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-rw-r--r--Core/regularisers_GPU/TGV_GPU_core.cu487
-rw-r--r--Core/regularisers_GPU/TGV_GPU_core.h2
-rw-r--r--Wrappers/Python/demos/SoftwareX_supp/Demo_RealData_Recon_SX.py231
-rw-r--r--Wrappers/Python/demos/SoftwareX_supp/Demo_SimulData_ParOptimis_SX.py161
-rw-r--r--Wrappers/Python/demos/SoftwareX_supp/Demo_SimulData_Recon_SX.py309
-rw-r--r--Wrappers/Python/demos/SoftwareX_supp/Demo_SimulData_SX.py117
-rw-r--r--Wrappers/Python/demos/SoftwareX_supp/Readme.md26
-rw-r--r--Wrappers/Python/demos/SoftwareX_supp/optim_param/Optim_admm_rofllt.h5bin0 -> 2408 bytes
-rw-r--r--Wrappers/Python/demos/SoftwareX_supp/optim_param/Optim_admm_sbtv.h5bin0 -> 2408 bytes
-rw-r--r--Wrappers/Python/demos/SoftwareX_supp/optim_param/Optim_admm_tgv.h5bin0 -> 2408 bytes
10 files changed, 1097 insertions, 236 deletions
diff --git a/Core/regularisers_GPU/TGV_GPU_core.cu b/Core/regularisers_GPU/TGV_GPU_core.cu
index e4abf72..849219b 100644
--- a/Core/regularisers_GPU/TGV_GPU_core.cu
+++ b/Core/regularisers_GPU/TGV_GPU_core.cu
@@ -38,49 +38,49 @@ limitations under the License.
* [1] K. Bredies "Total Generalized Variation"
*/
+
+#define BLKXSIZE2D 16
+#define BLKYSIZE2D 16
+
#define BLKXSIZE 8
#define BLKYSIZE 8
-#define BLKZSIZE 8
-
-#define BLKXSIZE2D 8
-#define BLKYSIZE2D 8
-#define EPS 1.0e-7
-#define idivup(a, b) ( ((a)%(b) != 0) ? (a)/(b)+1 : (a)/(b) )
+#define BLKZSIZE 8
+#define idivup(a, b) ( ((a)%(b) != 0) ? (a)/(b)+1 : (a)/(b) )
/********************************************************************/
/***************************2D Functions*****************************/
/********************************************************************/
-__global__ void DualP_2D_kernel(float *U, float *V1, float *V2, float *P1, float *P2, int dimX, int dimY, float sigma)
+__global__ void DualP_2D_kernel(float *U, float *V1, float *V2, float *P1, float *P2, long dimX, long dimY, float sigma)
{
- int num_total = dimX*dimY;
- const int i = blockDim.x * blockIdx.x + threadIdx.x;
- const int j = blockDim.y * blockIdx.y + threadIdx.y;
+ const long i = blockDim.x * blockIdx.x + threadIdx.x;
+ const long j = blockDim.y * blockIdx.y + threadIdx.y;
- int index = i + dimX*j;
+ long index = i + (dimX)*j;
- if (index < num_total) {
+ if ((i < dimX) && (j < dimY)) {
/* symmetric boundary conditions (Neuman) */
if ((i >= 0) && (i < dimX-1)) P1[index] += sigma*((U[(i+1) + dimX*j] - U[index]) - V1[index]);
- else P1[index] += sigma*(-V1[index]);
+ else if (i == dimX-1) P1[index] -= sigma*(V1[index]);
+ else P1[index] = 0.0f;
if ((j >= 0) && (j < dimY-1)) P2[index] += sigma*((U[i + dimX*(j+1)] - U[index]) - V2[index]);
- else P2[index] += sigma*(-V2[index]);
+ else if (j == dimY-1) P2[index] -= sigma*(V2[index]);
+ else P2[index] = 0.0f;
}
return;
}
-__global__ void ProjP_2D_kernel(float *P1, float *P2, int dimX, int dimY, float alpha1)
+__global__ void ProjP_2D_kernel(float *P1, float *P2, long dimX, long dimY, float alpha1)
{
float grad_magn;
- int num_total = dimX*dimY;
- const int i = blockDim.x * blockIdx.x + threadIdx.x;
- const int j = blockDim.y * blockIdx.y + threadIdx.y;
