code refactoring step1

32 files changed, 5923 insertions, 0 deletions

diff --git a/Wrappers/CMakeLists.txt b/Wrappers/CMakeLists.txt
new file mode 100644
index 0000000..cbe2fec
--- /dev/null
+++ b/Wrappers/CMakeLists.txt
@@ -0,0 +1,14 @@
+#   Copyright 2017 Edoardo Pasca
+#
+#   Licensed under the Apache License, Version 2.0 (the "License");
+#   you may not use this file except in compliance with the License.
+#   You may obtain a copy of the License at
+#
+#       http://www.apache.org/licenses/LICENSE-2.0
+#
+#   Unless required by applicable law or agreed to in writing, software
+#   distributed under the License is distributed on an "AS IS" BASIS,
+#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#   See the License for the specific language governing permissions and
+#   limitations under the License.
+add_subdirectory(Python)
\ No newline at end of file
diff --git a/Wrappers/Matlab/FISTA_REC.m b/Wrappers/Matlab/FISTA_REC.m
new file mode 100644
index 0000000..d717a03
--- /dev/null
+++ b/Wrappers/Matlab/FISTA_REC.m
@@ -0,0 +1,704 @@
+function [X,  output] = FISTA_REC(params)
+
+% <<<< FISTA-based reconstruction routine using ASTRA-toolbox >>>>
+% This code solves regularised PWLS problem using FISTA approach.
+% The code contains multiple regularisation penalties as well as it can be
+% accelerated by using ordered-subset version. Various projection
+% geometries supported.
+
+% DISCLAIMER
+% It is recommended to use ASTRA version 1.8 or later in order to avoid
+% crashing due to GPU memory overflow for big datasets
+
+% ___Input___:
+% params.[] file:
+%----------------General Parameters------------------------
+%       - .proj_geom (geometry of the projector) [required]
+%       - .vol_geom (geometry of the reconstructed object) [required]
+%       - .sino (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 for the PWLS model, size of the sinogram)
+%       - .fidelity (use 'studentt' fidelity)
+%       - .ROI (Region-of-interest, only if X_ideal is given)
+%       - .initialize (a 'warm start' using SIRT method from ASTRA)
+%----------------Regularization choices------------------------
+%       1 .Regul_Lambda_FGPTV (FGP-TV regularization parameter)
+%       2 .Regul_Lambda_SBTV (SplitBregman-TV regularization parameter)
+%       3 .Regul_LambdaLLT (Higher order LLT regularization parameter)
+%          3.1 .Regul_tauLLT (time step parameter for LLT (HO) term)
+%       4 .Regul_LambdaPatchBased_CPU (Patch-based nonlocal regularization parameter)
+%          4.1  .Regul_PB_SearchW (ratio of the searching window (e.g. 3 = (2*3+1) = 7 pixels window))
+%          4.2  .Regul_PB_SimilW (ratio of the similarity window (e.g. 1 = (2*1+1) = 3 pixels window))
+%          4.3  .Regul_PB_h (PB penalty function threshold)
+%       5 .Regul_LambdaPatchBased_GPU (Patch-based nonlocal regularization parameter)
+%          5.1  .Regul_PB_SearchW (ratio of the searching window (e.g. 3 = (2*3+1) = 7 pixels window))
+%          5.2  .Regul_PB_SimilW (ratio of the similarity window (e.g. 1 = (2*1+1) = 3 pixels window))
+%          5.3  .Regul_PB_h (PB penalty function threshold)
+%       6 .Regul_LambdaDiffHO (Higher-Order Diffusion regularization parameter)
+%          6.1  .Regul_DiffHO_EdgePar (edge-preserving noise related parameter)
+%       7 .Regul_LambdaTGV (Total Generalized variation regularization parameter)
+%       - .Regul_tol (tolerance to terminate regul iterations, default 1.0e-04)
+%       - .Regul_Iterations (iterations for the selected penalty, default 25)
+%       - .Regul_Dimension ('2D' or '3D' way to apply regularization, '3D' is the default)
+%----------------Ring removal------------------------
+%       - .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
+
+% Dealing with input parameters
+if (isfield(params,'proj_geom') == 0)
+    error('%s \n', 'Please provide ASTRA projection geometry - proj_geom');
+else
+    proj_geom = params.proj_geom;
+end
+if (isfield(params,'vol_geom') == 0)
+    error('%s \n', 'Please provide ASTRA object geometry - vol_geom');
+else
+    vol_geom = params.vol_geom;
+end
+N = params.vol_geom.GridColCount;
+if (isfield(params,'sino'))
+    sino = params.sino;
+    [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.');
+else
+    error('%s \n', 'Please provide a sinogram');
+end
+if (isfield(params,'iterFISTA'))
+    iterFISTA = params.iterFISTA;
+else
+    iterFISTA = 40;
+end
+if (isfield(params,'weights'))
+    weights = params.weights;
+else
+    weights = ones(size(sino));
+end
+if (isfield(params,'fidelity'))
+    studentt = 0;
+    if (strcmp(params.fidelity,'studentt') == 1)
+        studentt = 1;
+    end
+else
+    studentt = 0;
+end
+if (isfield(params,'L_const'))
+    L_const = params.L_const;
+else
+    % using Power method (PM) to establish L constant
+    fprintf('%s %s %s \n', 'Calculating Lipshitz constant for',proj_geom.type, 'beam geometry...');
+    if (strcmp(proj_geom.type,'parallel') || strcmp(proj_geom.type,'fanflat') || strcmp(proj_geom.type,'fanflat_vec'))
+        % for 2D geometry we can do just one selected slice
+        niter = 15; % number of iteration for the PM
+        x1 = rand(N,N,1);
+        sqweight = sqrt(weights(:,:,1));
+        [sino_id, y] = astra_create_sino_cuda(x1, proj_geom, vol_geom);
+        y = sqweight.*y';
+        astra_mex_data2d('delete', sino_id);
+        for i = 1:niter
+            [x1] = astra_create_backprojection_cuda((sqweight.*y)', proj_geom, vol_geom);
+            s = norm(x1(:));
+            x1 = x1./s;
+            [sino_id, y] = astra_create_sino_cuda(x1, proj_geom, vol_geom);
+            y = sqweight.*y';
+            astra_mex_data2d('delete', sino_id);
+        end
+    elseif (strcmp(proj_geom.type,'cone') || strcmp(proj_geom.type,'parallel3d') || strcmp(proj_geom.type,'parallel3d_vec') || strcmp(proj_geom.type,'cone_vec'))
+        % 3D geometry
+        niter = 8; % number of iteration for PM
+        x1 = rand(N,N,SlicesZ);
+        sqweight = sqrt(weights);
+        [sino_id, y] = astra_create_sino3d_cuda(x1, proj_geom, vol_geom);
+        y = sqweight.*y;
+        astra_mex_data3d('delete', sino_id);
+        
+        for i = 1:niter
+            [id,x1] = astra_create_backprojection3d_cuda(sqweight.*y, proj_geom, vol_geom);
+            s = norm(x1(:));
+            x1 = x1/s;
+            [sino_id, y] = astra_create_sino3d_cuda(x1, proj_geom, vol_geom);
+            y = sqweight.*y;
+            astra_mex_data3d('delete', sino_id);
+            astra_mex_data3d('delete', id);
+        end
+        clear x1
+    else
+        error('%s \n', 'No suitable geometry has been found!');
+    end
+    L_const = s;
+end
+if (isfield(params,'X_ideal'))
+    X_ideal = params.X_ideal;
+else
+    X_ideal = 'none';
+end
+if (isfield(params,'ROI'))
+    ROI = params.ROI;
+else
+    ROI = find(X_ideal>=0.0);
+end
+if (isfield(params,'Regul_Lambda_FGPTV'))
+    lambdaFGP_TV = params.Regul_Lambda_FGPTV;
+else
+    lambdaFGP_TV = 0;
+end
+if (isfield(params,'Regul_Lambda_SBTV'))
+    lambdaSB_TV = params.Regul_Lambda_SBTV;
+else
+    lambdaSB_TV = 0;
+end
+if (isfield(params,'Regul_tol'))
+    tol = params.Regul_tol;
+else
+    tol = 1.0e-05;
+end
+if (isfield(params,'Regul_Iterations'))
+    IterationsRegul = params.Regul_Iterations;
+else
+    IterationsRegul = 45;
+end
+if (isfield(params,'Regul_LambdaLLT'))
+    lambdaHO = params.Regul_LambdaLLT;
+else
+    lambdaHO = 0;
+end
+if (isfield(params,'Regul_iterHO'))
+    iterHO = params.Regul_iterHO;
+else
+    iterHO = 50;
+end
+if (isfield(params,'Regul_tauLLT'))
+    tauHO = params.Regul_tauLLT;
+else
+    tauHO = 0.0001;
+end
+if (isfield(params,'Regul_LambdaPatchBased_CPU'))
+    lambdaPB = params.Regul_LambdaPatchBased_CPU;
+else
+    lambdaPB = 0;
+end
+if (isfield(params,'Regul_LambdaPatchBased_GPU'))
+    lambdaPB_GPU = params.Regul_LambdaPatchBased_GPU;
+else
+    lambdaPB_GPU = 0;
+end
+if (isfield(params,'Regul_PB_SearchW'))
+    SearchW = params.Regul_PB_SearchW;
+else
+    SearchW = 3; % default
+end
+if (isfield(params,'Regul_PB_SimilW'))
+    SimilW = params.Regul_PB_SimilW;
+else
+    SimilW = 1; % default
+end
+if (isfield(params,'Regul_PB_h'))
+    h_PB = params.Regul_PB_h;
+else
+    h_PB = 0.1; % default
+end
+if (isfield(params,'Regul_LambdaDiffHO'))
+    LambdaDiff_HO = params.Regul_LambdaDiffHO;
+else
+    LambdaDiff_HO = 0;
+end
+if (isfield(params,'Regul_DiffHO_EdgePar'))
+    LambdaDiff_HO_EdgePar = params.Regul_DiffHO_EdgePar;
+else
+    LambdaDiff_HO_EdgePar = 0.01;
+end
+if (isfield(params,'Regul_LambdaTGV'))
+    LambdaTGV = params.Regul_LambdaTGV;
+else
+    LambdaTGV = 0;
+end
+if (isfield(params,'Ring_LambdaR_L1'))
+    lambdaR_L1 = params.Ring_LambdaR_L1;
+else
+    lambdaR_L1 = 0;
+end
+if (isfield(params,'Ring_Alpha'))
+    alpha_ring = params.Ring_Alpha; % higher values can accelerate ring removal procedure
+else
+    alpha_ring = 1;
+end
+if (isfield(params,'Regul_Dimension'))
+    Dimension = params.Regul_Dimension;
+    if ((strcmp('2D', Dimension) ~= 1) && (strcmp('3D', Dimension) ~= 1))
+        Dimension = '3D';
+    end
+else
+    Dimension = '3D';
+end
+if (isfield(params,'show'))
+    show = params.show;
+else
+    show = 0;
+end
+if (isfield(params,'maxvalplot'))
+    maxvalplot = params.maxvalplot;
+else
+    maxvalplot = 1;
+end
+if (isfield(params,'slice'))
+    slice = params.slice;
+else
+    slice = 1;
+end
+if (isfield(params,'initialize'))
+    X  = params.initialize;
+    if ((size(X,1) ~= N) || (size(X,2) ~= N) || (size(X,3) ~= SlicesZ))
+        error('%s \n', 'The initialized volume has different dimensions!');
+    end
+else
+    X = zeros(N,N,SlicesZ, 'single'); % storage for the solution
+end
+if (isfield(params,'subsets'))
+    % Ordered Subsets reorganisation of data and angles
+    subsets = params.subsets; % subsets number
+    angles = proj_geom.ProjectionAngles;
+    binEdges = linspace(min(angles),max(angles),subsets+1);
+    
+    % assign values to bins
+    [binsDiscr,~] = histc(angles, [binEdges(1:end-1) Inf]);
+    
+    % get rearranged subset indices
+    IndicesReorg = zeros(length(angles),1);
+    counterM = 0;
+    for ii = 1:max(binsDiscr(:))
+        counter = 0;
+        for jj = 1:subsets
+            curr_index = ii+jj-1 + counter;
+            if (binsDiscr(jj) >= ii)
+                counterM = counterM + 1;
+                IndicesReorg(counterM) = curr_index;
+            end
+            counter = (counter + binsDiscr(jj)) - 1;
+        end
+    end
+else
+    subsets = 0; % Classical FISTA
+end
+
+%----------------Reconstruction part------------------------
+Resid_error = zeros(iterFISTA,1); % errors vector (if the ground truth is given)
+objective = zeros(iterFISTA,1); % objective function values vector
+
+
+if (subsets == 0)
+    % Classical FISTA
+    t = 1;
+    X_t = X;
+    
+    r = zeros(Detectors,SlicesZ, 'single'); % 2D array (for 3D data) of sparse "ring" vectors
+    r_x = r; % another ring variable
+    residual = zeros(size(sino),'single');
+    
+    % Outer FISTA iterations loop
+    for i = 1:iterFISTA
+        
+        X_old = X;
+        t_old = t;
+        r_old = r;
+        
+        
+        if (strcmp(proj_geom.type,'parallel') || strcmp(proj_geom.type,'fanflat') || strcmp(proj_geom.type,'fanflat_vec'))
+            % if geometry is 2D use slice-by-slice projection-backprojection routine
+            sino_updt = zeros(size(sino),'single');
+            for kkk = 1:SlicesZ
+                [sino_id, sinoT] = astra_create_sino_cuda(X_t(:,:,kkk), proj_geom, vol_geom);
+                sino_updt(:,:,kkk) = sinoT';
+                astra_mex_data2d('delete', sino_id);
+            end
+        else
+            % for 3D geometry (watch the GPU memory overflow in earlier ASTRA versions < 1.8)
+            [sino_id, sino_updt] = astra_create_sino3d_cuda(X_t, proj_geom, vol_geom);
+            astra_mex_data3d('delete', sino_id);
+        end
+        
+        if (lambdaR_L1 > 0)
+            % 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
+        elseif (studentt > 0)
+            % artifacts removal with Students t penalty
+            residual = weights.*(sino_updt - sino);
+            for kkk = 1:SlicesZ
+                res_vec = reshape(residual(:,:,kkk), Detectors*anglesNumb, 1); % 1D vectorized sinogram
+                %s = 100;
+                %gr = (2)*res_vec./(s*2 + conj(res_vec).*res_vec);
+                [ff, gr] = studentst(res_vec, 1);
+                residual(:,:,kkk) = reshape(gr, Detectors, anglesNumb);
+            end
+            objective(i) = ff; % for the objective function output
+        else
+            % no ring removal (LS model)
+            residual = weights.*(sino_updt - sino);
+            objective(i) = 0.5*norm(residual(:)); % for the objective function output
+        end
+        
+        % if the geometry is 2D use slice-by-slice projection-backprojection routine
+        if (strcmp(proj_geom.type,'parallel') || strcmp(proj_geom.type,'fanflat') || strcmp(proj_geom.type,'fanflat_vec'))
+            x_temp = zeros(size(X),'single');
+            for kkk = 1:SlicesZ
+                [x_temp(:,:,kkk)] = astra_create_backprojection_cuda(squeeze(residual(:,:,kkk))', proj_geom, vol_geom);
+            end
+        else
+            [id, x_temp] = astra_create_backprojection3d_cuda(residual, proj_geom, vol_geom);
+            astra_mex_data3d('delete', id);
+        end
+        X = X_t - (1/L_const).*x_temp;
+        
+        % ----------------Regularization part------------------------%
+        if (lambdaFGP_TV > 0)
+            % FGP-TV regularization
+            if ((strcmp('2D', Dimension) == 1))
+                % 2D regularization
+                for kkk = 1:SlicesZ
+                    [X(:,:,kkk), f_val] = FGP_TV(single(X(:,:,kkk)), lambdaFGP_TV/L_const, IterationsRegul, tol, 'iso');
+                end
+            else
+                % 3D regularization
+                [X, f_val] = FGP_TV(single(X), lambdaFGP_TV/L_const, IterationsRegul, tol, 'iso');
+            end
+            objective(i) = (objective(i) + f_val)./(Detectors*anglesNumb*SlicesZ);
+        end
+        if (lambdaSB_TV > 0)
+            % Split Bregman regularization
+            if ((strcmp('2D', Dimension) == 1))
+                % 2D regularization
+                for kkk = 1:SlicesZ
+                    X(:,:,kkk) = SplitBregman_TV(single(X(:,:,kkk)), lambdaSB_TV/L_const, IterationsRegul, tol);  % (more memory efficent)
+                end
+            else
+                % 3D regularization
+                X = SplitBregman_TV(single(X), lambdaSB_TV/L_const, IterationsRegul, tol);  % (more memory efficent)
+            end
+        end
+        if (lambdaHO > 0)
+            % Higher Order (LLT) regularization
+            X2 = zeros(N,N,SlicesZ,'single');
+            if ((strcmp('2D', Dimension) == 1))
+                % 2D regularization
+                for kkk = 1:SlicesZ
+                    X2(:,:,kkk) = LLT_model(single(X(:,:,kkk)), lambdaHO/L_const, tauHO, iterHO, 3.0e-05, 0);
+                end
+            else
+                % 3D regularization
+                X2 = LLT_model(single(X), lambdaHO/L_const, tauHO, iterHO, 3.0e-05, 0);
+            end
+            X = 0.5.*(X + X2); % averaged combination of two solutions
+            
+        end
+        if (lambdaPB > 0)
+            % Patch-Based regularization (can be very slow on CPU)
+            if ((strcmp('2D', Dimension) == 1))
+                % 2D regularization
+                for kkk = 1:SlicesZ
+                    X(:,:,kkk) = PatchBased_Regul(single(X(:,:,kkk)), SearchW, SimilW, h_PB, lambdaPB/L_const);
+                end
+            else
+                X = PatchBased_Regul(single(X), SearchW, SimilW, h_PB, lambdaPB/L_const);
+            end
+        end
+        if (lambdaPB_GPU > 0)
+            % Patch-Based regularization (GPU CUDA implementation)
+            if ((strcmp('2D', Dimension) == 1))
+                % 2D regularization
+                for kkk = 1:SlicesZ
+                    X(:,:,kkk) = NLM_GPU(single(X(:,:,kkk)), SearchW, SimilW, h_PB, lambdaPB_GPU/L_const);
+                end
+            else
+                X = NLM_GPU(single(X), SearchW, SimilW, h_PB, lambdaPB_GPU/L_const);
+            end
+        end
+        if (LambdaDiff_HO > 0)
+            % Higher-order diffusion penalty (GPU CUDA implementation)
+            if ((strcmp('2D', Dimension) == 1))
+                % 2D regularization
+                for kkk = 1:SlicesZ
+                    X(:,:,kkk) = Diff4thHajiaboli_GPU(single(X(:,:,kkk)), LambdaDiff_HO_EdgePar, LambdaDiff_HO/L_const, IterationsRegul);
+                end
+            else
+                X = Diff4thHajiaboli_GPU(X, LambdaDiff_HO_EdgePar, LambdaDiff_HO/L_const, IterationsRegul);
+            end
+        end
+        if (LambdaTGV > 0)
+            % Total Generalized variation (currently only 2D)
+            lamTGV1 = 1.1; % smoothing trade-off parameters, see Pock's paper
+            lamTGV2 = 0.8; % second-order term
+            for kkk = 1:SlicesZ
+                X(:,:,kkk) = TGV_PD(single(X(:,:,kkk)), LambdaTGV/L_const, lamTGV1, lamTGV2, IterationsRegul);
+            end
+        end
+        
+        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
+    end
+else
+    % Ordered Subsets (OS) FISTA reconstruction routine (normally one order of magnitude faster than the classical version)
+    t = 1;
+    X_t = X;
+    proj_geomSUB = proj_geom;
+    
+    r = zeros(Detectors,SlicesZ, 'single'); % 2D array (for 3D data) of sparse "ring" vectors
+    r_x = r; % another ring variable
+    residual2 = zeros(size(sino),'single');
+    sino_updt_FULL = zeros(size(sino),'single');
+    
+    
+    % Outer FISTA iterations loop
+    for i = 1:iterFISTA
+        
+        if ((i > 1) && (lambdaR_L1 > 0))
+            % in order to make Group-Huber fidelity work with ordered subsets
+            % we still need to work with full sinogram
+            
+            % the offset variable must be calculated for the whole
+            % updated sinogram - sino_updt_FULL
+            for kkk = 1:anglesNumb
+                residual2(:,kkk,:) = squeeze(weights(:,kkk,:)).*(squeeze(sino_updt_FULL(:,kkk,:)) - (squeeze(sino(:,kkk,:)) - alpha_ring.*r_x));
+            end
+            
+            r_old = r;
+            vec = sum(residual2,2);
+            if (SlicesZ > 1)
+                vec = squeeze(vec(:,1,:));
+            end
+            r = r_x - (1./L_const).*vec; % update ring variable
+        end
+                
+       % subsets loop
+        counterInd = 1;        
+        for ss = 1:subsets
+            X_old = X;
+            t_old = t;
+            
+            numProjSub = binsDiscr(ss); % the number of projections per subset
+            sino_updt_Sub = zeros(Detectors, numProjSub, SlicesZ,'single');
+            CurrSubIndeces = IndicesReorg(counterInd:(counterInd + numProjSub - 1)); % extract indeces attached to the subset
+            proj_geomSUB.ProjectionAngles = angles(CurrSubIndeces);
+            
+            if (strcmp(proj_geom.type,'parallel') || strcmp(proj_geom.type,'fanflat') || strcmp(proj_geom.type,'fanflat_vec'))
+                % if geometry is 2D use slice-by-slice projection-backprojection routine
+                for kkk = 1:SlicesZ
+                    [sino_id, sinoT] = astra_create_sino_cuda(X_t(:,:,kkk), proj_geomSUB, vol_geom);
+                    sino_updt_Sub(:,:,kkk) = sinoT';
+                    astra_mex_data2d('delete', sino_id);
+                end
+            else
+                % for 3D geometry (watch the GPU memory overflow in earlier ASTRA versions < 1.8)
+                [sino_id, sino_updt_Sub] = astra_create_sino3d_cuda(X_t, proj_geomSUB, vol_geom);
+                astra_mex_data3d('delete', sino_id);
+            end
+            
+            if (lambdaR_L1 > 0)
+                % Group-Huber fidelity (ring removal)
+                residualSub = zeros(Detectors, numProjSub, SlicesZ,'single'); % residual for a chosen subset
+                for kkk = 1:numProjSub
+                    indC = CurrSubIndeces(kkk);
+                    residualSub(:,kkk,:) =  squeeze(weights(:,indC,:)).*(squeeze(sino_updt_Sub(:,kkk,:)) - (squeeze(sino(:,indC,:)) - alpha_ring.*r_x));
+                    sino_updt_FULL(:,indC,:) = squeeze(sino_updt_Sub(:,kkk,:)); % filling the full sinogram
+                end
+                
+            elseif (studentt > 0)
+                % student t data fidelity
+                
+                % artifacts removal with Students t penalty
+                residualSub = squeeze(weights(:,CurrSubIndeces,:)).*(sino_updt_Sub - squeeze(sino(:,CurrSubIndeces,:)));
+                
+                for kkk = 1:SlicesZ
+                    res_vec = reshape(residualSub(:,:,kkk), Detectors*numProjSub, 1); % 1D vectorized sinogram
+                    %s = 100;
+                    %gr = (2)*res_vec./(s*2 + conj(res_vec).*res_vec);
+                    [ff, gr] = studentst(res_vec, 1);
+                    residualSub(:,:,kkk) = reshape(gr, Detectors, numProjSub);
+                end
+                objective(i) = ff; % for the objective function output
+            else
+                % PWLS model                
+                residualSub = squeeze(weights(:,CurrSubIndeces,:)).*(sino_updt_Sub - squeeze(sino(:,CurrSubIndeces,:)));
