(10, 10))
 plt.subplot(1,3,1)
@@ -88,15 +103,12 @@ fig.subplots_adjust(bottom=0.1, top=0.9, left=0.1, right=0.8,
 
 plt.show()   
 
-#%%
 # Regularisation Parameter
 alpha = 0.3
 
 # Create operators
-#op1 = Gradient(ig)
 op1 = Gradient(ig, correlation='Space')
 op2 = Gradient(ig, correlation='SpaceChannels')
-
 op3 = Identity(ig, ag)
 
 # Create BlockOperator
@@ -138,7 +150,7 @@ pdhg2.run(2000)
 
 #%%
 
-tindex = [3, 6, 10]
+tindex = [8, 16, 24]
 fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(10, 8))
 
 plt.subplot(3,3,1)
@@ -177,7 +189,7 @@ plt.subplot(3,3,9)
 plt.imshow(pdhg2.get_output().as_array()[tindex[2],:,:])
 plt.axis('off')
 
-#%%
+
 im = plt.imshow(pdhg1.get_output().as_array()[tindex[0],:,:])
 
 
@@ -186,7 +198,46 @@ fig.subplots_adjust(bottom=0.1, top=0.9, left=0.1, right=0.8,
 
 cb_ax = fig.add_axes([0.83, 0.1, 0.02, 0.8])
 cbar = fig.colorbar(im, cax=cb_ax)
+plt.show()
+
+#%%
+import matplotlib.animation as animation
+fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(10, 30))
+ims1 = []
+ims2 = []
+ims3 = []
+for sl in range(0,np.shape(phantom_2Dt)[0]):
+    
+    plt.subplot(1,3,1)
+    im1 = plt.imshow(phantom_2Dt[sl,:,:], animated=True)
+    
+    plt.subplot(1,3,2)
+    im2 = plt.imshow(pdhg1.get_output().as_array()[sl,:,:])
+    
+    plt.subplot(1,3,3)
+    im3 = plt.imshow(pdhg2.get_output().as_array()[sl,:,:])
+    
+    ims1.append([im1])
+    ims2.append([im2])
+    ims3.append([im3])
+    
+    
+ani1 = animation.ArtistAnimation(fig, ims1, interval=500,
+                                repeat_delay=10)
+
+ani2 = animation.ArtistAnimation(fig, ims2, interval=500,
+                                repeat_delay=10)
+
+ani3 = animation.ArtistAnimation(fig, ims3, interval=500,
+                                repeat_delay=10)
+plt.show()        
+#    plt.pause(0.25)
+#    plt.show()
+
+
+
+
+
 
 
-plt.show()
 
diff --git a/Wrappers/Python/demos/PDHG_examples/GatherAll/PDHG_TV_Denoising_Gaussian_3D.py b/Wrappers/Python/demos/PDHG_examples/GatherAll/PDHG_TV_Denoising_Gaussian_3D.py
index 3d91bf9..15709cd 100644
--- a/Wrappers/Python/demos/PDHG_examples/GatherAll/PDHG_TV_Denoising_Gaussian_3D.py
+++ b/Wrappers/Python/demos/PDHG_examples/GatherAll/PDHG_TV_Denoising_Gaussian_3D.py
@@ -20,7 +20,7 @@
 #=========================================================================
 """ 
 
-Total Variation (3D) Denoising using PDHG algorithm:
+Total Variation (3D) Denoising using PDHG algorithm and Tomophantom:
 
 
 Problem:     min_{x} \alpha * ||\nabla x||_{2,1} + \frac{1}{2} * || x - g ||_{2}^{2}
@@ -39,19 +39,14 @@ Problem:     min_{x} \alpha * ||\nabla x||_{2,1} + \frac{1}{2} * || x - g ||_{2}
                                                                 
 """
 
