delete demos

4 files changed, 0 insertions, 593 deletions

diff --git a/Wrappers/Python/wip/Compare_Algs/FISTA_vs_PDHG.py b/Wrappers/Python/wip/Compare_Algs/FISTA_vs_PDHG.py
deleted file mode 100644
index eb62761..0000000
--- a/Wrappers/Python/wip/Compare_Algs/FISTA_vs_PDHG.py
+++ /dev/null
@@ -1,117 +0,0 @@
-# -*- coding: utf-8 -*-
-#   This work is part of the Core Imaging Library developed by
-#   Visual Analytics and Imaging System Group of the Science Technology
-#   Facilities Council, STFC
-
-#   Copyright 2018-2019 Evangelos Papoutsellis and Edoardo Pasca
-
-#   Licensed under the Apache License, Version 2.0 (the "License");
-#   you may not use this file except in compliance with the License.
-#   You may obtain a copy of the License at
-
-#       http://www.apache.org/licenses/LICENSE-2.0
-
-#   Unless required by applicable law or agreed to in writing, software
-#   distributed under the License is distributed on an "AS IS" BASIS,
-#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#   See the License for the specific language governing permissions and
-#   limitations under the License.
-
-
-""" 
-Compare FISTA & PDHG classes
-
-
-Problem:     min_x alpha * ||\grad x ||^{2}_{2} + || x - g ||_{1}
-
-             A: Projection operator
-             g: Sinogram
-
-"""
-
-from ccpi.framework import ImageData, ImageGeometry
-
-import numpy as np 
-import numpy                          
-import matplotlib.pyplot as plt
-
-from ccpi.optimisation.algorithms import FISTA, PDHG
-
-from ccpi.optimisation.operators import BlockOperator, Gradient, Identity
-from ccpi.optimisation.functions import L2NormSquared, L1Norm, \
-                                         FunctionOperatorComposition, BlockFunction, ZeroFunction
-                                                
-from skimage.util import random_noise
-
-
-# Create Ground truth and noisy data
-
-N = 100
-
-data = np.zeros((N,N))
-data[round(N/4):round(3*N/4),round(N/4):round(3*N/4)] = 0.5
-data[round(N/8):round(7*N/8),round(3*N/8):round(5*N/8)] = 1
-data = ImageData(data)
-ig = ImageGeometry(voxel_num_x = N, voxel_num_y = N)
-ag = ig
-
-n1 = random_noise(data.as_array(), mode = 's&p', salt_vs_pepper = 0.9, amount=0.2)
-noisy_data = ImageData(n1)
-
-# Regularisation Parameter
-alpha = 5
-
-
-# Setup and run the FISTA algorithm
-operator = Gradient(ig)
-fidelity = L1Norm(b=noisy_data)
-regulariser = FunctionOperatorComposition(alpha * L2NormSquared(), operator)
-
-x_init = ig.allocate()
-opt = {'memopt':True}
-fista = FISTA(x_init=x_init , f=regulariser, g=fidelity, opt=opt)
-fista.max_iteration = 2000
-fista.update_objective_interval = 50
-fista.run(2000, verbose=False)
-
-# Setup and run the PDHG algorithm
-op1 = Gradient(ig)
-op2 = Identity(ig, ag)
-
-operator = BlockOperator(op1, op2, shape=(2,1) )   
-f = BlockFunction(alpha * L2NormSquared(), fidelity)                                        
-g = ZeroFunction()
-  
-normK = operator.norm()
-
-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.update_objective_interval = 50
-pdhg.run(2000, verbose=False)
-
-#%%
-# Show results
-
-plt.figure(figsize=(15,15))
-
-plt.subplot(1,2,1)
-plt.imshow(pdhg.get_output().as_array())
-plt.title('PDHG reconstruction')
-
-plt.subplot(1,2,2)
-plt.imshow(fista.get_output().as_array())
-plt.title('FISTA reconstruction')
-
-plt.show()
-
-diff1 = pdhg.get_output() - fista.get_output()
-
-plt.imshow(diff1.abs().as_array())
-plt.title('Diff PDHG vs CGLS')
-plt.colorbar()
-plt.show()
-
-
diff --git a/Wrappers/Python/wip/Compare_Algs/LeastSq_CGLS_FISTA_PDHG.py b/Wrappers/Python/wip/Compare_Algs/LeastSq_CGLS_FISTA_PDHG.py
deleted file mode 100644
index 39f0907..0000000
--- a/Wrappers/Python/wip/Compare_Algs/LeastSq_CGLS_FISTA_PDHG.py
+++ /dev/null
@@ -1,197 +0,0 @@
-# -*- coding: utf-8 -*-
-#   This work is part of the Core Imaging Library developed by
-#   Visual Analytics and Imaging System Group of the Science Technology
-#   Facilities Council, STFC
-
-#   Copyright 2018-2019 Evangelos Papoutsellis and Edoardo Pasca
-
-#   Licensed under the Apache License, Version 2.0 (the "License");
-#   you may not use this file except in compliance with the License.