+                objective(i) = 0.5*norm(residualSub(:)); % for the objective function output
+            end
+            
+            % perform backprojection of a subset
+            if (strcmp(proj_geom.type,'parallel') || strcmp(proj_geom.type,'fanflat') || strcmp(proj_geom.type,'fanflat_vec'))
+                % if geometry is 2D use slice-by-slice projection-backprojection routine
+                x_temp = zeros(size(X),'single');
+                for kkk = 1:SlicesZ
+                    [x_temp(:,:,kkk)] = astra_create_backprojection_cuda(squeeze(residualSub(:,:,kkk))', proj_geomSUB, vol_geom);
+                end
+            else
+                [id, x_temp] = astra_create_backprojection3d_cuda(residualSub, proj_geomSUB, vol_geom);
+                astra_mex_data3d('delete', id);
+            end
+            
+            X = X_t - (1/L_const).*x_temp;
+            
+            % ----------------Regularization part------------------------%
+            if (lambdaFGP_TV > 0)
+                % FGP-TV regularization
+                if ((strcmp('2D', Dimension) == 1))
+                    % 2D regularization
+                    for kkk = 1:SlicesZ
+                        [X(:,:,kkk), f_val] = FGP_TV(single(X(:,:,kkk)), lambdaFGP_TV/(subsets*L_const), IterationsRegul, tol, 'iso');
+                    end
+                else
+                    % 3D regularization
+                    [X, f_val] = FGP_TV(single(X), lambdaFGP_TV/(subsets*L_const), IterationsRegul, tol, 'iso');
+                end
+                objective(i) = objective(i) + f_val;
+            end
+            if (lambdaSB_TV > 0)
+                % Split Bregman regularization
+                if ((strcmp('2D', Dimension) == 1))
+                    % 2D regularization
+                    for kkk = 1:SlicesZ
+                        X(:,:,kkk) = SplitBregman_TV(single(X(:,:,kkk)), lambdaSB_TV/(subsets*L_const), IterationsRegul, tol);  % (more memory efficent)
+                    end
+                else
+                    % 3D regularization
+                    X = SplitBregman_TV(single(X), lambdaSB_TV/(subsets*L_const), IterationsRegul, tol);  % (more memory efficent)
+                end
+            end
+            if (lambdaHO > 0)
+                % Higher Order (LLT) regularization
+                X2 = zeros(N,N,SlicesZ,'single');
+                if ((strcmp('2D', Dimension) == 1))
+                    % 2D regularization
+                    for kkk = 1:SlicesZ
+                        X2(:,:,kkk) = LLT_model(single(X(:,:,kkk)), lambdaHO/(subsets*L_const), tauHO/subsets, iterHO, 2.0e-05, 0);
+                    end
+                else
+                    % 3D regularization
+                    X2 = LLT_model(single(X), lambdaHO/(subsets*L_const), tauHO/subsets, iterHO, 2.0e-05, 0);
+                end
+                X = 0.5.*(X + X2); % the averaged combination of two solutions
+            end
+            if (lambdaPB > 0)
+                % Patch-Based regularization (can be slow on CPU)
+                if ((strcmp('2D', Dimension) == 1))
+                    % 2D regularization
+                    for kkk = 1:SlicesZ
+                        X(:,:,kkk) = PatchBased_Regul(single(X(:,:,kkk)), SearchW, SimilW, h_PB, lambdaPB/(subsets*L_const));
+                    end
+                else
+                    X = PatchBased_Regul(single(X), SearchW, SimilW, h_PB, lambdaPB/(subsets*L_const));
+                end
+            end
+            if (lambdaPB_GPU > 0)
+                % Patch-Based regularization (GPU CUDA implementation)
+                if ((strcmp('2D', Dimension) == 1))
+                    % 2D regularization
+                    for kkk = 1:SlicesZ
+                        X(:,:,kkk) = NLM_GPU(single(X(:,:,kkk)), SearchW, SimilW, h_PB, lambdaPB_GPU/(subsets*L_const));
+                    end
+                else
+                    X = NLM_GPU(single(X), SearchW, SimilW, h_PB, lambdaPB_GPU/(subsets*L_const));
+                end
+            end
+            if (LambdaDiff_HO > 0)
+                % Higher-order diffusion penalty (GPU CUDA implementation)
+                if ((strcmp('2D', Dimension) == 1))
+                    % 2D regularization
+                    for kkk = 1:SlicesZ
+                        X(:,:,kkk) = Diff4thHajiaboli_GPU(single(X(:,:,kkk)), LambdaDiff_HO_EdgePar, LambdaDiff_HO/(subsets*L_const), round(IterationsRegul/subsets));
+                    end
+                else
+                    X = Diff4thHajiaboli_GPU(X, LambdaDiff_HO_EdgePar, LambdaDiff_HO/(subsets*L_const), round(IterationsRegul/subsets));
+                end
+            end
+            if (LambdaTGV > 0)
+                % Total Generalized variation (currently only 2D)
+                lamTGV1 = 1.1; % smoothing trade-off parameters, see Pock's paper
+                lamTGV2 = 0.5; % second-order term
+                for kkk = 1:SlicesZ
+                    X(:,:,kkk) = TGV_PD(single(X(:,:,kkk)), LambdaTGV/(subsets*L_const), lamTGV1, lamTGV2, IterationsRegul);
+                end
+            end
+            
+            t = (1 + sqrt(1 + 4*t^2))/2; % updating t
+            X_t = X + ((t_old-1)/t).*(X - X_old); % updating X
+            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
+            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
+    end
+end
+
+output.Resid_error = Resid_error;
+output.objective = objective;
+output.L_const = L_const;
+
+end
diff --git a/Wrappers/Matlab/compile_mex.m b/Wrappers/Matlab/compile_mex.m
new file mode 100644
index 0000000..66c05da
--- /dev/null
+++ b/Wrappers/Matlab/compile_mex.m
@@ -0,0 +1,11 @@
+% compile mex's in Matlab once
+cd regularizers_CPU/
+
+mex LLT_model.c LLT_model_core.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp"
+mex FGP_TV.c FGP_TV_core.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp"
+mex SplitBregman_TV.c SplitBregman_TV_core.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp"
+mex TGV_PD.c TGV_PD_core.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp"
+mex PatchBased_Regul.c PatchBased_Regul_core.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp"
+
+cd ../../
+cd demos
diff --git a/Wrappers/Matlab/studentst.m b/Wrappers/Matlab/studentst.m
new file mode 100644
index 0000000..99fed1e
--- /dev/null
+++ b/Wrappers/Matlab/studentst.m
@@ -0,0 +1,47 @@
+function [f,g,h,s,k] = studentst(r,k,s)
+% Students T penalty with 'auto-tuning'
+%
+% use:
+%   [f,g,h,{k,{s}}] = studentst(r)     - automatically fits s and k
+%   [f,g,h,{k,{s}}] = studentst(r,k)   - automatically fits s
+%   [f,g,h,{k,{s}}] = studentst(r,k,s) - use given s and k
+%
+% input:
+%   r - residual as column vector
+%   s - scale (optional)
+%   k - degrees of freedom (optional)
+% 
+% output:
+%   f - misfit (scalar)
+%   g - gradient (column vector)
+%   h - positive approximation of the Hessian (column vector, Hessian is a diagonal matrix)
+%   s,k - scale and degrees of freedom
+%
+% Tristan van Leeuwen, 2012.
+% tleeuwen@eos.ubc.ca
+
+% fit both s and k
+if nargin == 1
+    opts = optimset('maxFunEvals',1e2);
+    tmp = fminsearch(@(x)st(r,x(1),x(2)),[1;2],opts);
+    s   = tmp(1);
+    k   = tmp(2);
+end
+
+
+if nargin == 2
+    opts = optimset('maxFunEvals',1e2);
+    tmp = fminsearch(@(x)st(r,x,k),[1],opts);
+    s   = tmp(1);
+end
+
+% evaulate penalty
+[f,g,h] = st(r,s,k);
+
+
+function [f,g,h] = st(r,s,k)
+n = length(r);
+c = -n*(gammaln((k+1)/2) - gammaln(k/2) - .5*log(pi*s*k));
+f = c + .5*(k+1)*sum(log(1 + conj(r).*r/(s*k)));
+g = (k+1)*r./(s*k + conj(r).*r);
+h = (k+1)./(s*k + conj(r).*r);
diff --git a/Wrappers/Python/CMakeLists.txt b/Wrappers/Python/CMakeLists.txt
new file mode 100644
index 0000000..506159a
--- /dev/null
+++ b/Wrappers/Python/CMakeLists.txt
@@ -0,0 +1,183 @@
+#   Copyright 2017 Edoardo Pasca
+#
+#   Licensed under the Apache License, Version 2.0 (the "License");
+#   you may not use this file except in compliance with the License.
+#   You may obtain a copy of the License at
+#
+#       http://www.apache.org/licenses/LICENSE-2.0
+#
+#   Unless required by applicable law or agreed to in writing, software
+#   distributed under the License is distributed on an "AS IS" BASIS,
+#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#   See the License for the specific language governing permissions and
+#   limitations under the License.
+
+# 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)
+
+## 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
+					ERROR_VARIABLE _CONDA_ERR)
+			if(NOT _CONDA_RESULT)
+				string(REPLACE "\n" ";" ENV_LIST ${_CONDA_ENVS})
+				foreach(line ${ENV_LIST})
+				  string(REGEX MATCHALL "(.+)[*](.+)" match ${line})
+				  if (NOT ${match} EQUAL "")
+				    #message("MATCHED " ${CMAKE_MATCH_0})
+					#message("MATCHED " ${CMAKE_MATCH_1})
+					#message("MATCHED " ${CMAKE_MATCH_2})
+					string(STRIP ${CMAKE_MATCH_1} CONDA_ENVIRONMENT)
+					string(STRIP ${CMAKE_MATCH_2} CONDA_ENVIRONMENT_PATH)   
+				  endif()
+				endforeach()
+			else()
+				message(FATAL_ERROR "ERROR with conda command " ${_CONDA_ERR})
+			endif()
+
+if (${CONDA_ENVIRONMENT} AND ${CONDA_ENVIRONMENT_PATH})
+  message (FATAL_ERROR "CONDA NOT FOUND")
+else()
+  message("**********************************************************")
+  message("Using current conda environmnet " ${CONDA_ENVIRONMENT})
+  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})
+
+## 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")
+  set (CONDA_ENVIRONMENT_PREFIX "${CONDA_ENVIRONMENT_PATH}\\Library" CACHE PATH "env dir")
+  set (CONDA_ENVIRONMENT_LIBRARY_INC "${CONDA_ENVIRONMENT_PREFIX}\\include" CACHE PATH "env dir")
+elseif (UNIX)
+  #set (CONDA_ENVIRONMENT_PATH "/apps/anaconda/2.4/envs/${CONDA_ENVIRONMENT}" CACHE PATH "Main environment directory")
+  set (CONDA_ENVIRONMENT_PREFIX "${CONDA_ENVIRONMENT_PATH}/lib/python${PYTHON_VERSION_MAJOR}.${PYTHON_VERSION_MINOR}" CACHE PATH "env dir")
+  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)
+
+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)
+file(COPY ${CMAKE_CURRENT_SOURCE_DIR}/ccpi/reconstruction/DeviceModel.py DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/ccpi/reconstruction)
+file(COPY ${CMAKE_CURRENT_SOURCE_DIR}/ccpi/reconstruction/AstraDevice.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}/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 ${CONDA_EXECUTABLE}
+		install --force --use-local ccpi-fista=${CIL_VERSION} -c ccpi -c conda-forge
+		WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}
+		)
+
+add_custom_target(install-regularizers
+		COMMAND ${CONDA_EXECUTABLE}
+		install --force --use-local ccpi-regularizers=${CIL_VERSION} -c ccpi -c conda-forge
+		WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}
+		)
+### add tests
+
+#add_executable(RegularizersTest )
+#find_package(tiff)
+#if (TIFF_FOUND)
+#  message("LibTIFF Found")
+#  message("TIFF_INCLUDE_DIR " ${TIFF_INCLUDE_DIR})
+#  message("TIFF_LIBRARIES" ${TIFF_LIBRARIES})
+#else()
+#  message("LibTIFF not found")
+#endif()
diff --git a/Wrappers/Python/FindAnacondaEnvironment.cmake b/Wrappers/Python/FindAnacondaEnvironment.cmake
new file mode 100644
index 0000000..6475128
--- /dev/null
+++ b/Wrappers/Python/FindAnacondaEnvironment.cmake
@@ -0,0 +1,154 @@
+#   Copyright 2017 Edoardo Pasca
+#
+#   Licensed under the Apache License, Version 2.0 (the "License");
+#   you may not use this file except in compliance with the License.
+#   You may obtain a copy of the License at
+#
+#       http://www.apache.org/licenses/LICENSE-2.0
+#
+#   Unless required by applicable law or agreed to in writing, software
+#   distributed under the License is distributed on an "AS IS" BASIS,
+#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#   See the License for the specific language governing permissions and
+#   limitations under the License.
+
+# #.rst:
+# FindAnacondaEnvironment
+# --------------
+#
+# Find Python executable and library for a specific Anaconda environment
+#
+# This module finds the Python interpreter for a specific Anaconda enviroment, 
+# if installed and determines where the include files and libraries are.  
+# This code sets the following variables:
+#
+# ::
+#   PYTHONINTERP_FOUND         - if the Python interpret has been found
+#   PYTHON_EXECUTABLE          - the Python interpret found
+#   PYTHON_LIBRARY             - path to the python library
+#   PYTHON_INCLUDE_PATH        - path to where Python.h is found (deprecated)
+#   PYTHON_INCLUDE_DIRS        - path to where Python.h is found
+#   PYTHONLIBS_VERSION_STRING  - version of the Python libs found (since CMake 2.8.8)
+#   PYTHON_VERSION_MAJOR       - major Python version
+#   PYTHON_VERSION_MINOR       - minor Python version
+#   PYTHON_VERSION_PATCH       - patch Python version
+
+
+
+function (findPythonForAnacondaEnvironment env)
+	if (WIN32)
+	  file(TO_CMAKE_PATH ${env}/python.exe PYTHON_EXECUTABLE)
+        elseif (UNIX)
+  	  file(TO_CMAKE_PATH ${env}/bin/python PYTHON_EXECUTABLE)
+	endif()
+
+	
+	message("findPythonForAnacondaEnvironment Found Python Executable" ${PYTHON_EXECUTABLE})
+	####### FROM FindPythonInterpr ########
+	# determine python version string
+	if(PYTHON_EXECUTABLE)
+		execute_process(COMMAND "${PYTHON_EXECUTABLE}" -c
+								"import sys; sys.stdout.write(';'.join([str(x) for x in sys.version_info[:3]]))"
+						OUTPUT_VARIABLE _VERSION
+						RESULT_VARIABLE _PYTHON_VERSION_RESULT
+						ERROR_QUIET)
+		if(NOT _PYTHON_VERSION_RESULT)
+			string(REPLACE ";" "." _PYTHON_VERSION_STRING "${_VERSION}")
+			list(GET _VERSION 0 _PYTHON_VERSION_MAJOR)
+			list(GET _VERSION 1 _PYTHON_VERSION_MINOR)
+			list(GET _VERSION 2 _PYTHON_VERSION_PATCH)
+			if(PYTHON_VERSION_PATCH EQUAL 0)
+				# it's called "Python 2.7", not "2.7.0"
+				string(REGEX REPLACE "\\.0$" "" _PYTHON_VERSION_STRING "${PYTHON_VERSION_STRING}")
+			endif()
+		else()
+			# sys.version predates sys.version_info, so use that
+			execute_process(COMMAND "${PYTHON_EXECUTABLE}" -c "import sys; sys.stdout.write(sys.version)"
+							OUTPUT_VARIABLE _VERSION
+							RESULT_VARIABLE _PYTHON_VERSION_RESULT
+							ERROR_QUIET)
+			if(NOT _PYTHON_VERSION_RESULT)
+				string(REGEX REPLACE " .*" "" _PYTHON_VERSION_STRING "${_VERSION}")
+				string(REGEX REPLACE "^([0-9]+)\\.[0-9]+.*" "\\1" _PYTHON_VERSION_MAJOR "${PYTHON_VERSION_STRING}")
+				string(REGEX REPLACE "^[0-9]+\\.([0-9])+.*" "\\1" _PYTHON_VERSION_MINOR "${PYTHON_VERSION_STRING}")
+				if(PYTHON_VERSION_STRING MATCHES "^[0-9]+\\.[0-9]+\\.([0-9]+)")
+					set(PYTHON_VERSION_PATCH "${CMAKE_MATCH_1}")
+				else()
+					set(PYTHON_VERSION_PATCH "0")
+				endif()
+			else()
+				# sys.version was first documented for Python 1.5, so assume
+				# this is older.
+				set(PYTHON_VERSION_STRING "1.4" PARENT_SCOPE)
+				set(PYTHON_VERSION_MAJOR "1" PARENT_SCOPE)
+				set(PYTHON_VERSION_MINOR "4" PARENT_SCOPE)
+				set(PYTHON_VERSION_PATCH "0" PARENT_SCOPE)
+			endif()
+		endif()
+		unset(_PYTHON_VERSION_RESULT)
+		unset(_VERSION)
+	endif()
+	###############################################
+	
+	set (PYTHON_EXECUTABLE ${PYTHON_EXECUTABLE} PARENT_SCOPE)
+	set (PYTHONINTERP_FOUND "ON" PARENT_SCOPE)
+	set (PYTHON_VERSION_STRING ${_PYTHON_VERSION_STRING} PARENT_SCOPE)
+	set (PYTHON_VERSION_MAJOR ${_PYTHON_VERSION_MAJOR} PARENT_SCOPE)
+	set (PYTHON_VERSION_MINOR ${_PYTHON_VERSION_MINOR} PARENT_SCOPE)
+	set (PYTHON_VERSION_PATCH ${_PYTHON_VERSION_PATCH} PARENT_SCOPE)
+	message("My version found " ${PYTHON_VERSION_STRING})
+	## find conda executable
+	if (WIN32)
+	  set (CONDA_EXECUTABLE ${env}/Script/conda PARENT_SCOPE)
+	elseif(UNIX)
+	  set (CONDA_EXECUTABLE ${env}/bin/conda PARENT_SCOPE)
+	endif()
+endfunction()
+
+
+
+set(Python_ADDITIONAL_VERSIONS 3.5)
+
+find_package(PythonInterp)
+if (PYTHONINTERP_FOUND)
+  
+  message("Found interpret " ${PYTHON_EXECUTABLE})
+  message("Python Library " ${PYTHON_LIBRARY})
+  message("Python Include Dir " ${PYTHON_INCLUDE_DIR})
+  message("Python Include Path " ${PYTHON_INCLUDE_PATH})
+  
+  foreach(pv ${PYTHON_VERSION_STRING})
+    message("Found interpret " ${pv})
+  endforeach()
+endif()
+
+
+
+find_package(PythonLibs)
+if (PYTHONLIB_FOUND) 
+  message("Found PythonLibs PYTHON_LIBRARIES " ${PYTHON_LIBRARIES})
+  message("Found PythonLibs PYTHON_INCLUDE_PATH " ${PYTHON_INCLUDE_PATH})
+  message("Found PythonLibs PYTHON_INCLUDE_DIRS " ${PYTHON_INCLUDE_DIRS})
+  message("Found PythonLibs PYTHONLIBS_VERSION_STRING " ${PYTHONLIBS_VERSION_STRING}  )
+else()
+  message("No PythonLibs Found")  
+endif()
+
+
+
+
+function(findPythonPackagesPath)
+   execute_process(COMMAND ${PYTHON_EXECUTABLE} -c "from distutils.sysconfig import *; print (get_python_lib())"
+                      RESULT_VARIABLE PYTHON_CVPY_PROCESS
+                      OUTPUT_VARIABLE PYTHON_STD_PACKAGES_PATH
+                      OUTPUT_STRIP_TRAILING_WHITESPACE)
+   #message("STD_PACKAGES " ${PYTHON_STD_PACKAGES_PATH})
+   if("${PYTHON_STD_PACKAGES_PATH}" MATCHES "site-packages")
+        set(_PYTHON_PACKAGES_PATH "python${PYTHON_VERSION_MAJOR_MINOR}/site-packages")
+   endif()
+
+    SET(PYTHON_PACKAGES_PATH "${PYTHON_STD_PACKAGES_PATH}" PARENT_SCOPE)
+
+endfunction()
+
+
diff --git a/Wrappers/Python/ccpi/reconstruction/AstraDevice.py b/Wrappers/Python/ccpi/reconstruction/AstraDevice.py
new file mode 100644
index 0000000..57435f8
--- /dev/null
+++ b/Wrappers/Python/ccpi/reconstruction/AstraDevice.py
@@ -0,0 +1,95 @@
+import astra
+from ccpi.reconstruction.DeviceModel import DeviceModel
+import numpy
+
+class AstraDevice(DeviceModel):
+    '''Concrete class for Astra Device'''
+
+    def __init__(self,
+                 device_type,
+                 data_aquisition_geometry,
+                 reconstructed_volume_geometry):
+        
+        super(AstraDevice, self).__init__(device_type,
+                                          data_aquisition_geometry,
+                                          reconstructed_volume_geometry)
+
+        self.type = device_type
+        self.proj_geom = astra.creators.create_proj_geom(
+            device_type,
+            self.acquisition_data_geometry['detectorSpacingX'],
+            self.acquisition_data_geometry['detectorSpacingY'],
+            self.acquisition_data_geometry['cameraX'],
+            self.acquisition_data_geometry['cameraY'],
+            self.acquisition_data_geometry['angles'],
+            )
+        
+        self.vol_geom = astra.creators.create_vol_geom(
+            self.reconstructed_volume_geometry['X'],
+            self.reconstructed_volume_geometry['Y'],
+            self.reconstructed_volume_geometry['Z']
+            )
+        
+    def doForwardProject(self, volume):
+        '''Forward projects the volume according to the device geometry
+
+Uses Astra-toolbox
+'''
+              
+        try:
+            sino_id, y = astra.creators.create_sino3d_gpu(
+                volume, self.proj_geom, self.vol_geom)
+            astra.matlab.data3d('delete', sino_id)
+            return y
+        except Exception as e:
+            print(e)
+            print("Value Error: ", self.proj_geom, self.vol_geom)
+
+    def doBackwardProject(self, projections):
+        '''Backward projects the projections according to the device geometry
+
+Uses Astra-toolbox
+'''
+        idx, volume = \
+               astra.creators.create_backprojection3d_gpu(
+                   projections,
+                   self.proj_geom,
+                   self.vol_geom)
+        
+        astra.matlab.data3d('delete', idx)
+        return volume
+    
+    def createReducedDevice(self, proj_par={'cameraY' : 1} , vol_par={'Z':1}):
+        '''Create a new device based on the current device by changing some parameter
+
+VERY RISKY'''
+        acquisition_data_geometry = self.acquisition_data_geometry.copy()
+        for k,v in proj_par.items():
+            if k in acquisition_data_geometry.keys():
+                acquisition_data_geometry[k] = v
+        proj_geom =  [ 
+            acquisition_data_geometry['cameraX'],
+            acquisition_data_geometry['cameraY'],
+            acquisition_data_geometry['detectorSpacingX'],
+            acquisition_data_geometry['detectorSpacingY'],
+            acquisition_data_geometry['angles']
+            ]
+
+        reconstructed_volume_geometry = self.reconstructed_volume_geometry.copy()
+        for k,v in vol_par.items():
+            if k in reconstructed_volume_geometry.keys():
+                reconstructed_volume_geometry[k] = v
+        
+        vol_geom = [
+            reconstructed_volume_geometry['X'],
+            reconstructed_volume_geometry['Y'],
+            reconstructed_volume_geometry['Z']
+            ]
+        return AstraDevice(self.type, proj_geom, vol_geom)
+        
+
+        
+if __name__=="main":
+    a = AstraDevice()
+
+
diff --git a/Wrappers/Python/ccpi/reconstruction/DeviceModel.py b/Wrappers/Python/ccpi/reconstruction/DeviceModel.py
new file mode 100644
index 0000000..eeb9a34
--- /dev/null
+++ b/Wrappers/Python/ccpi/reconstruction/DeviceModel.py
@@ -0,0 +1,63 @@
+from abc import ABCMeta, abstractmethod
+from enum import Enum
+
+class DeviceModel(metaclass=ABCMeta):
+    '''Abstract class that defines the device for projection and backprojection
+
+This class defines the methods that must be implemented by concrete classes.
