Merge branch origin/pythonize into pythonize

22 files changed, 1058 insertions, 1362 deletions

diff --git a/CMakeLists.txt b/CMakeLists.txt
index d05cdd9..4cfad7e 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -25,6 +25,6 @@ set (CIL_VERSION_MAJOR 0)
 set (CIL_VERSION_MINOR 9)
 set (CIL_VERSION_PATCH 1)
 
-set (CIL_VERSION '${CIL_VERSION_MAJOR}.${CIL_VERSION_MINOR}.${CIL_VERSION_PATCH}')
+set (CIL_VERSION '${CIL_VERSION_MAJOR}.${CIL_VERSION_MINOR}.${CIL_VERSION_PATCH}' CACHE INTERNAL "Core Imaging Library version" FORCE)
 
 add_subdirectory(src)
diff --git a/demos/DemoRD1.m b/demos/DemoRD1.m
deleted file mode 100644
index 5bb5f6b..0000000
--- a/demos/DemoRD1.m
+++ /dev/null
@@ -1,100 +0,0 @@
-% Demonstration of tomographic reconstruction from neutron tomography
-% dataset (basalt sample) using Student t data fidelity 
-clear all
-close all 
-
-% adding paths
-addpath('../data/'); 
-addpath('../main_func/'); addpath('../main_func/regularizers_CPU/');
-addpath('../supp/'); 
-
-load('sino_basalt.mat') % load real neutron data
-
-size_det = size(sino_basalt, 1); % detector size
-angSize = size(sino_basalt,2); % angles dim
-recon_size = 650; % reconstruction size
-
-FBP = iradon(sino_basalt, rad2deg(angles),recon_size);
-figure; imshow(FBP , [0, 0.45]); title ('FBP reconstruction');
-%%
-% set projection/reconstruction geometry here
-Z_slices = 1;
-det_row_count = Z_slices;
-proj_geom = astra_create_proj_geom('parallel3d', 1, 1, det_row_count, size_det, angles);
-vol_geom = astra_create_vol_geom(recon_size,recon_size,Z_slices);
-%%
-fprintf('%s\n', 'Reconstruction using FISTA-LS without regularization...');
-clear params
-params.proj_geom = proj_geom; % pass geometry to the function
-params.vol_geom = vol_geom;
-params.sino = sino_basalt;
-params.iterFISTA  = 50;
-params.show = 0;
-params.maxvalplot = 0.6; params.slice = 1;
-
-tic; [X_fista] = FISTA_REC(params); toc;
-figure; imshow(X_fista , [0, 0.45]); title ('FISTA-LS reconstruction');
-%%
-fprintf('%s\n', 'Reconstruction using FISTA-LS-TV...');
-clear params
-params.proj_geom = proj_geom; % pass geometry to the function
-params.vol_geom = vol_geom;
-params.sino = sino_basalt;
-params.iterFISTA  = 60;
-params.Regul_LambdaTV = 0.0003; % TV regularization parameter
-params.show = 0;
-params.maxvalplot = 0.6; params.slice = 1;
-
-tic; [X_fista_TV] = FISTA_REC(params); toc;
-figure; imshow(X_fista_TV , [0, 0.45]); title ('FISTA-LS-TV reconstruction');
-%%
-%%
-fprintf('%s\n', 'Reconstruction using FISTA-GH-TV...');
-clear params
-params.proj_geom = proj_geom; % pass geometry to the function
-params.vol_geom = vol_geom;
-params.sino = sino_basalt;
-params.iterFISTA  = 60;
-params.Regul_LambdaTV = 0.0003; % TV regularization parameter
-params.Ring_LambdaR_L1 = 0.001; % Soft-Thresh L1 ring variable parameter
-params.Ring_Alpha = 20; % acceleration for ring variable
-params.show = 0;
-params.maxvalplot = 0.6; params.slice = 1;
-
-tic; [X_fista_GH_TV] = FISTA_REC(params); toc;
-figure; imshow(X_fista_GH_TV , [0, 0.45]); title ('FISTA-GH-TV reconstruction');
-%%
-%%
-fprintf('%s\n', 'Reconstruction using FISTA-Student-TV...');
-clear params
-params.proj_geom = proj_geom; % pass geometry to the function
-params.vol_geom = vol_geom;
-params.sino = sino_basalt;
-params.iterFISTA  = 50;
-params.L_const = 3500; % Lipshitz constant
-params.Regul_LambdaTV = 0.0003; % TV regularization parameter
-params.fidelity = 'student'; % choosing Student t penalty
-params.show = 1;
-params.initialize = 1; % warm start with SIRT
-params.maxvalplot = 0.6; params.slice = 1;
-
-tic; [X_fistaStudentTV] = FISTA_REC(params); toc;
-figure; imshow(X_fistaStudentTV , [0, 0.45]); title ('FISTA-Student-TV reconstruction');
-%%
-
-fprintf('%s\n', 'Segmentation using OTSU method ...');
-level = graythresh(X_fista);
-Segm_FISTA = im2bw(X_fista,level);
-figure; imshow(Segm_FISTA, []); title ('Segmented FISTA-LS reconstruction');
-
-level = graythresh(X_fista_TV);
-Segm_FISTA_TV = im2bw(X_fista_TV,level);
-figure; imshow(Segm_FISTA_TV, []); title ('Segmented FISTA-LS-TV reconstruction');
-
-level = graythresh(X_fista_GH_TV);
-BW_FISTA_GH_TV = im2bw(X_fista_GH_TV,level);
-figure; imshow(BW_FISTA_GH_TV, []); title ('Segmented FISTA-GH-TV reconstruction');
-
-level = graythresh(X_fistaStudentTV);
-BW_FISTA_Student_TV = im2bw(X_fistaStudentTV,level);
-figure; imshow(BW_FISTA_Student_TV, []); title ('Segmented FISTA-Student-LS reconstruction');
\ No newline at end of file
diff --git a/demos/Demo_Phantom3D_Cone.m b/demos/Demo_Phantom3D_Cone.m
index 3a9178b..a8f2c92 100644
--- a/demos/Demo_Phantom3D_Cone.m
+++ b/demos/Demo_Phantom3D_Cone.m
@@ -8,7 +8,6 @@ addpath('../data/');
 addpath('../main_func/'); addpath('../main_func/regularizers_CPU/'); addpath('../main_func/regularizers_GPU/NL_Regul/'); addpath('../main_func/regularizers_GPU/Diffus_HO/');
 addpath('../supp/');
 
-
 %%
 % build 3D phantom using TomoPhantom 
 modelNo = 3; % see Phantom3DLibrary.dat file in TomoPhantom
@@ -16,9 +15,11 @@ N = 256; % x-y-z size (cubic image)
 angles = 0:1.5:360; % angles vector in degrees
 angles_rad = angles*(pi/180); % conversion to radians
 det_size = round(sqrt(2)*N); % detector size
-% in order to run functions you have to go to the directory:
+
+%---------TomoPhantom routines---------%
 pathTP = '/home/algol/Documents/MATLAB/TomoPhantom/functions/models/Phantom3DLibrary.dat'; % path to TomoPhantom parameters file
 TomoPhantom = buildPhantom3D(modelNo,N,pathTP); % generate 3D phantom
+%--------------------------------------%
 %%
 % using ASTRA-toolbox to set the projection geometry (cone beam)
 % eg: astra.create_proj_geom('cone', 1.0 (resol), 1.0 (resol), detectorRowCount, detectorColCount, angles, originToSource, originToDetector)
diff --git a/demos/Demo_Phantom3D_Parallel.m b/demos/Demo_Phantom3D_Parallel.m
index 6a54450..402bdd2 100644
--- a/demos/Demo_Phantom3D_Parallel.m
+++ b/demos/Demo_Phantom3D_Parallel.m
@@ -9,48 +9,74 @@ addpath('../main_func/'); addpath('../main_func/regularizers_CPU/'); addpath('..
 addpath('../supp/');

 

 %%

-% build 3D phantom using TomoPhantom and generate projection data

+% Main reconstruction/data generation parameters 

 modelNo = 2; % see Phantom3DLibrary.dat file in TomoPhantom

 N = 256; % x-y-z size (cubic image)

 angles = 1:0.5:180; % angles vector in degrees

 angles_rad = angles*(pi/180); % conversion to radians

 det_size = round(sqrt(2)*N); % detector size

-% in order to run functions you have to go to the directory:

+

+%---------TomoPhantom routines---------%

 pathTP = '/home/algol/Documents/MATLAB/TomoPhantom/functions/models/Phantom3DLibrary.dat'; % path to TomoPhantom parameters file

 TomoPhantom = buildPhantom3D(modelNo,N,pathTP); % generate 3D phantom

 sino_tomophan3D = buildSino3D(modelNo, N, det_size, single(angles),pathTP); % generate ideal data

+%--------------------------------------%

 % Adding noise and distortions if required

-sino_artifacts = sino_add_artifacts(sino_tomophan3D,'rings');

+sino_tomophan3D = sino_add_artifacts(sino_tomophan3D,'rings');

+% adding Poisson noise

+dose =  3e9; % photon flux (controls noise level)

+multifactor = max(sino_tomophan3D(:));

+dataExp = dose.*exp(-sino_tomophan3D/multifactor); % noiseless raw data

+dataRaw = astra_add_noise_to_sino(dataExp, dose); % pre-log noisy raw data (weights)

+sino3D_log = log(dose./max(dataRaw,1))*multifactor; %log corrected data -> sinogram

+clear dataExp sino_tomophan3D

 %

 %%

+%-------------Astra toolbox------------%

+% one can generate data using ASTRA toolbox

+proj_geom = astra_create_proj_geom('parallel', 1, det_size, angles_rad);

+vol_geom = astra_create_vol_geom(N,N);

+sino_ASTRA3D = zeros(det_size, length(angles), N, 'single');

+for i = 1:N

+[sino_id, sinoT] = astra_create_sino_cuda(TomoPhantom(:,:,i), proj_geom, vol_geom);

+sino_ASTRA3D(:,:,i) = sinoT';

+astra_mex_data2d('delete', sino_id);

+end

+%--------------------------------------%

+%%

 % using ASTRA-toolbox to set the projection geometry (parallel beam)

 proj_geom = astra_create_proj_geom('parallel', 1, det_size, angles_rad);

 vol_geom = astra_create_vol_geom(N,N);

 %%

 fprintf('%s\n', 'Reconstructing with FBP using ASTRA-toolbox ...');

-for i = 1:k

+reconASTRA_3D = zeros(size(TomoPhantom),'single');

+for k = 1:N

 vol_id = astra_mex_data2d('create', '-vol', vol_geom, 0);

-proj_id = astra_mex_data2d('create', '-proj3d', proj_geom, sino_artifacts(:,:,k)); 

+proj_id = astra_mex_data2d('create', '-sino', proj_geom, sino3D_log(:,:,k)'); 

 cfg = astra_struct('FBP_CUDA');

 cfg.ProjectionDataId = proj_id;

 cfg.ReconstructionDataId = vol_id;

 cfg.option.MinConstraint = 0;

 alg_id = astra_mex_algorithm('create', cfg);

-astra_mex_algorithm('iterate', alg_id, 15);

-reconASTRA_3D = astra_mex_data2d('get', vol_id);

+astra_mex_algorithm('iterate', alg_id, 1);

+rec = astra_mex_data2d('get', vol_id);

+reconASTRA_3D(:,:,k) = single(rec);

 end

+figure; imshow(reconASTRA_3D(:,:,128), [0 1.3]);

 %% 

-fprintf('%s\n', 'Reconstruction using FISTA-LS without regularization...');

+%% 

+fprintf('%s\n', 'Reconstruction using OS-FISTA-PWLS without regularization...');

 clear params

 % define parameters

 params.proj_geom = proj_geom; % pass geometry to the function

 params.vol_geom = vol_geom;

-params.sino = single(sino_tomophan3D); % sinogram

-params.iterFISTA = 5; %max number of outer iterations

+params.sino = single(sino3D_log); % sinogram

+params.iterFISTA = 12; %max number of outer iterations

 params.X_ideal = TomoPhantom; % ideal phantom

+params.weights = dataRaw./max(dataRaw(:)); % statistical weight for PWLS

+params.subsets = 12; % the number of subsets

 params.show = 1; % visualize reconstruction on each iteration

-params.subsets = 12; 

-params.slice = round(N/2); params.maxvalplot = 1; 

+params.slice = 1; params.maxvalplot = 1.3; 

 tic; [X_FISTA, output] = FISTA_REC(params); toc; 

 

 error_FISTA = output.Resid_error; obj_FISTA = output.objective;

@@ -63,4 +89,33 @@ subplot(1,2,2); imshow(Resid3D(:,:,params.slice),[0 0.1]);  title('residual'); c
 figure(3);

 subplot(1,2,1); plot(error_FISTA);  title('RMSE plot'); 

 subplot(1,2,2); plot(obj_FISTA);  title('Objective plot'); 

+%%

+%% 

+fprintf('%s\n', 'Reconstruction using OS-FISTA-GH without FGP-TV regularization...');

+clear params

+% define parameters

+params.proj_geom = proj_geom; % pass geometry to the function

+params.vol_geom = vol_geom;

+params.sino = single(sino3D_log); % sinogram

+params.iterFISTA = 15; %max number of outer iterations

+params.X_ideal = TomoPhantom; % ideal phantom

+params.weights = dataRaw./max(dataRaw(:)); % statistical weight for PWLS

+params.subsets = 8; % the number of subsets

+params.Regul_Lambda_FGPTV = 0.003; % TV regularization parameter for FGP-TV

+params.Ring_LambdaR_L1 = 0.02; % Soft-Thresh L1 ring variable parameter

+params.Ring_Alpha = 21; % to boost ring removal procedure

+params.show = 1; % visualize reconstruction on each iteration

+params.slice = 1; params.maxvalplot = 1.3; 

+tic; [X_FISTA_GH_TV, output] = FISTA_REC(params); toc; 

+

+error_FISTA_GH_TV = output.Resid_error; obj_FISTA_GH_TV = output.objective;

+fprintf('%s %.4f\n', 'Min RMSE for FISTA-PWLS reconstruction is:', min(error_FISTA_GH_TV(:)));

+

+Resid3D = (TomoPhantom - X_FISTA_GH_TV).^2;

+figure(2);

+subplot(1,2,1); imshow(X_FISTA_GH_TV(:,:,params.slice),[0 params.maxvalplot]); title('FISTA-LS reconstruction'); colorbar;

+subplot(1,2,2); imshow(Resid3D(:,:,params.slice),[0 0.1]);  title('residual'); colorbar;

+figure(3);

+subplot(1,2,1); plot(error_FISTA_GH_TV);  title('RMSE plot'); 

+subplot(1,2,2); plot(obj_FISTA_GH_TV);  title('Objective plot'); 

 %%
\ No newline at end of file
diff --git a/demos/DemoRD2.m b/demos/Demo_RealData3D_Parallel.m
index 717a55d..e4c9eb0 100644
--- a/demos/DemoRD2.m
+++ b/demos/Demo_RealData3D_Parallel.m
@@ -11,12 +11,12 @@ addpath('../supp/');
 
 load('DendrRawData.mat') % load raw data of 3D dendritic set
 angles_rad = angles*(pi/180); % conversion to radians
-size_det = size(data_raw3D,1); % detectors dim
+det_size = size(data_raw3D,1); % detectors dim
 angSize = size(data_raw3D, 2); % angles dim
 slices_tot = size(data_raw3D, 3); % no of slices
 recon_size = 950; % reconstruction size
 