-#   You may obtain a copy of the License at
-
-#       http://www.apache.org/licenses/LICENSE-2.0
-
-#   Unless required by applicable law or agreed to in writing, software
-#   distributed under the License is distributed on an "AS IS" BASIS,
-#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#   See the License for the specific language governing permissions and
-#   limitations under the License.
-
-
-""" 
-Compare Least Squares minimization problem using FISTA, PDHG, CGLS classes
-and Astra Built-in CGLS
-
-Problem:     min_x || A x - g ||_{2}^{2}
-
-             A: Projection operator
-             g: Sinogram
-
-"""
-
-from ccpi.framework import ImageData, ImageGeometry, AcquisitionGeometry
-
-import numpy as np 
-import numpy                          
-import matplotlib.pyplot as plt
-
-from ccpi.optimisation.algorithms import PDHG, CGLS, FISTA
-
-from ccpi.optimisation.functions import ZeroFunction, L2NormSquared, FunctionOperatorComposition
-from ccpi.astra.ops import AstraProjectorSimple
-import astra          
-
-# Create Ground truth phantom and Sinogram
-
-N = 128
-x = np.zeros((N,N))
-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
-
-data = ImageData(x)
-ig = ImageGeometry(voxel_num_x = N, voxel_num_y = N)
-
-detectors = N
-angles = np.linspace(0, np.pi, N, dtype=np.float32)
-
-ag = AcquisitionGeometry('parallel','2D', angles, detectors)
-Aop = AstraProjectorSimple(ig, ag, 'cpu')
-sin = Aop.direct(data)
-
-noisy_data = sin 
-
-# Setup and run Astra CGLS algorithm
-vol_geom = astra.create_vol_geom(N, N)
-proj_geom = astra.create_proj_geom('parallel', 1.0, detectors, angles)
-
-proj_id = astra.create_projector('strip', proj_geom, vol_geom)
-
-# Create a sinogram from a phantom
-sinogram_id, sinogram = astra.create_sino(x, proj_id)
-
-# Create a data object for the reconstruction
-rec_id = astra.data2d.create('-vol', vol_geom)
-
-cgls_astra = astra.astra_dict('CGLS')
-cgls_astra['ReconstructionDataId'] = rec_id
-cgls_astra['ProjectionDataId'] = sinogram_id
-cgls_astra['ProjectorId'] = proj_id
-
-# Create the algorithm object from the configuration structure
-alg_id = astra.algorithm.create(cgls_astra)
-
-astra.algorithm.run(alg_id, 1000)
-
-recon_cgls_astra = astra.data2d.get(rec_id)
-
-# Setup and run the CGLS algorithm  
-
-x_init = ig.allocate()      
-       
-cgls = CGLS(x_init=x_init, operator=Aop, data=noisy_data)
-cgls.max_iteration = 1000
-cgls.update_objective_interval = 200
-cgls.run(1000)
-
-# Setup and run the PDHG algorithm 
-
-operator = Aop
-f = L2NormSquared(b = noisy_data)
-g = ZeroFunction()
-  
-## Compute operator Norm
-normK = operator.norm()
-
-## Primal & dual stepsizes
-sigma = 1
-tau = 1/(sigma*normK**2)
-
-pdhg = PDHG(f=f,g=g,operator=operator, tau=tau, sigma=sigma, memopt=True)
-pdhg.max_iteration = 1000
-pdhg.update_objective_interval = 200
-pdhg.run(1000)
-
-# Setup and run the FISTA algorithm 
-
-fidelity = FunctionOperatorComposition(L2NormSquared(b=noisy_data), Aop)
-regularizer = ZeroFunction()
-
-opt = {'memopt':True}
-fista = FISTA(x_init=x_init , f=fidelity, g=regularizer, opt=opt)
-fista.max_iteration = 1000
-fista.update_objective_interval = 200
-fista.run(1000, verbose=True)
-
-#%% Show results
-
-plt.figure(figsize=(15,15))
-plt.suptitle('Reconstructions ')
-
-plt.subplot(2,2,1)
-plt.imshow(cgls.get_output().as_array())
-plt.title('CGLS reconstruction')
-
-plt.subplot(2,2,2)
-plt.imshow(fista.get_output().as_array())
-plt.title('FISTA reconstruction')
-
-plt.subplot(2,2,3)
-plt.imshow(pdhg.get_output().as_array())
-plt.title('PDHG reconstruction')
-
-plt.subplot(2,2,4)
-plt.imshow(recon_cgls_astra)
-plt.title('CGLS astra')
-
-#%%
-diff1 = pdhg.get_output() - cgls.get_output()
-diff2 = fista.get_output() - cgls.get_output()
-diff3 = ImageData(recon_cgls_astra) - cgls.get_output()
-
-print( diff1.squared_norm())
-print( diff2.squared_norm())
-print( diff3.squared_norm())
-
-plt.figure(figsize=(15,15))
-
-plt.subplot(3,1,1)
-plt.imshow(diff1.abs().as_array())
-plt.title('Diff PDHG vs CGLS')
-plt.colorbar()
-
-plt.subplot(3,1,2)