+ const long i = blockDim.x * blockIdx.x + threadIdx.x;
+ const long j = blockDim.y * blockIdx.y + threadIdx.y;
- int index = i + dimX*j;
+ long index = i + (dimX)*j;
- if (index < num_total) {
- grad_magn = sqrtf(pow(P1[index],2) + pow(P2[index],2));
+ if ((i < dimX) && (j < dimY)) {
+ grad_magn = sqrtf(powf(P1[index],2) + powf(P2[index],2));
grad_magn = grad_magn/alpha1;
if (grad_magn > 1.0f) {
P1[index] /= grad_magn;
@@ -90,17 +90,15 @@ __global__ void ProjP_2D_kernel(float *P1, float *P2, int dimX, int dimY, float
return;
}
-__global__ void DualQ_2D_kernel(float *V1, float *V2, float *Q1, float *Q2, float *Q3, int dimX, int dimY, float sigma)
+__global__ void DualQ_2D_kernel(float *V1, float *V2, float *Q1, float *Q2, float *Q3, long dimX, long dimY, float sigma)
{
float q1, q2, q11, q22;
- int num_total = dimX*dimY;
-
- const int i = blockDim.x * blockIdx.x + threadIdx.x;
- const int j = blockDim.y * blockIdx.y + threadIdx.y;
+ const long i = blockDim.x * blockIdx.x + threadIdx.x;
+ const long j = blockDim.y * blockIdx.y + threadIdx.y;
- int index = i + dimX*j;
+ long index = i + (dimX)*j;
- if (index < num_total) {
+ if ((i < dimX) && (j < dimY)) {
q1 = 0.0f; q2 = 0.0f; q11 = 0.0f; q22 = 0.0f;
if ((i >= 0) && (i < dimX-1)) {
@@ -120,18 +118,16 @@ __global__ void DualQ_2D_kernel(float *V1, float *V2, float *Q1, float *Q2, floa
return;
}
-__global__ void ProjQ_2D_kernel(float *Q1, float *Q2, float *Q3, int dimX, int dimY, float alpha0)
+__global__ void ProjQ_2D_kernel(float *Q1, float *Q2, float *Q3, long dimX, long dimY, float alpha0)
{
float grad_magn;
- int num_total = dimX*dimY;
-
- const int i = blockDim.x * blockIdx.x + threadIdx.x;
- const int j = blockDim.y * blockIdx.y + threadIdx.y;
+ const long i = blockDim.x * blockIdx.x + threadIdx.x;
+ const long j = blockDim.y * blockIdx.y + threadIdx.y;
- int index = i + dimX*j;
+ long index = i + (dimX)*j;
- if (index < num_total) {
- grad_magn = sqrt(pow(Q1[index],2) + pow(Q2[index],2) + 2*pow(Q3[index],2));
+ if ((i < dimX) && (j < dimY)) {
+ grad_magn = sqrt(powf(Q1[index],2) + powf(Q2[index],2) + 2*powf(Q3[index],2));
grad_magn = grad_magn/alpha0;
if (grad_magn > 1.0f) {
Q1[index] /= grad_magn;
@@ -142,26 +138,26 @@ __global__ void ProjQ_2D_kernel(float *Q1, float *Q2, float *Q3, int dimX, int d
return;
}
-__global__ void DivProjP_2D_kernel(float *U, float *U0, float *P1, float *P2, int dimX, int dimY, float lambda, float tau)
+__global__ void DivProjP_2D_kernel(float *U, float *U0, float *P1, float *P2, long dimX, long dimY, float lambda, float tau)
{
float P_v1, P_v2, div;
- int num_total = dimX*dimY;
-
- const int i = blockDim.x * blockIdx.x + threadIdx.x;
- const int j = blockDim.y * blockIdx.y + threadIdx.y;
+ const long i = blockDim.x * blockIdx.x + threadIdx.x;
+ const long j = blockDim.y * blockIdx.y + threadIdx.y;
- int index = i + dimX*j;
+ long index = i + (dimX)*j;
- if (index < num_total) {
- P_v1 = 0.0f; P_v2 = 0.0f;
-
- if (i == 0) P_v1 = P1[index];
- if (i == dimX-1) P_v1 = -P1[(i-1) + dimX*j];
+ if ((i < dimX) && (j < dimY)) {
+
if ((i > 0) && (i < dimX-1)) P_v1 = P1[index] - P1[(i-1) + dimX*j];