-Sino3D = zeros(size_det, angSize, slices_tot, 'single'); % log-corrected sino
+Sino3D = zeros(det_size, angSize, slices_tot, 'single'); % log-corrected sino
 % normalizing the data
 for  jj = 1:slices_tot
     sino = data_raw3D(:,:,jj);
@@ -30,10 +30,8 @@ Weights3D = single(data_raw3D); % weights for PW model
 clear data_raw3D
 %%
 % set projection/reconstruction geometry here
-Z_slices = 5;
-det_row_count = Z_slices;
-proj_geom = astra_create_proj_geom('parallel3d', 1, 1, det_row_count, size_det, angles_rad);
-vol_geom = astra_create_vol_geom(recon_size,recon_size,Z_slices);
+proj_geom = astra_create_proj_geom('parallel', 1, det_size, angles_rad);
+vol_geom = astra_create_vol_geom(recon_size,recon_size);
 %%
 fprintf('%s\n', 'Reconstruction using FBP...');
 FBP = iradon(Sino3D(:,:,10), angles,recon_size);
@@ -50,7 +48,7 @@ params.iterFISTA  = 12;
 params.weights = Weights3D;
 params.subsets = 16; % the number of ordered subsets 
 params.show = 1;
-params.maxvalplot = 2.5; params.slice = 2;
+params.maxvalplot = 2.5; params.slice = 1;
 
 tic; [X_fista, outputFISTA] = FISTA_REC(params); toc;
 figure; imshow(X_fista(:,:,params.slice) , [0, 2.5]); title ('FISTA-OS-PWLS reconstruction');
@@ -76,13 +74,13 @@ params.proj_geom = proj_geom; % pass geometry to the function
 params.vol_geom = vol_geom;
 params.sino = Sino3D;
 params.iterFISTA  = 12;
-params.Regul_Lambda_FGPTV = 0.005; % TV regularization parameter for FGP-TV
+% params.Regul_Lambda_FGPTV = 0.005; % TV regularization parameter for FGP-TV
 params.Ring_LambdaR_L1 = 0.002; % Soft-Thresh L1 ring variable parameter
 params.Ring_Alpha = 21; % to boost ring removal procedure
 params.weights = Weights3D;
 params.subsets = 16; % the number of ordered subsets 
 params.show = 1;
-params.maxvalplot = 2.5; params.slice = 2;
+params.maxvalplot = 2.5; params.slice = 1;
 
 tic; [X_fista_GH_TV, outputGHTV] = FISTA_REC(params); toc;
 figure; imshow(X_fista_GH_TV(:,:,params.slice) , [0, 2.5]); title ('FISTA-OS-GH-TV reconstruction');
diff --git a/main_func/FISTA_REC.m b/main_func/FISTA_REC.m
index 1e4228d..3d22b97 100644
--- a/main_func/FISTA_REC.m
+++ b/main_func/FISTA_REC.m
@@ -685,6 +685,10 @@ else
             counterInd = counterInd + numProjSub;
         end
         
+        if (i == 1)
+            r_old = r;
+        end
+        
         % working with a 'ring vector'
         if (lambdaR_L1 > 0)
             r =  max(abs(r)-lambdaR_L1, 0).*sign(r); % soft-thresholding operator for ring vector
diff --git a/main_func/regularizers_CPU/FGP_TV.c b/main_func/regularizers_CPU/FGP_TV.c
index 66442c9..30cea1a 100644
--- a/main_func/regularizers_CPU/FGP_TV.c
+++ b/main_func/regularizers_CPU/FGP_TV.c
@@ -66,7 +66,7 @@ void mexFunction(
     A  = (float *) mxGetData(prhs[0]); /*noisy image (2D/3D) */
     lambda =  (float) mxGetScalar(prhs[1]); /* regularization parameter */
     iter = 50; /* default iterations number */
-    epsil = 0.001; /* default tolerance constant */
+    epsil = 0.0001; /* default tolerance constant */
     methTV = 0;  /* default isotropic TV penalty */
     
     if ((nrhs == 3) || (nrhs == 4) || (nrhs == 5))  iter = (int) mxGetScalar(prhs[2]); /* iterations number */
@@ -89,7 +89,7 @@ void mexFunction(
     
     tk = 1.0f;
     tkp1=1.0f;
-    count = 1;
+    count = 0;
     re_old = 0.0f;
     
     if (number_of_dims == 2) {
@@ -128,7 +128,7 @@ void mexFunction(
             }
             re = sqrt(re)/sqrt(re1);
             if (re < epsil)  count++;
-            if (count > 3) {
+            if (count > 4) {
                 Obj_func_CALC2D(A, D, funcvalA, lambda, dimX, dimY); 
                 break; }
             
diff --git a/src/Python/CMakeLists.txt b/src/Python/CMakeLists.txt
index b464059..66630cb 100644
--- a/src/Python/CMakeLists.txt
+++ b/src/Python/CMakeLists.txt
@@ -12,21 +12,15 @@
 #   See the License for the specific language governing permissions and
 #   limitations under the License.
 
-message("CIL VERSION " ${CIL_VERSION})
-
-
 # variables that must be set for conda compilation
 
 #PREFIX=C:\Apps\Miniconda2\envs\cil\Library
 #LIBRARY_INC=C:\\Apps\\Miniconda2\\envs\\cil\\Library\\include
 set (NUMPY_VERSION 1.12)
-#set (PYTHON_VERSION 3.5)
-
-#https://groups.google.com/a/continuum.io/forum/#!topic/anaconda/R9gWjl09UFs
-#set (CONDA_ENVIRONMENT "cil")
 
 ## Tries to parse the output of conda env list to determine the current
 ## active conda environment
+message ("Trying to determine your active conda environment...")
 execute_process(COMMAND "conda" "env" "list"
 					OUTPUT_VARIABLE _CONDA_ENVS
 					RESULT_VARIABLE _CONDA_RESULT
@@ -44,7 +38,7 @@ execute_process(COMMAND "conda" "env" "list"
 				  endif()
 				endforeach()
 			else()
-				message("conda result false" ${_CONDA_ERR})
+				message(FATAL_ERROR "ERROR with conda command " ${_CONDA_ERR})
 			endif()
 
 if (${CONDA_ENVIRONMENT} AND ${CONDA_ENVIRONMENT_PATH})
@@ -55,24 +49,28 @@ else()
   message("Using current conda environmnet path " ${CONDA_ENVIRONMENT_PATH})
 endif()
 
-
-
 message("CIL VERSION " ${CIL_VERSION})
 
 # set the Python variables for the Conda environment
 include(FindAnacondaEnvironment.cmake)
 findPythonForAnacondaEnvironment(${CONDA_ENVIRONMENT_PATH})
+
 message("Python found " ${PYTHON_VERSION_STRING})
 message("Python found Major " ${PYTHON_VERSION_MAJOR})
 message("Python found Minor " ${PYTHON_VERSION_MINOR})
+
 findPythonPackagesPath()
 message("PYTHON_PACKAGES_FOUND " ${PYTHON_PACKAGES_PATH})
 
-# copy the Pyhon files of the package
-file(MAKE_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/ccpi/imaging/)
-file(COPY ${CMAKE_CURRENT_SOURCE_DIR}/ccpi/__init__.py DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/ccpi)
-file(COPY ${CMAKE_CURRENT_SOURCE_DIR}/ccpi/imaging/__init__.py DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/ccpi/imaging)
-file(COPY ${CMAKE_CURRENT_SOURCE_DIR}/ccpi/imaging/Regularizer.py DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/ccpi/imaging)
+## CACHE SOME VARIABLES ##
+set (CONDA_ENVIRONMENT ${CONDA_ENVIRONMENT} CACHE INTERNAL "active conda environment" FORCE)
+set (CONDA_ENVIRONMENT_PATH ${CONDA_ENVIRONMENT_PATH} CACHE INTERNAL "active conda environment" FORCE)
+
+set (PYTHON_VERSION_STRING ${PYTHON_VERSION_STRING} CACHE INTERNAL "conda environment Python version string" FORCE)
+set (PYTHON_VERSION_MAJOR ${PYTHON_VERSION_MAJOR} CACHE INTERNAL "conda environment Python version major" FORCE)
+set (PYTHON_VERSION_MINOR ${PYTHON_VERSION_MINOR} CACHE INTERNAL "conda environment Python version minor" FORCE)
+set (PYTHON_VERSION_PATCH ${PYTHON_VERSION_PATCH} CACHE INTERNAL "conda environment Python version patch" FORCE)
+set (PYTHON_PACKAGES_PATH ${PYTHON_PACKAGES_PATH} CACHE INTERNAL "conda environment Python packages path" FORCE)
 
 if (WIN32)
   #set (CONDA_ENVIRONMENT_PATH "C:\\Apps\\Miniconda2\\envs\\${CONDA_ENVIRONMENT}" CACHE PATH "Main environment directory")
@@ -84,21 +82,92 @@ elseif (UNIX)
   set (CONDA_ENVIRONMENT_LIBRARY_INC "${CONDA_ENVIRONMENT_PREFIX}/include" CACHE PATH "env dir")
 endif()
 
+######### CONFIGURE REGULARIZER PACKAGE #############
+
+# copy the Pyhon files of the package regularizer
+file(MAKE_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/ccpi/imaging/)
+file(COPY ${CMAKE_CURRENT_SOURCE_DIR}/ccpi/__init__.py DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/ccpi)
+# regularizers
+file(COPY ${CMAKE_CURRENT_SOURCE_DIR}/ccpi/imaging/__init__.py DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/ccpi/imaging)
+file(COPY ${CMAKE_CURRENT_SOURCE_DIR}/ccpi/imaging/Regularizer.py DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/ccpi/imaging)
+
 # Copy and configure the relative conda build and recipes
 configure_file(${CMAKE_CURRENT_SOURCE_DIR}/setup.py.in ${CMAKE_CURRENT_BINARY_DIR}/setup.py)
 file(MAKE_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/conda-recipe)
 file(COPY ${CMAKE_CURRENT_SOURCE_DIR}/conda-recipe/meta.yaml DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/conda-recipe)
 
 if (WIN32)
-	file(COPY ${CMAKE_CURRENT_SOURCE_DIR}/conda-recipe/bld.bat DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/conda-recipe/)
-	configure_file(${CMAKE_CURRENT_SOURCE_DIR}/compile.bat.in ${CMAKE_CURRENT_BINARY_DIR}/compile.bat)
+	
+  file(COPY ${CMAKE_CURRENT_SOURCE_DIR}/conda-recipe/bld.bat DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/conda-recipe/) 
+  configure_file(${CMAKE_CURRENT_SOURCE_DIR}/compile.bat.in ${CMAKE_CURRENT_BINARY_DIR}/compile.bat)
+
+elseif(UNIX)
+  
+  message ("We are on UNIX")
+  file(COPY ${CMAKE_CURRENT_SOURCE_DIR}/conda-recipe/build.sh DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/conda-recipe/)
+  # assumes we will use bash
+  configure_file(${CMAKE_CURRENT_SOURCE_DIR}/compile.sh.in ${CMAKE_CURRENT_BINARY_DIR}/compile.sh)
+
+endif()
+
+########## CONFIGURE FISTA RECONSTRUCTOR PACKAGE
+# fista reconstructor
+file(COPY ${CMAKE_CURRENT_SOURCE_DIR}/ccpi/reconstruction/FISTAReconstructor.py DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/ccpi/reconstruction)
+file(COPY ${CMAKE_CURRENT_SOURCE_DIR}/ccpi/reconstruction/__init__.py DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/ccpi/reconstruction)
+
+configure_file(${CMAKE_CURRENT_SOURCE_DIR}/setup-fista.py.in ${CMAKE_CURRENT_BINARY_DIR}/setup-fista.py)
+file(MAKE_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/fista-recipe)
+file(COPY ${CMAKE_CURRENT_SOURCE_DIR}/fista-recipe/meta.yaml DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/fista-recipe)
+
+if (WIN32)
+
+  file(COPY ${CMAKE_CURRENT_SOURCE_DIR}/fista-recipe/bld.bat DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/fista-recipe/)
+  configure_file(${CMAKE_CURRENT_SOURCE_DIR}/compile-fista.bat.in ${CMAKE_CURRENT_BINARY_DIR}/compile-fista.bat)
+
 elseif(UNIX)
-        message ("We are on UNIX")
-	file(COPY ${CMAKE_CURRENT_SOURCE_DIR}/conda-recipe/build.sh DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/conda-recipe/)
-    # assumes we will use bash
-	configure_file(${CMAKE_CURRENT_SOURCE_DIR}/compile.sh.in ${CMAKE_CURRENT_BINARY_DIR}/compile.sh)
+  message ("We are on UNIX")
+  file(COPY ${CMAKE_CURRENT_SOURCE_DIR}/fista-recipe/build.sh DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/fista-recipe/)
+  # assumes we will use bash
+  configure_file(${CMAKE_CURRENT_SOURCE_DIR}/compile-fista.sh.in ${CMAKE_CURRENT_BINARY_DIR}/compile-fista.sh)
 endif()
 
+#############################  TARGETS
+
+##########################  REGULARIZER PACKAGE ###############################
+
+# runs cmake on the build tree to update the code from source
+add_custom_target(update_code 
+		COMMAND ${CMAKE_COMMAND} 
+		ARGS ${CMAKE_SOURCE_DIR}
+		WORKING_DIRECTORY ${CMAKE_BINARY_DIR}
+		) 
+
+
+add_custom_target(fista
+		COMMAND bash
+		compile-fista.sh
+		WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}
+		DEPENDS ${update_code}
+		)
+
+add_custom_target(regularizers
+		COMMAND bash
+		compile.sh
+		WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}
+		DEPENDS ${update_code}
+		)
+
+add_custom_target(install-fista
+		COMMAND conda
+		install --force --use-local ccpi-fista=${CIL_VERSION} -c ccpi -c conda-forge
+		WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}
+		DEPENDS ${fista})
+
+add_custom_target(install-regularizers
+		COMMAND conda
+		install --force --use-local ccpi-regularizers=${CIL_VERSION} -c ccpi -c conda-forge
+		WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}
+		DEPENDS ${fista})
 ### add tests
 