+
+    '''
+    
+    class DeviceType(Enum):
+        '''Type of device
+PARALLEL BEAM
+PARALLEL BEAM 3D
+CONE BEAM
+HELICAL'''
+        
+        PARALLEL = 'parallel'
+        PARALLEL3D = 'parallel3d'
+        CONE_BEAM = 'cone-beam'
+        HELICAL = 'helical'
+        
+    def __init__(self,
+                 device_type,
+                 data_aquisition_geometry,
+                 reconstructed_volume_geometry):
+        '''Initializes the class
+
+Mandatory parameters are:
+device_type from DeviceType Enum
+data_acquisition_geometry: tuple (camera_X, camera_Y, detectorSpacingX,
+                                  detectorSpacingY, angles)
+reconstructed_volume_geometry: tuple (dimX,dimY,dimZ)
+'''
+        self.device_geometry = device_type
+        self.acquisition_data_geometry = {
+            'cameraX':           data_aquisition_geometry[0],
+            'cameraY':           data_aquisition_geometry[1],
+            'detectorSpacingX' : data_aquisition_geometry[2],
+            'detectorSpacingY' : data_aquisition_geometry[3],
+            'angles' :           data_aquisition_geometry[4],}
+        self.reconstructed_volume_geometry = {
+            'X': reconstructed_volume_geometry[0] ,
+            'Y': reconstructed_volume_geometry[1] ,
+            'Z': reconstructed_volume_geometry[2] }
+
+    @abstractmethod
+    def doForwardProject(self, volume):
+        '''Forward projects the volume according to the device geometry'''
+        return NotImplemented
+
+    
+    @abstractmethod
+    def doBackwardProject(self, projections):
+        '''Backward projects the projections according to the device geometry'''
+        return NotImplemented
+
+    @abstractmethod
+    def createReducedDevice(self):
+        '''Create a Device to do forward/backward projections on 2D slices'''
+        return NotImplemented
+    
+
diff --git a/Wrappers/Python/ccpi/reconstruction/FISTAReconstructor.py b/Wrappers/Python/ccpi/reconstruction/FISTAReconstructor.py
new file mode 100644
index 0000000..e40ad24
--- /dev/null
+++ b/Wrappers/Python/ccpi/reconstruction/FISTAReconstructor.py
@@ -0,0 +1,882 @@
+# -*- 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
+from ccpi.reconstruction.AstraDevice import AstraDevice
+
+   
+    
+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,
+                 device, 
+                 **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
+        self.pars['device_model'] = device
+
+        self.use_device = True
+        
+        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',
+              'device_model',
+              'reduced_device_model')
+        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
+
+        reduced_device = device.createReducedDevice()
+        self.setParameter(reduced_device_model=reduced_device)
+
+        
+                
+    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[0:1,:,:])
+            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
+        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):
+        if self.getParameter('subsets') == 0:
+            return self.iterateStandard(Xin)
+        else:
+            return self.iterateOrderedSubsets(Xin)
+        
+    def iterateStandard(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 , ring_lambda_R_L1 , weights = \
+                            self.getParameter([ 'projector_geometry' ,
+                                                'output_geometry',
+                                                'input_sinogram',
+                                                'SlicesZ' ,
+                                                'ring_lambda_R_L1',
+                                                'weights'])
+                   
+        t = 1
+
+        device = self.getParameter('device_model')
+        reduced_device = self.getParameter('reduced_device_model')
+        
+        for i in range(self.getParameter('number_of_iterations')):
+            print("iteration", i)
+            X_old = X.copy()
+            t_old = t
+            r_old = self.r.copy()
+            pg = self.getParameter('projector_geometry')['type']
+            if pg == 'parallel' or \
+               pg == 'fanflat' or \
+               pg == 'fanflat_vec':
+                # if the geometry is parallel use slice-by-slice
+                # projection-backprojection routine
+                #sino_updt = zeros(size(sino),'single');
+                
+                if self.use_device :
+                    self.sino_updt = numpy.zeros(numpy.shape(sino), dtype=numpy.float)
+                    
+                    for kkk in range(SlicesZ):
+                        self.sino_updt[kkk] = \
+                            reduced_device.doForwardProject( X_t[kkk:kkk+1] )
+                else:
+                    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);
+                
+                if self.use_device:
+                    self.sino_updt = device.doForwardProject(X_t)
+                else:
+                    sino_id, self.sino_updt = astra.creators.create_sino3d_gpu(
+                        X_t, proj_geom, vol_geom)
+                    astra.matlab.data3d('delete', sino_id)
+
+
+            ## RING REMOVAL
+            if ring_lambda_R_L1 != 0:
+                self.ringRemoval(i)
+            else:
+                self.residual = weights * (self.sino_updt - sino)
+                self.objective[i] = 0.5 * numpy.linalg.norm(self.residual)
+                #objective(i) = 0.5*norm(residual(:)); % for the objective function output
+            ## Projection/Backprojection Routine
+            X, X_t = self.projectionBackprojection(X, X_t)
+            
+            ## REGULARIZATION
+            Y = self.regularize(X)
+            X = Y.copy()
+            ## Update Loop
+            X , X_t, t = self.updateLoop(i, X, X_old, r_old, t, t_old)
+
+            print ("t" , t)
+            print ("X min {0} max {1}".format(X_t.min(),X_t.max()))
+            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'])
+        
+        device, reduced_device = self.getParameter(['device_model',
+                                                    'reduced_device_model'])
+        
+        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');
+            x_temp = numpy.zeros(numpy.shape(X),dtype=numpy.float32)
+                
+            if self.use_device:
+                proj_geomT = proj_geom.copy()
+                proj_geomT['DetectorRowCount'] = 1
+                vol_geomT = vol_geom.copy()
+                vol_geomT['GridSliceCount'] = 1;
+                
+                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:
+                for kkk in range(SliceZ):
+                    x_temp[kkk] = \
+                        reduced_device.doBackwardProject(residual[kkk:kkk+1])
+        else:
+            if self.use_device:
+                x_id, x_temp = \
+                  astra.creators.create_backprojection3d_gpu(
+                      residual, proj_geom, vol_geom)
+                astra.matlab.data3d('delete', x_id)
+            else:
+                x_temp = \
+                    device.doBackwardProject(residual)
+                       
+
+        X = X_t - (1/L_const) * x_temp
+        #astra.matlab.data3d('delete', sino_id)
+        return (X , X_t)
+        
+
+    def regularize(self, X , output_all=False):
+        #print ("FISTA Reconstructor: regularize")
+        
+        regularizer = self.getParameter('regularizer')
+        if regularizer is not None:
+            return regularizer(input=X,
+                               output_all=output_all)
+        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')
+            
+        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 = numpy.max(
+                numpy.abs(self.r) - lambdaR_L1 , 0) * \
+                numpy.sign(self.r)
+            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 iterateOrderedSubsets(self, Xin=None):
+        print ("FISTA Reconstructor: Ordered Subsets 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 , iterFISTA         = self.getParameter(
+            ['projector_geometry' , 'output_geometry', 'input_sinogram',
+             'SlicesZ' ,            'weights',         'ring_alpha' ,
+             'ring_lambda_R_L1',    'Lipschitz_constant',
+             'number_of_iterations'])
+
+            
+        # errors vector (if the ground truth is given)
+        Resid_error = numpy.zeros((iterFISTA));
+        # objective function values vector
+        #objective = numpy.zeros((iterFISTA));
+        objective = self.objective
+
+          
+        t = 1
+
+        ## additional for 
+        proj_geomSUB = proj_geom.copy()
+        self.residual2 = numpy.zeros(numpy.shape(sino))
+        residual2 = self.residual2
+        sino_updt_FULL = self.residual.copy()
+        r_x = self.r.copy()
+
+        print ("starting iterations")
+        ##    % Outer FISTA iterations loop
+        for i in range(self.getParameter('number_of_iterations')):
+            # With OS approach it becomes trickier to correlate independent
+            # subsets, hence additional work is required one solution is to
+            # work with a full sinogram at times
+
+            r_old = self.r.copy()
+            t_old = t
+            SlicesZ, anglesNumb, Detectors = \
+                        numpy.shape(self.getParameter('input_sinogram'))        ## https://github.com/vais-ral/CCPi-FISTA_Reconstruction/issues/4
+            if (i > 1 and lambdaR_L1 > 0) :
+                for kkk in range(anglesNumb):
+                     
+                     residual2[:,kkk,:] = (weights[:,kkk,:]).squeeze() * \
+                                           ((sino_updt_FULL[:,kkk,:]).squeeze() - \
+                                            (sino[:,kkk,:]).squeeze() -\
+                                            (alpha_ring * r_x)
+                                            )
+                
+                vec = self.residual.sum(axis = 1)
+                #if SlicesZ > 1:
+                #    vec = vec[:,1,:] # 1 or 0?
+                r_x = self.r_x
+                # update ring variable
+                self.r = (r_x - (1./L_const) * vec).copy()
+
+            # subset loop
+            counterInd = 1
+            geometry_type = self.getParameter('projector_geometry')['type']
+            angles = self.getParameter('projector_geometry')['ProjectionAngles']
+
+            for ss in range(self.getParameter('subsets')):
+                #print ("Subset {0}".format(ss))
+                X_old = X.copy()
+                t_old = t
+                
+                # the number of projections per subset
+                numProjSub = self.getParameter('os_bins')[ss]
+                CurrSubIndices = self.getParameter('os_indices')\
+                                 [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]
+                
+                if self.use_device:
+                    device = self.getParameter('device_model')\
+                             .createReducedDevice(
+                                 proj_par={'angles':angles[mask]},
+                                 vol_par={})
+        
+                shape = list(numpy.shape(self.getParameter('input_sinogram')))
+                shape[1] = numProjSub
+                sino_updt_Sub = numpy.zeros(shape)
+                if geometry_type == 'parallel' or \
+                   geometry_type == 'fanflat' or \
+                   geometry_type == 'fanflat_vec' :
+
+                    for kkk in range(SlicesZ):
+                        if self.use_device:
+                            sinoT = device.doForwardProject(X_t[kkk:kkk+1])
+                        else:
+                            sino_id, sinoT = astra.creators.create_sino3d_gpu (
+                                X_t[kkk:kkk+1] , proj_geomSUB, vol_geom)
+                            astra.matlab.data3d('delete', sino_id)
+                        sino_updt_Sub[kkk] = sinoT.T.copy()
+                        
+                else:
+                    # for 3D geometry (watch the GPU memory overflow in
+                    # ASTRA < 1.8)
+                    if self.use_device:
+                        sino_updt_Sub = device.doForwardProject(X_t)
+                        
+                    else:
+                        sino_id, sino_updt_Sub = \
+                             astra.creators.create_sino3d_gpu (X_t, proj_geomSUB, vol_geom)
+                        
+                        astra.matlab.data3d('delete', sino_id)
+        
+                #print ("shape(sino_updt_Sub)",numpy.shape(sino_updt_Sub))
+                if lambdaR_L1 > 0 :
+                    ## RING REMOVAL
+                    #print ("ring removal")
+                    residualSub , sino_updt_Sub, sino_updt_FULL = \
+                        self.ringRemovalOrderedSubsets(ss,
+                                                       counterInd,
+                                                       sino_updt_Sub,
+                                                       sino_updt_FULL)
+                else:
+                    #PWLS model
+                    #print ("PWLS model")
+                    residualSub = weights[:,CurrSubIndices,:] * \
+                                  ( sino_updt_Sub - \
+                                    sino[:,CurrSubIndices,:].squeeze() )
+                    objective[i] = 0.5 * numpy.linalg.norm(residualSub)
+
+                # projection/backprojection routine
+                if geometry_type == 'parallel' or \
+                   geometry_type == 'fanflat' or \
+                   geometry_type == 'fanflat_vec' :
+                    # if geometry is 2D use slice-by-slice projection-backprojection
+                    # routine
+                    x_temp = numpy.zeros(numpy.shape(X), dtype=numpy.float32)
+                    for kkk in range(SlicesZ):
+                        if self.use_device:
+                            x_temp[kkk] = device.doBackwardProject(
+                                residualSub[kkk:kkk+1])
+                        else:
+                            x_id, x_temp[kkk] = \
+                                     astra.creators.create_backprojection3d_gpu(
+                                         residualSub[kkk:kkk+1],
+                                         proj_geomSUB, vol_geom)
+                            astra.matlab.data3d('delete', x_id)
+                        
+                else:
+                    if self.use_device:
+                        x_temp = device.doBackwardProject(
+                            residualSub)
+                    else:
+                        x_id, x_temp = \
+                          astra.creators.create_backprojection3d_gpu(
+                              residualSub, proj_geomSUB, vol_geom)
+
+                        astra.matlab.data3d('delete', x_id)
+                
+                X = X_t - (1/L_const) * x_temp
+                
+                ## REGULARIZATION
+                X = self.regularize(X)
+                
+                ## Update subset Loop
+                t = (1 + numpy.sqrt(1 + 4 * t**2))/2
+                X_t = X + (((t_old -1)/t) * (X - X_old))
+            # FINAL
+            ## update iteration loop
+            if lambdaR_L1 > 0:
+                self.r = numpy.max(
+                    numpy.abs(self.r) - lambdaR_L1 , 0) * \
+                    numpy.sign(self.r)
+                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]))    
+            print("X min {0} max {1}".format(X.min(),X.max()))
+            self.setParameter(output_volume=X)
+            counterInd = counterInd + numProjSub
+
+        return X
+    
+    def ringRemovalOrderedSubsets(self, ss,counterInd,
+                                  sino_updt_Sub, sino_updt_FULL):
+        residual = self.residual
+        r_x = self.r_x
+        weights , alpha_ring , sino = \
+                self.getParameter( ['weights', 'ring_alpha', 'input_sinogram'])
+        numProjSub = self.getParameter('os_bins')[ss]
+        CurrSubIndices = self.getParameter('os_indices')\
+                         [counterInd:counterInd+numProjSub]
+
+        shape = list(numpy.shape(self.getParameter('input_sinogram')))
+        shape[1] = numProjSub
+            
+        residualSub = numpy.zeros(shape)
+
+        for kkk in range(numProjSub):
+            #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)
+            # filling the full sinogram
+            sino_updt_FULL[:,indC,:] = sino_updt_Sub[:,kkk,:].squeeze()
+
+        return (residualSub , sino_updt_Sub, sino_updt_FULL)
+
+
diff --git a/Wrappers/Python/ccpi/reconstruction/Reconstructor.py b/Wrappers/Python/ccpi/reconstruction/Reconstructor.py
new file mode 100644
index 0000000..ba67327
--- /dev/null
+++ b/Wrappers/Python/ccpi/reconstruction/Reconstructor.py
@@ -0,0 +1,598 @@
+# -*- 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 Reconstructor:
+    
+    class Algorithm(Enum):
+        CGLS = alg.cgls
+        CGLS_CONV = alg.cgls_conv
+        SIRT = alg.sirt
+        MLEM = alg.mlem
+        CGLS_TICHONOV = alg.cgls_tikhonov
+        CGLS_TVREG = alg.cgls_TVreg
+        FISTA = 'fista'
+        
+    def __init__(self, algorithm = None, projection_data = None,
+                 angles = None, center_of_rotation = None , 
+                 flat_field = None, dark_field = None, 
+                 iterations = None, resolution = None, isLogScale = False, threads = None, 
+                 normalized_projection = None):
+    
+        self.pars = dict()
+        self.pars['algorithm'] = algorithm
+        self.pars['projection_data'] = projection_data
+        self.pars['normalized_projection'] = normalized_projection
+        self.pars['angles'] = angles
+        self.pars['center_of_rotation'] = numpy.double(center_of_rotation)
+        self.pars['flat_field'] = flat_field
+        self.pars['iterations'] = iterations
+        self.pars['dark_field'] = dark_field
+        self.pars['resolution'] = resolution
+        self.pars['isLogScale'] = isLogScale
+        self.pars['threads'] = threads
+        if (iterations != None):
+            self.pars['iterationValues'] = numpy.zeros((iterations)) 
+        
+        if projection_data != None and dark_field != None and flat_field != None:
+            norm = self.normalize(projection_data, dark_field, flat_field, 0.1)
+            self.pars['normalized_projection'] = norm
+            
+    
+    def setPars(self, parameters):
+        keys = ['algorithm','projection_data' ,'normalized_projection', \
+                'angles' , 'center_of_rotation' , 'flat_field', \
+                'iterations','dark_field' , 'resolution', 'isLogScale' , \
+                'threads' , 'iterationValues', 'regularize']
+        
+        for k in keys:
+            if k not in parameters.keys():
+                self.pars[k] = None
+            else:
+                self.pars[k] = parameters[k]
+                
+        
+    def sanityCheck(self):
+        projection_data = self.pars['projection_data']
+        dark_field = self.pars['dark_field']
+        flat_field = self.pars['flat_field']
+        angles = self.pars['angles']
+        
+        if projection_data != None and dark_field != None and \
+            angles != None and flat_field != None:
+            data_shape =  numpy.shape(projection_data)
+            angle_shape = numpy.shape(angles)
+            
+            if angle_shape[0] != data_shape[0]:
+                #raise Exception('Projections and angles dimensions do not match: %d vs %d' % \
+                #                (angle_shape[0] , data_shape[0]) )
+                return (False , 'Projections and angles dimensions do not match: %d vs %d' % \
+                                (angle_shape[0] , data_shape[0]) )
+            
+            if data_shape[1:] != numpy.shape(flat_field):
+                #raise Exception('Projection and flat field dimensions do not match')
+                return (False , 'Projection and flat field dimensions do not match')
+            if data_shape[1:] != numpy.shape(dark_field):
+                #raise Exception('Projection and dark field dimensions do not match')
+                return (False , 'Projection and dark field dimensions do not match')
+            
+            return (True , '' )
+        elif self.pars['normalized_projection'] != None:
+            data_shape =  numpy.shape(self.pars['normalized_projection'])
+            angle_shape = numpy.shape(angles)
+            
+            if angle_shape[0] != data_shape[0]:
+                #raise Exception('Projections and angles dimensions do not match: %d vs %d' % \
+                #                (angle_shape[0] , data_shape[0]) )
+                return (False , 'Projections and angles dimensions do not match: %d vs %d' % \
+                                (angle_shape[0] , data_shape[0]) )
+            else:
+                return (True , '' )
+        else:
+            return (False , 'Not enough data')
+            
+    def reconstruct(self, parameters = None):
+        if parameters != None:
+            self.setPars(parameters)
+        
+        go , reason = self.sanityCheck()
+        if go:
+            return self._reconstruct()
+        else:
+            raise Exception(reason)
+            
+            
+    def _reconstruct(self, parameters=None):
+        if parameters!=None:
+            self.setPars(parameters)
+        parameters = self.pars
+        
+        if parameters['algorithm'] != None and \
+           parameters['normalized_projection'] != None and \
+           parameters['angles'] != None and \
+           parameters['center_of_rotation'] != None and \
+           parameters['iterations'] != None and \
+           parameters['resolution'] != None and\
+           parameters['threads'] != None and\
+           parameters['isLogScale'] != None:
+               
+               
+           if parameters['algorithm'] in (Reconstructor.Algorithm.CGLS,
+                        Reconstructor.Algorithm.MLEM, Reconstructor.Algorithm.SIRT):
+               #store parameters
+               self.pars = parameters
+               result = parameters['algorithm'](
+                           parameters['normalized_projection'] ,
+                           parameters['angles'],
+                           parameters['center_of_rotation'],
+                           parameters['resolution'],
+                           parameters['iterations'],
+                           parameters['threads'] ,
+                           parameters['isLogScale']
+                           )
+               return result
+           elif parameters['algorithm'] in (Reconstructor.Algorithm.CGLS_CONV,
+                          Reconstructor.Algorithm.CGLS_TICHONOV, 
+                          Reconstructor.Algorithm.CGLS_TVREG) :
+               self.pars = parameters
+               result = parameters['algorithm'](
+                           parameters['normalized_projection'] ,
+                           parameters['angles'],
+                           parameters['center_of_rotation'],
+                           parameters['resolution'],
+                           parameters['iterations'],
+                           parameters['threads'] ,
+                           parameters['regularize'],
+                           numpy.zeros((parameters['iterations'])),
+                           parameters['isLogScale']
+                           )
+               
+           elif parameters['algorithm'] == Reconstructor.Algorithm.FISTA:
+               pass
+             
+        else:
+           if parameters['projection_data'] != None and \
+                     parameters['dark_field'] != None and \
+                     parameters['flat_field'] != None:
+               norm = self.normalize(parameters['projection_data'],
+                                   parameters['dark_field'], 
+                                   parameters['flat_field'], 0.1)
+               self.pars['normalized_projection'] = norm
+               return self._reconstruct(parameters)
+              
+                
+                
+    def _normalize(self, projection, dark, flat, def_val=0):
+        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
+    
+    def normalize(self, projections, dark, flat, def_val=0):
+        norm = [self._normalize(projection, dark, flat, def_val) for projection in projections]
+        return numpy.asarray (norm, dtype=numpy.float32)
+        
+    
+    
+class FISTA():
+    '''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/Wrappers/Python/compile-fista.bat.in b/Wrappers/Python/compile-fista.bat.in
new file mode 100644
index 0000000..b1db686
--- /dev/null
+++ b/Wrappers/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/Wrappers/Python/compile-fista.sh.in b/Wrappers/Python/compile-fista.sh.in
new file mode 100644
index 0000000..267f014
--- /dev/null
+++ b/Wrappers/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/Wrappers/Python/compile.bat.in b/Wrappers/Python/compile.bat.in
new file mode 100644
index 0000000..e5342ed
--- /dev/null
+++ b/Wrappers/Python/compile.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 conda-recipe --python=@PYTHON_VERSION_MAJOR@.@PYTHON_VERSION_MINOR@ --numpy=@NUMPY_VERSION@ -c ccpi -c conda-forge
\ No newline at end of file
diff --git a/Wrappers/Python/compile.sh.in b/Wrappers/Python/compile.sh.in
new file mode 100644
index 0000000..93fdba2
--- /dev/null
+++ b/Wrappers/Python/compile.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 conda-recipe --python=@PYTHON_VERSION_MAJOR@.@PYTHON_VERSION_MINOR@ --numpy=@NUMPY_VERSION@ -c ccpi
diff --git a/Wrappers/Python/conda-recipe/bld.bat b/Wrappers/Python/conda-recipe/bld.bat
new file mode 100644
index 0000000..69491de
--- /dev/null
+++ b/Wrappers/Python/conda-recipe/bld.bat
@@ -0,0 +1,14 @@
+IF NOT DEFINED CIL_VERSION (
+ECHO CIL_VERSION Not Defined.