-from ccpi.framework import ImageData, ImageGeometry
-                         
+from ccpi.framework import ImageData, ImageGeometry                       
 import matplotlib.pyplot as plt
-
 from ccpi.optimisation.algorithms import PDHG
-
-from ccpi.optimisation.operators import BlockOperator, Identity, Gradient
-from ccpi.optimisation.functions import ZeroFunction, L2NormSquared, \
-                      MixedL21Norm, BlockFunction
+from ccpi.optimisation.operators import Gradient
+from ccpi.optimisation.functions import L2NormSquared, MixedL21Norm
 
 from skimage.util import random_noise
 
-# Create phantom for TV Gaussian denoising
 import timeit
 import os
 from tomophantom import TomoP3D
@@ -105,9 +100,9 @@ sigma = 1
 tau = 1/(sigma*normK**2)
 
 pdhg = PDHG(f=f,g=g,operator=operator, tau=tau, sigma=sigma, memopt=True)
-pdhg.max_iteration = 2000
+pdhg.max_iteration = 1000
 pdhg.update_objective_interval = 200
-pdhg.run(2000, verbose = True)
+pdhg.run(1000, verbose = True)
 
 # Show results
 fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(10, 8))
diff --git a/Wrappers/Python/demos/PDHG_examples/IMATDemo.py b/Wrappers/Python/demos/PDHG_examples/IMATDemo.py
new file mode 100644
index 0000000..2051860
--- /dev/null
+++ b/Wrappers/Python/demos/PDHG_examples/IMATDemo.py
@@ -0,0 +1,339 @@
+
+
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Mar 25 12:50:27 2019
+
+@author: vaggelis
+"""
+
+from ccpi.framework import ImageData, ImageGeometry, BlockDataContainer, AcquisitionGeometry, AcquisitionData
+from astropy.io import fits
+import numpy
+import matplotlib.pyplot as plt
+
+from ccpi.optimisation.algorithms import CGLS, PDHG
+from ccpi.optimisation.functions import MixedL21Norm, L2NormSquared, BlockFunction, ZeroFunction, KullbackLeibler, IndicatorBox
+from ccpi.optimisation.operators import Gradient, BlockOperator
+
+from ccpi.astra.operators import  AstraProjectorMC, AstraProjectorSimple
+
+import pickle
+
+
+# load file
+
+#filename_sino = '/media/newhd/shared/DataProcessed/IMAT_beamtime_Feb_2019/preprocessed_test_flat/sino/rebin_slice_350/sino_log_rebin_282.fits' 
+#filename_sino = '/media/newhd/shared/DataProcessed/IMAT_beamtime_Feb_2019/preprocessed_test_flat/sino/rebin_slice_350/sino_log_rebin_564.fits'
+#filename_sino = '/media/newhd/shared/DataProcessed/IMAT_beamtime_Feb_2019/preprocessed_test_flat/sino/rebin_slice_350/sino_log_rebin_141.fits'
+filename_sino = '/media/newhd/shared/DataProcessed/IMAT_beamtime_Feb_2019/preprocessed_test_flat/sino/rebin_slice_350/sino_log_rebin_80_channels.fits'
+
+sino_handler = fits.open(filename_sino)
+sino = numpy.array(sino_handler[0].data, dtype=float)
+
+# change axis order: channels, angles, detectors
+sino_new = numpy.rollaxis(sino, 2)
+sino_handler.close()
+
+
+sino_shape = sino_new.shape
+
+num_channels = sino_shape[0] # channelss
+num_pixels_h = sino_shape[2] # detectors
+num_pixels_v = sino_shape[2] # detectors