-plt.imshow(diff2.abs().as_array())
-plt.title('Diff FISTA vs CGLS')
-plt.colorbar()
-
-plt.subplot(3,1,3)
-plt.imshow(diff3.abs().as_array())
-plt.title('Diff CLGS astra vs CGLS')
-plt.colorbar()
-
-
-
-
-
-
-
-
-
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-
-
-
-
-
-
-
-
-
-#
-#
-#
-#
-#
-#
-#
-#
diff --git a/Wrappers/Python/wip/Compare_Algs/PDHG_vs_CGLS.py b/Wrappers/Python/wip/Compare_Algs/PDHG_vs_CGLS.py
deleted file mode 100644
index 3155654..0000000
--- a/Wrappers/Python/wip/Compare_Algs/PDHG_vs_CGLS.py
+++ /dev/null
@@ -1,127 +0,0 @@
-# -*- coding: utf-8 -*-
-#   This work is part of the Core Imaging Library developed by
-#   Visual Analytics and Imaging System Group of the Science Technology
-#   Facilities Council, STFC
-
-#   Copyright 2018-2019 Evangelos Papoutsellis and Edoardo Pasca
-
-#   Licensed under the Apache License, Version 2.0 (the "License");
-#   you may not use this file except in compliance with the License.
-#   You may obtain a copy of the License at
-
-#       http://www.apache.org/licenses/LICENSE-2.0
-
-#   Unless required by applicable law or agreed to in writing, software
-#   distributed under the License is distributed on an "AS IS" BASIS,
-#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#   See the License for the specific language governing permissions and
-#   limitations under the License.
-
-from ccpi.framework import ImageData, ImageGeometry, AcquisitionGeometry, AcquisitionData
-
-import numpy as np 
-import numpy                          
-import matplotlib.pyplot as plt
-
-from ccpi.optimisation.algorithms import PDHG, CGLS
-
-from ccpi.optimisation.operators import Gradient
-from ccpi.optimisation.functions import ZeroFunction, L2NormSquared, FunctionOperatorComposition
-from skimage.util import random_noise
-from ccpi.astra.ops import AstraProjectorSimple
-            
-#%%
-
-N = 128
-x = np.zeros((N,N))
-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
-
-data = ImageData(x)
-ig = ImageGeometry(voxel_num_x = N, voxel_num_y = N)
-
-detectors = N
-angles = np.linspace(0, np.pi, N, dtype=np.float32)
-
-ag = AcquisitionGeometry('parallel','2D',angles, detectors)
-Aop = AstraProjectorSimple(ig, ag, 'cpu')
-sin = Aop.direct(data)
-
-noisy_data = sin 
-
-x_init = ig.allocate()
-
-## Setup and run the CGLS algorithm        
-cgls = CGLS(x_init=x_init, operator=Aop, data=noisy_data)
-cgls.max_iteration = 500
-cgls.update_objective_interval = 50
-cgls.run(500, verbose=True)
-
-# Create BlockOperator
-operator = Aop
-f = 0.5 * L2NormSquared(b = noisy_data)
-g = ZeroFunction()
-  
-## Compute operator Norm
-normK = operator.norm()
-
-## Primal & dual stepsizes
-sigma = 0.1
-tau = 1/(sigma*normK**2)
-#
-#
-## Setup and run the PDHG algorithm
-pdhg = PDHG(f=f,g=g,operator=operator, tau=tau, sigma=sigma, memopt=True)
-pdhg.max_iteration = 2000
-pdhg.update_objective_interval = 50
-pdhg.run(2000)
-
-#%%
-
-diff = pdhg.get_output() - cgls.get_output()
-print( diff.norm())
-#
-plt.figure(figsize=(15,15))
-plt.subplot(3,1,1)
-plt.imshow(pdhg.get_output().as_array())
-plt.title('PDHG reconstruction')
-plt.colorbar()
-plt.subplot(3,1,2)
-plt.imshow(cgls.get_output().as_array())
-plt.title('CGLS reconstruction')
-plt.colorbar()
-plt.subplot(3,1,3)
-plt.imshow(diff.abs().as_array())
-plt.title('Difference reconstruction')
-plt.colorbar()
-plt.show()
-
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diff --git a/Wrappers/Python/wip/Compare_Algs/Tikhonov_CGLS_PDHG.py b/Wrappers/Python/wip/Compare_Algs/Tikhonov_CGLS_PDHG.py
deleted file mode 100644
index 984fca4..0000000
--- a/Wrappers/Python/wip/Compare_Algs/Tikhonov_CGLS_PDHG.py
+++ /dev/null
@@ -1,152 +0,0 @@
-# -*- coding: utf-8 -*-
-#   This work is part of the Core Imaging Library developed by
-#   Visual Analytics and Imaging System Group of the Science Technology
-#   Facilities Council, STFC
-
-#   Copyright 2018-2019 Evangelos Papoutsellis and Edoardo Pasca
-
-#   Licensed under the Apache License, Version 2.0 (the "License");
-#   you may not use this file except in compliance with the License.