+exit 1
+)
+
+mkdir "%SRC_DIR%\ccpi"
+xcopy /e "%RECIPE_DIR%\..\.." "%SRC_DIR%\ccpi"
+
+cd %SRC_DIR%\ccpi\Python
+
+%PYTHON% setup.py build_ext
+if errorlevel 1 exit 1
+%PYTHON% setup.py install
+if errorlevel 1 exit 1
diff --git a/Wrappers/Python/conda-recipe/build.sh b/Wrappers/Python/conda-recipe/build.sh
new file mode 100644
index 0000000..855047f
--- /dev/null
+++ b/Wrappers/Python/conda-recipe/build.sh
@@ -0,0 +1,14 @@
+
+if [ -z "$CIL_VERSION" ]; then
+    echo "Need to set CIL_VERSION"
+    exit 1
+fi  
+mkdir "$SRC_DIR/ccpi"
+cp -r "$RECIPE_DIR/../.." "$SRC_DIR/ccpi"
+
+cd $SRC_DIR/ccpi/Python
+
+$PYTHON setup.py build_ext
+$PYTHON setup.py install
+
+
diff --git a/Wrappers/Python/conda-recipe/meta.yaml b/Wrappers/Python/conda-recipe/meta.yaml
new file mode 100644
index 0000000..7068e9d
--- /dev/null
+++ b/Wrappers/Python/conda-recipe/meta.yaml
@@ -0,0 +1,30 @@
+package:
+  name: ccpi-regularizers
+  version: {{ environ['CIL_VERSION'] }}
+
+
+build:
+  preserve_egg_dir: False
+  script_env:
+    - CIL_VERSION   
+#  number: 0
+  
+requirements:
+  build:
+    - python
+    - numpy
+    - setuptools
+    - boost ==1.64
+    - boost-cpp ==1.64
+    - cython
+
+  run:
+    - python
+    - numpy
+    - boost ==1.64
+
+	
+about:
+  home: http://www.ccpi.ac.uk
+  license:  BSD license
+  summary: 'CCPi Core Imaging Library Quantification Toolbox'
diff --git a/Wrappers/Python/fista-recipe/bld.bat b/Wrappers/Python/fista-recipe/bld.bat
new file mode 100644
index 0000000..69c2afe
--- /dev/null
+++ b/Wrappers/Python/fista-recipe/bld.bat
@@ -0,0 +1,11 @@
+IF NOT DEFINED CIL_VERSION (
+ECHO CIL_VERSION Not Defined.
+exit 1
+)
+
+xcopy /e "%RECIPE_DIR%\.." "%SRC_DIR%"
+
+%PYTHON% setup.py -q bdist_egg
+:: %PYTHON% setup.py install --single-version-externally-managed --record=record.txt
+%PYTHON% setup.py install
+if errorlevel 1 exit 1
diff --git a/Wrappers/Python/fista-recipe/build.sh b/Wrappers/Python/fista-recipe/build.sh
new file mode 100644
index 0000000..e3f3552
--- /dev/null
+++ b/Wrappers/Python/fista-recipe/build.sh
@@ -0,0 +1,10 @@
+if [ -z "$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/Wrappers/Python/fista-recipe/meta.yaml b/Wrappers/Python/fista-recipe/meta.yaml
new file mode 100644
index 0000000..265541f
--- /dev/null
+++ b/Wrappers/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/Wrappers/Python/fista_module.cpp b/Wrappers/Python/fista_module.cpp
new file mode 100644
index 0000000..f3add76
--- /dev/null
+++ b/Wrappers/Python/fista_module.cpp
@@ -0,0 +1,1047 @@
+/*
+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 Daniil Kazantsev
+Copyright 2017 Srikanth Nagella, Edoardo Pasca
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+http://www.apache.org/licenses/LICENSE-2.0
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+*/
+
+#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
+
+#include <iostream>
+#include <cmath>
+
+#include <boost/python.hpp>
+#include <boost/python/numpy.hpp>
+#include "boost/tuple/tuple.hpp"
+
+#include "SplitBregman_TV_core.h"
+#include "FGP_TV_core.h"
+#include "LLT_model_core.h"
+#include "PatchBased_Regul_core.h"
+#include "TGV_PD_core.h"
+#include "utils.h"
+
+
+
+#if defined(_WIN32) || defined(_WIN32) || defined(__WIN32__) || defined(_WIN64)
+#include <windows.h>
+// this trick only if compiler is MSVC
+__if_not_exists(uint8_t) { typedef __int8 uint8_t; }
+__if_not_exists(uint16_t) { typedef __int8 uint16_t; }
+#endif
+
+namespace bp = boost::python;
+namespace np = boost::python::numpy;
+
+/*! in the Matlab implementation this is called as
+void mexFunction(
+int nlhs, mxArray *plhs[],
+int nrhs, const mxArray *prhs[])
+where:
+prhs Array of pointers to the INPUT mxArrays
+nrhs int number of INPUT mxArrays
+
+nlhs Array of pointers to the OUTPUT mxArrays
+plhs int number of OUTPUT mxArrays
+
+***********************************************************
+
+***********************************************************
+double mxGetScalar(const mxArray *pm);
+args: pm Pointer to an mxArray; cannot be a cell mxArray, a structure mxArray, or an empty mxArray.
+Returns: Pointer to the value of the first real (nonimaginary) element of the mxArray.	In C, mxGetScalar returns a double.
+***********************************************************
+char *mxArrayToString(const mxArray *array_ptr);
+args: array_ptr Pointer to mxCHAR array.
+Returns: C-style string. Returns NULL on failure. Possible reasons for failure include out of memory and specifying an array that is not an mxCHAR array.
+Description: Call mxArrayToString to copy the character data of an mxCHAR array into a C-style string.
+***********************************************************
+mxClassID mxGetClassID(const mxArray *pm);
+args: pm Pointer to an mxArray
+Returns: Numeric identifier of the class (category) of the mxArray that pm points to.For user-defined types,
+mxGetClassId returns a unique value identifying the class of the array contents.
+Use mxIsClass to determine whether an array is of a specific user-defined type.
+
+mxClassID Value	  MATLAB Type   MEX Type	 C Primitive Type
+mxINT8_CLASS 	  int8	        int8_T	     char, byte
+mxUINT8_CLASS	  uint8	        uint8_T	     unsigned char, byte
+mxINT16_CLASS	  int16	        int16_T	     short
+mxUINT16_CLASS	  uint16	    uint16_T	 unsigned short
+mxINT32_CLASS	  int32	        int32_T	     int
+mxUINT32_CLASS	  uint32	    uint32_T	 unsigned int
+mxINT64_CLASS	  int64	        int64_T	     long long
+mxUINT64_CLASS	  uint64	    uint64_T 	 unsigned long long
+mxSINGLE_CLASS	  single	    float	     float
+mxDOUBLE_CLASS	  double	    double	     double
+
+****************************************************************
+double *mxGetPr(const mxArray *pm);
+args: pm Pointer to an mxArray of type double
+Returns: Pointer to the first element of the real data. Returns NULL in C (0 in Fortran) if there is no real data.
+****************************************************************
+mxArray *mxCreateNumericArray(mwSize ndim, const mwSize *dims,
+mxClassID classid, mxComplexity ComplexFlag);
+args: ndimNumber of dimensions. If you specify a value for ndim that is less than 2, mxCreateNumericArray automatically sets the number of dimensions to 2.
+dims Dimensions array. Each element in the dimensions array contains the size of the array in that dimension.
+For example, in C, setting dims[0] to 5 and dims[1] to 7 establishes a 5-by-7 mxArray. Usually there are ndim elements in the dims array.
+classid Identifier for the class of the array, which determines the way the numerical data is represented in memory.
+For example, specifying mxINT16_CLASS in C causes each piece of numerical data in the mxArray to be represented as a 16-bit signed integer.
+ComplexFlag  If the mxArray you are creating is to contain imaginary data, set ComplexFlag to mxCOMPLEX in C (1 in Fortran). Otherwise, set ComplexFlag to mxREAL in C (0 in Fortran).
+Returns: Pointer to the created mxArray, if successful. If unsuccessful in a standalone (non-MEX file) application, returns NULL in C (0 in Fortran).
+If unsuccessful in a MEX file, the MEX file terminates and returns control to the MATLAB prompt. The function is unsuccessful when there is not
+enough free heap space to create the mxArray.
+*/
+
+
+
+bp::list SplitBregman_TV(np::ndarray input, double d_mu, int iter, double d_epsil, int methTV) {
+	
+	// the result is in the following list
+	bp::list result;
+		
+	int number_of_dims, dimX, dimY, dimZ, ll, j, count;
+	//const int  *dim_array;
+	float *A, *U = NULL, *U_old = NULL, *Dx = NULL, *Dy = NULL, *Dz = NULL, *Bx = NULL, *By = NULL, *Bz = NULL, lambda, mu, epsil, re, re1, re_old;
+	
+	//number_of_dims = mxGetNumberOfDimensions(prhs[0]);
+	//dim_array = mxGetDimensions(prhs[0]);
+
+	number_of_dims = input.get_nd();
+	int dim_array[3];
+
+	dim_array[0] = input.shape(0);
+	dim_array[1] = input.shape(1);
+	if (number_of_dims == 2) {
+		dim_array[2] = -1;
+	}
+	else {
+		dim_array[2] = input.shape(2);
+	}
+
+	// Parameter handling is be done in Python
+	///*Handling Matlab input data*/
+	//if ((nrhs < 2) || (nrhs > 5)) mexErrMsgTxt("At least 2 parameters is required: Image(2D/3D), Regularization parameter. The full list of parameters: Image(2D/3D), Regularization parameter, iterations number, tolerance, penalty type ('iso' or 'l1')");
+
+	///*Handling Matlab input data*/
+	//A = (float *)mxGetData(prhs[0]); /*noisy image (2D/3D) */
+	A = reinterpret_cast<float *>(input.get_data());
+
+	//mu = (float)mxGetScalar(prhs[1]); /* regularization parameter */
+	mu = (float)d_mu;
+
+	//iter = 35; /* default iterations number */
+	
+	//epsil = 0.0001; /* default tolerance constant */
+	epsil = (float)d_epsil;
+	//methTV = 0;  /* default isotropic TV penalty */
+	//if ((nrhs == 3) || (nrhs == 4) || (nrhs == 5))  iter = (int)mxGetScalar(prhs[2]); /* iterations number */
+	//if ((nrhs == 4) || (nrhs == 5))  epsil = (float)mxGetScalar(prhs[3]); /* tolerance constant */
+	//if (nrhs == 5) {
+	//	char *penalty_type;
+	//	penalty_type = mxArrayToString(prhs[4]); /* choosing TV penalty: 'iso' or 'l1', 'iso' is the default */
+	//	if ((strcmp(penalty_type, "l1") != 0) && (strcmp(penalty_type, "iso") != 0)) mexErrMsgTxt("Choose TV type: 'iso' or 'l1',");
+	//	if (strcmp(penalty_type, "l1") == 0)  methTV = 1;  /* enable 'l1' penalty */
+	//	mxFree(penalty_type);
+	//}
+	//if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) { mexErrMsgTxt("The input image must be in a single precision"); }
+
+	lambda = 2.0f*mu;
+	count = 1;
+	re_old = 0.0f;
+	/*Handling Matlab output data*/
+	dimY = dim_array[0]; dimX = dim_array[1]; dimZ = dim_array[2];
+
+	if (number_of_dims == 2) {
+		dimZ = 1; /*2D case*/
+		//U = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+		//U_old = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+		//Dx = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+		//Dy = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+		//Bx = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+		//By = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+		bp::tuple shape = bp::make_tuple(dim_array[0], dim_array[1]);
+		np::dtype dtype = np::dtype::get_builtin<float>();
+
+		np::ndarray npU = np::zeros(shape, dtype);
+		np::ndarray npU_old = np::zeros(shape, dtype);
+		np::ndarray npDx = np::zeros(shape, dtype);
+		np::ndarray npDy = np::zeros(shape, dtype);
+		np::ndarray npBx = np::zeros(shape, dtype);
+		np::ndarray npBy = np::zeros(shape, dtype);
+
+		U = reinterpret_cast<float *>(npU.get_data());
+		U_old = reinterpret_cast<float *>(npU_old.get_data());
+		Dx = reinterpret_cast<float *>(npDx.get_data());
+		Dy = reinterpret_cast<float *>(npDy.get_data());
+		Bx = reinterpret_cast<float *>(npBx.get_data());
+		By = reinterpret_cast<float *>(npBy.get_data());
+
+
+
+		copyIm(A, U, dimX, dimY, dimZ); /*initialize */
+
+										/* begin outer SB iterations */
+		for (ll = 0; ll < iter; ll++) {
+
+			/*storing old values*/
+			copyIm(U, U_old, dimX, dimY, dimZ);
+
+			/*GS iteration */
+			gauss_seidel2D(U, A, Dx, Dy, Bx, By, dimX, dimY, lambda, mu);
+
+			if (methTV == 1)  updDxDy_shrinkAniso2D(U, Dx, Dy, Bx, By, dimX, dimY, lambda);
+			else updDxDy_shrinkIso2D(U, Dx, Dy, Bx, By, dimX, dimY, lambda);
+
+			updBxBy2D(U, Dx, Dy, Bx, By, dimX, dimY);
+
+			/* calculate norm to terminate earlier */
+			re = 0.0f; re1 = 0.0f;
+			for (j = 0; j < dimX*dimY*dimZ; j++)
+			{
+				re += pow(U_old[j] - U[j], 2);
+				re1 += pow(U_old[j], 2);
+			}
+			re = sqrt(re) / sqrt(re1);
+			if (re < epsil)  count++;
+			if (count > 4) break;
+
+			/* check that the residual norm is decreasing */
+			if (ll > 2) {
+				if (re > re_old) break;
+			}
+			re_old = re;
+			/*printf("%f %i %i \n", re, ll, count); */
+
+			/*copyIm(U_old, U, dimX, dimY, dimZ); */
+			
+		}
+		//printf("SB iterations stopped at iteration: %i\n", ll);
+		result.append<np::ndarray>(npU);
+		result.append<int>(ll);
+	}
+	if (number_of_dims == 3) {
+			/*U = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
+			U_old = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
+			Dx = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
+			Dy = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
+			Dz = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
+			Bx = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
+			By = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
+			Bz = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));*/
+			bp::tuple shape = bp::make_tuple(dim_array[0], dim_array[1], dim_array[2]);
+			np::dtype dtype = np::dtype::get_builtin<float>();
+
+			np::ndarray npU     = np::zeros(shape, dtype);
+			np::ndarray npU_old = np::zeros(shape, dtype);
+			np::ndarray npDx    = np::zeros(shape, dtype);
+			np::ndarray npDy    = np::zeros(shape, dtype);
+			np::ndarray npDz    = np::zeros(shape, dtype);
+			np::ndarray npBx    = np::zeros(shape, dtype);
+			np::ndarray npBy    = np::zeros(shape, dtype);
+			np::ndarray npBz    = np::zeros(shape, dtype);
+
+			U     = reinterpret_cast<float *>(npU.get_data());
+			U_old = reinterpret_cast<float *>(npU_old.get_data());
+			Dx    = reinterpret_cast<float *>(npDx.get_data());
+			Dy    = reinterpret_cast<float *>(npDy.get_data());
+			Dz    = reinterpret_cast<float *>(npDz.get_data());
+			Bx    = reinterpret_cast<float *>(npBx.get_data());
+			By    = reinterpret_cast<float *>(npBy.get_data());
+			Bz    = reinterpret_cast<float *>(npBz.get_data());
+
+			copyIm(A, U, dimX, dimY, dimZ); /*initialize */
+
+											/* begin outer SB iterations */
+			for (ll = 0; ll<iter; ll++) {
+
+				/*storing old values*/
+				copyIm(U, U_old, dimX, dimY, dimZ);
+
+				/*GS iteration */
+				gauss_seidel3D(U, A, Dx, Dy, Dz, Bx, By, Bz, dimX, dimY, dimZ, lambda, mu);
+
+				if (methTV == 1) updDxDyDz_shrinkAniso3D(U, Dx, Dy, Dz, Bx, By, Bz, dimX, dimY, dimZ, lambda);
+				else updDxDyDz_shrinkIso3D(U, Dx, Dy, Dz, Bx, By, Bz, dimX, dimY, dimZ, lambda);
+
+				updBxByBz3D(U, Dx, Dy, Dz, Bx, By, Bz, dimX, dimY, dimZ);
+
+				/* calculate norm to terminate earlier */
+				re = 0.0f; re1 = 0.0f;
+				for (j = 0; j<dimX*dimY*dimZ; j++)
+				{
+					re += pow(U[j] - U_old[j], 2);
+					re1 += pow(U[j], 2);
+				}
+				re = sqrt(re) / sqrt(re1);
+				if (re < epsil)  count++;
+				if (count > 4) break;
+
+				/* check that the residual norm is decreasing */
+				if (ll > 2) {
+					if (re > re_old) break;
+				}
+				/*printf("%f %i %i \n", re, ll, count); */
+				re_old = re;
+			}
+			//printf("SB iterations stopped at iteration: %i\n", ll);
+			result.append<np::ndarray>(npU);
+			result.append<int>(ll);
+		}
+	return result;
+
+	}
+
+
+
+bp::list FGP_TV(np::ndarray input, double d_mu, int iter, double d_epsil, int methTV) {
+
+	// the result is in the following list
+	bp::list result;
+
+	int number_of_dims, dimX, dimY, dimZ, ll, j, count;
+	float *A, *D = NULL, *D_old = NULL, *P1 = NULL, *P2 = NULL, *P3 = NULL, *P1_old = NULL, *P2_old = NULL, *P3_old = NULL, *R1 = NULL, *R2 = NULL, *R3 = NULL;
+	float lambda, tk, tkp1, re, re1, re_old, epsil, funcval;
+
+	//number_of_dims = mxGetNumberOfDimensions(prhs[0]);
+	//dim_array = mxGetDimensions(prhs[0]);
+
+	number_of_dims = input.get_nd();
+	int dim_array[3];
+
+	dim_array[0] = input.shape(0);
+	dim_array[1] = input.shape(1);
+	if (number_of_dims == 2) {
+		dim_array[2] = -1;
+	}
+	else {
+		dim_array[2] = input.shape(2);
+	}
+	// Parameter handling is be done in Python
+	///*Handling Matlab input data*/
+	//if ((nrhs < 2) || (nrhs > 5)) mexErrMsgTxt("At least 2 parameters is required: Image(2D/3D), Regularization parameter. The full list of parameters: Image(2D/3D), Regularization parameter, iterations number, tolerance, penalty type ('iso' or 'l1')");
+
+	///*Handling Matlab input data*/
+	//A = (float *)mxGetData(prhs[0]); /*noisy image (2D/3D) */
+	A = reinterpret_cast<float *>(input.get_data());
+
+	//mu = (float)mxGetScalar(prhs[1]); /* regularization parameter */
+	lambda = (float)d_mu;
+
+	//iter = 35; /* default iterations number */
+
+	//epsil = 0.0001; /* default tolerance constant */
+	epsil = (float)d_epsil;
+	//methTV = 0;  /* default isotropic TV penalty */
+	//if ((nrhs == 3) || (nrhs == 4) || (nrhs == 5))  iter = (int)mxGetScalar(prhs[2]); /* iterations number */
+	//if ((nrhs == 4) || (nrhs == 5))  epsil = (float)mxGetScalar(prhs[3]); /* tolerance constant */
+	//if (nrhs == 5) {
+	//	char *penalty_type;
+	//	penalty_type = mxArrayToString(prhs[4]); /* choosing TV penalty: 'iso' or 'l1', 'iso' is the default */
+	//	if ((strcmp(penalty_type, "l1") != 0) && (strcmp(penalty_type, "iso") != 0)) mexErrMsgTxt("Choose TV type: 'iso' or 'l1',");
+	//	if (strcmp(penalty_type, "l1") == 0)  methTV = 1;  /* enable 'l1' penalty */
+	//	mxFree(penalty_type);
+	//}
+	//if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) { mexErrMsgTxt("The input image must be in a single precision"); }
+
+	//plhs[1] = mxCreateNumericMatrix(1, 1, mxSINGLE_CLASS, mxREAL);
+	bp::tuple shape1 = bp::make_tuple(dim_array[0], dim_array[1]);
+	np::dtype dtype = np::dtype::get_builtin<float>();
+	np::ndarray out1 = np::zeros(shape1, dtype);
+	
+	//float *funcvalA = (float *)mxGetData(plhs[1]);
+	float * funcvalA = reinterpret_cast<float *>(out1.get_data());
+	//if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) { mexErrMsgTxt("The input image must be in a single precision"); }
+
+	/*Handling Matlab output data*/
+	dimX = dim_array[0]; dimY = dim_array[1]; dimZ = dim_array[2];
+
+	tk = 1.0f;
+	tkp1 = 1.0f;
+	count = 1;
+	re_old = 0.0f;
+
+	if (number_of_dims == 2) {
+		dimZ = 1; /*2D case*/
+		/*D = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+		D_old = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+		P1 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+		P2 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+		P1_old = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+		P2_old = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+		R1 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+		R2 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));*/
+
+		bp::tuple shape = bp::make_tuple(dim_array[0], dim_array[1]);
+		np::dtype dtype = np::dtype::get_builtin<float>();
+
+
+		np::ndarray npD      = np::zeros(shape, dtype);
+		np::ndarray npD_old  = np::zeros(shape, dtype);
+		np::ndarray npP1     = np::zeros(shape, dtype);
+		np::ndarray npP2     = np::zeros(shape, dtype);
+		np::ndarray npP1_old = np::zeros(shape, dtype);
+		np::ndarray npP2_old = np::zeros(shape, dtype);