+num_angles = sino_shape[1] # angles
+
+
+ig = ImageGeometry(voxel_num_x = num_pixels_h, voxel_num_y = num_pixels_v, channels = num_channels)
+
+with open("/media/newhd/vaggelis/CCPi/IMAT_reconstruction/CCPi-Framework/Wrappers/Python/ccpi/optimisation/IMAT_data/golden_angles_new.txt") as f:
+    angles_string = [line.rstrip() for line in f]
+    angles = numpy.array(angles_string).astype(float)
+                                                                                 
+
+ag = AcquisitionGeometry('parallel', '2D',  angles * numpy.pi / 180, pixel_num_h = num_pixels_h, channels = num_channels)
+op_MC = AstraProjectorMC(ig, ag, 'gpu')
+
+sino_aqdata = AcquisitionData(sino_new, ag)
+result_bp = op_MC.adjoint(sino_aqdata)
+
+#%%
+
+channel = [40, 60]
+for j in range(2):        
+    z4 = sino_aqdata.as_array()[channel[j]] 
+    plt.figure(figsize=(10,6))
+    plt.imshow(z4, cmap='viridis')
+    plt.axis('off')
+    plt.savefig('Sino_141/Sinogram_ch_{}_.png'.format(channel[j]), bbox_inches='tight', transparent=True)
+    plt.show() 
+
+#%%
+
+def callback(iteration, objective, x):
+    plt.imshow(x.as_array()[40])
+    plt.colorbar()
+    plt.show()
+
+#%%
+# CGLS 
+
+x_init = ig.allocate()      
+cgls1 = CGLS(x_init=x_init, operator=op_MC, data=sino_aqdata)
+cgls1.max_iteration = 100
+cgls1.update_objective_interval = 2
+cgls1.run(20,verbose=True, callback=callback)
+
+plt.imshow(cgls1.get_output().subset(channel=20).array)
+plt.title('CGLS')
+plt.colorbar()
+plt.show()
+
+#%%
+with open('Sino_141/CGLS/CGLS_{}_iter.pkl'.format(20), 'wb') as f:
+    z = cgls1.get_output()
+    pickle.dump(z, f)  
+    
+#%% 
+#% Tikhonov Space
+    
+x_init = ig.allocate() 
+alpha = [1,3,5,10,20,50]
+
+for a in alpha:
+    
+    Grad = Gradient(ig, correlation = Gradient.CORRELATION_SPACE)
+    operator = BlockOperator(op_MC, a * Grad, shape=(2,1))
+    blockData = BlockDataContainer(sino_aqdata, \
+                                   Grad.range_geometry().allocate())
+    cgls2 = CGLS()
+    cgls2.max_iteration = 500
+    cgls2.set_up(x_init, operator, blockData)
+    cgls2.update_objective_interval = 50
+    cgls2.run(100,verbose=True)
+        
+    with open('Sino_141/CGLS_Space/CGLS_a_{}.pkl'.format(a), 'wb') as f:
+        z = cgls2.get_output()
+        pickle.dump(z, f)
+   
+#% Tikhonov SpaceChannels
+
+for a1 in alpha:
+    
+    Grad1 = Gradient(ig, correlation = Gradient.CORRELATION_SPACECHANNEL)
+    operator1 = BlockOperator(op_MC, a1 * Grad1, shape=(2,1))
+    blockData1 = BlockDataContainer(sino_aqdata, \
+                                   Grad1.range_geometry().allocate())
+    cgls3 = CGLS()
+    cgls3.max_iteration = 500
+    cgls3.set_up(x_init, operator1, blockData1)
+    cgls3.update_objective_interval = 10
+    cgls3.run(100, verbose=True)
+    
+    with open('Sino_141/CGLS_SpaceChannels/CGLS_a_{}.pkl'.format(a1), 'wb') as f1:
+        z1 = cgls3.get_output()
+        pickle.dump(z1, f1)