 #add_executable(RegularizersTest )
diff --git a/src/Python/ccpi/fista/FISTAReconstructor.py b/src/Python/ccpi/fista/FISTAReconstructor.py
deleted file mode 100644
index 85bfac5..0000000
--- a/src/Python/ccpi/fista/FISTAReconstructor.py
+++ /dev/null
@@ -1,609 +0,0 @@
-# -*- 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 2017 Edoardo Pasca, Srikanth Nagella
-#Copyright 2017 Daniil Kazantsev
-#
-#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.
-###############################################################################
-
-
-
-import numpy
-#from ccpi.reconstruction.parallelbeam import alg
-
-#from ccpi.imaging.Regularizer import Regularizer
-from enum import Enum
-
-import astra
-
-   
-    
-class FISTAReconstructor():
-    '''FISTA-based reconstruction algorithm using ASTRA-toolbox
-    
-    '''
-    # <<<< FISTA-based reconstruction algorithm using ASTRA-toolbox >>>>
-    # ___Input___:
-    # params.[] file:
-    #       - .proj_geom (geometry of the projector) [required]
-    #       - .vol_geom (geometry of the reconstructed object) [required]
-    #       - .sino (vectorized in 2D or 3D sinogram) [required]
-    #       - .iterFISTA (iterations for the main loop, default 40)
-    #       - .L_const (Lipschitz constant, default Power method)                                                                                                    )
-    #       - .X_ideal (ideal image, if given)
-    #       - .weights (statisitcal weights, size of the sinogram)
-    #       - .ROI (Region-of-interest, only if X_ideal is given)
-    #       - .initialize (a 'warm start' using SIRT method from ASTRA)
-    #----------------Regularization choices------------------------
-    #       - .Regul_Lambda_FGPTV (FGP-TV regularization parameter)
-    #       - .Regul_Lambda_SBTV (SplitBregman-TV regularization parameter)
-    #       - .Regul_Lambda_TVLLT (Higher order SB-LLT regularization parameter)
-    #       - .Regul_tol (tolerance to terminate regul iterations, default 1.0e-04)
-    #       - .Regul_Iterations (iterations for the selected penalty, default 25)
-    #       - .Regul_tauLLT (time step parameter for LLT term)
-    #       - .Ring_LambdaR_L1 (regularization parameter for L1-ring minimization, if lambdaR_L1 > 0 then switch on ring removal)
-    #       - .Ring_Alpha (larger values can accelerate convergence but check stability, default 1)
-    #----------------Visualization parameters------------------------
-    #       - .show (visualize reconstruction 1/0, (0 default))
-    #       - .maxvalplot (maximum value to use for imshow[0 maxvalplot])
-    #       - .slice (for 3D volumes - slice number to imshow)
-    # ___Output___:
-    # 1. X - reconstructed image/volume
-    # 2. output - a structure with
-    #    - .Resid_error - residual error (if X_ideal is given)
-    #    - .objective: value of the objective function
-    #    - .L_const: Lipshitz constant to avoid recalculations
-    
-    # References:
-    # 1. "A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse
-    # Problems" by A. Beck and M Teboulle
-    # 2. "Ring artifacts correction in compressed sensing..." by P. Paleo
-    # 3. "A novel tomographic reconstruction method based on the robust
-    # Student's t function for suppressing data outliers" D. Kazantsev et.al.
-    # D. Kazantsev, 2016-17
-    def __init__(self, projector_geometry, output_geometry, input_sinogram,
-                 **kwargs):
-        # handle parmeters:
-        # obligatory parameters
-        self.pars = dict()
-        self.pars['projector_geometry'] = projector_geometry # proj_geom
-        self.pars['output_geometry'] = output_geometry       # vol_geom
-        self.pars['input_sinogram'] = input_sinogram         # sino
-        sliceZ, nangles, detectors = numpy.shape(input_sinogram)
-        self.pars['detectors'] = detectors
-        self.pars['number_of_angles'] = nangles
-        self.pars['SlicesZ'] = sliceZ
-        self.pars['output_volume'] = None
-
-        print (self.pars)
-        # handle optional input parameters (at instantiation)
-        
-        # Accepted input keywords
-        kw = (
-              # mandatory fields
-              'projector_geometry',
-              'output_geometry',
-              'input_sinogram',
-              'detectors',
-              'number_of_angles',
-              'SlicesZ',
-              # optional fields
-              'number_of_iterations', 
-              'Lipschitz_constant' , 
-              'ideal_image' ,
-              'weights' , 
-              'region_of_interest' , 
-              'initialize' , 
-              'regularizer' , 
-              'ring_lambda_R_L1',
-              'ring_alpha',
-              'subsets',
-              'output_volume',
-              'os_subsets',
-              'os_indices',
-              'os_bins')
-        self.acceptedInputKeywords = list(kw)
-        
-        # handle keyworded parameters
-        if kwargs is not None:
-            for key, value in kwargs.items():
-                if key in kw:
-                    #print("{0} = {1}".format(key, value))                        
-                    self.pars[key] = value
-                    
-        # set the default values for the parameters if not set
-        if 'number_of_iterations' in kwargs.keys():
-            self.pars['number_of_iterations'] = kwargs['number_of_iterations']
-        else:
-            self.pars['number_of_iterations'] = 40
-        if 'weights' in kwargs.keys():
-            self.pars['weights'] = kwargs['weights']
-        else:
-            self.pars['weights'] = \
-                                 numpy.ones(numpy.shape(
-                                     self.pars['input_sinogram']))
-        if 'Lipschitz_constant' in kwargs.keys():
-            self.pars['Lipschitz_constant'] = kwargs['Lipschitz_constant']
-        else:
-            self.pars['Lipschitz_constant'] = None
-        
-        if not 'ideal_image' in kwargs.keys():
-            self.pars['ideal_image'] = None
-        
-        if not 'region_of_interest'in kwargs.keys() :
-            if self.pars['ideal_image'] == None:
-                self.pars['region_of_interest'] = None
-            else:
-                ## nonzero if the image is larger than m
-                fsm = numpy.frompyfunc(lambda x,m: 1 if x>m else 0, 2,1)
-                
-                self.pars['region_of_interest'] = fsm(self.pars['ideal_image'], 0)
-                
-        # the regularizer must be a correctly instantiated object    
-        if not 'regularizer' in kwargs.keys() :
-            self.pars['regularizer'] = None
-
-        #RING REMOVAL
-        if not 'ring_lambda_R_L1' in kwargs.keys():
-            self.pars['ring_lambda_R_L1'] = 0
-        if not 'ring_alpha' in kwargs.keys():
-            self.pars['ring_alpha'] = 1
-
-        # ORDERED SUBSET
-        if not 'subsets' in kwargs.keys():
-            self.pars['subsets'] = 0
-        else:
-            self.createOrderedSubsets()
-
-        if not 'initialize' in kwargs.keys():
-            self.pars['initialize'] = False
-
-        
-            
-            
-    def setParameter(self, **kwargs):
-        '''set named parameter for the reconstructor engine
-        
-        raises Exception if the named parameter is not recognized
-        
-        '''
-        for key , value in kwargs.items():
-            if key in self.acceptedInputKeywords:
-                self.pars[key] = value
-            else:
-                raise Exception('Wrong parameter {0} for '.format(key) +
-                                'reconstructor')
-    # setParameter
-
-    def getParameter(self, key):
-        if type(key) is str:
-            if key in self.acceptedInputKeywords:
-                return self.pars[key]
-            else:
-                raise Exception('Unrecongnised parameter: {0} '.format(key) )
-        elif type(key) is list:
-            outpars = []
-            for k in key:
-                outpars.append(self.getParameter(k))
-            return outpars
-        else:
-            raise Exception('Unhandled input {0}' .format(str(type(key))))
-            
-    
-    def calculateLipschitzConstantWithPowerMethod(self):
-        ''' using Power method (PM) to establish L constant'''
-        
-        N = self.pars['output_geometry']['GridColCount']
-        proj_geom = self.pars['projector_geometry']
-        vol_geom = self.pars['output_geometry']
-        weights = self.pars['weights']
-        SlicesZ = self.pars['SlicesZ']
-        
-            
-                               
-        if (proj_geom['type'] == 'parallel') or \
-           (proj_geom['type'] == 'parallel3d'):
-            #% for parallel geometry we can do just one slice
-            #print('Calculating Lipshitz constant for parallel beam geometry...')
-            niter = 5;# % number of iteration for the PM
-            #N = params.vol_geom.GridColCount;
-            #x1 = rand(N,N,1);
-            x1 = numpy.random.rand(1,N,N)
-            #sqweight = sqrt(weights(:,:,1));
-            sqweight = numpy.sqrt(weights[0])
-            proj_geomT = proj_geom.copy();
-            proj_geomT['DetectorRowCount'] = 1;
-            vol_geomT = vol_geom.copy();
-            vol_geomT['GridSliceCount'] = 1;
-            
-            #[sino_id, y] = astra_create_sino3d_cuda(x1, proj_geomT, vol_geomT);
-            
-            
-            for i in range(niter):
-            #        [id,x1] = astra_create_backprojection3d_cuda(sqweight.*y, proj_geomT, vol_geomT);
-            #            s = norm(x1(:));
-            #            x1 = x1/s;
-            #            [sino_id, y] = astra_create_sino3d_cuda(x1, proj_geomT, vol_geomT);
-            #            y = sqweight.*y;
-            #            astra_mex_data3d('delete', sino_id);
-            #            astra_mex_data3d('delete', id);
-                #print ("iteration {0}".format(i))
-                                
-                sino_id, y = astra.creators.create_sino3d_gpu(x1,
-                                                          proj_geomT,
-                                                          vol_geomT)
-                
-                y = (sqweight * y).copy() # element wise multiplication
-                
-                #b=fig.add_subplot(2,1,2)
-                #imgplot = plt.imshow(x1[0])
-                #plt.show()
-                
-                #astra_mex_data3d('delete', sino_id);
-                astra.matlab.data3d('delete', sino_id)
-                del x1
-                    
-                idx,x1 = astra.creators.create_backprojection3d_gpu((sqweight*y).copy(), 
-                                                                    proj_geomT,
-                                                                    vol_geomT)
-                del y
-                
-                                                                    
-                s = numpy.linalg.norm(x1)
-                ### this line?
-                x1 = (x1/s).copy();
-                
-            #        ### this line?
-            #        sino_id, y = astra.creators.create_sino3d_gpu(x1, 
-            #                                                      proj_geomT, 
-            #                                                      vol_geomT);
-            #        y = sqweight * y;
-                astra.matlab.data3d('delete', sino_id);
-                astra.matlab.data3d('delete', idx)
-                print ("iteration {0} s= {1}".format(i,s))
-                
-            #end
-            del proj_geomT
-            del vol_geomT
-            #plt.show()
-        else:
-            #% divergen beam geometry
-            print('Calculating Lipshitz constant for divergen beam geometry...')
-            niter = 8; #% number of iteration for PM
-            x1 = numpy.random.rand(SlicesZ , N , N);
-            #sqweight = sqrt(weights);
-            sqweight = numpy.sqrt(weights[0])
-            
-            sino_id, y = astra.creators.create_sino3d_gpu(x1, proj_geom, vol_geom);
-            y = sqweight*y;
-            #astra_mex_data3d('delete', sino_id);
-            astra.matlab.data3d('delete', sino_id);
-            
-            for i in range(niter):
-                #[id,x1] = astra_create_backprojection3d_cuda(sqweight.*y, proj_geom, vol_geom);
-                idx,x1 = astra.creators.create_backprojection3d_gpu(sqweight*y, 
-                                                                    proj_geom, 
-                                                                    vol_geom)
-                s = numpy.linalg.norm(x1)
-                ### this line?
-                x1 = x1/s;
-                ### this line?
-                #[sino_id, y] = astra_create_sino3d_gpu(x1, proj_geom, vol_geom);
-                sino_id, y = astra.creators.create_sino3d_gpu(x1, 
-                                                              proj_geom, 
-                                                              vol_geom);
-                
-                y = sqweight*y;
-                #astra_mex_data3d('delete', sino_id);
-                #astra_mex_data3d('delete', id);
-                astra.matlab.data3d('delete', sino_id);
-                astra.matlab.data3d('delete', idx);
-            #end
-            #clear x1
-            del x1
-
-        
-        return s
-    
-    
-    def setRegularizer(self, regularizer):
-        if regularizer is not None:
-            self.pars['regularizer'] = regularizer
-        
-
-    def initialize(self):
-        # convenience variable storage
-        proj_geom = self.pars['projector_geometry']
-        vol_geom = self.pars['output_geometry']
-        sino = self.pars['input_sinogram']
-        
-        # a 'warm start' with SIRT method
-        # Create a data object for the reconstruction
-        rec_id = astra.matlab.data3d('create', '-vol',
-                                    vol_geom);
-        
-        #sinogram_id = astra_mex_data3d('create', '-proj3d', proj_geom, sino);
-        sinogram_id = astra.matlab.data3d('create', '-proj3d',
-                                          proj_geom,
-                                          sino)
-
-        sirt_config = astra.astra_dict('SIRT3D_CUDA')
-        sirt_config['ReconstructionDataId' ] = rec_id
-        sirt_config['ProjectionDataId'] = sinogram_id
-
-        sirt = astra.algorithm.create(sirt_config)
-        astra.algorithm.run(sirt, iterations=35)
-        X = astra.matlab.data3d('get', rec_id)
-
-        # clean up memory
-        astra.matlab.data3d('delete', rec_id)
-        astra.matlab.data3d('delete', sinogram_id)
-        astra.algorithm.delete(sirt)
-
-        
-
-        return X
-
-    def createOrderedSubsets(self, subsets=None):
-        if subsets is None:
-            try:
-                subsets = self.getParameter('subsets')
-            except Exception():
-                subsets = 0
-            #return subsets
-
-        angles = self.getParameter('projector_geometry')['ProjectionAngles'] 
-        
-        #binEdges = numpy.linspace(angles.min(),
-        #                          angles.max(),
-        #                          subsets + 1)
-        binsDiscr, binEdges = numpy.histogram(angles, bins=subsets)
-        # get rearranged subset indices
-        IndicesReorg = numpy.zeros((numpy.shape(angles)))
-        counterM = 0
-        for ii in range(binsDiscr.max()):
-            counter = 0
-            for jj in range(subsets):
-                curr_index = ii + jj  + counter
-                #print ("{0} {1} {2}".format(binsDiscr[jj] , ii, counterM))
-                if binsDiscr[jj] > ii:
-                    if (counterM < numpy.size(IndicesReorg)):
-                        IndicesReorg[counterM] = curr_index
-                    counterM = counterM + 1
-                    
-                counter = counter + binsDiscr[jj] - 1    
-                
-        # store the OS in parameters
-        self.setParameter(os_subsets=subsets,
-                          os_bins=binsDiscr,
-                          os_indices=IndicesReorg)
-            
-
-    def prepareForIteration(self):
-        print ("FISTA Reconstructor: prepare for iteration")
-        
-        self.residual_error = numpy.zeros((self.pars['number_of_iterations']))
-        self.objective = numpy.zeros((self.pars['number_of_iterations']))
-
-        #2D array (for 3D data) of sparse "ring" 
-        detectors, nangles, sliceZ  = numpy.shape(self.pars['input_sinogram'])
-        self.r = numpy.zeros((detectors, sliceZ), dtype=numpy.float)
-        # another ring variable
-        self.r_x = self.r.copy()
-
-        self.residual = numpy.zeros(numpy.shape(self.pars['input_sinogram']))
-        
-        if self.getParameter('Lipschitz_constant') is None:
-            self.pars['Lipschitz_constant'] = \
-                            self.calculateLipschitzConstantWithPowerMethod()
-        # errors vector (if the ground truth is given)
-        self.Resid_error = numpy.zeros((self.getParameter('number_of_iterations')));
-        # objective function values vector
-        self.objective = numpy.zeros((self.getParameter('number_of_iterations')));      
-        
-
-    # prepareForIteration
-
-    def iterate(self, Xin=None):
-        print ("FISTA Reconstructor: iterate")
-        
-        if Xin is None:    
-            if self.getParameter('initialize'):
-                X = self.initialize()
-            else:
-                N = vol_geom['GridColCount']
-                X = numpy.zeros((N,N,SlicesZ), dtype=numpy.float)
-        else:
-            # copy by reference
-            X = Xin
-        # store the output volume in the parameters
-        self.setParameter(output_volume=X)
-        X_t = X.copy()
-        # convenience variable storage
-        proj_geom , vol_geom, sino , \
-          SlicesZ  = self.getParameter([ 'projector_geometry' ,
-                                                'output_geometry',
-                                                'input_sinogram',
-                                                'SlicesZ' ])
-                   
-        t = 1
-        
-        for i in range(self.getParameter('number_of_iterations')):
-            X_old = X.copy()
-            t_old = t
-            r_old = self.r.copy()
-            if self.getParameter('projector_geometry')['type'] == 'parallel' or \
-               self.getParameter('projector_geometry')['type'] == 'fanflat' or \
-               self.getParameter('projector_geometry')['type'] == 'fanflat_vec':
-                # if the geometry is parallel use slice-by-slice
-                # projection-backprojection routine
-                #sino_updt = zeros(size(sino),'single');
-                proj_geomT = proj_geom.copy()
-                proj_geomT['DetectorRowCount'] = 1
-                vol_geomT = vol_geom.copy()
-                vol_geomT['GridSliceCount'] = 1;
-                self.sino_updt = numpy.zeros(numpy.shape(sino), dtype=numpy.float)
-                for kkk in range(SlicesZ):
-                    sino_id, self.sino_updt[kkk] = \
-                             astra.creators.create_sino3d_gpu(
-                                 X_t[kkk:kkk+1], proj_geomT, vol_geomT)
-                    astra.matlab.data3d('delete', sino_id)
-            else:
-                # for divergent 3D geometry (watch the GPU memory overflow in
-                # ASTRA versions < 1.8)
-                #[sino_id, sino_updt] = astra_create_sino3d_cuda(X_t, proj_geom, vol_geom);
-                sino_id, self.sino_updt = astra.creators.create_sino3d_gpu(
-                    X_t, proj_geom, vol_geom)
-
-
-            ## RING REMOVAL
-            self.ringRemoval(i)
-            ## Projection/Backprojection Routine
-            self.projectionBackprojection(X, X_t)
-            astra.matlab.data3d('delete', sino_id)
-            ## REGULARIZATION
-            X = self.regularize(X)
-            ## Update Loop
-            X , X_t, t = self.updateLoop(i, X, X_old, r_old, t, t_old)
-            self.setParameter(output_volume=X)
-        return X
-    ## iterate
-    
-    def ringRemoval(self, i):
-        print ("FISTA Reconstructor: ring removal")
-        residual = self.residual
-        lambdaR_L1 , alpha_ring , weights , L_const , sino= \
-                   self.getParameter(['ring_lambda_R_L1',
-                                      'ring_alpha' , 'weights',
-                                      'Lipschitz_constant',
-                                      'input_sinogram'])
-        r_x = self.r_x
-        sino_updt = self.sino_updt
-        
-        SlicesZ, anglesNumb, Detectors = \
-                    numpy.shape(self.getParameter('input_sinogram'))
-        if lambdaR_L1 > 0 :
-             for kkk in range(anglesNumb):
-                 
-                 residual[:,kkk,:] = (weights[:,kkk,:]).squeeze() * \
-                                       ((sino_updt[:,kkk,:]).squeeze() - \
-                                        (sino[:,kkk,:]).squeeze() -\
-                                        (alpha_ring * r_x)
-                                        )
-             vec = residual.sum(axis = 1)
-             #if SlicesZ > 1:
-             #    vec = vec[:,1,:].squeeze()
-             self.r = (r_x - (1./L_const) * vec).copy()
-             self.objective[i] = (0.5 * (residual ** 2).sum())
-
-    def projectionBackprojection(self, X, X_t):
-        print ("FISTA Reconstructor: projection-backprojection routine")
-        
