delete old demos

2 files changed, 0 insertions, 373 deletions

diff --git a/Wrappers/Python/demos/PDHG_examples/MultiChannel/PDHG_TV_Denoising_2D_time.py b/Wrappers/Python/demos/PDHG_examples/MultiChannel/PDHG_TV_Denoising_2D_time.py
deleted file mode 100644
index 14608db..0000000
--- a/Wrappers/Python/demos/PDHG_examples/MultiChannel/PDHG_TV_Denoising_2D_time.py
+++ /dev/null
@@ -1,192 +0,0 @@
-#========================================================================
-# Copyright 2019 Science Technology Facilities Council
-# Copyright 2019 University of Manchester
-#
-# This work is part of the Core Imaging Library developed by Science Technology
-# Facilities Council and University of Manchester
-#
-# 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.txt
-#
-# 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
-
-from ccpi.optimisation.operators import BlockOperator, Gradient, Identity
-from ccpi.optimisation.functions import ZeroFunction, L2NormSquared, \
-                      MixedL21Norm, BlockFunction
-
-from ccpi.astra.ops import AstraProjectorMC
-
-import os
-import tomophantom
-from tomophantom import TomoP2D
-
-# Create phantom for TV 2D dynamic tomography 
-
-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')
-plt.figure(1)
-plt.rcParams.update({'font.size': 21})
-plt.title('{}''{}'.format('2D+t phantom using model no.',model))
-for sl in range(0,np.shape(phantom_2Dt)[0]):
-    im = phantom_2Dt[sl,:,:]
-    plt.imshow(im, vmin=0, vmax=1)
-#    plt.pause(.1)
-#    plt.draw
-
-    
-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
-np.random.seed(10)
-noisy_data = ImageData( data.as_array() + np.random.normal(0, 0.25, size=ig.shape) )
-
-tindex = [3, 6, 10]
-
-fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(10, 10))
-plt.subplot(1,3,1)
-plt.imshow(noisy_data.as_array()[tindex[0],:,:])
-plt.axis('off')
-plt.title('Time {}'.format(tindex[0]))
-plt.subplot(1,3,2)
-plt.imshow(noisy_data.as_array()[tindex[1],:,:])
-plt.axis('off')
-plt.title('Time {}'.format(tindex[1]))
-plt.subplot(1,3,3)
-plt.imshow(noisy_data.as_array()[tindex[2],:,:])
-plt.axis('off')
-plt.title('Time {}'.format(tindex[2]))
-
-fig.subplots_adjust(bottom=0.1, top=0.9, left=0.1, right=0.8,
-                    wspace=0.02, hspace=0.02)
-
-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
-operator1 = BlockOperator(op1, op3, shape=(2,1) ) 
-operator2 = BlockOperator(op2, op3, shape=(2,1) )
-
-# Create functions
-      
-f1 = alpha * MixedL21Norm()
-f2 = 0.5 * L2NormSquared(b = noisy_data)    
-f = BlockFunction(f1, f2)  
-                                                                        
-g = ZeroFunction()
-    
-# Compute operator Norm
-normK1 = operator1.norm()
-normK2 = operator2.norm()
-
-#%%
-# Primal & dual stepsizes
-sigma1 = 1
-tau1 = 1/(sigma1*normK1**2)
-
-sigma2 = 1
-tau2 = 1/(sigma2*normK2**2)
-
-# Setup and run the PDHG algorithm
-pdhg1 = PDHG(f=f,g=g,operator=operator1, tau=tau1, sigma=sigma1)
-pdhg1.max_iteration = 2000
-pdhg1.update_objective_interval = 200
-pdhg1.run(2000)
-
-# Setup and run the PDHG algorithm
-pdhg2 = PDHG(f=f,g=g,operator=operator2, tau=tau2, sigma=sigma2)
-pdhg2.max_iteration = 2000
-pdhg2.update_objective_interval = 200
-pdhg2.run(2000)
-
-
-#%%
-
-tindex = [3, 6, 10]
-fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(10, 8))
-
-plt.subplot(3,3,1)
-plt.imshow(phantom_2Dt[tindex[0],:,:])
-plt.axis('off')
-plt.title('Time {}'.format(tindex[0]))
-
-plt.subplot(3,3,2)
-plt.imshow(phantom_2Dt[tindex[1],:,:])
-plt.axis('off')
-plt.title('Time {}'.format(tindex[1]))
-
-plt.subplot(3,3,3)
-plt.imshow(phantom_2Dt[tindex[2],:,:])
-plt.axis('off')
-plt.title('Time {}'.format(tindex[2]))
-
-plt.subplot(3,3,4)
-plt.imshow(pdhg1.get_output().as_array()[tindex[0],:,:])
-plt.axis('off')
-plt.subplot(3,3,5)
-plt.imshow(pdhg1.get_output().as_array()[tindex[1],:,:])
-plt.axis('off')
-plt.subplot(3,3,6)
-plt.imshow(pdhg1.get_output().as_array()[tindex[2],:,:])
-plt.axis('off')
-
-
-plt.subplot(3,3,7)
-plt.imshow(pdhg2.get_output().as_array()[tindex[0],:,:])
-plt.axis('off')
-plt.subplot(3,3,8)
-plt.imshow(pdhg2.get_output().as_array()[tindex[1],:,:])
-plt.axis('off')
-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],:,:])
-
-
-fig.subplots_adjust(bottom=0.1, top=0.9, left=0.1, right=0.8,
-                    wspace=0.02, hspace=0.02)
-
-cb_ax = fig.add_axes([0.83, 0.1, 0.02, 0.8])
-cbar = fig.colorbar(im, cax=cb_ax)
-
-
-plt.show()
-
diff --git a/Wrappers/Python/demos/PDHG_examples/MultiChannel/PDHG_TV_Denoising_Gaussian_3D.py b/Wrappers/Python/demos/PDHG_examples/MultiChannel/PDHG_TV_Denoising_Gaussian_3D.py
deleted file mode 100644
index 03dc2ef..0000000
--- a/Wrappers/Python/demos/PDHG_examples/MultiChannel/PDHG_TV_Denoising_Gaussian_3D.py
+++ /dev/null
@@ -1,181 +0,0 @@
-#========================================================================
-# Copyright 2019 Science Technology Facilities Council
-# Copyright 2019 University of Manchester
-#
-# This work is part of the Core Imaging Library developed by Science Technology
-# Facilities Council and University of Manchester
-#
-# 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.txt
-#
-# 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.