+    
+  
+    
+#%%
+#
+ig_tmp = ImageGeometry(voxel_num_x = num_pixels_h, voxel_num_y = num_pixels_v)
+ag_tmp = AcquisitionGeometry('parallel', '2D',  angles * numpy.pi / 180, pixel_num_h = num_pixels_h)
+op_tmp = AstraProjectorSimple(ig_tmp, ag_tmp, 'gpu')
+normK1 = op_tmp.norm()
+
+alpha_TV = [2, 5, 10] # for powder
+
+# Create operators
+op1 = Gradient(ig, correlation=Gradient.CORRELATION_SPACECHANNEL)
+op2 = op_MC
+
+# Create BlockOperator
+operator = BlockOperator(op1, op2, shape=(2,1) ) 
+
+
+for alpha in alpha_TV:
+# Create functions      
+    f1 = alpha * MixedL21Norm()
+    
+    f2 = KullbackLeibler(sino_aqdata)    
+    f = BlockFunction(f1, f2)  
+    g = IndicatorBox(lower=0)
+    
+    # Compute operator Norm
+    normK = numpy.sqrt(8 + normK1**2) 
+    
+    # Primal & dual stepsizes
+    sigma = 1
+    tau = 1/(sigma*normK**2)
+    
+    # Setup and run the PDHG algorithm
+    pdhg = PDHG(f=f,g=g,operator=operator, tau=tau, sigma=sigma)
+    pdhg.max_iteration = 5000
+    pdhg.update_objective_interval = 500
+#    pdhg.run(2000, verbose=True, callback=callback)   
+    pdhg.run(5000, verbose=True, callback=callback)  
+#    
+    with open('Sino_141/TV_SpaceChannels/TV_a = {}.pkl'.format(alpha), 'wb') as f3:
+        z3 = pdhg.get_output()
+        pickle.dump(z3, f3)    
+#    
+#
+#
+#
+##%%
+#        
+#ig_tmp = ImageGeometry(voxel_num_x = num_pixels_h, voxel_num_y = num_pixels_v)
+#ag_tmp = AcquisitionGeometry('parallel', '2D',  angles * numpy.pi / 180, pixel_num_h = num_pixels_h)
+#op_tmp = AstraProjectorSimple(ig_tmp, ag_tmp, 'gpu')
+#normK1 = op_tmp.norm()
+#
+#alpha_TV = 10 # for powder
+#
+## Create operators
+#op1 = Gradient(ig, correlation=Gradient.CORRELATION_SPACECHANNEL)
+#op2 = op_MC
+#
+## Create BlockOperator
+#operator = BlockOperator(op1, op2, shape=(2,1) ) 
+#
+#
+## Create functions      
+#f1 = alpha_TV * MixedL21Norm()
+#f2 = 0.5 * L2NormSquared(b=sino_aqdata)    
+#f = BlockFunction(f1, f2)  
+#g = ZeroFunction()
+#
+## Compute operator Norm
+##normK = 8.70320267279591 # For powder Run one time no need to compute again takes time
+#normK = numpy.sqrt(8 + normK1**2) # for carbon
+#
+## Primal & dual stepsizes
+#sigma = 0.1
+#tau = 1/(sigma*normK**2)
+#
+#def callback(iteration, objective, x):
+#    plt.imshow(x.as_array()[100])
+#    plt.colorbar()
+#    plt.show()
+#
+## Setup and run the PDHG algorithm
+#pdhg = PDHG(f=f,g=g,operator=operator, tau=tau, sigma=sigma)
+#pdhg.max_iteration = 2000
+#pdhg.update_objective_interval = 100
+#pdhg.run(2000, verbose=True)       
+#        
+#        
+#        
+#        
+#        
+#        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+        
+#%%
+    
+#with open('/media/newhd/vaggelis/CCPi/IMAT_reconstruction/CCPi-Framework/Wrappers/Python/ccpi/optimisation/CGLS_Tikhonov/CGLS_Space/CGLS_Space_a = 50.pkl', 'wb') as f:
+#    z = cgls2.get_output()
+#    pickle.dump(z, f)
+#      
+        