-        # a few useful variables
-        SlicesZ, anglesNumb, Detectors = \
-                    numpy.shape(self.getParameter('input_sinogram'))
-        residual = self.residual
-        proj_geom , vol_geom , L_const = \
-                  self.getParameter(['projector_geometry' ,
-                                                  'output_geometry',
-                                                  'Lipschitz_constant'])
-        
-        
-        if self.getParameter('projector_geometry')['type'] == 'parallel' or \
-           self.getParameter('projector_geometry')['type'] == 'fanflat' or \
-           self.getParameter('projector_geometry')['type'] == 'fanflat_vec':
-            # if the geometry is parallel use slice-by-slice
-            # projection-backprojection routine
-            #sino_updt = zeros(size(sino),'single');
-            proj_geomT = proj_geom.copy()
-            proj_geomT['DetectorRowCount'] = 1
-            vol_geomT = vol_geom.copy()
-            vol_geomT['GridSliceCount'] = 1;
-            x_temp = numpy.zeros(numpy.shape(X),dtype=numpy.float32)
-            
-            for kkk in range(SlicesZ):
-                
-                x_id, x_temp[kkk] = \
-                         astra.creators.create_backprojection3d_gpu(
-                             residual[kkk:kkk+1],
-                             proj_geomT, vol_geomT)
-                astra.matlab.data3d('delete', x_id)
-        else:
-            x_id, x_temp = \
-                  astra.creators.create_backprojection3d_gpu(
-                      residual, proj_geom, vol_geom)            
-
-        X = X_t - (1/L_const) * x_temp
-        #astra.matlab.data3d('delete', sino_id)
-        astra.matlab.data3d('delete', x_id)
-
-    def regularize(self, X):
-        print ("FISTA Reconstructor: regularize")
-        
-        regularizer = self.getParameter('regularizer')
-        if regularizer is not None:
-            return regularizer(input=X)
-        else:
-            return X
-
-    def updateLoop(self, i, X, X_old, r_old, t, t_old):
-        print ("FISTA Reconstructor: update loop")
-        lambdaR_L1 = self.getParameter('ring_lambda_R_L1')
-        if lambdaR_L1 > 0:
-            self.r = numpy.max(
-                numpy.abs(self.r) - lambdaR_L1 , 0) * \
-                numpy.sign(self.r)
-        t = (1 + numpy.sqrt(1 + 4 * t**2))/2
-        X_t = X + (((t_old -1)/t) * (X - X_old))
-
-        if lambdaR_L1 > 0:
-            self.r_x = self.r + \
-                             (((t_old-1)/t) * (self.r - r_old))
-
-        if self.getParameter('region_of_interest') is None:
-            string = 'Iteration Number {0} | Objective {1} \n'
-            print (string.format( i, self.objective[i]))
-        else:
-            ROI , X_ideal = fistaRecon.getParameter('region_of_interest',
-                                                    'ideal_image')
-            
-            Resid_error[i] = RMSE(X*ROI, X_ideal*ROI)
-            string = 'Iteration Number {0} | RMS Error {1} | Objective {2} \n'
-            print (string.format(i,Resid_error[i], self.objective[i]))
-        return (X , X_t, t)
-
-    def os_iterate(self, Xin=None):
-        print ("FISTA Reconstructor: iterate")
-        
-        if Xin is None:    
-            if self.getParameter('initialize'):
-                X = self.initialize()
-            else:
-                N = vol_geom['GridColCount']
-                X = numpy.zeros((N,N,SlicesZ), dtype=numpy.float)
-        else:
-            # copy by reference
-            X = Xin
-        # store the output volume in the parameters
-        self.setParameter(output_volume=X)
-        X_t = X.copy()
-
-        # some useful constants
-        proj_geom , vol_geom, sino , \
-          SlicesZ, weights , alpha_ring ,
-          lambdaR_L1 , L_const = self.getParameter(
-            ['projector_geometry' , 'output_geometry',
-             'input_sinogram', 'SlicesZ' ,  'weights', 'ring_alpha' ,
-             'ring_lambda_R_L1', 'Lipschitz_constant'])
diff --git a/src/Python/ccpi/fista/Reconstructor.py b/src/Python/ccpi/fista/Reconstructor.py
deleted file mode 100644
index d29ac0d..0000000
--- a/src/Python/ccpi/fista/Reconstructor.py
+++ /dev/null
@@ -1,425 +0,0 @@
-# -*- 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 2017 Edoardo Pasca, Srikanth Nagella
-#Copyright 2017 Daniil Kazantsev
-#
-#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.
-###############################################################################
-
-
-
-import numpy
-import h5py
-from ccpi.reconstruction.parallelbeam import alg
-
-from Regularizer import Regularizer
-from enum import Enum
-
-import astra
-
-   
-    
-class FISTAReconstructor():
-    '''FISTA-based reconstruction algorithm using ASTRA-toolbox
-    
-    '''
-    # <<<< FISTA-based reconstruction algorithm using ASTRA-toolbox >>>>
-    # ___Input___:
-    # params.[] file:
-    #       - .proj_geom (geometry of the projector) [required]
-    #       - .vol_geom (geometry of the reconstructed object) [required]
-    #       - .sino (vectorized in 2D or 3D sinogram) [required]
-    #       - .iterFISTA (iterations for the main loop, default 40)
-    #       - .L_const (Lipschitz constant, default Power method)                                                                                                    )
-    #       - .X_ideal (ideal image, if given)
-    #       - .weights (statisitcal weights, size of the sinogram)
-    #       - .ROI (Region-of-interest, only if X_ideal is given)
-    #       - .initialize (a 'warm start' using SIRT method from ASTRA)
-    #----------------Regularization choices------------------------
-    #       - .Regul_Lambda_FGPTV (FGP-TV regularization parameter)
-    #       - .Regul_Lambda_SBTV (SplitBregman-TV regularization parameter)
-    #       - .Regul_Lambda_TVLLT (Higher order SB-LLT regularization parameter)
-    #       - .Regul_tol (tolerance to terminate regul iterations, default 1.0e-04)
-    #       - .Regul_Iterations (iterations for the selected penalty, default 25)
-    #       - .Regul_tauLLT (time step parameter for LLT term)
-    #       - .Ring_LambdaR_L1 (regularization parameter for L1-ring minimization, if lambdaR_L1 > 0 then switch on ring removal)
-    #       - .Ring_Alpha (larger values can accelerate convergence but check stability, default 1)
-    #----------------Visualization parameters------------------------
-    #       - .show (visualize reconstruction 1/0, (0 default))
-    #       - .maxvalplot (maximum value to use for imshow[0 maxvalplot])
-    #       - .slice (for 3D volumes - slice number to imshow)
-    # ___Output___:
-    # 1. X - reconstructed image/volume
-    # 2. output - a structure with
-    #    - .Resid_error - residual error (if X_ideal is given)
-    #    - .objective: value of the objective function
-    #    - .L_const: Lipshitz constant to avoid recalculations
-    
-    # References:
-    # 1. "A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse
-    # Problems" by A. Beck and M Teboulle
-    # 2. "Ring artifacts correction in compressed sensing..." by P. Paleo
-    # 3. "A novel tomographic reconstruction method based on the robust
-    # Student's t function for suppressing data outliers" D. Kazantsev et.al.
-    # D. Kazantsev, 2016-17
-    def __init__(self, projector_geometry, output_geometry, input_sinogram, **kwargs):
-        self.params = dict()
-        self.params['projector_geometry'] = projector_geometry
-        self.params['output_geometry'] = output_geometry
-        self.params['input_sinogram'] = input_sinogram
-        detectors, nangles, sliceZ = numpy.shape(input_sinogram)
-        self.params['detectors'] = detectors
-        self.params['number_og_angles'] = nangles
-        self.params['SlicesZ'] = sliceZ
-        
-        # Accepted input keywords
-        kw = ('number_of_iterations', 'Lipschitz_constant' , 'ideal_image' ,
-              'weights' , 'region_of_interest' , 'initialize' , 
-              'regularizer' , 
-              'ring_lambda_R_L1',
-              'ring_alpha')
-        
-        # handle keyworded parameters
-        if kwargs is not None:
-            for key, value in kwargs.items():
-                if key in kw:
-                    #print("{0} = {1}".format(key, value))                        
-                    self.pars[key] = value
-                    
-        # set the default values for the parameters if not set
-        if 'number_of_iterations' in kwargs.keys():
-            self.pars['number_of_iterations'] = kwargs['number_of_iterations']
-        else:
-            self.pars['number_of_iterations'] = 40
-        if 'weights' in kwargs.keys():
-            self.pars['weights'] = kwargs['weights']
-        else:
-            self.pars['weights'] = numpy.ones(numpy.shape(self.params['input_sinogram']))
-        if 'Lipschitz_constant' in kwargs.keys():
-            self.pars['Lipschitz_constant'] = kwargs['Lipschitz_constant']
-        else:
-            self.pars['Lipschitz_constant'] = self.calculateLipschitzConstantWithPowerMethod()
-        
-        if not self.pars['ideal_image'] in kwargs.keys():
-            self.pars['ideal_image'] = None
-        
-        if not self.pars['region_of_interest'] :
-            if self.pars['ideal_image'] == None:
-                pass
-            else:
-                self.pars['region_of_interest'] = numpy.nonzero(self.pars['ideal_image']>0.0)
-            
-        if not self.pars['regularizer'] :
-            self.pars['regularizer'] = None
-        else:
-            # the regularizer must be a correctly instantiated object
-            if not self.pars['ring_lambda_R_L1']:
-                self.pars['ring_lambda_R_L1'] = 0
-            if not self.pars['ring_alpha']:
-                self.pars['ring_alpha'] = 1
-        
-            
-            
-        
-    def calculateLipschitzConstantWithPowerMethod(self):
-        ''' using Power method (PM) to establish L constant'''
-        
-        #N = params.vol_geom.GridColCount
-        N = self.pars['output_geometry'].GridColCount
-        proj_geom = self.params['projector_geometry']
-        vol_geom = self.params['output_geometry']
-        weights = self.pars['weights']
-        SlicesZ = self.pars['SlicesZ']
-        
-        if (proj_geom['type'] == 'parallel') or (proj_geom['type'] == 'parallel3d'):
-            #% for parallel geometry we can do just one slice
-            #fprintf('%s \n', 'Calculating Lipshitz constant for parallel beam geometry...');
-            niter = 15;# % number of iteration for the PM
-            #N = params.vol_geom.GridColCount;
-            #x1 = rand(N,N,1);
-            x1 = numpy.random.rand(1,N,N)
-            #sqweight = sqrt(weights(:,:,1));
-            sqweight = numpy.sqrt(weights.T[0])
-            proj_geomT = proj_geom.copy();
-            proj_geomT.DetectorRowCount = 1;
-            vol_geomT = vol_geom.copy();
-            vol_geomT['GridSliceCount'] = 1;
-            
-            
-            for i in range(niter):
-                if i == 0:
-                    #[sino_id, y] = astra_create_sino3d_cuda(x1, proj_geomT, vol_geomT);
-                    sino_id, y = astra.creators.create_sino3d_gpu(x1, proj_geomT, vol_geomT);
-                    y = sqweight * y # element wise multiplication
-                    #astra_mex_data3d('delete', sino_id);
-                    astra.matlab.data3d('delete', sino_id)
-                    
-                idx,x1 = astra.creators.create_backprojection3d_gpu(sqweight*y, proj_geomT, vol_geomT);
-                s = numpy.linalg.norm(x1)
-                ### this line?
-                x1 = x1/s;
-                ### this line?
-                sino_id, y = astra_create_sino3d_cuda(x1, proj_geomT, vol_geomT);
-                y = sqweight*y;
-                astra.matlab.data3d('delete', sino_id);
-                astra.matlab.data3d('delete', idx);
-            #end
-            del proj_geomT
-            del vol_geomT
-        else
-            #% divergen beam geometry
-            #fprintf('%s \n', 'Calculating Lipshitz constant for divergen beam geometry...');
-            niter = 8; #% number of iteration for PM
-            x1 = numpy.random.rand(SlicesZ , N , N);
-            #sqweight = sqrt(weights);
-            sqweight = numpy.sqrt(weights.T[0])
-            
-            sino_id, y = astra.creators.create_sino3d_gpu(x1, proj_geom, vol_geom);
-            y = sqweight*y;
-            #astra_mex_data3d('delete', sino_id);
-            astra.matlab.data3d('delete', sino_id);
-            
-            for i in range(niter):
-                #[id,x1] = astra_create_backprojection3d_cuda(sqweight.*y, proj_geom, vol_geom);
-                idx,x1 = astra.creators.create_backprojection3d_gpu(sqweight*y, 
-                                                                    proj_geom, 
-                                                                    vol_geom)
-                s = numpy.linalg.norm(x1)
-                ### this line?
-                x1 = x1/s;
-                ### this line?
-                #[sino_id, y] = astra_create_sino3d_gpu(x1, proj_geom, vol_geom);
-                sino_id, y = astra.creators.create_sino3d_gpu(x1, 
-                                                              proj_geom, 
-                                                              vol_geom);
-                
-                y = sqweight*y;
-                #astra_mex_data3d('delete', sino_id);
-                #astra_mex_data3d('delete', id);
-                astra.matlab.data3d('delete', sino_id);
-                astra.matlab.data3d('delete', idx);
-            #end
-            #clear x1
-            del x1
-        
-        return s
-    
-    
-    def setRegularizer(self, regularizer):
-        if regularizer
-        self.pars['regularizer'] = regularizer
-        
-    
-    
-
-
-def getEntry(location):
-    for item in nx[location].keys():
-        print (item)
-
-
-print ("Loading Data")
-
-##fname = "D:\\Documents\\Dataset\\IMAT\\20170419_crabtomo\\crabtomo\\Sample\\IMAT00005153_crabstomo_Sample_000.tif"
-####ind = [i * 1049 for i in range(360)]
-#### use only 360 images
-##images = 200
-##ind = [int(i * 1049 / images) for i in range(images)]
-##stack_image = dxchange.reader.read_tiff_stack(fname, ind, digit=None, slc=None)
-
-#fname = "D:\\Documents\\Dataset\\CGLS\\24737_fd.nxs"
-fname = "C:\\Users\\ofn77899\\Documents\\CCPi\\CGLS\\24737_fd_2.nxs"
-nx = h5py.File(fname, "r")
-
-# the data are stored in a particular location in the hdf5
-for item in nx['entry1/tomo_entry/data'].keys():
-    print (item)
-
-data = nx.get('entry1/tomo_entry/data/rotation_angle')
-angles = numpy.zeros(data.shape)
-data.read_direct(angles)
-print (angles)
-# angles should be in degrees
-
-data = nx.get('entry1/tomo_entry/data/data')
-stack = numpy.zeros(data.shape)
-data.read_direct(stack)
-print (data.shape)
-
-print ("Data Loaded")
-
-
-# Normalize
-data = nx.get('entry1/tomo_entry/instrument/detector/image_key')
-itype = numpy.zeros(data.shape)
-data.read_direct(itype)
-# 2 is dark field
-darks = [stack[i] for i in range(len(itype)) if itype[i] == 2 ]
-dark = darks[0]
-for i in range(1, len(darks)):
-    dark += darks[i]
-dark = dark / len(darks)
-#dark[0][0] = dark[0][1]
-
-# 1 is flat field
-flats = [stack[i] for i in range(len(itype)) if itype[i] == 1 ]
-flat = flats[0]
-for i in range(1, len(flats)):
-    flat += flats[i]
-flat = flat / len(flats)
-#flat[0][0] = dark[0][1]
-
-
-# 0 is projection data
-proj = [stack[i] for i in range(len(itype)) if itype[i] == 0 ]
-angle_proj = [angles[i] for i in range(len(itype)) if itype[i] == 0 ]
-angle_proj = numpy.asarray (angle_proj)
-angle_proj = angle_proj.astype(numpy.float32)
-
-# normalized data are
-# norm = (projection - dark)/(flat-dark)
-
-def normalize(projection, dark, flat, def_val=0.1):
-    a = (projection - dark)
-    b = (flat-dark)
-    with numpy.errstate(divide='ignore', invalid='ignore'):
-        c = numpy.true_divide( a, b )
-        c[ ~ numpy.isfinite( c )] = def_val  # set to not zero if 0/0 
-    return c
-    
-
-norm = [normalize(projection, dark, flat) for projection in proj]
-norm = numpy.asarray (norm)
-norm = norm.astype(numpy.float32)
-
-#recon = Reconstructor(algorithm = Algorithm.CGLS, normalized_projection = norm,
-#                 angles = angle_proj, center_of_rotation = 86.2 , 
-#                 flat_field = flat, dark_field = dark, 
-#                 iterations = 15, resolution = 1, isLogScale = False, threads = 3)
-
-#recon = Reconstructor(algorithm = Reconstructor.Algorithm.CGLS, projection_data = proj,
-#                 angles = angle_proj, center_of_rotation = 86.2 , 
-#                 flat_field = flat, dark_field = dark, 
-#                 iterations = 15, resolution = 1, isLogScale = False, threads = 3)
-#img_cgls = recon.reconstruct()
-#
-#pars = dict()
-#pars['algorithm'] = Reconstructor.Algorithm.SIRT
-#pars['projection_data'] = proj
-#pars['angles'] = angle_proj
-#pars['center_of_rotation'] = numpy.double(86.2)
-#pars['flat_field'] = flat
-#pars['iterations'] = 15
-#pars['dark_field'] = dark
-#pars['resolution'] = 1
-#pars['isLogScale'] = False
-#pars['threads'] = 3
-#
-#img_sirt = recon.reconstruct(pars)
-#
-#recon.pars['algorithm'] = Reconstructor.Algorithm.MLEM
-#img_mlem = recon.reconstruct()
-
-############################################################
-############################################################
-#recon.pars['algorithm'] = Reconstructor.Algorithm.CGLS_CONV
-#recon.pars['regularize'] = numpy.double(0.1)
-#img_cgls_conv = recon.reconstruct()
-
-niterations = 15
-threads = 3
-
-img_cgls = alg.cgls(norm, angle_proj, numpy.double(86.2), 1 , niterations, threads, False)
-img_mlem = alg.mlem(norm, angle_proj, numpy.double(86.2), 1 , niterations, threads, False)
-img_sirt = alg.sirt(norm, angle_proj, numpy.double(86.2), 1 , niterations, threads, False)
-
-iteration_values = numpy.zeros((niterations,))
-img_cgls_conv = alg.cgls_conv(norm, angle_proj, numpy.double(86.2), 1 , niterations, threads,
-                              iteration_values, False)
-print ("iteration values %s" % str(iteration_values))
-
-iteration_values = numpy.zeros((niterations,))
-img_cgls_tikhonov = alg.cgls_tikhonov(norm, angle_proj, numpy.double(86.2), 1 , niterations, threads,
-                                      numpy.double(1e-5), iteration_values , False)
-print ("iteration values %s" % str(iteration_values))
-iteration_values = numpy.zeros((niterations,))
-img_cgls_TVreg = alg.cgls_TVreg(norm, angle_proj, numpy.double(86.2), 1 , niterations, threads,
-                                      numpy.double(1e-5), iteration_values , False)
-print ("iteration values %s" % str(iteration_values))
-
-
-##numpy.save("cgls_recon.npy", img_data)
-import matplotlib.pyplot as plt
-fig, ax = plt.subplots(1,6,sharey=True)
-ax[0].imshow(img_cgls[80])
-ax[0].axis('off')  # clear x- and y-axes
-ax[1].imshow(img_sirt[80])
-ax[1].axis('off')  # clear x- and y-axes
-ax[2].imshow(img_mlem[80])
-ax[2].axis('off')  # clear x- and y-axesplt.show()
-ax[3].imshow(img_cgls_conv[80])
-ax[3].axis('off')  # clear x- and y-axesplt.show()
-ax[4].imshow(img_cgls_tikhonov[80])
-ax[4].axis('off')  # clear x- and y-axesplt.show()
-ax[5].imshow(img_cgls_TVreg[80])
-ax[5].axis('off')  # clear x- and y-axesplt.show()
-
-
-plt.show()
-
-#viewer = edo.CILViewer()
-#viewer.setInputAsNumpy(img_cgls2)
-#viewer.displaySliceActor(0)
-#viewer.startRenderLoop()
-
-import vtk
-
-def NumpyToVTKImageData(numpyarray):
-    if (len(numpy.shape(numpyarray)) == 3):
-        doubleImg = vtk.vtkImageData()
-        shape = numpy.shape(numpyarray)
-        doubleImg.SetDimensions(shape[0], shape[1], shape[2])
-        doubleImg.SetOrigin(0,0,0)
-        doubleImg.SetSpacing(1,1,1)
-        doubleImg.SetExtent(0, shape[0]-1, 0, shape[1]-1, 0, shape[2]-1)
-        #self.img3D.SetScalarType(vtk.VTK_UNSIGNED_SHORT, vtk.vtkInformation())
-        doubleImg.AllocateScalars(vtk.VTK_DOUBLE,1)
-        
-        for i in range(shape[0]):
-            for j in range(shape[1]):
-                for k in range(shape[2]):
-                    doubleImg.SetScalarComponentFromDouble(
-                        i,j,k,0, numpyarray[i][j][k])
-    #self.setInput3DData( numpy_support.numpy_to_vtk(numpyarray) )
-        # rescale to appropriate VTK_UNSIGNED_SHORT
-        stats = vtk.vtkImageAccumulate()
-        stats.SetInputData(doubleImg)
-        stats.Update()
-        iMin = stats.GetMin()[0]
-        iMax = stats.GetMax()[0]
-        scale = vtk.VTK_UNSIGNED_SHORT_MAX / (iMax - iMin)
-
-        shiftScaler = vtk.vtkImageShiftScale ()
-        shiftScaler.SetInputData(doubleImg)
-        shiftScaler.SetScale(scale)
-        shiftScaler.SetShift(iMin)
-        shiftScaler.SetOutputScalarType(vtk.VTK_UNSIGNED_SHORT)
-        shiftScaler.Update()
-        return shiftScaler.GetOutput()
-        
-#writer = vtk.vtkMetaImageWriter()
-#writer.SetFileName(alg + "_recon.mha")
-#writer.SetInputData(NumpyToVTKImageData(img_cgls2))
-#writer.Write()
diff --git a/src/Python/ccpi/fista/__init__.py b/src/Python/ccpi/fista/__init__.py
deleted file mode 100644
index e69de29..0000000
--- a/src/Python/ccpi/fista/__init__.py
+++ /dev/null
diff --git a/src/Python/ccpi/reconstruction/FISTAReconstructor.py b/src/Python/ccpi/reconstruction/FISTAReconstructor.py
index ea96b53..c903712 100644
--- a/src/Python/ccpi/reconstruction/FISTAReconstructor.py
+++ b/src/Python/ccpi/reconstruction/FISTAReconstructor.py
@@ -21,10 +21,9 @@
 
