Merge pull request #7 from vais-ral/demo_fix

Demo fix

4 files changed, 3 insertions, 113 deletions

diff --git a/Wrappers/Python/wip/DemoRecIP.py b/Wrappers/Python/wip/DemoRecIP.py
deleted file mode 100755
index 442e40e..0000000
--- a/Wrappers/Python/wip/DemoRecIP.py
+++ /dev/null
@@ -1,110 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Reading multi-channel data and reconstruction using FISTA modular
-"""
-
-import numpy as np
-import matplotlib.pyplot as plt
-
-#import sys
-#sys.path.append('../../../data/')
-from read_IPdata import read_IPdata
-
-from ccpi.astra.astra_ops import AstraProjectorSimple, AstraProjectorMC
-from ccpi.reconstruction.funcs import Norm2sq, Norm1, BaseFunction
-from ccpi.reconstruction.algs import FISTA
-#from ccpi.reconstruction.funcs import BaseFunction
-
-from ccpi.framework import ImageData, AcquisitionData, AcquisitionGeometry, ImageGeometry
-
-# read IP paper data into a dictionary
-dataDICT = read_IPdata('..\..\..\data\IP_data70channels.mat')
-
-# Set ASTRA Projection-backprojection class (fan-beam geometry)
-DetWidth = dataDICT.get('im_size')[0] * dataDICT.get('det_width')[0] / \
-            dataDICT.get('detectors_numb')[0]
-SourceOrig = dataDICT.get('im_size')[0] * dataDICT.get('src_to_rotc')[0] / \
-            dataDICT.get('dom_width')[0]
-OrigDetec = dataDICT.get('im_size')[0] * \
-            (dataDICT.get('src_to_det')[0] - dataDICT.get('src_to_rotc')[0]) /\
-            dataDICT.get('dom_width')[0]
-
-N = dataDICT.get('im_size')[0]
-
-vg = ImageGeometry(voxel_num_x=dataDICT.get('im_size')[0],
-                   voxel_num_y=dataDICT.get('im_size')[0],
-                   channels=1)
-
-pg = AcquisitionGeometry('cone',
-                         '2D',
-                         angles=(np.pi/180)*dataDICT.get('theta')[0],
-                         pixel_num_h=dataDICT.get('detectors_numb')[0],
-                         pixel_size_h=DetWidth,
-                         dist_source_center=SourceOrig, 
-                         dist_center_detector=OrigDetec,
-                         channels=1)
-
-
-sino = dataDICT.get('data_norm')[0][:,:,34] # select mid-channel 
-b = AcquisitionData(sino,geometry=pg)
-
-# Initial guess
-x_init = ImageData(np.zeros((N, N)),geometry=vg)
-
-
-
-
-
-Aop = AstraProjectorSimple(vg,pg,'gpu')
-f = Norm2sq(Aop,b,c=0.5)
-
-# Run FISTA for least squares without regularization
-opt = {'tol': 1e-4, 'iter': 10}
-x_fista0, it0, timing0, criter0 = FISTA(x_init, f, None, opt)
-
-plt.imshow(x_fista0.array)
-plt.show()
-
-# Now least squares plus 1-norm regularization
-g1 = Norm1(10)
-
-# Run FISTA for least squares plus 1-norm function.
-x_fista1, it1, timing1, criter1 = FISTA(x_init, f, g1, opt)
-
-plt.imshow(x_fista1.array)
-plt.show()
-
-# Multiple channels
-sino_mc = dataDICT.get('data_norm')[0][:,:,32:37] # select mid-channel 
-
-vg_mc = ImageGeometry(voxel_num_x=dataDICT.get('im_size')[0],
-                   voxel_num_y=dataDICT.get('im_size')[0],
-                   channels=5)
-
-pg_mc = AcquisitionGeometry('cone',
-                         '2D',
-                         angles=(np.pi/180)*dataDICT.get('theta')[0],
-                         pixel_num_h=dataDICT.get('detectors_numb')[0],
-                         pixel_size_h=DetWidth,
-                         dist_source_center=SourceOrig, 
-                         dist_center_detector=OrigDetec,
-                         channels=5)
-
-b_mc = AcquisitionData(np.transpose(sino_mc,(2,0,1)),
-                       geometry=pg_mc, 
-                       dimension_labels=("channel","angle","horizontal"))
-
-# ASTRA operator using volume and sinogram geometries
-Aop_mc = AstraProjectorMC(vg_mc, pg_mc, 'gpu')
-
-f_mc = Norm2sq(Aop_mc,b_mc,c=0.5)
-
-# Initial guess
-x_init_mc = ImageData(np.zeros((5, N, N)),geometry=vg_mc)
-
-
-x_fista0_mc, it0_mc, timing0_mc, criter0_mc = FISTA(x_init_mc, f_mc, None, opt)
-
-plt.imshow(x_fista0_mc.as_array()[4,:,:])
-plt.show()
\ No newline at end of file
diff --git a/Wrappers/Python/wip/simple_mc_demo.py b/Wrappers/Python/wip/demo_astra_mc.py
index 3b78eb3..3b78eb3 100755
--- a/Wrappers/Python/wip/simple_mc_demo.py
+++ b/Wrappers/Python/wip/demo_astra_mc.py
diff --git a/Wrappers/Python/wip/simple_demo_astra.py b/Wrappers/Python/wip/demo_astra_simple.py
index 8529c98..8529c98 100755
--- a/Wrappers/Python/wip/simple_demo_astra.py
+++ b/Wrappers/Python/wip/demo_astra_simple.py
diff --git a/Wrappers/Python/wip/demo_sophiabeads.py b/Wrappers/Python/wip/demo_astra_sophiabeads.py
index 53a0bf8..d8100ea 100755
--- a/Wrappers/Python/wip/demo_sophiabeads.py
+++ b/Wrappers/Python/wip/demo_astra_sophiabeads.py
@@ -16,7 +16,7 @@ from ccpi.optimisation.algs import CGLS
 
 # Set up reader object and read the data
 datareader = XTEKReader("REPLACE_THIS_BY_PATH_TO_DATASET/SophiaBeads_256_averaged.xtekct")
-data = datareader.getAcquisitionData()
+data = datareader.get_acquisition_data()
 
 # Extract central slice, scale and negative-log transform
 sino = -np.log(data.as_array()[:,:,1000]/60000.0)
@@ -65,8 +65,8 @@ Aop = AstraProjectorSimple(ig2d, ag2d,"gpu")
 x_init = ImageData(np.zeros((N,N)),geometry=ig2d)
 
 # Run 50-iteration CGLS reconstruction
-num_iter = 50
-x, it, timing, criter = CGLS(Aop,data2d,num_iter,x_init)
+opt = {'tol': 1e-4, 'iter': 100}
+x, it, timing, criter = CGLS(x_init,Aop,data2d,opt=opt)
 
 # Display reconstruction
 plt.figure()