+		np::ndarray npR1     = np::zeros(shape, dtype);
+		np::ndarray npR2     = np::zeros(shape, dtype);
+
+		D      = reinterpret_cast<float *>(npD.get_data());
+		D_old  = reinterpret_cast<float *>(npD_old.get_data());
+		P1     = reinterpret_cast<float *>(npP1.get_data());
+		P2     = reinterpret_cast<float *>(npP2.get_data());
+		P1_old = reinterpret_cast<float *>(npP1_old.get_data());
+		P2_old = reinterpret_cast<float *>(npP2_old.get_data());
+		R1     = reinterpret_cast<float *>(npR1.get_data());
+		R2     = reinterpret_cast<float *>(npR2.get_data());
+
+		/* begin iterations */
+		for (ll = 0; ll<iter; ll++) {
+			/* computing the gradient of the objective function */
+			Obj_func2D(A, D, R1, R2, lambda, dimX, dimY);
+
+			/*Taking a step towards minus of the gradient*/
+			Grad_func2D(P1, P2, D, R1, R2, lambda, dimX, dimY);
+
+
+
+
+			/*updating R and t*/
+			tkp1 = (1.0f + sqrt(1.0f + 4.0f*tk*tk))*0.5f;
+			Rupd_func2D(P1, P1_old, P2, P2_old, R1, R2, tkp1, tk, dimX, dimY);
+
+			/* calculate norm */
+			re = 0.0f; re1 = 0.0f;
+			for (j = 0; j<dimX*dimY*dimZ; j++)
+			{
+				re += pow(D[j] - D_old[j], 2);
+				re1 += pow(D[j], 2);
+			}
+			re = sqrt(re) / sqrt(re1);
+			if (re < epsil)  count++;
+			if (count > 3) {
+				Obj_func2D(A, D, P1, P2, lambda, dimX, dimY);
+				funcval = 0.0f;
+				for (j = 0; j<dimX*dimY*dimZ; j++) funcval += pow(D[j], 2);
+				//funcvalA[0] = sqrt(funcval);
+				float fv = sqrt(funcval);
+				std::memcpy(funcvalA, &fv, sizeof(float));
+				break;
+			}
+
+			/* check that the residual norm is decreasing */
+			if (ll > 2) {
+				if (re > re_old) {
+					Obj_func2D(A, D, P1, P2, lambda, dimX, dimY);
+					funcval = 0.0f;
+					for (j = 0; j<dimX*dimY*dimZ; j++) funcval += pow(D[j], 2);
+					//funcvalA[0] = sqrt(funcval);
+					float fv = sqrt(funcval);
+					std::memcpy(funcvalA, &fv, sizeof(float));
+					break;
+				}
+			}
+			re_old = re;
+			/*printf("%f %i %i \n", re, ll, count); */
+
+			/*storing old values*/
+			copyIm(D, D_old, dimX, dimY, dimZ);
+			copyIm(P1, P1_old, dimX, dimY, dimZ);
+			copyIm(P2, P2_old, dimX, dimY, dimZ);
+			tk = tkp1;
+
+			/* calculating the objective function value */
+			if (ll == (iter - 1)) {
+				Obj_func2D(A, D, P1, P2, lambda, dimX, dimY);
+				funcval = 0.0f;
+				for (j = 0; j<dimX*dimY*dimZ; j++) funcval += pow(D[j], 2);
+				//funcvalA[0] = sqrt(funcval);
+				float fv = sqrt(funcval);
+				std::memcpy(funcvalA, &fv, sizeof(float));
+			}
+		}
+		//printf("FGP-TV iterations stopped at iteration %i with the function value %f \n", ll, funcvalA[0]);
+		result.append<np::ndarray>(npD);
+		result.append<np::ndarray>(out1);
+		result.append<int>(ll);
+	}
+	if (number_of_dims == 3) {
+		/*D = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
+		D_old = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
+		P1 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
+		P2 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
+		P3 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
+		P1_old = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
+		P2_old = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
+		P3_old = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
+		R1 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
+		R2 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
+		R3 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));*/
+		bp::tuple shape = bp::make_tuple(dim_array[0], dim_array[1], dim_array[2]);
+		np::dtype dtype = np::dtype::get_builtin<float>();
+		
+		np::ndarray npD      = np::zeros(shape, dtype);
+		np::ndarray npD_old  = np::zeros(shape, dtype);
+		np::ndarray npP1     = np::zeros(shape, dtype);
+		np::ndarray npP2     = np::zeros(shape, dtype);
+		np::ndarray npP3     = np::zeros(shape, dtype);
+		np::ndarray npP1_old = np::zeros(shape, dtype);
+		np::ndarray npP2_old = np::zeros(shape, dtype);
+		np::ndarray npP3_old = np::zeros(shape, dtype);
+		np::ndarray npR1     = np::zeros(shape, dtype);
+		np::ndarray npR2     = np::zeros(shape, dtype);
+		np::ndarray npR3     = np::zeros(shape, dtype);
+
+		D      = reinterpret_cast<float *>(npD.get_data());
+		D_old  = reinterpret_cast<float *>(npD_old.get_data());
+		P1     = reinterpret_cast<float *>(npP1.get_data());
+		P2     = reinterpret_cast<float *>(npP2.get_data());
+		P3     = reinterpret_cast<float *>(npP3.get_data());
+		P1_old = reinterpret_cast<float *>(npP1_old.get_data());
+		P2_old = reinterpret_cast<float *>(npP2_old.get_data());
+		P3_old = reinterpret_cast<float *>(npP3_old.get_data());
+		R1     = reinterpret_cast<float *>(npR1.get_data());
+		R2     = reinterpret_cast<float *>(npR2.get_data());
+		R3     = reinterpret_cast<float *>(npR3.get_data());
+		/* begin iterations */
+		for (ll = 0; ll<iter; ll++) {
+			/* computing the gradient of the objective function */
+			Obj_func3D(A, D, R1, R2, R3, lambda, dimX, dimY, dimZ);
+			/*Taking a step towards minus of the gradient*/
+			Grad_func3D(P1, P2, P3, D, R1, R2, R3, lambda, dimX, dimY, dimZ);
+
+			/* projection step */
+			Proj_func3D(P1, P2, P3, dimX, dimY, dimZ);
+
+			/*updating R and t*/
+			tkp1 = (1.0f + sqrt(1.0f + 4.0f*tk*tk))*0.5f;
+			Rupd_func3D(P1, P1_old, P2, P2_old, P3, P3_old, R1, R2, R3, tkp1, tk, dimX, dimY, dimZ);
+
+			/* calculate norm - stopping rules*/
+			re = 0.0f; re1 = 0.0f;
+			for (j = 0; j<dimX*dimY*dimZ; j++)
+			{
+				re += pow(D[j] - D_old[j], 2);
+				re1 += pow(D[j], 2);
+			}
+			re = sqrt(re) / sqrt(re1);
+			/* stop if the norm residual is less than the tolerance EPS */
+			if (re < epsil)  count++;
+			if (count > 3) {
+				Obj_func3D(A, D, P1, P2, P3, lambda, dimX, dimY, dimZ);
+				funcval = 0.0f;
+				for (j = 0; j<dimX*dimY*dimZ; j++) funcval += pow(D[j], 2);
+				//funcvalA[0] = sqrt(funcval);
+				float fv = sqrt(funcval);
+				std::memcpy(funcvalA, &fv, sizeof(float));
+				break;
+			}
+
+			/* check that the residual norm is decreasing */
+			if (ll > 2) {
+				if (re > re_old) {
+					Obj_func3D(A, D, P1, P2, P3, lambda, dimX, dimY, dimZ);
+					funcval = 0.0f;
+					for (j = 0; j<dimX*dimY*dimZ; j++) funcval += pow(D[j], 2);
+					//funcvalA[0] = sqrt(funcval);
+					float fv = sqrt(funcval);
+					std::memcpy(funcvalA, &fv, sizeof(float));
+					break;
+				}
+			}
+
+			re_old = re;
+			/*printf("%f %i %i \n", re, ll, count); */
+
+			/*storing old values*/
+			copyIm(D, D_old, dimX, dimY, dimZ);
+			copyIm(P1, P1_old, dimX, dimY, dimZ);
+			copyIm(P2, P2_old, dimX, dimY, dimZ);
+			copyIm(P3, P3_old, dimX, dimY, dimZ);
+			tk = tkp1;
+
+			if (ll == (iter - 1)) {
+				Obj_func3D(A, D, P1, P2, P3, lambda, dimX, dimY, dimZ);
+				funcval = 0.0f;
+				for (j = 0; j<dimX*dimY*dimZ; j++) funcval += pow(D[j], 2);
+				//funcvalA[0] = sqrt(funcval);
+				float fv = sqrt(funcval);
+				std::memcpy(funcvalA, &fv, sizeof(float));
+			}
+
+		}
+		//printf("FGP-TV iterations stopped at iteration %i with the function value %f \n", ll, funcvalA[0]);
+		result.append<np::ndarray>(npD);
+		result.append<np::ndarray>(out1);
+		result.append<int>(ll);
+	}
+
+	return result;
+}
+
+bp::list LLT_model(np::ndarray input, double d_lambda, double d_tau, int iter, double d_epsil, int switcher) {
+	// the result is in the following list
+	bp::list result;
+
+	int number_of_dims, dimX, dimY, dimZ, ll, j, count;
+	//const int  *dim_array;
+	float *U0, *U = NULL, *U_old = NULL, *D1 = NULL, *D2 = NULL, *D3 = NULL, lambda, tau, re, re1, epsil, re_old;
+	unsigned short *Map = NULL;
+
+	number_of_dims = input.get_nd();
+	int dim_array[3];
+
+	dim_array[0] = input.shape(0);
+	dim_array[1] = input.shape(1);
+	if (number_of_dims == 2) {
+		dim_array[2] = -1;
+	}
+	else {
+		dim_array[2] = input.shape(2);
+	}
+
+	///*Handling Matlab input data*/
+	//U0 = (float *)mxGetData(prhs[0]); /*origanal noise image/volume*/
+	//if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) { mexErrMsgTxt("The input in single precision is required"); }
+	//lambda = (float)mxGetScalar(prhs[1]); /*regularization parameter*/
+	//tau = (float)mxGetScalar(prhs[2]); /* time-step */
+	//iter = (int)mxGetScalar(prhs[3]); /*iterations number*/
+	//epsil = (float)mxGetScalar(prhs[4]); /* tolerance constant */
+	//switcher = (int)mxGetScalar(prhs[5]); /*switch on (1) restrictive smoothing in Z dimension*/
+	
+	U0 = reinterpret_cast<float *>(input.get_data());
+	lambda = (float)d_lambda;
+	tau = (float)d_tau;
+	// iter is passed as parameter
+	epsil = (float)d_epsil;
+	// switcher is passed as parameter
+										  /*Handling Matlab output data*/
+	dimX = dim_array[0]; dimY = dim_array[1];  dimZ = 1;
+
+	if (number_of_dims == 2) {
+		/*2D case*/
+		/*U = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+		U_old = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+		D1 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+		D2 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));*/
+
+		bp::tuple shape = bp::make_tuple(dim_array[0], dim_array[1]);
+		np::dtype dtype = np::dtype::get_builtin<float>();
+
+
+		np::ndarray npU = np::zeros(shape, dtype);
+		np::ndarray npU_old = np::zeros(shape, dtype);
+		np::ndarray npD1 = np::zeros(shape, dtype);
+		np::ndarray npD2 = np::zeros(shape, dtype);
+		
+
+		U = reinterpret_cast<float *>(npU.get_data());
+		U_old = reinterpret_cast<float *>(npU_old.get_data());
+		D1 = reinterpret_cast<float *>(npD1.get_data());
+		D2 = reinterpret_cast<float *>(npD2.get_data());
+		
+		/*Copy U0 to U*/
+		copyIm(U0, U, dimX, dimY, dimZ);
+
+		count = 1;
+		re_old = 0.0f;
+
+		for (ll = 0; ll < iter; ll++) {
+
+			copyIm(U, U_old, dimX, dimY, dimZ);
+
+			/*estimate inner derrivatives */
+			der2D(U, D1, D2, dimX, dimY, dimZ);
+			/* calculate div^2 and update */
+			div_upd2D(U0, U, D1, D2, dimX, dimY, dimZ, lambda, tau);
+
+			/* calculate norm to terminate earlier */
+			re = 0.0f; re1 = 0.0f;
+			for (j = 0; j<dimX*dimY*dimZ; j++)
+			{
+				re += pow(U_old[j] - U[j], 2);
+				re1 += pow(U_old[j], 2);
+			}
+			re = sqrt(re) / sqrt(re1);
+			if (re < epsil)  count++;
+			if (count > 4) break;
+
+			/* check that the residual norm is decreasing */
+			if (ll > 2) {
+				if (re > re_old) break;
+			}
+			re_old = re;
+
+		} /*end of iterations*/
+		  //printf("HO iterations stopped at iteration: %i\n", ll);
+
+		result.append<np::ndarray>(npU);
+	}
+	else if (number_of_dims == 3) {
+		/*3D case*/
+		dimZ = dim_array[2];
+		/*U = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
+		U_old = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
+		D1 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
+		D2 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
+		D3 = (float*)mxGetPr(mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
+		if (switcher != 0) {
+			Map = (unsigned short*)mxGetPr(plhs[1] = mxCreateNumericArray(3, dim_array, mxUINT16_CLASS, mxREAL));
+		}*/
+		bp::tuple shape = bp::make_tuple(dim_array[0], dim_array[1], dim_array[2]);
+		np::dtype dtype = np::dtype::get_builtin<float>();
+
+
+		np::ndarray npU = np::zeros(shape, dtype);
+		np::ndarray npU_old = np::zeros(shape, dtype);
+		np::ndarray npD1 = np::zeros(shape, dtype);
+		np::ndarray npD2 = np::zeros(shape, dtype);
+		np::ndarray npD3 = np::zeros(shape, dtype);
+		np::ndarray npMap = np::zeros(shape, np::dtype::get_builtin<unsigned short>());
+		Map = reinterpret_cast<unsigned short *>(npMap.get_data());
+		if (switcher != 0) {
+			//Map = (unsigned short*)mxGetPr(plhs[1] = mxCreateNumericArray(3, dim_array, mxUINT16_CLASS, mxREAL));
+			
+			Map = reinterpret_cast<unsigned short *>(npMap.get_data());
+		}
+
+		U = reinterpret_cast<float *>(npU.get_data());
+		U_old = reinterpret_cast<float *>(npU_old.get_data());
+		D1 = reinterpret_cast<float *>(npD1.get_data());
+		D2 = reinterpret_cast<float *>(npD2.get_data());
+		D3 = reinterpret_cast<float *>(npD2.get_data());
+		
+		/*Copy U0 to U*/
+		copyIm(U0, U, dimX, dimY, dimZ);
+
+		count = 1;
+		re_old = 0.0f;
+	
+
+		if (switcher == 1) {
+			/* apply restrictive smoothing */
+			calcMap(U, Map, dimX, dimY, dimZ);
+			/*clear outliers */
+			cleanMap(Map, dimX, dimY, dimZ);
+		}
+		for (ll = 0; ll < iter; ll++) {
+
+			copyIm(U, U_old, dimX, dimY, dimZ);
+
+			/*estimate inner derrivatives */
+			der3D(U, D1, D2, D3, dimX, dimY, dimZ);
+			/* calculate div^2 and update */
+			div_upd3D(U0, U, D1, D2, D3, Map, switcher, dimX, dimY, dimZ, lambda, tau);
+
+			/* calculate norm to terminate earlier */
+			re = 0.0f; re1 = 0.0f;
+			for (j = 0; j<dimX*dimY*dimZ; j++)
+			{
+				re += pow(U_old[j] - U[j], 2);
+				re1 += pow(U_old[j], 2);
+			}
+			re = sqrt(re) / sqrt(re1);
+			if (re < epsil)  count++;
+			if (count > 4) break;
+
+			/* check that the residual norm is decreasing */
+			if (ll > 2) {
+				if (re > re_old) break;
+			}
+			re_old = re;
+
+		} /*end of iterations*/
+		//printf("HO iterations stopped at iteration: %i\n", ll);
+		result.append<np::ndarray>(npU);
+		if (switcher != 0) result.append<np::ndarray>(npMap);
+
+	}
+	return result;
+}
+
+
+bp::list PatchBased_Regul(np::ndarray input, double d_lambda, int SearchW_real, int SimilW,  double d_h) {
+	// the result is in the following list
+	bp::list result;
+
+	int N, M, Z, numdims, SearchW, /*SimilW, SearchW_real,*/ padXY, newsizeX, newsizeY, newsizeZ, switchpad_crop;
+	//const int  *dims;
+	float *A, *B = NULL, *Ap = NULL, *Bp = NULL, h, lambda;
+
+	numdims = input.get_nd();
+	int dims[3];
+
+	dims[0] = input.shape(0);
+	dims[1] = input.shape(1);
+	if (numdims == 2) {
+		dims[2] = -1;
+	}
+	else {
+		dims[2] = input.shape(2);
+	}
+	/*numdims = mxGetNumberOfDimensions(prhs[0]);
+	dims = mxGetDimensions(prhs[0]);*/
+
+	N = dims[0];
+	M = dims[1];
+	Z = dims[2];
+
+	//if ((numdims < 2) || (numdims > 3)) { mexErrMsgTxt("The input should be 2D image or 3D volume"); }
+	//if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) { mexErrMsgTxt("The input in single precision is required"); }
+
+	//if (nrhs != 5) mexErrMsgTxt("Five inputs reqired: Image(2D,3D), SearchW, SimilW, Threshold, Regularization parameter");
+
+	///*Handling inputs*/
+	//A = (float *)mxGetData(prhs[0]);    /* the image to regularize/filter */
+	A = reinterpret_cast<float *>(input.get_data());
+	//SearchW_real = (int)mxGetScalar(prhs[1]); /* the searching window ratio */
+	//SimilW = (int)mxGetScalar(prhs[2]);  /* the similarity window ratio */
+	//h = (float)mxGetScalar(prhs[3]);  /* parameter for the PB filtering function */
+	//lambda = (float)mxGetScalar(prhs[4]); /* regularization parameter */
+
+	//if (h <= 0) mexErrMsgTxt("Parmeter for the PB penalty function should be > 0");
+	//if (lambda <= 0) mexErrMsgTxt(" Regularization parmeter should be > 0");
+
+	lambda = (float)d_lambda;
+	h = (float)d_h;
+	SearchW = SearchW_real + 2 * SimilW;
+
+	/* SearchW_full = 2*SearchW + 1; */ /* the full searching window  size */
+										/* SimilW_full = 2*SimilW + 1;  */  /* the full similarity window  size */
+
+
+	padXY = SearchW + 2 * SimilW; /* padding sizes */
+	newsizeX = N + 2 * (padXY); /* the X size of the padded array */
+	newsizeY = M + 2 * (padXY); /* the Y size of the padded array */
+	newsizeZ = Z + 2 * (padXY); /* the Z size of the padded array */
+	int N_dims[] = { newsizeX, newsizeY, newsizeZ };
+	/******************************2D case ****************************/
+	if (numdims == 2) {
+		///*Handling output*/
+		//B = (float*)mxGetData(plhs[0] = mxCreateNumericMatrix(N, M, mxSINGLE_CLASS, mxREAL));
+		///*allocating memory for the padded arrays */
+		//Ap = (float*)mxGetData(mxCreateNumericMatrix(newsizeX, newsizeY, mxSINGLE_CLASS, mxREAL));
+		//Bp = (float*)mxGetData(mxCreateNumericMatrix(newsizeX, newsizeY, mxSINGLE_CLASS, mxREAL));
+		///**************************************************************************/
+
+		bp::tuple shape = bp::make_tuple(N, M);
+		np::dtype dtype = np::dtype::get_builtin<float>();
+
+		np::ndarray npB = np::zeros(shape, dtype);
+
+		shape = bp::make_tuple(newsizeX, newsizeY);
+		np::ndarray npAp = np::zeros(shape, dtype);
+		np::ndarray npBp = np::zeros(shape, dtype);
+		B = reinterpret_cast<float *>(npB.get_data());
+		Ap = reinterpret_cast<float *>(npAp.get_data());
+		Bp = reinterpret_cast<float *>(npBp.get_data());		
+
+		/*Perform padding of image A to the size of [newsizeX * newsizeY] */
+		switchpad_crop = 0; /*padding*/
+		pad_crop(A, Ap, M, N, 0, newsizeY, newsizeX, 0, padXY, switchpad_crop);
+		
+		/* Do PB regularization with the padded array  */
+		PB_FUNC2D(Ap, Bp, newsizeY, newsizeX, padXY, SearchW, SimilW, (float)h, (float)lambda);
+		
+		switchpad_crop = 1; /*cropping*/
+		pad_crop(Bp, B, M, N, 0, newsizeY, newsizeX, 0, padXY, switchpad_crop);
+		
+		result.append<np::ndarray>(npB);
+	}
+	else
+	{
+		/******************************3D case ****************************/
+		///*Handling output*/
+		//B = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dims, mxSINGLE_CLASS, mxREAL));
+		///*allocating memory for the padded arrays */
+		//Ap = (float*)mxGetPr(mxCreateNumericArray(3, N_dims, mxSINGLE_CLASS, mxREAL));
+		//Bp = (float*)mxGetPr(mxCreateNumericArray(3, N_dims, mxSINGLE_CLASS, mxREAL));
+		/**************************************************************************/
+		bp::tuple shape = bp::make_tuple(dims[0], dims[1], dims[2]);
+		bp::tuple shape_AB = bp::make_tuple(N_dims[0], N_dims[1], N_dims[2]);
+		np::dtype dtype = np::dtype::get_builtin<float>();
+
+		np::ndarray npB = np::zeros(shape, dtype);
+		np::ndarray npAp = np::zeros(shape_AB, dtype);
+		np::ndarray npBp = np::zeros(shape_AB, dtype);
+		B = reinterpret_cast<float *>(npB.get_data());
+		Ap = reinterpret_cast<float *>(npAp.get_data());
+		Bp = reinterpret_cast<float *>(npBp.get_data());
+		/*Perform padding of image A to the size of [newsizeX * newsizeY * newsizeZ] */
+		switchpad_crop = 0; /*padding*/
+		pad_crop(A, Ap, M, N, Z, newsizeY, newsizeX, newsizeZ, padXY, switchpad_crop);
+
+		/* Do PB regularization with the padded array  */
+		PB_FUNC3D(Ap, Bp, newsizeY, newsizeX, newsizeZ, padXY, SearchW, SimilW, (float)h, (float)lambda);
+
+		switchpad_crop = 1; /*cropping*/
+		pad_crop(Bp, B, M, N, Z, newsizeY, newsizeX, newsizeZ, padXY, switchpad_crop);
+
+		result.append<np::ndarray>(npB);
+	} /*end else ndims*/
+
+	return result;
+}
+
+bp::list TGV_PD(np::ndarray input, double d_lambda, double d_alpha1, double d_alpha0, int iter) {
+	// the result is in the following list
+	bp::list result;
+	int number_of_dims, /*iter,*/ dimX, dimY, dimZ, ll;
+	//const int  *dim_array;