 
 import numpy
-import h5py
 #from ccpi.reconstruction.parallelbeam import alg
 
-from ccpi.imaging.Regularizer import Regularizer
+#from ccpi.imaging.Regularizer import Regularizer
 from enum import Enum
 
 import astra
@@ -74,18 +73,34 @@ class FISTAReconstructor():
     # 3. "A novel tomographic reconstruction method based on the robust
     # Student's t function for suppressing data outliers" D. Kazantsev et.al.
     # D. Kazantsev, 2016-17
-    def __init__(self, projector_geometry, output_geometry, input_sinogram, **kwargs):
-        self.params = dict()
-        self.params['projector_geometry'] = projector_geometry
-        self.params['output_geometry'] = output_geometry
-        self.params['input_sinogram'] = input_sinogram
-        detectors, nangles, sliceZ = numpy.shape(input_sinogram)
-        self.params['detectors'] = detectors
-        self.params['number_og_angles'] = nangles
-        self.params['SlicesZ'] = sliceZ
+    def __init__(self, projector_geometry, output_geometry, input_sinogram,
+                 **kwargs):
+        # handle parmeters:
+        # obligatory parameters
+        self.pars = dict()
+        self.pars['projector_geometry'] = projector_geometry # proj_geom
+        self.pars['output_geometry'] = output_geometry       # vol_geom
+        self.pars['input_sinogram'] = input_sinogram         # sino
+        sliceZ, nangles, detectors = numpy.shape(input_sinogram)
+        self.pars['detectors'] = detectors
+        self.pars['number_of_angles'] = nangles
+        self.pars['SlicesZ'] = sliceZ
+        self.pars['output_volume'] = None
+
+        print (self.pars)
+        # handle optional input parameters (at instantiation)
         
         # Accepted input keywords
-        kw = ('number_of_iterations', 
+        kw = (
+              # mandatory fields
+              'projector_geometry',
+              'output_geometry',
+              'input_sinogram',
+              'detectors',
+              'number_of_angles',
+              'SlicesZ',
+              # optional fields
+              'number_of_iterations', 
               'Lipschitz_constant' , 
               'ideal_image' ,
               'weights' , 
@@ -93,7 +108,13 @@ class FISTAReconstructor():
               'initialize' , 
               'regularizer' , 
               'ring_lambda_R_L1',
-              'ring_alpha')
+              'ring_alpha',
+              'subsets',
+              'output_volume',
+              'os_subsets',
+              'os_indices',
+              'os_bins')
+        self.acceptedInputKeywords = list(kw)
         
         # handle keyworded parameters
         if kwargs is not None:
@@ -110,85 +131,160 @@ class FISTAReconstructor():
         if 'weights' in kwargs.keys():
             self.pars['weights'] = kwargs['weights']
         else:
-            self.pars['weights'] = numpy.ones(numpy.shape(self.params['input_sinogram']))
+            self.pars['weights'] = \
+                                 numpy.ones(numpy.shape(
+                                     self.pars['input_sinogram']))
         if 'Lipschitz_constant' in kwargs.keys():
             self.pars['Lipschitz_constant'] = kwargs['Lipschitz_constant']
         else:
-            self.pars['Lipschitz_constant'] = self.calculateLipschitzConstantWithPowerMethod()
+            self.pars['Lipschitz_constant'] = None
         
-        if not self.pars['ideal_image'] in kwargs.keys():
+        if not 'ideal_image' in kwargs.keys():
             self.pars['ideal_image'] = None
         
-        if not self.pars['region_of_interest'] :
+        if not 'region_of_interest'in kwargs.keys() :
             if self.pars['ideal_image'] == None:
-                pass
+                self.pars['region_of_interest'] = None
             else:
-                self.pars['region_of_interest'] = numpy.nonzero(self.pars['ideal_image']>0.0)
-            
-        if not self.pars['regularizer'] :
+                ## nonzero if the image is larger than m
+                fsm = numpy.frompyfunc(lambda x,m: 1 if x>m else 0, 2,1)
+                
+                self.pars['region_of_interest'] = fsm(self.pars['ideal_image'], 0)
+                
+        # the regularizer must be a correctly instantiated object    
+        if not 'regularizer' in kwargs.keys() :
             self.pars['regularizer'] = None
+
+        #RING REMOVAL
+        if not 'ring_lambda_R_L1' in kwargs.keys():
+            self.pars['ring_lambda_R_L1'] = 0
+        if not 'ring_alpha' in kwargs.keys():
+            self.pars['ring_alpha'] = 1
+
+        # ORDERED SUBSET
+        if not 'subsets' in kwargs.keys():
+            self.pars['subsets'] = 0
         else:
-            # the regularizer must be a correctly instantiated object
-            if not self.pars['ring_lambda_R_L1']:
-                self.pars['ring_lambda_R_L1'] = 0
-            if not self.pars['ring_alpha']:
-                self.pars['ring_alpha'] = 1
+            self.createOrderedSubsets()
+
+        if not 'initialize' in kwargs.keys():
+            self.pars['initialize'] = False
+
         
             
             
+    def setParameter(self, **kwargs):
+        '''set named parameter for the reconstructor engine
+        
+        raises Exception if the named parameter is not recognized
         
+        '''
+        for key , value in kwargs.items():
+            if key in self.acceptedInputKeywords:
+                self.pars[key] = value
+            else:
+                raise Exception('Wrong parameter {0} for '.format(key) +
+                                'reconstructor')
+    # setParameter
+
+    def getParameter(self, key):
+        if type(key) is str:
+            if key in self.acceptedInputKeywords:
+                return self.pars[key]
+            else:
+                raise Exception('Unrecongnised parameter: {0} '.format(key) )
+        elif type(key) is list:
+            outpars = []
+            for k in key:
+                outpars.append(self.getParameter(k))
+            return outpars
+        else:
+            raise Exception('Unhandled input {0}' .format(str(type(key))))
+            
+    
     def calculateLipschitzConstantWithPowerMethod(self):
         ''' using Power method (PM) to establish L constant'''
         