+        #%%
+with open('Sino_141/CGLS_Space/CGLS_Space_a_20.pkl', 'rb') as f1:
+    x = pickle.load(f1)
+    
+with open('Sino_141/CGLS_SpaceChannels/CGLS_SpaceChannels_a_20.pkl', 'rb') as f1:
+    x1 = pickle.load(f1)    
+    
+   
+    
+#    
+plt.imshow(x.as_array()[40]*mask)
+plt.colorbar()
+plt.show()
+
+plt.imshow(x1.as_array()[40]*mask)
+plt.colorbar()
+plt.show()
+
+plt.plot(x.as_array()[40,100,:])
+plt.plot(x1.as_array()[40,100,:])
+plt.show()
+
+#%%
+
+# Show results
+
+def circ_mask(h, w, center=None, radius=None):
+
+    if center is None: # use the middle of the image
+        center = [int(w/2), int(h/2)]
+    if radius is None: # use the smallest distance between the center and image walls
+        radius = min(center[0], center[1], w-center[0], h-center[1])
+
+    Y, X = numpy.ogrid[:h, :w]
+    dist_from_center = numpy.sqrt((X - center[0])**2 + (Y-center[1])**2)
+
+    mask = dist_from_center <= radius
+    return mask
+
+mask = circ_mask(141, 141, center=None, radius = 55)
+plt.imshow(numpy.multiply(x.as_array()[40],mask))
+plt.show()
+#%%
+#channel = [100, 200, 300]
+#
+#for i in range(3):     
+#    tmp = cgls1.get_output().as_array()[channel[i]] 
+#    
+#    z =  tmp * mask
+#    plt.figure(figsize=(10,6))
+#    plt.imshow(z, vmin=0, cmap='viridis')
+#    plt.axis('off')
+##    plt.clim(0, 0.02)
+##    plt.colorbar()
+##    del z
+#    plt.savefig('CGLS_282/CGLS_Chan_{}.png'.format(channel[i]), bbox_inches='tight', transparent=True)
+#    plt.show()
+#    
+#    
+##%% Line Profiles
+#    
+#n1, n2, n3 =  cgs.get_output().as_array().shape
+#mask = circ_mask(564, 564, center=None, radius = 220)
+#material = ['Cu', 'Fe', 'Ni']
+#ycoords = [200, 300, 380]
+#    
+#for i in range(3):
+#    z = cgs.get_output().as_array()[channel[i]]  * mask
+#    
+#    for k1 in range(len(ycoords)):
+#        plt.plot(numpy.arange(0,n2), z[ycoords[k1],:])
+#        plt.title('Channel {}: {}'.format(channel[i], material[k1]))
+#        plt.savefig('CGLS/line_profile_chan_{}_material_{}.png'.\
+#                        format(channel[i], material[k1]), bbox_inches='tight')
+#        plt.show()
+#    
+#    
+#
+#
+#
+##%%
+#
+#%%
+        
+
+
+#%%
+
+#plt.imshow(pdhg.get_output().subset(channel=100).as_array())
+#plt.show()
diff --git a/Wrappers/Python/wip/demo_colourbay.py b/Wrappers/Python/wip/demo_colourbay.py
index 5dbf2e1..0536b07 100644
--- a/Wrappers/Python/wip/demo_colourbay.py
+++ b/Wrappers/Python/wip/demo_colourbay.py
@@ -18,7 +18,7 @@ from ccpi.optimisation.funcs import Norm2sq, Norm1
 # Permute (numpy.transpose) puts into our default ordering which is 
 # (channel, angle, vertical, horizontal).
 
-pathname = '/media/jakob/050d8d45-fab3-4285-935f-260e6c5f162c1/Data/ColourBay/spectral_data_sets/CarbonPd/'
+pathname = '/media/newhd/shared/Data/ColourBay/spectral_data_sets/CarbonPd/'
 filename = 'carbonPd_full_sinogram_stripes_removed.mat'
 
 X = loadmat(pathname + filename)