+	float *A, *U, *U_old, *P1, *P2, *Q1, *Q2, *Q3, *V1, *V1_old, *V2, *V2_old, lambda, L2, tau, sigma, alpha1, alpha0;
+
+	//number_of_dims = mxGetNumberOfDimensions(prhs[0]);
+	//dim_array = mxGetDimensions(prhs[0]);
+	number_of_dims = input.get_nd();
+	int dim_array[3];
+
+	dim_array[0] = input.shape(0);
+	dim_array[1] = input.shape(1);
+	if (number_of_dims == 2) {
+		dim_array[2] = -1;
+	}
+	else {
+		dim_array[2] = input.shape(2);
+	}
+	/*Handling Matlab input data*/
+	//A = (float *)mxGetData(prhs[0]); /*origanal noise image/volume*/
+	//if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) { mexErrMsgTxt("The input in single precision is required"); }
+	
+	A = reinterpret_cast<float *>(input.get_data());
+
+	//lambda = (float)mxGetScalar(prhs[1]); /*regularization parameter*/
+	//alpha1 = (float)mxGetScalar(prhs[2]); /*first-order term*/
+	//alpha0 = (float)mxGetScalar(prhs[3]); /*second-order term*/
+	//iter = (int)mxGetScalar(prhs[4]); /*iterations number*/
+	//if (nrhs != 5) mexErrMsgTxt("Five input parameters is reqired: Image(2D/3D), Regularization parameter, alpha1, alpha0, Iterations");
+	lambda = (float)d_lambda;
+	alpha1 = (float)d_alpha1;
+	alpha0 = (float)d_alpha0;
+
+	/*Handling Matlab output data*/
+	dimX = dim_array[0]; dimY = dim_array[1];
+
+	if (number_of_dims == 2) {
+		/*2D case*/
+		dimZ = 1;
+		bp::tuple shape = bp::make_tuple(dim_array[0], dim_array[1]);
+		np::dtype dtype = np::dtype::get_builtin<float>();
+
+		np::ndarray npU = np::zeros(shape, dtype);
+		np::ndarray npP1 = np::zeros(shape, dtype);
+		np::ndarray npP2 = np::zeros(shape, dtype);
+		np::ndarray npQ1 = np::zeros(shape, dtype);
+		np::ndarray npQ2 = np::zeros(shape, dtype);
+		np::ndarray npQ3 = np::zeros(shape, dtype);
+		np::ndarray npV1 = np::zeros(shape, dtype);
+		np::ndarray npV1_old = np::zeros(shape, dtype);
+		np::ndarray npV2 = np::zeros(shape, dtype);
+		np::ndarray npV2_old = np::zeros(shape, dtype);
+		np::ndarray npU_old = np::zeros(shape, dtype);
+
+		U = reinterpret_cast<float *>(npU.get_data());
+		U_old = reinterpret_cast<float *>(npU_old.get_data());
+		P1 = reinterpret_cast<float *>(npP1.get_data());
+		P2 = reinterpret_cast<float *>(npP2.get_data());
+		Q1 = reinterpret_cast<float *>(npQ1.get_data());
+		Q2 = reinterpret_cast<float *>(npQ2.get_data());
+		Q3 = reinterpret_cast<float *>(npQ3.get_data());
+		V1 = reinterpret_cast<float *>(npV1.get_data());
+		V1_old = reinterpret_cast<float *>(npV1_old.get_data());
+		V2 = reinterpret_cast<float *>(npV2.get_data());
+		V2_old = reinterpret_cast<float *>(npV2_old.get_data());
+		//U = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+
+		/*dual variables*/
+		/*P1 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+		P2 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+
+		Q1 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+		Q2 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+		Q3 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+
+		U_old = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+
+		V1 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+		V1_old = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+		V2 = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+		V2_old = (float*)mxGetPr(mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));*/
+		/*printf("%i \n", i);*/
+		L2 = 12.0; /*Lipshitz constant*/
+		tau = 1.0 / pow(L2, 0.5);
+		sigma = 1.0 / pow(L2, 0.5);
+
+		/*Copy A to U*/
+		copyIm(A, U, dimX, dimY, dimZ);
+		/* Here primal-dual iterations begin for 2D */
+		for (ll = 0; ll < iter; ll++) {
+
+			/* Calculate Dual Variable P */
+			DualP_2D(U, V1, V2, P1, P2, dimX, dimY, dimZ, sigma);
+
+			/*Projection onto convex set for P*/
+			ProjP_2D(P1, P2, dimX, dimY, dimZ, alpha1);
+
+			/* Calculate Dual Variable Q */
+			DualQ_2D(V1, V2, Q1, Q2, Q3, dimX, dimY, dimZ, sigma);
+
+			/*Projection onto convex set for Q*/
+			ProjQ_2D(Q1, Q2, Q3, dimX, dimY, dimZ, alpha0);
+
+			/*saving U into U_old*/
+			copyIm(U, U_old, dimX, dimY, dimZ);
+
+			/*adjoint operation  -> divergence and projection of P*/
+			DivProjP_2D(U, A, P1, P2, dimX, dimY, dimZ, lambda, tau);
+
+			/*get updated solution U*/
+			newU(U, U_old, dimX, dimY, dimZ);
+
+			/*saving V into V_old*/
+			copyIm(V1, V1_old, dimX, dimY, dimZ);
+			copyIm(V2, V2_old, dimX, dimY, dimZ);
+
+			/* upd V*/
+			UpdV_2D(V1, V2, P1, P2, Q1, Q2, Q3, dimX, dimY, dimZ, tau);
+
+			/*get new V*/
+			newU(V1, V1_old, dimX, dimY, dimZ);
+			newU(V2, V2_old, dimX, dimY, dimZ);
+		} /*end of iterations*/
+	
+		result.append<np::ndarray>(npU);
+	}
+	
+
+	
+	
+	return result;
+}
+
+BOOST_PYTHON_MODULE(cpu_regularizers)
+{
+	np::initialize();
+
+	//To specify that this module is a package
+	bp::object package = bp::scope();
+	package.attr("__path__") = "cpu_regularizers";
+
+	np::dtype dt1 = np::dtype::get_builtin<uint8_t>();
+	np::dtype dt2 = np::dtype::get_builtin<uint16_t>();
+
+	def("SplitBregman_TV", SplitBregman_TV);
+	def("FGP_TV", FGP_TV);
+	def("LLT_model", LLT_model);
+	def("PatchBased_Regul", PatchBased_Regul);
+	def("TGV_PD", TGV_PD);
+}
diff --git a/Wrappers/Python/setup-fista.py.in b/Wrappers/Python/setup-fista.py.in
new file mode 100644
index 0000000..c5c9f4d
--- /dev/null
+++ b/Wrappers/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/Wrappers/Python/setup.py.in b/Wrappers/Python/setup.py.in
new file mode 100644
index 0000000..12e8af1
--- /dev/null
+++ b/Wrappers/Python/setup.py.in
@@ -0,0 +1,69 @@
+#!/usr/bin/env python
+
+import setuptools
+from distutils.core import setup
+from distutils.extension import Extension
+from Cython.Distutils import build_ext
+
+import os
+import sys
+import numpy
+import platform	
+
+cil_version=@CIL_VERSION@
+
+library_include_path = ""
+library_lib_path = ""
+try:
+    library_include_path = os.environ['LIBRARY_INC']
+    library_lib_path = os.environ['LIBRARY_LIB']
+except:
+    library_include_path = os.environ['PREFIX']+'/include'
+    pass
+    
+extra_include_dirs = [numpy.get_include(), library_include_path]
+extra_library_dirs = [os.path.join(library_include_path, "..", "lib")]
+extra_compile_args = ['-fopenmp','-O2', '-funsigned-char', '-Wall', '-std=c++0x']
+extra_libraries = []
+extra_include_dirs += [os.path.join("@CMAKE_SOURCE_DIR@" , "main_func" ,  "regularizers_CPU"),
+                       	   os.path.join("@CMAKE_SOURCE_DIR@" , "main_func" ,  "regularizers_GPU") , 
+						   "@CMAKE_CURRENT_SOURCE_DIR@"]
+						   
+if platform.system() == 'Windows':
+    extra_compile_args[0:] = ['/DWIN32','/EHsc','/DBOOST_ALL_NO_LIB' , '/openmp' ]   
+    
+    if sys.version_info.major == 3 :   
+        extra_libraries += ['boost_python3-vc140-mt-1_64', 'boost_numpy3-vc140-mt-1_64']
+    else:
+        extra_libraries += ['boost_python-vc90-mt-1_64', 'boost_numpy-vc90-mt-1_64']
+else:
+    if sys.version_info.major == 3:
+        extra_libraries += ['boost_python3', 'boost_numpy3','gomp']
+    else:
+        extra_libraries += ['boost_python', 'boost_numpy','gomp']
+
+setup(
+    name='ccpi',
+	description='CCPi Core Imaging Library - Image Regularizers',
+	version=cil_version,
+    cmdclass = {'build_ext': build_ext},
+    ext_modules = [Extension("ccpi.imaging.cpu_regularizers",
+                             sources=[os.path.join("@CMAKE_CURRENT_SOURCE_DIR@" , "fista_module.cpp" ),
+                                      os.path.join("@CMAKE_SOURCE_DIR@" , "main_func" ,  "regularizers_CPU", "FGP_TV_core.c"),
+									  os.path.join("@CMAKE_SOURCE_DIR@" , "main_func" ,  "regularizers_CPU", "SplitBregman_TV_core.c"),
+									  os.path.join("@CMAKE_SOURCE_DIR@" , "main_func" ,  "regularizers_CPU", "LLT_model_core.c"),
+									  os.path.join("@CMAKE_SOURCE_DIR@" , "main_func" ,  "regularizers_CPU", "PatchBased_Regul_core.c"),
+									  os.path.join("@CMAKE_SOURCE_DIR@" , "main_func" ,  "regularizers_CPU", "TGV_PD_core.c"),
+									  os.path.join("@CMAKE_SOURCE_DIR@" , "main_func" ,  "regularizers_CPU", "utils.c")
+                                        ],
+                             include_dirs=extra_include_dirs, 
+							 library_dirs=extra_library_dirs, 
+							 extra_compile_args=extra_compile_args, 
+							 libraries=extra_libraries ), 
+    
+    ],
+	zip_safe = False,	
+	packages = {'ccpi','ccpi.imaging'},
+)
+
+
diff --git a/Wrappers/Python/test/astra_test.py b/Wrappers/Python/test/astra_test.py
new file mode 100644
index 0000000..42c375a
--- /dev/null
+++ b/Wrappers/Python/test/astra_test.py
@@ -0,0 +1,85 @@
+import astra
+import numpy
+import filefun
+
+
+# read in the same data as the DemoRD2
+angles = filefun.dlmread("DemoRD2/angles.csv")
+darks_ar = filefun.dlmread("DemoRD2/darks_ar.csv", separator=",")
+flats_ar = filefun.dlmread("DemoRD2/flats_ar.csv", separator=",")
+
+if True:
+    Sino3D = numpy.load("DemoRD2/Sino3D.npy")
+else:
+    sino = filefun.dlmread("DemoRD2/sino_01.csv", separator=",")
+    a = map (lambda x:x, numpy.shape(sino))
+    a.append(20)
+
+    Sino3D = numpy.zeros(tuple(a), dtype="float")
+
+    for i in range(1,numpy.shape(Sino3D)[2]+1):
+        print("Read file DemoRD2/sino_%02d.csv" % i)
+        sino = filefun.dlmread("DemoRD2/sino_%02d.csv" % i, separator=",")
+        Sino3D.T[i-1] = sino.T
+    
+Weights3D = numpy.asarray(Sino3D, dtype="float")
+
+##angles_rad = angles*(pi/180); % conversion to radians
+##size_det = 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
+
+
+angles_rad = angles * numpy.pi /180.
+size_det, angSize, slices_tot = numpy.shape(Sino3D)
+size_det, angSize, slices_tot = [int(i) for i in numpy.shape(Sino3D)]
+recon_size = 950
+Z_slices = 3;
+det_row_count = Z_slices;
+
+#proj_geom = astra_create_proj_geom('parallel3d', 1, 1,
+# det_row_count, size_det, angles_rad);
+
+detectorSpacingX = 1.0
+detectorSpacingY = detectorSpacingX
+proj_geom = astra.create_proj_geom('parallel3d',
+                                            detectorSpacingX,
+                                            detectorSpacingY,
+                                            det_row_count,
+                                            size_det,
+                                            angles_rad)
+
+#vol_geom = astra_create_vol_geom(recon_size,recon_size,Z_slices);
+vol_geom = astra.create_vol_geom(recon_size,recon_size,Z_slices);
+
+sino = numpy.zeros((size_det, angSize, slices_tot), dtype="float")
+
+#weights = ones(size(sino));
+weights = numpy.ones(numpy.shape(sino))
+
+#####################################################################
+## PowerMethod for Lipschitz constant
+
+N = vol_geom['GridColCount']
+x1 = numpy.random.rand(1,N,N)
+#sqweight = sqrt(weights(:,:,1));
+sqweight = numpy.sqrt(weights.T[0]).T
+##proj_geomT = proj_geom;
+proj_geomT = proj_geom.copy()
+##proj_geomT.DetectorRowCount = 1;
+proj_geomT['DetectorRowCount'] = 1
+##vol_geomT = vol_geom;
+vol_geomT = vol_geom.copy()
+##vol_geomT.GridSliceCount = 1;
+vol_geomT['GridSliceCount'] = 1
+
+##[sino_id, y] = astra_create_sino3d_cuda(x1, proj_geomT, vol_geomT);
+
+#sino_id, y = astra.create_sino3d_gpu(x1, proj_geomT, vol_geomT);
+sino_id, y = astra.create_sino(x1, proj_geomT, vol_geomT);
+
+##y = sqweight.*y;
+##astra_mex_data3d('delete', sino_id);
+        
+
diff --git a/Wrappers/Python/test/create_phantom_projections.py b/Wrappers/Python/test/create_phantom_projections.py
new file mode 100644
index 0000000..20a9278
--- /dev/null
+++ b/Wrappers/Python/test/create_phantom_projections.py
@@ -0,0 +1,49 @@
+from ccpi.reconstruction.AstraDevice import AstraDevice
+from ccpi.reconstruction.DeviceModel import DeviceModel
+import h5py
+import numpy
+import matplotlib.pyplot as plt
+
+nx = h5py.File('phant3D_256.h5', "r")
+phantom = numpy.asarray(nx.get('/dataset1'))
+pX,pY,pZ = numpy.shape(phantom)
+
+filename = r'/home/ofn77899/Reconstruction/CCPi-FISTA_Reconstruction/demos/DendrData.h5'
+nxa = h5py.File(filename, "r")
+#getEntry(nx, '/')
+# I have exported the entries as children of /
+entries = [entry for entry in nxa['/'].keys()]
+print (entries)
+
+angles_rad = numpy.asarray(nxa.get('/angles_rad'), dtype="float32")
+
+
+device = AstraDevice(
+    DeviceModel.DeviceType.PARALLEL3D.value,
+    [ pX , pY , 1., 1., angles_rad],
+    [ pX, pY, pZ ] )
+
+
+proj = device.doForwardProject(phantom)
+stack = [proj[:,i,:] for i in range(len(angles_rad))]
+stack = numpy.asarray(stack)
+
+
+fig = plt.figure()
+a=fig.add_subplot(1,2,1)
+a.set_title('proj')
+imgplot = plt.imshow(proj[:,100,:])
+a=fig.add_subplot(1,2,2)
+a.set_title('stack')
+imgplot = plt.imshow(stack[100])
+plt.show()
+
+pf = h5py.File("phantom3D256_projections.h5" , "w")
+pf.create_dataset("/projections", data=stack)
+pf.create_dataset("/sinogram", data=proj)
+pf.create_dataset("/angles", data=angles_rad)
+pf.create_dataset("/reconstruction_volume" , data=numpy.asarray([pX, pY, pZ]))
+pf.create_dataset("/camera/size" , data=numpy.asarray([pX , pY ]))
+pf.create_dataset("/camera/spacing" , data=numpy.asarray([1.,1.]))
+pf.flush()
+pf.close()
diff --git a/Wrappers/Python/test/readhd5.py b/Wrappers/Python/test/readhd5.py
new file mode 100644
index 0000000..eff6c43
--- /dev/null
+++ b/Wrappers/Python/test/readhd5.py
@@ -0,0 +1,42 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Aug 23 16:34:49 2017
+
+@author: ofn77899
+"""
+
+import h5py
+import numpy
+
+def getEntry(nx, location):
+    for item in nx[location].keys():
+        print (item)
+        
+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'))
+Weights3D = numpy.asarray(nx.get('/Weights3D'))
+angSize = numpy.asarray(nx.get('/angSize'), dtype=int)[0]
+angles_rad = numpy.asarray(nx.get('/angles_rad'))
+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]
+
+#from ccpi.viewer.CILViewer2D import CILViewer2D
+#v = CILViewer2D()
+#v.setInputAsNumpy(Weights3D)
+#v.startRenderLoop()
+
+import matplotlib.pyplot as plt
+fig = plt.figure()
+
+a=fig.add_subplot(1,1,1)
+a.set_title('noise')
+imgplot = plt.imshow(Weights3D[0].T)
+plt.show()
diff --git a/Wrappers/Python/test/simple_astra_test.py b/Wrappers/Python/test/simple_astra_test.py
new file mode 100644
index 0000000..905eeea
--- /dev/null
+++ b/Wrappers/Python/test/simple_astra_test.py
@@ -0,0 +1,25 @@
+import astra
+import numpy
+
+detectorSpacingX = 1.0
+detectorSpacingY = 1.0
+det_row_count = 128
+det_col_count = 128
+
+angles_rad = numpy.asarray([i for i in range(360)], dtype=float) / 180. * numpy.pi
+
+proj_geom = astra.creators.create_proj_geom('parallel3d',
+                                            detectorSpacingX,
+                                            detectorSpacingY,
+                                            det_row_count,
+                                            det_col_count,
+                                            angles_rad)
+
+image_size_x = 64
+image_size_y = 64
+image_size_z = 32
+
+vol_geom = astra.creators.create_vol_geom(image_size_x,image_size_y,image_size_z)
+
+x1 = numpy.random.rand(image_size_z,image_size_y,image_size_x)
+sino_id, y = astra.creators.create_sino3d_gpu(x1, proj_geom, vol_geom)
diff --git a/Wrappers/Python/test/test_reconstructor-os_phantom.py b/Wrappers/Python/test/test_reconstructor-os_phantom.py
new file mode 100644
index 0000000..01f1354
--- /dev/null
+++ b/Wrappers/Python/test/test_reconstructor-os_phantom.py
@@ -0,0 +1,480 @@
+# -*- 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
+from ccpi.imaging.Regularizer import Regularizer
+from ccpi.reconstruction.AstraDevice import AstraDevice
+from ccpi.reconstruction.DeviceModel import DeviceModel
+
+#from ccpi.viewer.CILViewer2D import *
+
+
+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/src/Python/test/phantom3D256_projections.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)
+
+projections = numpy.asarray(nx.get('/projections'), 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'), dtype="float32")
+angSize = numpy.size(angles_rad)
+image_size_x, image_size_y, image_size_z = \
+              numpy.asarray(nx.get('/reconstruction_volume'), dtype=int)
+det_col_count, det_row_count = \
+              numpy.asarray(nx.get('/camera/size'))
+#slices_tot = numpy.asarray(nx.get('/slices_tot'), dtype=int)[0]
+detectorSpacingX, detectorSpacingY = numpy.asarray(nx.get('/camera/spacing'), dtype=int)
+
+Z_slices = 20
+#det_row_count = image_size_y
+# next definition is just for consistency of naming
+#det_col_count = image_size_x
+
+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
+astradevice = AstraDevice(DeviceModel.DeviceType.PARALLEL3D.value,
+                [proj_geom['DetectorRowCount'] ,
+                 proj_geom['DetectorColCount'] ,
+                 proj_geom['DetectorSpacingX'] ,
+                 proj_geom['DetectorSpacingY'] ,
+                 proj_geom['ProjectionAngles']
+                 ],
+                [
+                    vol_geom['GridColCount'],
+                    vol_geom['GridRowCount'], 
+                    vol_geom['GridSliceCount'] ] )
+## create the sinogram 
+Sino3D = numpy.transpose(projections, axes=[1,0,2])
+
+fistaRecon = FISTAReconstructor(proj_geom,
+                                vol_geom,
+                                Sino3D ,
+                                #weights=Weights3D,
+                                device=astradevice)
+
+print ("Lipschitz Constant {0}".format(fistaRecon.pars['Lipschitz_constant']))
+fistaRecon.setParameter(number_of_iterations = 4)
+#fistaRecon.setParameter(Lipschitz_constant = 767893952.0)
+fistaRecon.setParameter(ring_alpha = 21)
+fistaRecon.setParameter(ring_lambda_R_L1 = 0.002)
+#fistaRecon.setParameter(ring_lambda_R_L1 = 0)
+subsets = 8
+fistaRecon.setParameter(subsets=subsets)
+
+
+#reg = Regularizer(Regularizer.Algorithm.FGP_TV)
+#reg.setParameter(regularization_parameter=0.005,
+#                          number_of_iterations=50)
+reg = Regularizer(Regularizer.Algorithm.FGP_TV)
+reg.setParameter(regularization_parameter=5e6,
+                          tolerance_constant=0.0001,
+                          number_of_iterations=50)
+
+#fistaRecon.setParameter(regularizer=reg)
+#lc = fistaRecon.getParameter('Lipschitz_constant')
+#reg.setParameter(regularization_parameter=5e6/lc)
+
+## Ordered subset
+if True:
+    #subsets = 8
+    fistaRecon.setParameter(subsets=subsets)
+    fistaRecon.createOrderedSubsets()
+else:
+    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 True:
+    print ("Lipschitz Constant {0}".format(fistaRecon.pars['Lipschitz_constant']))
+    print ("prepare for iteration")
+    fistaRecon.prepareForIteration()
+    
+    
+
+    print("initializing  ...")