-        #N = params.vol_geom.GridColCount
-        N = self.pars['output_geometry'].GridColCount
-        proj_geom = self.params['projector_geometry']
-        vol_geom = self.params['output_geometry']
+        N = self.pars['output_geometry']['GridColCount']
+        proj_geom = self.pars['projector_geometry']
+        vol_geom = self.pars['output_geometry']
         weights = self.pars['weights']
         SlicesZ = self.pars['SlicesZ']
         
-        if (proj_geom['type'] == 'parallel') or (proj_geom['type'] == 'parallel3d'):
+            
+                               
+        if (proj_geom['type'] == 'parallel') or \
+           (proj_geom['type'] == 'parallel3d'):
             #% for parallel geometry we can do just one slice
-            #fprintf('%s \n', 'Calculating Lipshitz constant for parallel beam geometry...');
-            niter = 15;# % number of iteration for the PM
+            #print('Calculating Lipshitz constant for parallel beam geometry...')
+            niter = 5;# % number of iteration for the PM
             #N = params.vol_geom.GridColCount;
             #x1 = rand(N,N,1);
             x1 = numpy.random.rand(1,N,N)
             #sqweight = sqrt(weights(:,:,1));
-            sqweight = numpy.sqrt(weights.T[0])
+            sqweight = numpy.sqrt(weights[0])
             proj_geomT = proj_geom.copy();
-            proj_geomT.DetectorRowCount = 1;
+            proj_geomT['DetectorRowCount'] = 1;
             vol_geomT = vol_geom.copy();
             vol_geomT['GridSliceCount'] = 1;
             
+            #[sino_id, y] = astra_create_sino3d_cuda(x1, proj_geomT, vol_geomT);
+            
             
             for i in range(niter):
-                if i == 0:
-                    #[sino_id, y] = astra_create_sino3d_cuda(x1, proj_geomT, vol_geomT);
-                    sino_id, y = astra.creators.create_sino3d_gpu(x1, proj_geomT, vol_geomT);
-                    y = sqweight * y # element wise multiplication
-                    #astra_mex_data3d('delete', sino_id);
-                    astra.matlab.data3d('delete', sino_id)
+            #        [id,x1] = astra_create_backprojection3d_cuda(sqweight.*y, proj_geomT, vol_geomT);
+            #            s = norm(x1(:));
+            #            x1 = x1/s;
+            #            [sino_id, y] = astra_create_sino3d_cuda(x1, proj_geomT, vol_geomT);
+            #            y = sqweight.*y;
+            #            astra_mex_data3d('delete', sino_id);
+            #            astra_mex_data3d('delete', id);
+                #print ("iteration {0}".format(i))
+                                
+                sino_id, y = astra.creators.create_sino3d_gpu(x1,
+                                                          proj_geomT,
+                                                          vol_geomT)
+                
+                y = (sqweight * y).copy() # element wise multiplication
+                
+                #b=fig.add_subplot(2,1,2)
+                #imgplot = plt.imshow(x1[0])
+                #plt.show()
+                
+                #astra_mex_data3d('delete', sino_id);
+                astra.matlab.data3d('delete', sino_id)
+                del x1
                     
-                idx,x1 = astra.creators.create_backprojection3d_gpu(sqweight*y, proj_geomT, vol_geomT);
+                idx,x1 = astra.creators.create_backprojection3d_gpu((sqweight*y).copy(), 
+                                                                    proj_geomT,
+                                                                    vol_geomT)
+                del y
+                
+                                                                    
                 s = numpy.linalg.norm(x1)
                 ### this line?
-                x1 = x1/s;
-                ### this line?
-                sino_id, y = astra.creators.create_sino3d_gpu(x1, proj_geomT, vol_geomT);
-                y = sqweight*y;
+                x1 = (x1/s).copy();
+                
+            #        ### this line?
+            #        sino_id, y = astra.creators.create_sino3d_gpu(x1, 
+            #                                                      proj_geomT, 
+            #                                                      vol_geomT);
+            #        y = sqweight * y;
                 astra.matlab.data3d('delete', sino_id);
-                astra.matlab.data3d('delete', idx);
+                astra.matlab.data3d('delete', idx)
+                print ("iteration {0} s= {1}".format(i,s))
+                
             #end
             del proj_geomT
             del vol_geomT
+            #plt.show()
         else:
             #% divergen beam geometry
-            #fprintf('%s \n', 'Calculating Lipshitz constant for divergen beam geometry...');
+            print('Calculating Lipshitz constant for divergen beam geometry...')
             niter = 8; #% number of iteration for PM
             x1 = numpy.random.rand(SlicesZ , N , N);
             #sqweight = sqrt(weights);
-            sqweight = numpy.sqrt(weights.T[0])
+            sqweight = numpy.sqrt(weights[0])
             
             sino_id, y = astra.creators.create_sino3d_gpu(x1, proj_geom, vol_geom);
             y = sqweight*y;
@@ -217,6 +313,7 @@ class FISTAReconstructor():
             #end
             #clear x1
             del x1
+
         
         return s
     
@@ -225,130 +322,291 @@ class FISTAReconstructor():
         if regularizer is not None:
             self.pars['regularizer'] = regularizer
         
+
+    def initialize(self):
+        # convenience variable storage
+        proj_geom = self.pars['projector_geometry']
+        vol_geom = self.pars['output_geometry']
+        sino = self.pars['input_sinogram']
+        
+        # a 'warm start' with SIRT method
+        # Create a data object for the reconstruction
+        rec_id = astra.matlab.data3d('create', '-vol',
+                                    vol_geom);
+        
+        #sinogram_id = astra_mex_data3d('create', '-proj3d', proj_geom, sino);
+        sinogram_id = astra.matlab.data3d('create', '-proj3d',
+                                          proj_geom,
+                                          sino)
+
+        sirt_config = astra.astra_dict('SIRT3D_CUDA')
+        sirt_config['ReconstructionDataId' ] = rec_id
+        sirt_config['ProjectionDataId'] = sinogram_id
+
+        sirt = astra.algorithm.create(sirt_config)
+        astra.algorithm.run(sirt, iterations=35)
+        X = astra.matlab.data3d('get', rec_id)
+
+        # clean up memory
+        astra.matlab.data3d('delete', rec_id)
+        astra.matlab.data3d('delete', sinogram_id)
+        astra.algorithm.delete(sirt)
+
+        
+
+        return X
+
+    def createOrderedSubsets(self, subsets=None):
+        if subsets is None:
+            try:
+                subsets = self.getParameter('subsets')
+            except Exception():
+                subsets = 0
+            #return subsets
+        else:
+            self.setParameter(subsets=subsets)
+            
+
+        angles = self.getParameter('projector_geometry')['ProjectionAngles'] 
+        
+        #binEdges = numpy.linspace(angles.min(),
+        #                          angles.max(),
+        #                          subsets + 1)
+        binsDiscr, binEdges = numpy.histogram(angles, bins=subsets)
+        # get rearranged subset indices
+        IndicesReorg = numpy.zeros((numpy.shape(angles)), dtype=numpy.int32)
+        counterM = 0
+        for ii in range(binsDiscr.max()):
+            counter = 0
+            for jj in range(subsets):
+                curr_index = ii + jj  + counter
+                #print ("{0} {1} {2}".format(binsDiscr[jj] , ii, counterM))
+                if binsDiscr[jj] > ii:
+                    if (counterM < numpy.size(IndicesReorg)):
+                        IndicesReorg[counterM] = curr_index
+                    counterM = counterM + 1
+                    
+                counter = counter + binsDiscr[jj] - 1    
+                
+        # store the OS in parameters
+        self.setParameter(os_subsets=subsets,
+                          os_bins=binsDiscr,
+                          os_indices=IndicesReorg)
+            
+
+    def prepareForIteration(self):
+        print ("FISTA Reconstructor: prepare for iteration")
+        
+        self.residual_error = numpy.zeros((self.pars['number_of_iterations']))
+        self.objective = numpy.zeros((self.pars['number_of_iterations']))
+
+        #2D array (for 3D data) of sparse "ring" 
+        detectors, nangles, sliceZ  = numpy.shape(self.pars['input_sinogram'])
+        self.r = numpy.zeros((detectors, sliceZ), dtype=numpy.float)
+        # another ring variable
+        self.r_x = self.r.copy()
+
+        self.residual = numpy.zeros(numpy.shape(self.pars['input_sinogram']))
+        
+        if self.getParameter('Lipschitz_constant') is None:
+            self.pars['Lipschitz_constant'] = \
+                            self.calculateLipschitzConstantWithPowerMethod()
+        # errors vector (if the ground truth is given)
+        self.Resid_error = numpy.zeros((self.getParameter('number_of_iterations')));
+        # objective function values vector
+        self.objective = numpy.zeros((self.getParameter('number_of_iterations')));      
+        
+
+    # prepareForIteration
+
+    def iterate(self, Xin=None):
+        print ("FISTA Reconstructor: iterate")
+        
+        if Xin is None:    
+            if self.getParameter('initialize'):
+                X = self.initialize()
+            else:
+                N = vol_geom['GridColCount']
+                X = numpy.zeros((N,N,SlicesZ), dtype=numpy.float)
+        else:
+            # copy by reference
+            X = Xin
+        # store the output volume in the parameters
+        self.setParameter(output_volume=X)
+        X_t = X.copy()
+        # convenience variable storage
+        proj_geom , vol_geom, sino , \
+          SlicesZ  = self.getParameter([ 'projector_geometry' ,
+                                                'output_geometry',
+                                                'input_sinogram',
+                                                'SlicesZ' ])
+                   
+        t = 1
+        
+        for i in range(self.getParameter('number_of_iterations')):
+            X_old = X.copy()
+            t_old = t
+            r_old = self.r.copy()
+            if self.getParameter('projector_geometry')['type'] == 'parallel' or \
+               self.getParameter('projector_geometry')['type'] == 'fanflat' or \
+               self.getParameter('projector_geometry')['type'] == 'fanflat_vec':
+                # if the geometry is parallel use slice-by-slice
+                # projection-backprojection routine
+                #sino_updt = zeros(size(sino),'single');
+                proj_geomT = proj_geom.copy()
+                proj_geomT['DetectorRowCount'] = 1
+                vol_geomT = vol_geom.copy()
+                vol_geomT['GridSliceCount'] = 1;
+                self.sino_updt = numpy.zeros(numpy.shape(sino), dtype=numpy.float)
+                for kkk in range(SlicesZ):
+                    sino_id, self.sino_updt[kkk] = \
+                             astra.creators.create_sino3d_gpu(
+                                 X_t[kkk:kkk+1], proj_geomT, vol_geomT)
+                    astra.matlab.data3d('delete', sino_id)
+            else:
+                # for divergent 3D geometry (watch the GPU memory overflow in
+                # ASTRA versions < 1.8)
+                #[sino_id, sino_updt] = astra_create_sino3d_cuda(X_t, proj_geom, vol_geom);
+                sino_id, self.sino_updt = astra.creators.create_sino3d_gpu(
+                    X_t, proj_geom, vol_geom)
+
+
+            ## RING REMOVAL
+            self.ringRemoval(i)
+            ## Projection/Backprojection Routine
+            self.projectionBackprojection(X, X_t)
+            astra.matlab.data3d('delete', sino_id)
+            ## REGULARIZATION
+            X = self.regularize(X)
+            ## Update Loop
+            X , X_t, t = self.updateLoop(i, X, X_old, r_old, t, t_old)
+            self.setParameter(output_volume=X)
+        return X
+    ## iterate
     
-    
+    def ringRemoval(self, i):
+        print ("FISTA Reconstructor: ring removal")
+        residual = self.residual
+        lambdaR_L1 , alpha_ring , weights , L_const , sino= \
+                   self.getParameter(['ring_lambda_R_L1',
+                                      'ring_alpha' , 'weights',
+                                      'Lipschitz_constant',
+                                      'input_sinogram'])
+        r_x = self.r_x
+        sino_updt = self.sino_updt
+        
+        SlicesZ, anglesNumb, Detectors = \
+                    numpy.shape(self.getParameter('input_sinogram'))
+        if lambdaR_L1 > 0 :
+             for kkk in range(anglesNumb):
+                 
+                 residual[:,kkk,:] = (weights[:,kkk,:]).squeeze() * \
+                                       ((sino_updt[:,kkk,:]).squeeze() - \
+                                        (sino[:,kkk,:]).squeeze() -\
+                                        (alpha_ring * r_x)
+                                        )
+             vec = residual.sum(axis = 1)
+             #if SlicesZ > 1:
+             #    vec = vec[:,1,:].squeeze()
+             self.r = (r_x - (1./L_const) * vec).copy()
+             self.objective[i] = (0.5 * (residual ** 2).sum())
 
+    def projectionBackprojection(self, X, X_t):
+        print ("FISTA Reconstructor: projection-backprojection routine")
+        
+        # a few useful variables
+        SlicesZ, anglesNumb, Detectors = \
+                    numpy.shape(self.getParameter('input_sinogram'))
+        residual = self.residual
+        proj_geom , vol_geom , L_const = \
+                  self.getParameter(['projector_geometry' ,
+                                                  'output_geometry',
+                                                  'Lipschitz_constant'])
+        
+        
+        if self.getParameter('projector_geometry')['type'] == 'parallel' or \
+           self.getParameter('projector_geometry')['type'] == 'fanflat' or \
+           self.getParameter('projector_geometry')['type'] == 'fanflat_vec':
+            # if the geometry is parallel use slice-by-slice
+            # projection-backprojection routine
+            #sino_updt = zeros(size(sino),'single');
+            proj_geomT = proj_geom.copy()
+            proj_geomT['DetectorRowCount'] = 1
+            vol_geomT = vol_geom.copy()
+            vol_geomT['GridSliceCount'] = 1;
+            x_temp = numpy.zeros(numpy.shape(X),dtype=numpy.float32)
+            
+            for kkk in range(SlicesZ):
+                
+                x_id, x_temp[kkk] = \
+                         astra.creators.create_backprojection3d_gpu(
+                             residual[kkk:kkk+1],
+                             proj_geomT, vol_geomT)
+                astra.matlab.data3d('delete', x_id)
+        else:
+            x_id, x_temp = \
+                  astra.creators.create_backprojection3d_gpu(
+                      residual, proj_geom, vol_geom)            
+
+        X = X_t - (1/L_const) * x_temp
+        #astra.matlab.data3d('delete', sino_id)
+        astra.matlab.data3d('delete', x_id)
 
