Ip removal (#109)

* Removing IP data and files. Still in IP_parking branch. #102

* Also remove IP pyc file

4 files changed, 0 insertions, 168 deletions

diff --git a/Wrappers/Python/wip/DemoRecIP.py b/Wrappers/Python/wip/DemoRecIP.py
deleted file mode 100644
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/data/IP_data70channels.mat b/data/IP_data70channels.mat
deleted file mode 100644
index 19b9421..0000000
--- a/data/IP_data70channels.mat
+++ /dev/null
diff --git a/data/__pycache__/read_IPdata.cpython-35.pyc b/data/__pycache__/read_IPdata.cpython-35.pyc
deleted file mode 100644
index b062cf9..0000000
--- a/data/__pycache__/read_IPdata.cpython-35.pyc
+++ /dev/null
diff --git a/data/read_IPdata.py b/data/read_IPdata.py
deleted file mode 100644
index a7565d7..0000000
--- a/data/read_IPdata.py
+++ /dev/null
@@ -1,58 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-function to read IP data and provide a dictionary with data and parameters as an output
-"""
-from scipy import io
-import numpy as np
-from collections import defaultdict
-
-def read_IPdata(datafile):
-    # read data from mat file (specify the location)
-    alldata = io.loadmat(datafile)
-    data_raw = alldata.get('Data_raw') # here is raw projection data
-    Phantom_ideal = alldata.get('Phantom_ideal') # here is 70 channels ideal phantom
-    Photon_flux = alldata.get('Photon_flux') # photon flux for normalization
-    del alldata
-
-    # extract geometry-related parameters
-    proj_numb,detectors_numb,channels = np.shape(data_raw)
-    im_size  = np.size(Phantom_ideal,1)
-
-    theta = np.linspace(0,proj_numb-1,proj_numb)*360/proj_numb   # projection angles
-    dom_width   = 1.0       # width of domain in cm
-    src_to_rotc = 3.0       # dist. from source to rotation center
-    src_to_det  = 5.0       # dist. from source to detector
-    det_width   = 2.0       # detector width
-
-    # negative log normalisation of the raw data (avoiding of log(0))
-    data_norm = np.zeros(np.shape(data_raw))
-    for i in range(0,channels):
-        slice1 = data_raw[:,:,i]
-        indx = np.nonzero(slice1>0)
-        slice2 = np.zeros((proj_numb,detectors_numb), 'float32')
-        slice2[indx] = -np.log(slice1[indx]/Photon_flux[i])
-        indx2 = np.nonzero(slice1==0)
-        slice3 = np.zeros((proj_numb,detectors_numb), 'float32')
-        slice3[indx2] = np.log(slice2[indx2]+Photon_flux[i])
-        data_norm[:,:,i] = slice2 + slice3
-        del indx, indx2, slice1, slice2, slice3
-    data_norm = np.float32(data_norm*(im_size/dom_width))
-    
-    #build a dictionary for data and related parameters
-    dataDICT = defaultdict(list)
-    dataDICT['data_norm'].append(data_norm)
-    dataDICT['data_raw'].append(data_raw)
-    dataDICT['Photon_flux'].append(Photon_flux)
-    dataDICT['Phantom_ideal'].append(Phantom_ideal)
-    dataDICT['theta'].append(theta)
-    dataDICT['proj_numb'].append(proj_numb)
-    dataDICT['detectors_numb'].append(detectors_numb)
-    dataDICT['channels'].append(channels)
-    dataDICT['im_size'].append(im_size)
-    dataDICT['dom_width'].append(dom_width)
-    dataDICT['src_to_rotc'].append(src_to_rotc)
-    dataDICT['src_to_det'].append(src_to_det)
-    dataDICT['det_width'].append(det_width)
-    
-    return (dataDICT)
\ No newline at end of file