+    if True:
+        # if X doesn't exist
+        #N = params.vol_geom.GridColCount
+        N = vol_geom['GridColCount']
+        print ("N " + str(N))
+        X = numpy.asarray(numpy.ones((image_size_x,image_size_y,image_size_z)),
+                        dtype=numpy.float) * 0.001
+        X = numpy.asarray(numpy.zeros((image_size_x,image_size_y,image_size_z)),
+                        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, weights , alpha_ring = fistaRecon.getParameter(
+            ['projector_geometry' , 'output_geometry',
+             'input_sinogram', 'SlicesZ' ,  'weights', 'ring_alpha'])
+    lambdaR_L1 , alpha_ring , weights , L_const= \
+                       fistaRecon.getParameter(['ring_lambda_R_L1',
+                                               'ring_alpha' , 'weights',
+                                               'Lipschitz_constant'])
+
+    #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
+    
+
+    ## additional for 
+    proj_geomSUB = proj_geom.copy()
+    fistaRecon.residual2 = numpy.zeros(numpy.shape(fistaRecon.pars['input_sinogram']))
+    residual2 = fistaRecon.residual2
+    sino_updt_FULL = fistaRecon.residual.copy()
+    r_x = fistaRecon.r.copy()
+
+    results = []
+    print ("starting iterations")
+##    % Outer FISTA iterations loop
+    for i in range(fistaRecon.getParameter('number_of_iterations')):
+##        % With OS approach it becomes trickier to correlate independent subsets, hence additional work is required
+##        % one solution is to work with a full sinogram at times
+##        if ((i >= 3) && (lambdaR_L1 > 0))
+##            [sino_id2, sino_updt2] = astra_create_sino3d_cuda(X, proj_geom, vol_geom);
+##            astra_mex_data3d('delete', sino_id2);
+##        end
+        # With OS approach it becomes trickier to correlate independent subsets,
+        # hence additional work is required one solution is to work with a full
+        # sinogram at times
+
+        
+        #t_old = t
+        SlicesZ, anglesNumb, Detectors = \
+                    numpy.shape(fistaRecon.getParameter('input_sinogram'))
+        ## https://github.com/vais-ral/CCPi-FISTA_Reconstruction/issues/4
+        r_old = fistaRecon.r.copy()
+            
+        if (i > 1 and lambdaR_L1 > 0) :
+            for kkk in range(anglesNumb):
+                 
+                 residual2[:,kkk,:] = (weights[:,kkk,:]).squeeze() * \
+                                       ((sino_updt_FULL[:,kkk,:]).squeeze() - \
+                                        (sino[:,kkk,:]).squeeze() -\
+                                        (alpha_ring * r_x)
+                                        )
+            #r_old = fistaRecon.r.copy()
+            vec = fistaRecon.residual.sum(axis = 1)
+            #if SlicesZ > 1:
+            #    vec = vec[:,1,:] # 1 or 0?
+            r_x = fistaRecon.r_x
+            # update ring variable
+            fistaRecon.r = (r_x - (1./L_const) * vec)
+
+        # subset loop
+        counterInd = 1
+        geometry_type = fistaRecon.getParameter('projector_geometry')['type']
+        angles = fistaRecon.getParameter('projector_geometry')['ProjectionAngles']
+    
+##        if geometry_type == 'parallel' or \
+##           geometry_type == 'fanflat' or \
+##           geometry_type == 'fanflat_vec' :
+##            
+##            for kkk in range(SlicesZ):
+##                sino_id, sinoT[kkk] = \
+##                         astra.creators.create_sino3d_gpu(
+##                             X_t[kkk:kkk+1], proj_geomSUB, vol_geom)
+##                sino_updt_Sub[kkk] = sinoT.T.copy()
+##                
+##        else:
+##            sino_id, sino_updt_Sub = \
+##                astra.creators.create_sino3d_gpu(X_t, proj_geomSUB, vol_geom)
+##        
+##        astra.matlab.data3d('delete', sino_id)
+  
+        for ss in range(fistaRecon.getParameter('subsets')):
+            print ("Subset {0}".format(ss))
+            X_old = X.copy()
+            t_old = t
+            print ("X[0][0][0] {0} t {1}".format(X[0][0][0], t))
+            
+            # the number of projections per subset
+            numProjSub = fistaRecon.getParameter('os_bins')[ss]
+            CurrSubIndices = fistaRecon.getParameter('os_indices')\
+                             [counterInd:counterInd+numProjSub]
+            shape = list(numpy.shape(fistaRecon.getParameter('input_sinogram')))
+            shape[1] = numProjSub
+            sino_updt_Sub = numpy.zeros(shape)
+            
+            #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
+
+            ## this is a reduced device
+            rdev = fistaRecon.getParameter('device_model')\
+                   .createReducedDevice(proj_par={'angles' : angles[mask]},
+                                        vol_par={})
+            proj_geomSUB['ProjectionAngles'] = angles[mask]
+
+            
+
+            if geometry_type == 'parallel' or \
+               geometry_type == 'fanflat' or \
+               geometry_type == 'fanflat_vec' :
+
+                for kkk in range(SlicesZ):
+                    sino_id, sinoT = astra.creators.create_sino3d_gpu (
+                        X_t[kkk:kkk+1] , proj_geomSUB, vol_geom)
+                    sino_updt_Sub[kkk] = sinoT.T.copy()
+                    astra.matlab.data3d('delete', sino_id)
+            else:
+                # for 3D geometry (watch the GPU memory overflow in ASTRA < 1.8)
+                sino_id, sino_updt_Sub = \
+                     astra.creators.create_sino3d_gpu (X_t,
+                                                       proj_geomSUB,
+                                                       vol_geom)
+                
+                astra.matlab.data3d('delete', sino_id)
+                
+            
+
+
+            ## RING REMOVAL
+            residual = fistaRecon.residual
+            
+            
+            if lambdaR_L1 > 0 :
+                 print ("ring removal")
+                 residualSub = numpy.zeros(shape)
+    ##             for a chosen subset
+    ##                for kkk = 1:numProjSub
+    ##                    indC = CurrSubIndeces(kkk);
+    ##                    residualSub(:,kkk,:) =  squeeze(weights(:,indC,:)).*(squeeze(sino_updt_Sub(:,kkk,:)) - (squeeze(sino(:,indC,:)) - alpha_ring.*r_x));
+    ##                    sino_updt_FULL(:,indC,:) = squeeze(sino_updt_Sub(:,kkk,:)); % filling the full sinogram
+    ##                end
+                 for kkk in range(numProjSub):
+                     #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)
+                     # filling the full sinogram
+                     sino_updt_FULL[:,indC,:] = sino_updt_Sub[:,kkk,:].squeeze()
+
+            else:
+                #PWLS model
+                # I guess we need to use mask here instead
+                residualSub = weights[:,CurrSubIndices,:] * \
+                              ( sino_updt_Sub - \
+                                sino[:,CurrSubIndices,:].squeeze() )
+            # it seems that in the original code the following like is not
+            # calculated in the case of ring removal
+            objective[i] = 0.5 * numpy.linalg.norm(residualSub)
+
+            #backprojection
+            if geometry_type == 'parallel' or \
+               geometry_type == 'fanflat' or \
+               geometry_type == 'fanflat_vec' :
+                # if geometry is 2D use slice-by-slice projection-backprojection
+                # routine
+                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(
+                                 residualSub[kkk:kkk+1],
+                                 proj_geomSUB, vol_geom)
+                    astra.matlab.data3d('delete', x_id)
+                    
+            else:
+                x_id, x_temp = \
+                      astra.creators.create_backprojection3d_gpu(
+                          residualSub, proj_geomSUB, vol_geom)
+
+                astra.matlab.data3d('delete', x_id)
+                
+            X = X_t - (1/L_const) * x_temp
+
+        
+
+            ## REGULARIZATION
+            ## SKIPPING FOR NOW
+            ## Should be simpli
+            # regularizer = fistaRecon.getParameter('regularizer')
+            # for slices:
+            # out = regularizer(input=X)
+            print ("regularizer")
+            reg = fistaRecon.getParameter('regularizer')
+
+            if reg is not None:
+                X = reg(input=X,
+                        output_all=False)
+
+            t = (1 + numpy.sqrt(1 + 4 * t **2))/2
+            X_t = X + (((t_old -1)/t) * (X-X_old))
+            counterInd = counterInd + numProjSub - 1
+        if i == 1:
+            r_old = fistaRecon.r.copy()
+            
+        ## 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)
+            # updating r
+            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]))
+
+        results.append(X[10])
+    numpy.save("X_out_os.npy", X)
+
+else:
+    
+    
+    
+    astradevice = AstraDevice(DeviceModel.DeviceType.PARALLEL3D.value,
+                [proj_geom['DetectorRowCount'] ,
+                 proj_geom['DetectorColCount'] ,
+                 proj_geom['DetectorSpacingX'] ,
+                 proj_geom['DetectorSpacingY'] ,
+                 proj_geom['ProjectionAngles']
+                 ],
+                [
+                    vol_geom['GridColCount'],
+                    vol_geom['GridRowCount'], 
+                    vol_geom['GridSliceCount'] ] )
+    regul = Regularizer(Regularizer.Algorithm.FGP_TV)
+    regul.setParameter(regularization_parameter=5e6,
+                       number_of_iterations=50,
+                       tolerance_constant=1e-4,
+                       TV_penalty=Regularizer.TotalVariationPenalty.isotropic)
+
+    fistaRecon = FISTAReconstructor(proj_geom,
+                                vol_geom,
+                                Sino3D ,
+                                weights=Weights3D,
+                                device=astradevice,
+                                #regularizer = regul,
+                                subsets=8)
+
+    print ("Lipschitz Constant {0}".format(fistaRecon.pars['Lipschitz_constant']))
+    fistaRecon.setParameter(number_of_iterations = 1)
+    fistaRecon.setParameter(Lipschitz_constant = 767893952.0)
+    fistaRecon.setParameter(ring_alpha = 21)
+    fistaRecon.setParameter(ring_lambda_R_L1 = 0.002)
+    #fistaRecon.setParameter(subsets=8)
+    
+    #lc = fistaRecon.getParameter('Lipschitz_constant')
+    #fistaRecon.getParameter('regularizer').setParameter(regularization_parameter=5e6/lc)
+    
+    fistaRecon.prepareForIteration()
+    X = fistaRecon.iterate(numpy.load("X.npy"))
+    
+
+# plot
+fig = plt.figure()
+cols = 3
+
+## add the difference
+rd = []
+for i in range(1,len(results)):
+    rd.append(results[i-1])
+    rd.append(results[i])
+    rd.append(results[i] - results[i-1])
+
+rows = (lambda x: int(numpy.floor(x/cols) + 1) if x%cols != 0  else int(x/cols)) \
+       (len (rd))
+for i in range(len (results)):
+    a=fig.add_subplot(rows,cols,i+1)
+    imgplot = plt.imshow(results[i], vmin=0, vmax=1)
+    a.text(0.05, 0.95, "iteration {0}".format(i),
+               verticalalignment='top')
+##    i = i + 1
+##    a=fig.add_subplot(rows,cols,i+1)
+##    imgplot = plt.imshow(results[i], vmin=0, vmax=10)
+##    a.text(0.05, 0.95, "iteration {0}".format(i),
+##               verticalalignment='top')
+    
+##    a=fig.add_subplot(rows,cols,i+2)
+##    imgplot = plt.imshow(results[i]-results[i-1], vmin=0, vmax=10)
+##    a.text(0.05, 0.95, "difference {0}-{1}".format(i, i-1),
+##               verticalalignment='top')
+        
+        
+
+plt.show()
diff --git a/Wrappers/Python/test/test_reconstructor.py b/Wrappers/Python/test/test_reconstructor.py
new file mode 100644
index 0000000..40065e7
--- /dev/null
+++ b/Wrappers/Python/test/test_reconstructor.py
@@ -0,0 +1,359 @@
+# -*- 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
+from ccpi.imaging.Regularizer import Regularizer
+from ccpi.reconstruction.AstraDevice import AstraDevice
+from ccpi.reconstruction.DeviceModel import DeviceModel
+
+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')
+
+def createAstraDevice(projector_geometry, output_geometry):
+    '''TODO remove'''
+    
+    device = AstraDevice(DeviceModel.DeviceType.PARALLEL3D.value,
+                [projector_geometry['DetectorRowCount'] ,
+                 projector_geometry['DetectorColCount'] ,
+                 projector_geometry['DetectorSpacingX'] ,
+                 projector_geometry['DetectorSpacingY'] ,
+                 projector_geometry['ProjectionAngles']
+                 ],
+                [
+                    output_geometry['GridColCount'],
+                    output_geometry['GridRowCount'], 
+                    output_geometry['GridSliceCount'] ] )
+    return device
+
+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:
+    
+    # create a device for forward/backprojection
+    #astradevice = createAstraDevice(proj_geom, vol_geom)
+    
+    astradevice = AstraDevice(DeviceModel.DeviceType.PARALLEL3D.value,
+                [proj_geom['DetectorRowCount'] ,
+                 proj_geom['DetectorColCount'] ,
+                 proj_geom['DetectorSpacingX'] ,
+                 proj_geom['DetectorSpacingY'] ,
+                 proj_geom['ProjectionAngles']
+                 ],
+                [
+                    vol_geom['GridColCount'],
+                    vol_geom['GridRowCount'], 
+                    vol_geom['GridSliceCount'] ] )
+
+    regul = Regularizer(Regularizer.Algorithm.FGP_TV)
+    regul.setParameter(regularization_parameter=5e6,
+                       number_of_iterations=50,
+                       tolerance_constant=1e-4,
+                       TV_penalty=Regularizer.TotalVariationPenalty.isotropic)
+    
+    fistaRecon = FISTAReconstructor(proj_geom,
+                                vol_geom,
+                                Sino3D ,
+                                device = astradevice,
+                                weights=Weights3D,
+                                regularizer = regul
+                                )
+
+    print ("Lipschitz Constant {0}".format(fistaRecon.pars['Lipschitz_constant']))
+    fistaRecon.setParameter(number_of_iterations = 18)
+    fistaRecon.setParameter(Lipschitz_constant = 767893952.0)
+    fistaRecon.setParameter(ring_alpha = 21)
+    fistaRecon.setParameter(ring_lambda_R_L1 = 0.002)
+
+    
+    
+    fistaRecon.prepareForIteration()
+    X = numpy.load("X.npy")
+
+    
+    X = fistaRecon.iterate(X)
+    #numpy.save("X_out.npy", X)
diff --git a/Wrappers/Python/test/test_regularizers.py b/Wrappers/Python/test/test_regularizers.py
new file mode 100644
index 0000000..27e4ed3
--- /dev/null
+++ b/Wrappers/Python/test/test_regularizers.py
@@ -0,0 +1,412 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Fri Aug  4 11:10:05 2017
+
+@author: ofn77899
+"""
+
+#from ccpi.viewer.CILViewer2D import Converter
+#import vtk
+
+import matplotlib.pyplot as plt
+import numpy as np
+import os    
+from enum import Enum
+import timeit
+#from PIL import Image
+#from Regularizer import Regularizer
+from ccpi.imaging.Regularizer import Regularizer
+
+###############################################################################
+#https://stackoverflow.com/questions/13875989/comparing-image-in-url-to-image-in-filesystem-in-python/13884956#13884956
+#NRMSE a normalization of the root of the mean squared error
+#NRMSE is simply 1 - [RMSE / (maxval - minval)]. Where maxval is the maximum
+# intensity from the two images being compared, and respectively the same for
+# minval. RMSE is given by the square root of MSE: 
+# sqrt[(sum(A - B) ** 2) / |A|],
+# where |A| means the number of elements in A. By doing this, the maximum value
+# given by RMSE is maxval.
+
+def nrmse(im1, im2):
+    a, b = im1.shape
+    rmse = np.sqrt(np.sum((im2 - im1) ** 2) / float(a * b))
+    max_val = max(np.max(im1), np.max(im2))
+    min_val = min(np.min(im1), np.min(im2))
+    return 1 - (rmse / (max_val - min_val))
+###############################################################################
+
+###############################################################################
+#
+#  2D Regularizers
+#
+###############################################################################
+#Example:
+# figure;
+# Im = double(imread('lena_gray_256.tif'))/255;  % loading image
+# u0 = Im + .05*randn(size(Im)); u0(u0 < 0) = 0;
+# u = SplitBregman_TV(single(u0), 10, 30, 1e-04);
+
+
+#filename = r"C:\Users\ofn77899\Documents\GitHub\CCPi-FISTA_reconstruction\data\lena_gray_512.tif"
+filename = r"/home/ofn77899/Reconstruction/CCPi-FISTA_Reconstruction/data/lena_gray_512.tif"
+#filename = r'/home/algol/Documents/Python/STD_test_images/lena_gray_512.tif'
+
+#reader = vtk.vtkTIFFReader()
+#reader.SetFileName(os.path.normpath(filename))
+#reader.Update()
+Im = plt.imread(filename)                     
+#Im = Image.open('/home/algol/Documents/Python/STD_test_images/lena_gray_512.tif')/255
+#img.show()
+Im = np.asarray(Im, dtype='float32')
+
+
+
+
+#imgplot = plt.imshow(Im)
+perc = 0.05
+u0 = Im + (perc* np.random.normal(size=np.shape(Im)))
+# map the u0 u0->u0>0
+f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1)
+u0 = f(u0).astype('float32')
+
+## plot 
+fig = plt.figure()
+#a=fig.add_subplot(3,3,1)
+#a.set_title('Original')
+#imgplot = plt.imshow(Im)
+
+a=fig.add_subplot(2,3,1)
+a.set_title('noise')
+imgplot = plt.imshow(u0,cmap="gray")
+
+reg_output = []
+##############################################################################
+# Call regularizer
+
+####################### SplitBregman_TV #####################################
+# u = SplitBregman_TV(single(u0), 10, 30, 1e-04);
+
+use_object = True
+if use_object:
+    reg = Regularizer(Regularizer.Algorithm.SplitBregman_TV)
+    print (reg.pars)
+    reg.setParameter(input=u0)    
+    reg.setParameter(regularization_parameter=10.)