-def getEntry(location):
-    for item in nx[location].keys():
-        print (item)
-
-
-print ("Loading Data")
-
-##fname = "D:\\Documents\\Dataset\\IMAT\\20170419_crabtomo\\crabtomo\\Sample\\IMAT00005153_crabstomo_Sample_000.tif"
-####ind = [i * 1049 for i in range(360)]
-#### use only 360 images
-##images = 200
-##ind = [int(i * 1049 / images) for i in range(images)]
-##stack_image = dxchange.reader.read_tiff_stack(fname, ind, digit=None, slc=None)
-
-#fname = "D:\\Documents\\Dataset\\CGLS\\24737_fd.nxs"
-#fname = "C:\\Users\\ofn77899\\Documents\\CCPi\\CGLS\\24737_fd_2.nxs"
-##fname = "/home/ofn77899/Reconstruction/CCPi-FISTA_Reconstruction/data/dendr.h5"
-##nx = h5py.File(fname, "r")
-##
-### the data are stored in a particular location in the hdf5
-##for item in nx['entry1/tomo_entry/data'].keys():
-##    print (item)
-##
-##data = nx.get('entry1/tomo_entry/data/rotation_angle')
-##angles = numpy.zeros(data.shape)
-##data.read_direct(angles)
-##print (angles)
-### angles should be in degrees
-##
-##data = nx.get('entry1/tomo_entry/data/data')
-##stack = numpy.zeros(data.shape)
-##data.read_direct(stack)
-##print (data.shape)
-##
-##print ("Data Loaded")
-##
-##
-### Normalize
-##data = nx.get('entry1/tomo_entry/instrument/detector/image_key')
-##itype = numpy.zeros(data.shape)
-##data.read_direct(itype)
-### 2 is dark field
-##darks = [stack[i] for i in range(len(itype)) if itype[i] == 2 ]
-##dark = darks[0]
-##for i in range(1, len(darks)):
-##    dark += darks[i]
-##dark = dark / len(darks)
-###dark[0][0] = dark[0][1]
-##
-### 1 is flat field
-##flats = [stack[i] for i in range(len(itype)) if itype[i] == 1 ]
-##flat = flats[0]
-##for i in range(1, len(flats)):
-##    flat += flats[i]
-##flat = flat / len(flats)
-###flat[0][0] = dark[0][1]
-##
-##
-### 0 is projection data
-##proj = [stack[i] for i in range(len(itype)) if itype[i] == 0 ]
-##angle_proj = [angles[i] for i in range(len(itype)) if itype[i] == 0 ]
-##angle_proj = numpy.asarray (angle_proj)
-##angle_proj = angle_proj.astype(numpy.float32)
-##
-### normalized data are
-### norm = (projection - dark)/(flat-dark)
-##
-##def normalize(projection, dark, flat, def_val=0.1):
-##    a = (projection - dark)
-##    b = (flat-dark)
-##    with numpy.errstate(divide='ignore', invalid='ignore'):
-##        c = numpy.true_divide( a, b )
-##        c[ ~ numpy.isfinite( c )] = def_val  # set to not zero if 0/0 
-##    return c
-##    
-##
-##norm = [normalize(projection, dark, flat) for projection in proj]
-##norm = numpy.asarray (norm)
-##norm = norm.astype(numpy.float32)
-
-
-##niterations = 15
-##threads = 3
-##
-##img_cgls = alg.cgls(norm, angle_proj, numpy.double(86.2), 1 , niterations, threads, False)
-##img_mlem = alg.mlem(norm, angle_proj, numpy.double(86.2), 1 , niterations, threads, False)
-##img_sirt = alg.sirt(norm, angle_proj, numpy.double(86.2), 1 , niterations, threads, False)
-##
-##iteration_values = numpy.zeros((niterations,))
-##img_cgls_conv = alg.cgls_conv(norm, angle_proj, numpy.double(86.2), 1 , niterations, threads,
-##                              iteration_values, False)
-##print ("iteration values %s" % str(iteration_values))
-##
-##iteration_values = numpy.zeros((niterations,))
-##img_cgls_tikhonov = alg.cgls_tikhonov(norm, angle_proj, numpy.double(86.2), 1 , niterations, threads,
-##                                      numpy.double(1e-5), iteration_values , False)
-##print ("iteration values %s" % str(iteration_values))
-##iteration_values = numpy.zeros((niterations,))
-##img_cgls_TVreg = alg.cgls_TVreg(norm, angle_proj, numpy.double(86.2), 1 , niterations, threads,
-##                                      numpy.double(1e-5), iteration_values , False)
-##print ("iteration values %s" % str(iteration_values))
-##
-##
-####numpy.save("cgls_recon.npy", img_data)
-##import matplotlib.pyplot as plt
-##fig, ax = plt.subplots(1,6,sharey=True)
-##ax[0].imshow(img_cgls[80])
-##ax[0].axis('off')  # clear x- and y-axes
-##ax[1].imshow(img_sirt[80])
-##ax[1].axis('off')  # clear x- and y-axes
-##ax[2].imshow(img_mlem[80])
-##ax[2].axis('off')  # clear x- and y-axesplt.show()
-##ax[3].imshow(img_cgls_conv[80])
-##ax[3].axis('off')  # clear x- and y-axesplt.show()
-##ax[4].imshow(img_cgls_tikhonov[80])
-##ax[4].axis('off')  # clear x- and y-axesplt.show()
-##ax[5].imshow(img_cgls_TVreg[80])
-##ax[5].axis('off')  # clear x- and y-axesplt.show()
-##
-##
-##plt.show()
-##
+    def regularize(self, X):
+        print ("FISTA Reconstructor: regularize")
+        
+        regularizer = self.getParameter('regularizer')
+        if regularizer is not None:
+            return regularizer(input=X)
+        else:
+            return X
+
+    def updateLoop(self, i, X, X_old, r_old, t, t_old):
+        print ("FISTA Reconstructor: update loop")
+        lambdaR_L1 = self.getParameter('ring_lambda_R_L1')
+        if lambdaR_L1 > 0:
+            self.r = numpy.max(
+                numpy.abs(self.r) - lambdaR_L1 , 0) * \
+                numpy.sign(self.r)
+        t = (1 + numpy.sqrt(1 + 4 * t**2))/2
+        X_t = X + (((t_old -1)/t) * (X - X_old))
+
+        if lambdaR_L1 > 0:
+            self.r_x = self.r + \
+                             (((t_old-1)/t) * (self.r - r_old))
+
+        if self.getParameter('region_of_interest') is None:
+            string = 'Iteration Number {0} | Objective {1} \n'
+            print (string.format( i, self.objective[i]))
+        else:
+            ROI , X_ideal = fistaRecon.getParameter('region_of_interest',
+                                                    'ideal_image')
+            
+            Resid_error[i] = RMSE(X*ROI, X_ideal*ROI)
+            string = 'Iteration Number {0} | RMS Error {1} | Objective {2} \n'
+            print (string.format(i,Resid_error[i], self.objective[i]))
+        return (X , X_t, t)
+
+    def os_iterate(self, Xin=None):
+        print ("FISTA Reconstructor: iterate")
+        
+        if Xin is None:    
+            if self.getParameter('initialize'):
+                X = self.initialize()
+            else:
+                N = vol_geom['GridColCount']
+                X = numpy.zeros((N,N,SlicesZ), dtype=numpy.float)
+        else:
+            # copy by reference
+            X = Xin
+        # store the output volume in the parameters
+        self.setParameter(output_volume=X)
+        X_t = X.copy()
 
+        # some useful constants
+        proj_geom , vol_geom, sino , \
+          SlicesZ, weights , alpha_ring ,\
+          lambdaR_L1 , L_const = self.getParameter(
+            ['projector_geometry' , 'output_geometry',
+             'input_sinogram', 'SlicesZ' ,  'weights', 'ring_alpha' ,
+             'ring_lambda_R_L1', 'Lipschitz_constant'])
diff --git a/src/Python/compile-fista.bat.in b/src/Python/compile-fista.bat.in
new file mode 100644
index 0000000..b1db686
--- /dev/null
+++ b/src/Python/compile-fista.bat.in
@@ -0,0 +1,7 @@
+set CIL_VERSION=@CIL_VERSION@
+
+set PREFIX=@CONDA_ENVIRONMENT_PREFIX@
+set LIBRARY_INC=@CONDA_ENVIRONMENT_LIBRARY_INC@
+
+REM activate @CONDA_ENVIRONMENT@
+conda build fista-recipe --python=@PYTHON_VERSION_MAJOR@.@PYTHON_VERSION_MINOR@ --numpy=@NUMPY_VERSION@ -c ccpi -c conda-forge
diff --git a/src/Python/compile-fista.sh.in b/src/Python/compile-fista.sh.in
new file mode 100644
index 0000000..267f014
--- /dev/null
+++ b/src/Python/compile-fista.sh.in
@@ -0,0 +1,9 @@
+#!/bin/sh
+# compile within the right conda environment
+#module load python/anaconda
+#source activate @CONDA_ENVIRONMENT@
+
+export CIL_VERSION=@CIL_VERSION@
+export LIBRARY_INC=@CONDA_ENVIRONMENT_LIBRARY_INC@
+
+conda build fista-recipe --python=@PYTHON_VERSION_MAJOR@.@PYTHON_VERSION_MINOR@ --numpy=@NUMPY_VERSION@ -c ccpi
diff --git a/src/Python/conda-recipe/meta.yaml b/src/Python/conda-recipe/meta.yaml
index c5b7a89..7068e9d 100644
--- a/src/Python/conda-recipe/meta.yaml
+++ b/src/Python/conda-recipe/meta.yaml
@@ -1,5 +1,5 @@
 package:
-  name: ccpi-fista
+  name: ccpi-regularizers
   version: {{ environ['CIL_VERSION'] }}
 
 
diff --git a/src/Python/fista-recipe/build.sh b/src/Python/fista-recipe/build.sh
new file mode 100644
index 0000000..e3f3552
--- /dev/null
+++ b/src/Python/fista-recipe/build.sh
@@ -0,0 +1,10 @@
+if [ -z "$CIL_VERSION" ]; then
+    echo "Need to set CIL_VERSION"
+    exit 1
+fi  
+mkdir "$SRC_DIR/ccpifista"
+cp -r "$RECIPE_DIR/.." "$SRC_DIR/ccpifista"
+
+cd $SRC_DIR/ccpifista
+
+$PYTHON setup-fista.py install
diff --git a/src/Python/fista-recipe/meta.yaml b/src/Python/fista-recipe/meta.yaml
new file mode 100644
index 0000000..265541f
--- /dev/null
+++ b/src/Python/fista-recipe/meta.yaml
@@ -0,0 +1,29 @@
+package:
+  name: ccpi-fista
+  version: {{ environ['CIL_VERSION'] }}
+
+
+build:
+  preserve_egg_dir: False
+  script_env:
+    - CIL_VERSION   
+#  number: 0
+  
+requirements:
+  build:
+    - python
+    - numpy
+    - setuptools
+ 
+  run:
+    - python
+    - numpy
+    #- astra-toolbox
+    - ccpi-regularizers
+
+
+	
+about:
+  home: http://www.ccpi.ac.uk
+  license:  Apache v.2.0 license
+  summary: 'CCPi Core Imaging Library (Viewer)'
diff --git a/src/Python/setup-fista.py.in b/src/Python/setup-fista.py.in
new file mode 100644
index 0000000..c5c9f4d
--- /dev/null
+++ b/src/Python/setup-fista.py.in
@@ -0,0 +1,27 @@
+from distutils.core import setup
+#from setuptools import setup, find_packages
+import os
+
+cil_version=os.environ['CIL_VERSION']
+if  cil_version == '':
+    print("Please set the environmental variable CIL_VERSION")
+    sys.exit(1)
+
+setup(
+    name="ccpi-fista",
+    version=cil_version,
+    packages=['ccpi','ccpi.reconstruction'],
+	install_requires=['numpy'],
+
+     zip_safe = False,
+
+    # metadata for upload to PyPI
+    author="Edoardo Pasca",
+    author_email="edo.paskino@gmail.com",
+    description='CCPi Core Imaging Library - FISTA Reconstructor module',
+    license="Apache v2.0",
+    keywords="tomography interative reconstruction",
+    url="http://www.ccpi.ac.uk",   # project home page, if any
+
+    # could also include long_description, download_url, classifiers, etc.
+)
diff --git a/src/Python/setup.py.in b/src/Python/setup.py.in
index 0a1f4ad..12e8af1 100644
--- a/src/Python/setup.py.in
+++ b/src/Python/setup.py.in
@@ -44,7 +44,7 @@ else:
 
 setup(
     name='ccpi',
-	description='CCPi Core Imaging Library - FISTA Reconstruction Module',
+	description='CCPi Core Imaging Library - Image Regularizers',
 	version=cil_version,
     cmdclass = {'build_ext': build_ext},
     ext_modules = [Extension("ccpi.imaging.cpu_regularizers",
@@ -65,3 +65,5 @@ setup(
 	zip_safe = False,	
 	packages = {'ccpi','ccpi.imaging'},
 )
+
+
diff --git a/src/Python/test_reconstructor-os.py b/src/Python/test/test_reconstructor-os.py
index aee70a4..6c82ae0 100644
--- a/src/Python/test_reconstructor-os.py
+++ b/src/Python/test/test_reconstructor-os.py
@@ -9,9 +9,10 @@ Based on DemoRD2.m
 import h5py
 import numpy
 
-from ccpi.fista.FISTAReconstructor import FISTAReconstructor
+from ccpi.reconstruction.FISTAReconstructor import FISTAReconstructor
 import astra
 import matplotlib.pyplot as plt
+from ccpi.imaging.Regularizer import Regularizer
 
 def RMSE(signal1, signal2):
     '''RMSE Root Mean Squared Error'''
@@ -76,9 +77,18 @@ fistaRecon.setParameter(Lipschitz_constant = 767893952.0)
 fistaRecon.setParameter(ring_alpha = 21)
 fistaRecon.setParameter(ring_lambda_R_L1 = 0.002)
 
+
+reg = Regularizer(Regularizer.Algorithm.LLT_model)
+reg.setParameter(regularization_parameter=25,
+                          time_step=0.0003,
+                          tolerance_constant=0.0001,
+                          number_of_iterations=300)
+
 ## Ordered subset
 if True:
     subsets = 16
+    fistaRecon.setParameter(subsets=subsets)
+    fistaRecon.createOrderedSubsets()
     angles = fistaRecon.getParameter('projector_geometry')['ProjectionAngles']
     #binEdges = numpy.linspace(angles.min(),
     #                          angles.max(),
@@ -146,6 +156,7 @@ if True:
     fistaRecon.residual2 = numpy.zeros(numpy.shape(fistaRecon.pars['input_sinogram']))
     residual2 = fistaRecon.residual2
     sino_updt_FULL = fistaRecon.residual.copy()
+    r_x = fistaRecon.r.copy()
     
     print ("starting iterations")
 ##    % Outer FISTA iterations loop
@@ -206,8 +217,13 @@ if True:
             # the number of projections per subset
             numProjSub = fistaRecon.getParameter('os_bins')[ss]
             CurrSubIndices = fistaRecon.getParameter('os_indices')\
-                             [counterInd:counterInd+numProjSub-1]
-            proj_geomSUB['ProjectionAngles'] = angles[CurrSubIndeces]
+                             [counterInd:counterInd+numProjSub]
+            #print ("Len CurrSubIndices {0}".format(numProjSub))
+            mask = numpy.zeros(numpy.shape(angles), dtype=bool)
+            cc = 0
+            for j in range(len(CurrSubIndices)):
+                mask[int(CurrSubIndices[j])] = True
+            proj_geomSUB['ProjectionAngles'] = angles[mask]
 
             shape = list(numpy.shape(fistaRecon.getParameter('input_sinogram')))
             shape[1] = numProjSub
@@ -246,7 +262,8 @@ if True:
     ##                    sino_updt_FULL(:,indC,:) = squeeze(sino_updt_Sub(:,kkk,:)); % filling the full sinogram
     ##                end
                  for kkk in range(numProjSub):
-                     indC = CurrSubIndices[kkk]
+                     #print ("ring removal indC ... {0}".format(kkk))
+                     indC = int(CurrSubIndices[kkk])
                      residualSub[:,kkk,:] = weights[:,indC,:].squeeze() * \
                             (sino_updt_Sub[:,kkk,:].squeeze() - \
                              sino[:,indC,:].squeeze() - alpha_ring * r_x)
@@ -288,7 +305,8 @@ if True:
         # regularizer = fistaRecon.getParameter('regularizer')
         # for slices:
         # out = regularizer(input=X)
-        print ("skipping regularizer")
+        print ("regularizer")
+        #X = reg(input=X)
 
