Delete test_pdhg_gap.py

1 files changed, 0 insertions, 140 deletions

diff --git a/Wrappers/Python/wip/test_pdhg_gap.py b/Wrappers/Python/wip/test_pdhg_gap.py
deleted file mode 100644
index 6c7ccc9..0000000
--- a/Wrappers/Python/wip/test_pdhg_gap.py
+++ /dev/null
@@ -1,140 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Tue Apr  2 12:26:24 2019
-
-@author: vaggelis
-"""
-
-
-from ccpi.framework import ImageData, ImageGeometry, BlockDataContainer, AcquisitionGeometry, AcquisitionData
-
-import numpy as np                           
-import matplotlib.pyplot as plt
-
-from ccpi.optimisation.algorithms import PDHG, PDHG_old
-
-from ccpi.optimisation.operators import BlockOperator, Identity, Gradient
-from ccpi.optimisation.functions import ZeroFun, L2NormSquared, \
-                      MixedL21Norm, BlockFunction, ScaledFunction
-
-from ccpi.astra.ops import AstraProjectorSimple
-from skimage.util import random_noise
-
-
-#%%###############################################################################
-# Create phantom for TV tomography
-
-#import os
-#import tomophantom
-#from tomophantom import TomoP2D
-#from tomophantom.supp.qualitymetrics import QualityTools
-
-#model = 1 # select a model number from the library
-#N = 150 # 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 phantom (2D)
-#phantom_2D = TomoP2D.Model(model, N, path_library2D)
-#ig = ImageGeometry(voxel_num_x = N, voxel_num_y = N)
-#data = ImageData(phantom_2D, geometry=ig)
-
-N = 150
-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 = 150
-angles = np.linspace(0,np.pi,100)
-
-ag = AcquisitionGeometry('parallel','2D',angles, detectors)
-Aop = AstraProjectorSimple(ig, ag, 'cpu')
-sin = Aop.direct(data)
-
-plt.imshow(sin.as_array())
-plt.title('Sinogram')
-plt.colorbar()
-plt.show()
-
-# Add Gaussian noise to the sinogram data
-np.random.seed(10)
-n1 = np.random.random(sin.shape)
-
-noisy_data = sin + ImageData(5*n1)
-
-plt.imshow(noisy_data.as_array())
-plt.title('Noisy Sinogram')
-plt.colorbar()
-plt.show()
-
-
-#%% Works only with Composite Operator Structure of PDHG
-
-#ig = ImageGeometry(voxel_num_x = N, voxel_num_y = N)
-
-# Create operators
-op1 = Gradient(ig)
-op2 = Aop
-
-# Form Composite Operator
-operator = BlockOperator(op1, op2, shape=(2,1) ) 
-
-alpha = 50
-f = BlockFunction( alpha * MixedL21Norm(), \
-                   0.5 * L2NormSquared(b = noisy_data) )
-g = ZeroFun()
-
-# Compute operator Norm
-normK = operator.norm()
-
-## Primal & dual stepsizes
-
-sigma = 10
-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(5000)
-#%%
-
-opt = {'niter':2000}
-
-res = PDHG_old(f, g, operator, tau = tau, sigma = sigma, opt = opt)
-
-#%%
-sol = pdhg.get_output().as_array()
-sol_old = res[0].as_array()
-fig = plt.figure(figsize=(20,10))
-plt.subplot(1,3,1)
-plt.imshow(noisy_data.as_array())
-#plt.colorbar()
-plt.subplot(1,3,2)
-plt.imshow(sol)
-#plt.colorbar()
-
-plt.subplot(1,3,3)
-plt.imshow(sol_old)
-plt.show()
-
-plt.imshow(np.abs(sol-sol_old))
-plt.colorbar()
-plt.show()
-
-
-#
-#
-##%%
-#plt.plot(np.linspace(0,N,N), data.as_array()[int(N/2),:], label = 'GTruth')
-#plt.plot(np.linspace(0,N,N), sol[int(N/2),:], label = 'Recon')
-#plt.legend()
-#plt.show()
-
-