+    # or 
+    # reg.setParameter(input=u0, regularization_parameter=10., #number_of_iterations=30,
+              #tolerance_constant=1e-4, 
+              #TV_Penalty=Regularizer.TotalVariationPenalty.l1)
+    plotme = reg() [0]
+    pars = reg.pars
+    textstr = reg.printParametersToString() 
+    
+    #out = reg(input=u0, regularization_parameter=10., #number_of_iterations=30,
+              #tolerance_constant=1e-4, 
+    #          TV_Penalty=Regularizer.TotalVariationPenalty.l1)
+    
+#out2 = Regularizer.SplitBregman_TV(input=u0, regularization_parameter=10., number_of_iterations=30,
+#          tolerance_constant=1e-4, 
+#          TV_Penalty=Regularizer.TotalVariationPenalty.l1)
+
+else:
+    out2 = Regularizer.SplitBregman_TV(input=u0, regularization_parameter=10. )
+    pars = out2[2]
+    reg_output.append(out2)
+    plotme = reg_output[-1][0]
+    textstr = out2[-1]
+
+a=fig.add_subplot(2,3,2)
+
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
+# place a text box in upper left in axes coords
+a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
+        verticalalignment='top', bbox=props)
+imgplot = plt.imshow(plotme,cmap="gray")
+
+###################### FGP_TV #########################################
+# u = FGP_TV(single(u0), 0.05, 100, 1e-04);
+out2 = Regularizer.FGP_TV(input=u0, regularization_parameter=0.0005,
+                          number_of_iterations=50)
+pars = out2[-2]
+
+reg_output.append(out2)
+
+a=fig.add_subplot(2,3,3)
+
+textstr = out2[-1]
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
+# place a text box in upper left in axes coords
+a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(reg_output[-1][0])
+# place a text box in upper left in axes coords
+a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
+        verticalalignment='top', bbox=props)
+imgplot = plt.imshow(reg_output[-1][0],cmap="gray")
+
+###################### LLT_model #########################################
+# * u0 = Im + .03*randn(size(Im)); % adding noise
+# [Den] = LLT_model(single(u0), 10, 0.1, 1);
+#Den = LLT_model(single(u0), 25, 0.0003, 300, 0.0001, 0); 
+#input, regularization_parameter , time_step, number_of_iterations,
+#                  tolerance_constant, restrictive_Z_smoothing=0
+out2 = Regularizer.LLT_model(input=u0, regularization_parameter=25,
+                          time_step=0.0003,
+                          tolerance_constant=0.0001,
+                          number_of_iterations=300)
+pars = out2[-2]
+
+reg_output.append(out2)
+
+a=fig.add_subplot(2,3,4)
+
+textstr = out2[-1]
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
+# place a text box in upper left in axes coords
+a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(reg_output[-1][0],cmap="gray")
+
+
+# ###################### PatchBased_Regul #########################################
+# # Quick 2D denoising example in Matlab:   
+# #   Im = double(imread('lena_gray_256.tif'))/255;  % loading image
+# #   u0 = Im + .03*randn(size(Im)); u0(u0<0) = 0; % adding noise
+# #   ImDen = PB_Regul_CPU(single(u0), 3, 1, 0.08, 0.05); 
+
+out2 = Regularizer.PatchBased_Regul(input=u0, regularization_parameter=0.05,
+                       searching_window_ratio=3,
+                       similarity_window_ratio=1,
+                       PB_filtering_parameter=0.08)
+pars = out2[-2]
+reg_output.append(out2)
+
+a=fig.add_subplot(2,3,5)
+
+
+textstr = out2[-1]
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
+# place a text box in upper left in axes coords
+a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
+        verticalalignment='top', bbox=props)
+imgplot = plt.imshow(reg_output[-1][0],cmap="gray")
+
+
+# ###################### TGV_PD #########################################
+# # Quick 2D denoising example in Matlab:   
+# #   Im = double(imread('lena_gray_256.tif'))/255;  % loading image
+# #   u0 = Im + .03*randn(size(Im)); u0(u0<0) = 0; % adding noise
+# #   u = PrimalDual_TGV(single(u0), 0.02, 1.3, 1, 550);
+
+
+out2 = Regularizer.TGV_PD(input=u0, regularization_parameter=0.05,
+                           first_order_term=1.3,
+                           second_order_term=1,
+                           number_of_iterations=550)
+pars = out2[-2]
+reg_output.append(out2)
+
+a=fig.add_subplot(2,3,6)
+
+
+textstr = out2[-1]
+
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
+# place a text box in upper left in axes coords
+a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(reg_output[-1][0],cmap="gray")
+
+
+plt.show()
+
+################################################################################
+##
+##  3D Regularizers
+##
+################################################################################
+##Example:
+## figure;
+## Im = double(imread('lena_gray_256.tif'))/255;  % loading image
+## u0 = Im + .05*randn(size(Im)); u0(u0 < 0) = 0;
+## u = SplitBregman_TV(single(u0), 10, 30, 1e-04);
+#
+##filename = r"C:\Users\ofn77899\Documents\GitHub\CCPi-Reconstruction\python\test\reconstruction_example.mha"
+#filename = r"C:\Users\ofn77899\Documents\GitHub\CCPi-Simpleflex\data\head.mha"
+#
+#reader = vtk.vtkMetaImageReader()
+#reader.SetFileName(os.path.normpath(filename))
+#reader.Update()
+##vtk returns 3D images, let's take just the one slice there is as 2D
+#Im = Converter.vtk2numpy(reader.GetOutput())
+#Im = Im.astype('float32')
+##imgplot = plt.imshow(Im)
+#perc = 0.05
+#u0 = Im + (perc* np.random.normal(size=np.shape(Im)))
+## map the u0 u0->u0>0
+#f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1)
+#u0 = f(u0).astype('float32')
+#converter = Converter.numpy2vtkImporter(u0, reader.GetOutput().GetSpacing(),
+#                                        reader.GetOutput().GetOrigin())
+#converter.Update()
+#writer = vtk.vtkMetaImageWriter()
+#writer.SetInputData(converter.GetOutput())
+#writer.SetFileName(r"C:\Users\ofn77899\Documents\GitHub\CCPi-FISTA_reconstruction\data\noisy_head.mha")
+##writer.Write()
+#
+#
+### plot 
+#fig3D = plt.figure()
+##a=fig.add_subplot(3,3,1)
+##a.set_title('Original')
+##imgplot = plt.imshow(Im)
+#sliceNo = 32
+#
+#a=fig3D.add_subplot(2,3,1)
+#a.set_title('noise')
+#imgplot = plt.imshow(u0.T[sliceNo])
+#
+#reg_output3d = []
+#
+###############################################################################
+## Call regularizer
+#
+######################## SplitBregman_TV #####################################
+## u = SplitBregman_TV(single(u0), 10, 30, 1e-04);
+#
+##reg = Regularizer(Regularizer.Algorithm.SplitBregman_TV)
+#
+##out = reg(input=u0, regularization_parameter=10., #number_of_iterations=30,
+##          #tolerance_constant=1e-4, 
+##          TV_Penalty=Regularizer.TotalVariationPenalty.l1)
+#
+#out2 = Regularizer.SplitBregman_TV(input=u0, regularization_parameter=10., number_of_iterations=30,
+#          tolerance_constant=1e-4, 
+#          TV_Penalty=Regularizer.TotalVariationPenalty.l1)
+#
+#
+#pars = out2[-2]
+#reg_output3d.append(out2)
+#
+#a=fig3D.add_subplot(2,3,2)
+#
+#
+#textstr = out2[-1]
+#
+#
+## these are matplotlib.patch.Patch properties
+#props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
+## place a text box in upper left in axes coords
+#a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
+#        verticalalignment='top', bbox=props)
+#imgplot = plt.imshow(reg_output3d[-1][0].T[sliceNo])
+#
+####################### FGP_TV #########################################
+## u = FGP_TV(single(u0), 0.05, 100, 1e-04);
+#out2 = Regularizer.FGP_TV(input=u0, regularization_parameter=0.005,
+#                          number_of_iterations=200)
+#pars = out2[-2]
+#reg_output3d.append(out2)
+#
+#a=fig3D.add_subplot(2,3,2)
+#
+#
+#textstr = out2[-1]
+#
+#
+## these are matplotlib.patch.Patch properties
+#props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
+## place a text box in upper left in axes coords
+#a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
+#        verticalalignment='top', bbox=props)
+#imgplot = plt.imshow(reg_output3d[-1][0].T[sliceNo])
+#
+####################### LLT_model #########################################
+## * u0 = Im + .03*randn(size(Im)); % adding noise
+## [Den] = LLT_model(single(u0), 10, 0.1, 1);
+##Den = LLT_model(single(u0), 25, 0.0003, 300, 0.0001, 0); 
+##input, regularization_parameter , time_step, number_of_iterations,
+##                  tolerance_constant, restrictive_Z_smoothing=0
+#out2 = Regularizer.LLT_model(input=u0, regularization_parameter=25,
+#                          time_step=0.0003,
+#                          tolerance_constant=0.0001,
+#                          number_of_iterations=300)
+#pars = out2[-2]
+#reg_output3d.append(out2)
+#
+#a=fig3D.add_subplot(2,3,2)
+#
+#
+#textstr = out2[-1]
+#
+#
+## these are matplotlib.patch.Patch properties
+#props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
+## place a text box in upper left in axes coords
+#a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
+#        verticalalignment='top', bbox=props)
+#imgplot = plt.imshow(reg_output3d[-1][0].T[sliceNo])
+#
+####################### PatchBased_Regul #########################################
+## Quick 2D denoising example in Matlab:   
+##   Im = double(imread('lena_gray_256.tif'))/255;  % loading image
+##   u0 = Im + .03*randn(size(Im)); u0(u0<0) = 0; % adding noise
+##   ImDen = PB_Regul_CPU(single(u0), 3, 1, 0.08, 0.05); 
+#
+#out2 = Regularizer.PatchBased_Regul(input=u0, regularization_parameter=0.05,
+#                          searching_window_ratio=3,
+#                          similarity_window_ratio=1,
+#                          PB_filtering_parameter=0.08)
+#pars = out2[-2]
+#reg_output3d.append(out2)
+#
+#a=fig3D.add_subplot(2,3,2)
+#
+#
+#textstr = out2[-1]
+#
+#
+## these are matplotlib.patch.Patch properties
+#props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
+## place a text box in upper left in axes coords
+#a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
+#        verticalalignment='top', bbox=props)
+#imgplot = plt.imshow(reg_output3d[-1][0].T[sliceNo])
+#
+
+###################### TGV_PD #########################################
+# Quick 2D denoising example in Matlab:   
+#   Im = double(imread('lena_gray_256.tif'))/255;  % loading image
+#   u0 = Im + .03*randn(size(Im)); u0(u0<0) = 0; % adding noise
+#   u = PrimalDual_TGV(single(u0), 0.02, 1.3, 1, 550);
+
+
+#out2 = Regularizer.TGV_PD(input=u0, regularization_parameter=0.05,
+#                          first_order_term=1.3,
+#                          second_order_term=1,
+#                          number_of_iterations=550)
+#pars = out2[-2]
+#reg_output3d.append(out2)
+#
+#a=fig3D.add_subplot(2,3,2)
+#
+#
+#textstr = out2[-1]
+#
+#
+## these are matplotlib.patch.Patch properties
+#props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
+## place a text box in upper left in axes coords
+#a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
+#        verticalalignment='top', bbox=props)
+#imgplot = plt.imshow(reg_output3d[-1][0].T[sliceNo])
diff --git a/Wrappers/Python/test/test_regularizers_3d.py b/Wrappers/Python/test/test_regularizers_3d.py
new file mode 100644
index 0000000..2d11a7e
--- /dev/null
+++ b/Wrappers/Python/test/test_regularizers_3d.py
@@ -0,0 +1,425 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Fri Aug  4 11:10:05 2017
+
+@author: ofn77899
+"""
+
+#from ccpi.viewer.CILViewer2D import Converter
+#import vtk
+
+import matplotlib.pyplot as plt
+import numpy as np
+import os    
+from enum import Enum
+import timeit
+#from PIL import Image
+#from Regularizer import Regularizer
+from ccpi.imaging.Regularizer import Regularizer
+
+###############################################################################
+#https://stackoverflow.com/questions/13875989/comparing-image-in-url-to-image-in-filesystem-in-python/13884956#13884956
+#NRMSE a normalization of the root of the mean squared error
+#NRMSE is simply 1 - [RMSE / (maxval - minval)]. Where maxval is the maximum
+# intensity from the two images being compared, and respectively the same for
+# minval. RMSE is given by the square root of MSE: 
+# sqrt[(sum(A - B) ** 2) / |A|],
+# where |A| means the number of elements in A. By doing this, the maximum value
+# given by RMSE is maxval.
+
+def nrmse(im1, im2):
+    a, b = im1.shape
+    rmse = np.sqrt(np.sum((im2 - im1) ** 2) / float(a * b))
+    max_val = max(np.max(im1), np.max(im2))
+    min_val = min(np.min(im1), np.min(im2))
+    return 1 - (rmse / (max_val - min_val))
+###############################################################################
+
+###############################################################################
+#
+#  2D Regularizers
+#
+###############################################################################
+#Example:
+# figure;
+# Im = double(imread('lena_gray_256.tif'))/255;  % loading image
+# u0 = Im + .05*randn(size(Im)); u0(u0 < 0) = 0;
+# u = SplitBregman_TV(single(u0), 10, 30, 1e-04);
+
+
+#filename = r"C:\Users\ofn77899\Documents\GitHub\CCPi-FISTA_reconstruction\data\lena_gray_512.tif"
+filename = r"/home/ofn77899/Reconstruction/CCPi-FISTA_Reconstruction/data/lena_gray_512.tif"
+#filename = r'/home/algol/Documents/Python/STD_test_images/lena_gray_512.tif'
+
+#reader = vtk.vtkTIFFReader()
+#reader.SetFileName(os.path.normpath(filename))
+#reader.Update()
+Im = plt.imread(filename)                     
+#Im = Image.open('/home/algol/Documents/Python/STD_test_images/lena_gray_512.tif')/255
+#img.show()
+Im = np.asarray(Im, dtype='float32')
+
+# create a 3D image by stacking N of this images
+
+
+#imgplot = plt.imshow(Im)
+perc = 0.05
+u_n = Im + (perc* np.random.normal(size=np.shape(Im)))
+y,z = np.shape(u_n)
+u_n = np.reshape(u_n , (1,y,z))
+
+u0 = u_n.copy()
+for i in range (19):
+    u_n = Im + (perc* np.random.normal(size=np.shape(Im)))
+    u_n = np.reshape(u_n , (1,y,z))
+
+    u0 = np.vstack ( (u0, u_n) )
+
+# map the u0 u0->u0>0
+f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1)
+u0 = f(u0).astype('float32')
+
+print ("Passed image shape {0}".format(np.shape(u0)))
+
+## plot 
+fig = plt.figure()
+#a=fig.add_subplot(3,3,1)
+#a.set_title('Original')
+#imgplot = plt.imshow(Im)
+sliceno = 10
+
+a=fig.add_subplot(2,3,1)
+a.set_title('noise')
+imgplot = plt.imshow(u0[sliceno],cmap="gray")
+
+reg_output = []
+##############################################################################
+# Call regularizer
+
+####################### SplitBregman_TV #####################################
+# u = SplitBregman_TV(single(u0), 10, 30, 1e-04);
+
+use_object = True
+if use_object:
+    reg = Regularizer(Regularizer.Algorithm.SplitBregman_TV)
+    print (reg.pars)
+    reg.setParameter(input=u0)    
+    reg.setParameter(regularization_parameter=10.)
+    # or 
+    # reg.setParameter(input=u0, regularization_parameter=10., #number_of_iterations=30,
+              #tolerance_constant=1e-4, 
+              #TV_Penalty=Regularizer.TotalVariationPenalty.l1)
+    plotme = reg() [0]
+    pars = reg.pars
+    textstr = reg.printParametersToString() 
+    
+    #out = reg(input=u0, regularization_parameter=10., #number_of_iterations=30,
+              #tolerance_constant=1e-4, 
+    #          TV_Penalty=Regularizer.TotalVariationPenalty.l1)
+    
+#out2 = Regularizer.SplitBregman_TV(input=u0, regularization_parameter=10., number_of_iterations=30,
+#          tolerance_constant=1e-4, 
+#          TV_Penalty=Regularizer.TotalVariationPenalty.l1)
+
+else:
+    out2 = Regularizer.SplitBregman_TV(input=u0, regularization_parameter=10. )
+    pars = out2[2]
+    reg_output.append(out2)
+    plotme = reg_output[-1][0]
+    textstr = out2[-1]
+
+a=fig.add_subplot(2,3,2)
+
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
+# place a text box in upper left in axes coords
+a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
+        verticalalignment='top', bbox=props)
+imgplot = plt.imshow(plotme[sliceno],cmap="gray")
+
+###################### FGP_TV #########################################
+# u = FGP_TV(single(u0), 0.05, 100, 1e-04);
+out2 = Regularizer.FGP_TV(input=u0, regularization_parameter=0.0005,
+                          number_of_iterations=50)
+pars = out2[-2]
+
+reg_output.append(out2)
+
+a=fig.add_subplot(2,3,3)
+
+textstr = out2[-1]
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
+# place a text box in upper left in axes coords
+a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(reg_output[-1][0][sliceno])
+# place a text box in upper left in axes coords
+a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
+        verticalalignment='top', bbox=props)
+imgplot = plt.imshow(reg_output[-1][0][sliceno],cmap="gray")
+
+###################### LLT_model #########################################
+# * u0 = Im + .03*randn(size(Im)); % adding noise
+# [Den] = LLT_model(single(u0), 10, 0.1, 1);
+#Den = LLT_model(single(u0), 25, 0.0003, 300, 0.0001, 0); 
+#input, regularization_parameter , time_step, number_of_iterations,
+#                  tolerance_constant, restrictive_Z_smoothing=0
+out2 = Regularizer.LLT_model(input=u0, regularization_parameter=25,
+                          time_step=0.0003,
+                          tolerance_constant=0.0001,
+                          number_of_iterations=300)
+pars = out2[-2]
+
+reg_output.append(out2)
+
+a=fig.add_subplot(2,3,4)
+
+textstr = out2[-1]
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
+# place a text box in upper left in axes coords
+a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(reg_output[-1][0][sliceno],cmap="gray")
+
+
+# ###################### PatchBased_Regul #########################################
+# # Quick 2D denoising example in Matlab:   
+# #   Im = double(imread('lena_gray_256.tif'))/255;  % loading image
+# #   u0 = Im + .03*randn(size(Im)); u0(u0<0) = 0; % adding noise
+# #   ImDen = PB_Regul_CPU(single(u0), 3, 1, 0.08, 0.05); 
+
+out2 = Regularizer.PatchBased_Regul(input=u0, regularization_parameter=0.05,
+                       searching_window_ratio=3,
+                       similarity_window_ratio=1,
+                       PB_filtering_parameter=0.08)
+pars = out2[-2]
+reg_output.append(out2)
+
+a=fig.add_subplot(2,3,5)
+
+
+textstr = out2[-1]
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
+# place a text box in upper left in axes coords
+a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
+        verticalalignment='top', bbox=props)
+imgplot = plt.imshow(reg_output[-1][0][sliceno],cmap="gray")
+
+
+# ###################### TGV_PD #########################################
+# # Quick 2D denoising example in Matlab:   
+# #   Im = double(imread('lena_gray_256.tif'))/255;  % loading image
+# #   u0 = Im + .03*randn(size(Im)); u0(u0<0) = 0; % adding noise
+# #   u = PrimalDual_TGV(single(u0), 0.02, 1.3, 1, 550);
+
+
+out2 = Regularizer.TGV_PD(input=u0, regularization_parameter=0.05,
+                           first_order_term=1.3,
+                           second_order_term=1,
+                           number_of_iterations=550)
+pars = out2[-2]
+reg_output.append(out2)
+
+a=fig.add_subplot(2,3,6)
+
+
+textstr = out2[-1]
+
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
+# place a text box in upper left in axes coords
+a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(reg_output[-1][0][sliceno],cmap="gray")
+
+
+plt.show()
+
+################################################################################
+##
+##  3D Regularizers
+##
+################################################################################
+##Example:
+## figure;
+## Im = double(imread('lena_gray_256.tif'))/255;  % loading image
+## u0 = Im + .05*randn(size(Im)); u0(u0 < 0) = 0;
+## u = SplitBregman_TV(single(u0), 10, 30, 1e-04);
+#
+##filename = r"C:\Users\ofn77899\Documents\GitHub\CCPi-Reconstruction\python\test\reconstruction_example.mha"
+#filename = r"C:\Users\ofn77899\Documents\GitHub\CCPi-Simpleflex\data\head.mha"
+#
+#reader = vtk.vtkMetaImageReader()
+#reader.SetFileName(os.path.normpath(filename))
+#reader.Update()
+##vtk returns 3D images, let's take just the one slice there is as 2D
+#Im = Converter.vtk2numpy(reader.GetOutput())
+#Im = Im.astype('float32')
+##imgplot = plt.imshow(Im)
+#perc = 0.05
+#u0 = Im + (perc* np.random.normal(size=np.shape(Im)))
+## map the u0 u0->u0>0
+#f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1)
+#u0 = f(u0).astype('float32')
+#converter = Converter.numpy2vtkImporter(u0, reader.GetOutput().GetSpacing(),
+#                                        reader.GetOutput().GetOrigin())
+#converter.Update()
+#writer = vtk.vtkMetaImageWriter()
+#writer.SetInputData(converter.GetOutput())
+#writer.SetFileName(r"C:\Users\ofn77899\Documents\GitHub\CCPi-FISTA_reconstruction\data\noisy_head.mha")
+##writer.Write()
+#
+#
+### plot 
+#fig3D = plt.figure()
+##a=fig.add_subplot(3,3,1)
+##a.set_title('Original')
+##imgplot = plt.imshow(Im)
+#sliceNo = 32
+#
+#a=fig3D.add_subplot(2,3,1)
+#a.set_title('noise')
+#imgplot = plt.imshow(u0.T[sliceNo])
+#
+#reg_output3d = []
+#
+###############################################################################
+## Call regularizer
+#
+######################## SplitBregman_TV #####################################
+## u = SplitBregman_TV(single(u0), 10, 30, 1e-04);
+#
+##reg = Regularizer(Regularizer.Algorithm.SplitBregman_TV)
+#
+##out = reg(input=u0, regularization_parameter=10., #number_of_iterations=30,
+##          #tolerance_constant=1e-4, 
+##          TV_Penalty=Regularizer.TotalVariationPenalty.l1)
+#
+#out2 = Regularizer.SplitBregman_TV(input=u0, regularization_parameter=10., number_of_iterations=30,
+#          tolerance_constant=1e-4, 
+#          TV_Penalty=Regularizer.TotalVariationPenalty.l1)
+#
+#
+#pars = out2[-2]
+#reg_output3d.append(out2)
+#
+#a=fig3D.add_subplot(2,3,2)
+#
+#
+#textstr = out2[-1]
+#
+#
+## these are matplotlib.patch.Patch properties
+#props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
+## place a text box in upper left in axes coords
+#a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
+#        verticalalignment='top', bbox=props)
+#imgplot = plt.imshow(reg_output3d[-1][0].T[sliceNo])
+#
+####################### FGP_TV #########################################
+## u = FGP_TV(single(u0), 0.05, 100, 1e-04);
+#out2 = Regularizer.FGP_TV(input=u0, regularization_parameter=0.005,
+#                          number_of_iterations=200)
+#pars = out2[-2]
+#reg_output3d.append(out2)
+#
+#a=fig3D.add_subplot(2,3,2)
+#
+#
+#textstr = out2[-1]
+#
+#
+## these are matplotlib.patch.Patch properties
+#props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
+## place a text box in upper left in axes coords
+#a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
+#        verticalalignment='top', bbox=props)
+#imgplot = plt.imshow(reg_output3d[-1][0].T[sliceNo])
+#
+####################### LLT_model #########################################
+## * u0 = Im + .03*randn(size(Im)); % adding noise
+## [Den] = LLT_model(single(u0), 10, 0.1, 1);
+##Den = LLT_model(single(u0), 25, 0.0003, 300, 0.0001, 0); 
+##input, regularization_parameter , time_step, number_of_iterations,
+##                  tolerance_constant, restrictive_Z_smoothing=0
+#out2 = Regularizer.LLT_model(input=u0, regularization_parameter=25,
+#                          time_step=0.0003,
+#                          tolerance_constant=0.0001,
+#                          number_of_iterations=300)
+#pars = out2[-2]
+#reg_output3d.append(out2)
+#
+#a=fig3D.add_subplot(2,3,2)
+#
+#
+#textstr = out2[-1]
+#
+#
+## these are matplotlib.patch.Patch properties
+#props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
+## place a text box in upper left in axes coords
+#a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
+#        verticalalignment='top', bbox=props)
+#imgplot = plt.imshow(reg_output3d[-1][0].T[sliceNo])
+#
+####################### PatchBased_Regul #########################################
+## Quick 2D denoising example in Matlab:   
+##   Im = double(imread('lena_gray_256.tif'))/255;  % loading image
+##   u0 = Im + .03*randn(size(Im)); u0(u0<0) = 0; % adding noise
+##   ImDen = PB_Regul_CPU(single(u0), 3, 1, 0.08, 0.05); 
+#
+#out2 = Regularizer.PatchBased_Regul(input=u0, regularization_parameter=0.05,
+#                          searching_window_ratio=3,
+#                          similarity_window_ratio=1,
+#                          PB_filtering_parameter=0.08)
+#pars = out2[-2]
+#reg_output3d.append(out2)
+#
+#a=fig3D.add_subplot(2,3,2)
+#
+#
+#textstr = out2[-1]
+#
+#
+## these are matplotlib.patch.Patch properties
+#props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
+## place a text box in upper left in axes coords
+#a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
+#        verticalalignment='top', bbox=props)
+#imgplot = plt.imshow(reg_output3d[-1][0].T[sliceNo])
+#
+
+###################### TGV_PD #########################################
+# Quick 2D denoising example in Matlab:   
+#   Im = double(imread('lena_gray_256.tif'))/255;  % loading image
+#   u0 = Im + .03*randn(size(Im)); u0(u0<0) = 0; % adding noise
+#   u = PrimalDual_TGV(single(u0), 0.02, 1.3, 1, 550);
+
+
+#out2 = Regularizer.TGV_PD(input=u0, regularization_parameter=0.05,
+#                          first_order_term=1.3,
+#                          second_order_term=1,
+#                          number_of_iterations=550)
+#pars = out2[-2]
+#reg_output3d.append(out2)
+#
+#a=fig3D.add_subplot(2,3,2)
+#
+#
+#textstr = out2[-1]
+#
+#
+## these are matplotlib.patch.Patch properties
+#props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
+## place a text box in upper left in axes coords
+#a.text(0.05, 0.95, textstr, transform=a.transAxes, fontsize=14,
+#        verticalalignment='top', bbox=props)
+#imgplot = plt.imshow(reg_output3d[-1][0].T[sliceNo])
diff --git a/Wrappers/Python/test/view_result.py b/Wrappers/Python/test/view_result.py
new file mode 100644
index 0000000..f89a90c
--- /dev/null
+++ b/Wrappers/Python/test/view_result.py
@@ -0,0 +1,12 @@
+import numpy
+from ccpi.viewer.CILViewer2D import *
+import sys
+#reader = vtk.vtkMetaImageReader()
+#reader.SetFileName("X_out_os_s.mhd")
+#reader.Update()
+
+X = numpy.load(sys.argv[1])
+
+v = CILViewer2D()
+v.setInputAsNumpy(X)
+v.startRenderLoop()