-#
-#=========================================================================
-""" 
-
-Total Variation (3D) Denoising using PDHG algorithm:
-
-
-Problem:     min_{x} \alpha * ||\nabla x||_{2,1} + \frac{1}{2} * || x - g ||_{2}^{2}
-
-             \alpha: Regularization parameter
-             
-             \nabla: Gradient operator 
-             
-             g: Noisy Data with Gaussian Noise
-                          
-             Method = 0 ( PDHG - split ) :  K = [ \nabla,
-                                                 Identity]
-                          
-                                                                    
-             Method = 1 (PDHG - explicit ):  K = \nabla  
-                                                                
-"""
-
-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 skimage.util import random_noise
-
-# Create phantom for TV Gaussian denoising
-import timeit
-import os
-from tomophantom import TomoP3D
-import tomophantom
-
-print ("Building 3D phantom using TomoPhantom software")
-tic=timeit.default_timer()
-model = 13 # select a model number from the library
-N = 64 # 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 x N x N phantom (3D)
-phantom_tm = TomoP3D.Model(model, N, path_library3D)
-
-#%%
-
-# Create noisy data. Add Gaussian noise
-ig = ImageGeometry(voxel_num_x=N, voxel_num_y=N, voxel_num_z=N)
-ag = ig
-n1 = random_noise(phantom_tm, mode = 'gaussian', mean=0, var = 0.001, seed=10)
-noisy_data = ImageData(n1)
-
-sliceSel = int(0.5*N)
-plt.figure(figsize=(15,15))
-plt.subplot(3,1,1)
-plt.imshow(noisy_data.as_array()[sliceSel,:,:],vmin=0, vmax=1)
-plt.title('Axial View')
-plt.colorbar()
-plt.subplot(3,1,2)
-plt.imshow(noisy_data.as_array()[:,sliceSel,:],vmin=0, vmax=1)
-plt.title('Coronal View')
-plt.colorbar()
-plt.subplot(3,1,3)
-plt.imshow(noisy_data.as_array()[:,:,sliceSel],vmin=0, vmax=1)
-plt.title('Sagittal View')
-plt.colorbar()
-plt.show()   
-
-#%%
-
-# Regularisation Parameter
-alpha = 0.05
-
-method = '0'
-
-if method == '0':
-
-    # Create operators
-    op1 = Gradient(ig)
-    op2 = Identity(ig, ag)
-
-    # Create BlockOperator
-    operator = BlockOperator(op1, op2, shape=(2,1) ) 
-
-    # Create functions
-      
-    f1 = alpha * MixedL21Norm()
-    f2 = 0.5 * L2NormSquared(b = noisy_data)    
-    f = BlockFunction(f1, f2)  
-                                      
-    g = ZeroFunction()
-    
-else:
-    
-    # Without the "Block Framework"
-    operator = Gradient(ig)
-    f =  alpha * MixedL21Norm()
-    g =  0.5 * L2NormSquared(b = noisy_data)
-        
-    
-# Compute operator Norm
-normK = operator.norm()
-
-# 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, memopt=True)
-pdhg.max_iteration = 2000
-pdhg.update_objective_interval = 200
-pdhg.run(2000, verbose = True)
-
-
-#%%
-fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(10, 8))
-fig.suptitle('TV Reconstruction',fontsize=20)
-
-
-plt.subplot(2,3,1)
-plt.imshow(noisy_data.as_array()[sliceSel,:,:],vmin=0, vmax=1)
-plt.axis('off')
-plt.title('Axial View')
-
-plt.subplot(2,3,2)
-plt.imshow(noisy_data.as_array()[:,sliceSel,:],vmin=0, vmax=1)
-plt.axis('off')
-plt.title('Coronal View')
-
-plt.subplot(2,3,3)
-plt.imshow(noisy_data.as_array()[:,:,sliceSel],vmin=0, vmax=1)
-plt.axis('off')
-plt.title('Sagittal View')
-
-
-plt.subplot(2,3,4)
-plt.imshow(pdhg.get_output().as_array()[sliceSel,:,:],vmin=0, vmax=1)
-plt.axis('off')
-plt.subplot(2,3,5)
-plt.imshow(pdhg.get_output().as_array()[:,sliceSel,:],vmin=0, vmax=1)
-plt.axis('off')
-plt.subplot(2,3,6)
-plt.imshow(pdhg.get_output().as_array()[:,:,sliceSel],vmin=0, vmax=1)
-plt.axis('off')
-im = plt.imshow(pdhg.get_output().as_array()[:,:,sliceSel],vmin=0, vmax=1)
-
-
-fig.subplots_adjust(bottom=0.1, top=0.9, left=0.1, right=0.8,
-                    wspace=0.02, hspace=0.02)
-
-cb_ax = fig.add_axes([0.83, 0.1, 0.02, 0.8])
-cbar = fig.colorbar(im, cax=cb_ax)
-
-
-plt.show()
-