1 files changed, 154 insertions, 0 deletions
diff --git a/Wrappers/Python/wip/Demos/LeastSq_CGLS_FISTA_PDHG.py b/Wrappers/Python/wip/Demos/LeastSq_CGLS_FISTA_PDHG.py
new file mode 100644
index 0000000..97c71ba
--- /dev/null
+++ b/Wrappers/Python/wip/Demos/LeastSq_CGLS_FISTA_PDHG.py
@@ -0,0 +1,154 @@
+# -*- 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
+
+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
+            
+#%%
+
+N = 68
+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 the CGLS algorithm  
+
+x_init = ig.allocate()      
+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)
+
+#%%
+plt.imshow(cgls.get_output().as_array())
+#%%
+###############################################################################
+## 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 = 0.1
+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)
+
+
+#%%
+###############################################################################
+## Setup and run the FISTA algorithm 
+
+fidelity = FunctionOperatorComposition(L2NormSquared(b=noisy_data), Aop)
+regularizer = ZeroFunction()
+
+## Setup and run the FISTA algorithm
+opt = {'memopt':True}
+fista = FISTA(x_init=x_init , f=fidelity, g=regularizer, opt=opt)
+fista.max_iteration = 2000
+fista.update_objective_interval = 200
+fista.run(2000, verbose=True)
+
+#%% Show results
+
+diff1 = pdhg.get_output() - cgls.get_output()
+diff2 = fista.get_output() - cgls.get_output()
+
+print( diff1.norm())
+print( diff2.norm())
+
+plt.figure(figsize=(10,10))
+plt.subplot(2,3,1)
+plt.imshow(cgls.get_output().as_array())
+plt.title('CGLS reconstruction')
+plt.subplot(2,3,2)
+plt.imshow(pdhg.get_output().as_array())
+plt.title('PDHG reconstruction')
+plt.subplot(2,3,3)
+plt.imshow(fista.get_output().as_array())
+plt.title('FISTA reconstruction')
+plt.subplot(2,3,4)
+plt.imshow(diff1.abs().as_array())
+plt.title('Diff PDHG vs CGLS')
+plt.colorbar()
+plt.subplot(2,3,5)
+plt.imshow(diff2.abs().as_array())
+plt.title('Diff FISTA vs CGLS')
+plt.colorbar()
+plt.show()
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+#
+#
+#
+#
+#
+#
+#
+#