-#   You may obtain a copy of the License at
-
-#       http://www.apache.org/licenses/LICENSE-2.0
-
-#   Unless required by applicable law or agreed to in writing, software
-#   distributed under the License is distributed on an "AS IS" BASIS,
-#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#   See the License for the specific language governing permissions and
-#   limitations under the License.
-
-""" 
-Compare Tikhonov with PDHG, CGLS classes
-
-
-Problem:     min_x alpha * ||\grad x ||^{2}_{2} + || A x - g ||_{2}^{2}
-
-             A: Projection operator
-             g: Sinogram
-
-"""
-
-
-from ccpi.framework import ImageData, ImageGeometry, \
-                            AcquisitionGeometry, BlockDataContainer, AcquisitionData
-
-import numpy as np 
-import numpy                          
-import matplotlib.pyplot as plt
-
-from ccpi.optimisation.algorithms import PDHG, CGLS
-from ccpi.optimisation.operators import BlockOperator, Gradient
-
-from ccpi.optimisation.functions import ZeroFunction, BlockFunction, L2NormSquared
-from ccpi.astra.ops import AstraProjectorSimple         
-
-# Create Ground truth phantom and Sinogram
-
-N = 128
-x = np.zeros((N,N))
-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
-
-data = ImageData(x)
-ig = ImageGeometry(voxel_num_x = N, voxel_num_y = N)
-
-detectors = N
-angles = np.linspace(0, np.pi, N, dtype=np.float32)
-
-ag = AcquisitionGeometry('parallel','2D', angles, detectors)
-Aop = AstraProjectorSimple(ig, ag, 'cpu')
-sin = Aop.direct(data)
-
-noisy_data = AcquisitionData( sin.as_array() + np.random.normal(0,3,ig.shape))
-
-# 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)
-
-#%%
-#Setup and run the PDHG algorithm 
-
-# Create BlockOperator
-op_PDHG = BlockOperator(Grad, Aop, shape=(2,1) ) 
-
-# Create functions
-      
-f1 = 0.5 * alpha**2 * L2NormSquared()
-f2 = 0.5 * L2NormSquared(b = noisy_data)    
-f = BlockFunction(f1, f2)                                       
-g = ZeroFunction()
-
-## Compute operator Norm
-normK = op_PDHG.norm()
-
-## Primal & dual stepsizes
-sigma = 10
-tau = 1/(sigma*normK**2)
-
-pdhg = PDHG(f=f,g=g,operator=op_PDHG, tau=tau, sigma=sigma, memopt=True)
-pdhg.max_iteration = 1000
-pdhg.update_objective_interval = 200
-pdhg.run(1000)
-
-#%%
-# Show results
-
-plt.figure(figsize=(15,15))
-
-plt.subplot(1,2,1)
-plt.imshow(cgls.get_output().as_array())
-plt.title('CGLS reconstruction')
-
-plt.subplot(1,2,2)
-plt.imshow(pdhg.get_output().as_array())
-plt.title('PDHG reconstruction')
-
-plt.show()
-
-diff1 = pdhg.get_output() - cgls.get_output()
-
-plt.imshow(diff1.abs().as_array())
-plt.title('Diff PDHG vs CGLS')
-plt.colorbar()
-plt.show()
-
-#%%
-
-plt.plot(np.linspace(0,N,N), pdhg.get_output().as_array()[int(N/2),:], label = 'PDHG')
-plt.plot(np.linspace(0,N,N), cgls.get_output().as_array()[int(N/2),:], label = 'CGLS')
-plt.legend()
-plt.title('Middle Line Profiles')
-plt.show()
-            
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-#
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-#
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