 
         ## FINAL
@@ -312,7 +330,8 @@ if True:
             Resid_error[i] = RMSE(X*ROI, X_ideal*ROI)
             string = 'Iteration Number {0} | RMS Error {1} | Objective {2} \n'
             print (string.format(i,Resid_error[i], objective[i]))
-            
+
+    numpy.save("X_out_os.npy", X)
 
 else:
     fistaRecon = FISTAReconstructor(proj_geom,
diff --git a/src/Python/test/test_reconstructor.py b/src/Python/test/test_reconstructor.py
new file mode 100644
index 0000000..3342301
--- /dev/null
+++ b/src/Python/test/test_reconstructor.py
@@ -0,0 +1,309 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Aug 23 16:34:49 2017
+
+@author: ofn77899
+Based on DemoRD2.m
+"""
+
+import h5py
+import numpy
+
+from ccpi.reconstruction.FISTAReconstructor import FISTAReconstructor
+import astra
+import matplotlib.pyplot as plt
+
+def RMSE(signal1, signal2):
+    '''RMSE Root Mean Squared Error'''
+    if numpy.shape(signal1) == numpy.shape(signal2):
+        err = (signal1 - signal2)
+        err = numpy.sum( err * err )/numpy.size(signal1);  # MSE
+        err = sqrt(err);                                   # RMSE
+        return err
+    else:
+        raise Exception('Input signals must have the same shape')
+  
+filename = r'/home/ofn77899/Reconstruction/CCPi-FISTA_Reconstruction/demos/DendrData.h5'
+nx = h5py.File(filename, "r")
+#getEntry(nx, '/')
+# I have exported the entries as children of /
+entries = [entry for entry in nx['/'].keys()]
+print (entries)
+
+Sino3D = numpy.asarray(nx.get('/Sino3D'), dtype="float32")
+Weights3D = numpy.asarray(nx.get('/Weights3D'), dtype="float32")
+angSize = numpy.asarray(nx.get('/angSize'), dtype=int)[0]
+angles_rad = numpy.asarray(nx.get('/angles_rad'), dtype="float32")
+recon_size = numpy.asarray(nx.get('/recon_size'), dtype=int)[0]
+size_det = numpy.asarray(nx.get('/size_det'), dtype=int)[0]
+slices_tot = numpy.asarray(nx.get('/slices_tot'), dtype=int)[0]
+
+Z_slices = 20
+det_row_count = Z_slices
+# next definition is just for consistency of naming
+det_col_count = size_det
+
+detectorSpacingX = 1.0
+detectorSpacingY = detectorSpacingX
+
+
+proj_geom = astra.creators.create_proj_geom('parallel3d',
+                                            detectorSpacingX,
+                                            detectorSpacingY,
+                                            det_row_count,
+                                            det_col_count,
+                                            angles_rad)
+
+#vol_geom = astra_create_vol_geom(recon_size,recon_size,Z_slices);
+image_size_x = recon_size
+image_size_y = recon_size
+image_size_z = Z_slices
+vol_geom = astra.creators.create_vol_geom( image_size_x,
+                                           image_size_y,
+                                           image_size_z)
+
+## First pass the arguments to the FISTAReconstructor and test the
+## Lipschitz constant
+
+fistaRecon = FISTAReconstructor(proj_geom,
+                                vol_geom,
+                                Sino3D ,
+                                weights=Weights3D)
+
+print ("Lipschitz Constant {0}".format(fistaRecon.pars['Lipschitz_constant']))
+fistaRecon.setParameter(number_of_iterations = 12)
+fistaRecon.setParameter(Lipschitz_constant = 767893952.0)
+fistaRecon.setParameter(ring_alpha = 21)
+fistaRecon.setParameter(ring_lambda_R_L1 = 0.002)
+
+reg = Regularizer(Regularizer.Algorithm.LLT_model)
+reg.setParameter(regularization_parameter=25,
+                          time_step=0.0003,
+                          tolerance_constant=0.0001,
+                          number_of_iterations=300)
+fistaRecon.setParameter(regularizer = reg)
+
+## Ordered subset
+if False:
+    subsets = 16
+    angles = fistaRecon.getParameter('projector_geometry')['ProjectionAngles']
+    #binEdges = numpy.linspace(angles.min(),
+    #                          angles.max(),
+    #                          subsets + 1)
+    binsDiscr, binEdges = numpy.histogram(angles, bins=subsets)
+    # get rearranged subset indices
+    IndicesReorg = numpy.zeros((numpy.shape(angles)))
+    counterM = 0
+    for ii in range(binsDiscr.max()):
+        counter = 0
+        for jj in range(subsets):
+            curr_index = ii + jj  + counter
+            #print ("{0} {1} {2}".format(binsDiscr[jj] , ii, counterM))
+            if binsDiscr[jj] > ii:
+                if (counterM < numpy.size(IndicesReorg)):
+                    IndicesReorg[counterM] = curr_index
+                counterM = counterM + 1
+                
+            counter = counter + binsDiscr[jj] - 1
+
+
+if False:
+    print ("Lipschitz Constant {0}".format(fistaRecon.pars['Lipschitz_constant']))
+    print ("prepare for iteration")
+    fistaRecon.prepareForIteration()
+    
+    
+
+    print("initializing  ...")
+    if False:
+        # if X doesn't exist
+        #N = params.vol_geom.GridColCount
+        N = vol_geom['GridColCount']
+        print ("N " + str(N))
+        X = numpy.zeros((N,N,SlicesZ), dtype=numpy.float)
+    else:
+        #X = fistaRecon.initialize()
+        X = numpy.load("X.npy")
+    
+    print (numpy.shape(X))
+    X_t = X.copy()
+    print ("initialized")
+    proj_geom , vol_geom, sino , \
+                      SlicesZ = fistaRecon.getParameter(['projector_geometry' ,
+                                                            'output_geometry',
+                                                            'input_sinogram',
+                                                         'SlicesZ'])
+
+    #fistaRecon.setParameter(number_of_iterations = 3)
+    iterFISTA = fistaRecon.getParameter('number_of_iterations')
+    # errors vector (if the ground truth is given)
+    Resid_error = numpy.zeros((iterFISTA));
+    # objective function values vector
+    objective = numpy.zeros((iterFISTA)); 
+
+      
+    t = 1
+
+
+    print ("starting iterations")
+##    % Outer FISTA iterations loop
+    for i in range(fistaRecon.getParameter('number_of_iterations')):
+        X_old = X.copy()
+        t_old = t
+        r_old = fistaRecon.r.copy()
+        if fistaRecon.getParameter('projector_geometry')['type'] == 'parallel' or \
+           fistaRecon.getParameter('projector_geometry')['type'] == 'fanflat' or \
+           fistaRecon.getParameter('projector_geometry')['type'] == 'fanflat_vec' :
+            # if the geometry is parallel use slice-by-slice
+            # projection-backprojection routine
+            #sino_updt = zeros(size(sino),'single');
+            proj_geomT = proj_geom.copy()
+            proj_geomT['DetectorRowCount'] = 1
+            vol_geomT = vol_geom.copy()
+            vol_geomT['GridSliceCount'] = 1;
+            sino_updt = numpy.zeros(numpy.shape(sino), dtype=numpy.float)
+            for kkk in range(SlicesZ):
+                sino_id, sino_updt[kkk] = \
+                         astra.creators.create_sino3d_gpu(
+                             X_t[kkk:kkk+1], proj_geom, vol_geom)
+                astra.matlab.data3d('delete', sino_id)
+        else:
+            # for divergent 3D geometry (watch the GPU memory overflow in
+            # ASTRA versions < 1.8)
+            #[sino_id, sino_updt] = astra_create_sino3d_cuda(X_t, proj_geom, vol_geom);
+            sino_id, sino_updt = astra.creators.create_sino3d_gpu(
+                X_t, proj_geom, vol_geom)
+
+        ## RING REMOVAL
+        residual = fistaRecon.residual
+        lambdaR_L1 , alpha_ring , weights , L_const= \
+                   fistaRecon.getParameter(['ring_lambda_R_L1',
+                                           'ring_alpha' , 'weights',
+                                           'Lipschitz_constant'])
+        r_x = fistaRecon.r_x
+        SlicesZ, anglesNumb, Detectors = \
+                    numpy.shape(fistaRecon.getParameter('input_sinogram'))
+        if lambdaR_L1 > 0 :
+             print ("ring removal")
+             for kkk in range(anglesNumb):
+                 
+                 residual[:,kkk,:] = (weights[:,kkk,:]).squeeze() * \
+                                       ((sino_updt[:,kkk,:]).squeeze() - \
+                                        (sino[:,kkk,:]).squeeze() -\
+                                        (alpha_ring * r_x)
+                                        )
+             vec = residual.sum(axis = 1)
+             #if SlicesZ > 1:
+             #    vec = vec[:,1,:].squeeze()
+             fistaRecon.r = (r_x - (1./L_const) * vec).copy()
+             objective[i] = (0.5 * (residual ** 2).sum())
+##            % the ring removal part (Group-Huber fidelity)
+##            for kkk = 1:anglesNumb
+##                residual(:,kkk,:) =  squeeze(weights(:,kkk,:)).*
+##                 (squeeze(sino_updt(:,kkk,:)) -
+##                 (squeeze(sino(:,kkk,:)) - alpha_ring.*r_x));
+##            end
+##            vec = sum(residual,2);
+##            if (SlicesZ > 1)
+##                vec = squeeze(vec(:,1,:));
+##            end
+##            r = r_x - (1./L_const).*vec;
+##            objective(i) = (0.5*sum(residual(:).^2)); % for the objective function output
+
+        
+
+        # Projection/Backprojection Routine
+        if fistaRecon.getParameter('projector_geometry')['type'] == 'parallel' or \
+           fistaRecon.getParameter('projector_geometry')['type'] == 'fanflat' or\
+           fistaRecon.getParameter('projector_geometry')['type'] == 'fanflat_vec':
+            x_temp = numpy.zeros(numpy.shape(X),dtype=numpy.float32)
+            print ("Projection/Backprojection Routine")
+            for kkk in range(SlicesZ):
+                
+                x_id, x_temp[kkk] = \
+                         astra.creators.create_backprojection3d_gpu(
+                             residual[kkk:kkk+1],
+                             proj_geomT, vol_geomT)
+                astra.matlab.data3d('delete', x_id)
+        else:
+            x_id, x_temp = \
+                  astra.creators.create_backprojection3d_gpu(
+                      residual, proj_geom, vol_geom)            
+
+        X = X_t - (1/L_const) * x_temp
+        astra.matlab.data3d('delete', sino_id)
+        astra.matlab.data3d('delete', x_id)
+        
+
+        ## REGULARIZATION
+        ## SKIPPING FOR NOW
+        ## Should be simpli
+        # regularizer = fistaRecon.getParameter('regularizer')
+        # for slices:
+        # out = regularizer(input=X)
+        print ("skipping regularizer")
+
+
+        ## FINAL
+        print ("final")
+        lambdaR_L1 = fistaRecon.getParameter('ring_lambda_R_L1')
+        if lambdaR_L1 > 0:
+            fistaRecon.r = numpy.max(
+                numpy.abs(fistaRecon.r) - lambdaR_L1 , 0) * \
+                numpy.sign(fistaRecon.r)
+        t = (1 + numpy.sqrt(1 + 4 * t**2))/2
+        X_t = X + (((t_old -1)/t) * (X - X_old))
+
+        if lambdaR_L1 > 0:
+            fistaRecon.r_x = fistaRecon.r + \
+                             (((t_old-1)/t) * (fistaRecon.r - r_old))
+
+        if fistaRecon.getParameter('region_of_interest') is None:
+            string = 'Iteration Number {0} | Objective {1} \n'
+            print (string.format( i, objective[i]))
+        else:
+            ROI , X_ideal = fistaRecon.getParameter('region_of_interest',
+                                                    'ideal_image')
+            
+            Resid_error[i] = RMSE(X*ROI, X_ideal*ROI)
+            string = 'Iteration Number {0} | RMS Error {1} | Objective {2} \n'
+            print (string.format(i,Resid_error[i], objective[i]))
+            
+##        if (lambdaR_L1 > 0)
+##            r =  max(abs(r)-lambdaR_L1, 0).*sign(r); % soft-thresholding operator for ring vector
+##        end
+##        
+##        t = (1 + sqrt(1 + 4*t^2))/2; % updating t
+##        X_t = X + ((t_old-1)/t).*(X - X_old); % updating X
+##        
+##        if (lambdaR_L1 > 0)
+##            r_x = r + ((t_old-1)/t).*(r - r_old); % updating r
+##        end
+##        
+##        if (show == 1)
+##            figure(10); imshow(X(:,:,slice), [0 maxvalplot]);
+##            if (lambdaR_L1 > 0)
+##                figure(11); plot(r); title('Rings offset vector')
+##            end
+##            pause(0.01);
+##        end
+##        if (strcmp(X_ideal, 'none' ) == 0)
+##            Resid_error(i) = RMSE(X(ROI), X_ideal(ROI));
+##            fprintf('%s %i %s %s %.4f  %s %s %f \n', 'Iteration Number:', i, '|', 'Error RMSE:', Resid_error(i), '|', 'Objective:', objective(i));
+##        else
+##            fprintf('%s %i  %s %s %f \n', 'Iteration Number:', i, '|', 'Objective:', objective(i));
+##        end
+else:
+    fistaRecon = FISTAReconstructor(proj_geom,
+                                vol_geom,
+                                Sino3D ,
+                                weights=Weights3D)
+
+    print ("Lipschitz Constant {0}".format(fistaRecon.pars['Lipschitz_constant']))
+    fistaRecon.setParameter(number_of_iterations = 12)
+    fistaRecon.setParameter(Lipschitz_constant = 767893952.0)
+    fistaRecon.setParameter(ring_alpha = 21)
+    fistaRecon.setParameter(ring_lambda_R_L1 = 0.002)
+    fistaRecon.prepareForIteration()
+    X = fistaRecon.iterate(numpy.load("X.npy"))
+    numpy.save("X_out.npy", X)
diff --git a/supp/sino_add_artifacts.m b/supp/sino_add_artifacts.m
new file mode 100644
index 0000000..f601914
--- /dev/null
+++ b/supp/sino_add_artifacts.m
@@ -0,0 +1,33 @@
+function sino_artifacts = sino_add_artifacts(sino,artifact_type)
+% function to add various distortions to the sinogram space, current
+% version includes: random rings and zingers (streaks)
+% Input: 
+% 1. sinogram
+% 2. artifact type: 'rings' or 'zingers' (streaks)
+
+
+[Detectors, anglesNumb, SlicesZ] = size(sino);
+fprintf('%s %i %s %i %s %i %s \n', 'Sinogram has a dimension of', Detectors, 'detectors;', anglesNumb, 'projections;', SlicesZ, 'vertical slices.');
+
+sino_artifacts = sino;
+
+if (strcmp(artifact_type,'rings'))
+    fprintf('%s \n', 'Adding rings...');    
+    NumRings = round(Detectors/20); % Number of rings relatively to the size of Detectors
+    IntenOff = linspace(0.05,0.5,NumRings); % the intensity of rings in the selected range
+    
+    for k = 1:SlicesZ
+        % generate random indices to propagate rings
+        RandInd = randperm(Detectors,Detectors);
+        for jj = 1:NumRings
+            ind_c = RandInd(jj);
+            sino_artifacts(ind_c,1:end,k) = sino_artifacts(ind_c,1:end,k) + IntenOff(jj).*sino_artifacts(ind_c,1:end,k); % generate a constant offset            
+        end
+        
+    end
+elseif (strcmp(artifact_type,'zingers'))
+    fprintf('%s \n', 'Adding zingers...');
+else
+    fprintf('%s \n', 'Nothing selected, the same sinogram returned...');
+end
+end
\ No newline at end of file