+ else if (i == dimX-1) P_v1 = -P1[(i-1) + dimX*j];
+ else if (i == 0) P_v1 = P1[index];
+ else P_v1 = 0.0f;
- if (j == 0) P_v2 = P2[index];
- if (j == dimY-1) P_v2 = -P2[i + dimX*(j-1)];
if ((j > 0) && (j < dimY-1)) P_v2 = P2[index] - P2[i + dimX*(j-1)];
+ else if (j == dimY-1) P_v2 = -P2[i + dimX*(j-1)];
+ else if (j == 0) P_v2 = P2[index];
+ else P_v2 = 0.0f;
+
div = P_v1 + P_v2;
U[index] = (lambda*(U[index] + tau*div) + tau*U0[index])/(lambda + tau);
@@ -169,18 +165,19 @@ __global__ void DivProjP_2D_kernel(float *U, float *U0, float *P1, float *P2, in
return;
}
-__global__ void UpdV_2D_kernel(float *V1, float *V2, float *P1, float *P2, float *Q1, float *Q2, float *Q3, int dimX, int dimY, float tau)
+__global__ void UpdV_2D_kernel(float *V1, float *V2, float *P1, float *P2, float *Q1, float *Q2, float *Q3, long dimX, long dimY, float tau)
{
float q1, q3_x, q2, q3_y, div1, div2;
- int num_total = dimX*dimY;
- int i1, j1;
+ long i1, j1;
- const int i = blockDim.x * blockIdx.x + threadIdx.x;
- const int j = blockDim.y * blockIdx.y + threadIdx.y;
+ const long i = blockDim.x * blockIdx.x + threadIdx.x;
+ const long j = blockDim.y * blockIdx.y + threadIdx.y;
- int index = i + dimX*j;
+ long index = i + (dimX)*j;
- if (index < num_total) {
+ if ((i < dimX) && (j < dimY)) {
+
+ q1 = 0.0f; q3_x = 0.0f; q2 = 0.0f; q3_y = 0.0f; div1 = 0.0f; div2= 0.0f;
i1 = (i-1) + dimX*j;
j1 = (i) + dimX*(j-1);
@@ -222,94 +219,95 @@ __global__ void UpdV_2D_kernel(float *V1, float *V2, float *P1, float *P2, float
return;
}
-__global__ void copyIm_TGV_kernel(float *U, float *U_old, int N, int M, int num_total)
+__global__ void copyIm_TGV_kernel(float *U, float *U_old, long dimX, long dimY)
{
- int xIndex = blockDim.x * blockIdx.x + threadIdx.x;
- int yIndex = blockDim.y * blockIdx.y + threadIdx.y;
-
- int index = xIndex + N*yIndex;
+ const long i = blockDim.x * blockIdx.x + threadIdx.x;
+ const long j = blockDim.y * blockIdx.y + threadIdx.y;
+
+ long index = i + (dimX)*j;
- if (index < num_total) {
+ if ((i < dimX) && (j < dimY)) {
U_old[index] = U[index];
}
}
-__global__ void copyIm_TGV_kernel_ar2(float *V1, float *V2, float *V1_old, float *V2_old, int N, int M, int num_total)
+__global__ void copyIm_TGV_kernel_ar2(float *V1, float *V2, float *V1_old, float *V2_old, long dimX, long dimY)
{
- int xIndex = blockDim.x * blockIdx.x + threadIdx.x;
- int yIndex = blockDim.y * blockIdx.y + threadIdx.y;
-
- int index = xIndex + N*yIndex;
+ const long i = blockDim.x * blockIdx.x + threadIdx.x;
+ const long j = blockDim.y * blockIdx.y + threadIdx.y;
+
+ long index = i + (dimX)*j;
- if (index < num_total) {
+ if ((i < dimX) && (j < dimY)) {
V1_old[index] = V1[index];
V2_old[index] = V2[index];
}
}
-__global__ void newU_kernel(float *U, float *U_old, int N, int M, int num_total)
+__global__ void newU_kernel(float *U, float *U_old, long dimX, long dimY)
{
- int xIndex = blockDim.x * blockIdx.x + threadIdx.x;
- int yIndex = blockDim.y * blockIdx.y + threadIdx.y;
-
- int index = xIndex + N*yIndex;
+ const long i = blockDim.x * blockIdx.x + threadIdx.x;
+ const long j = blockDim.y * blockIdx.y + threadIdx.y;
+
+ long index = i + (dimX)*j;
- if (index < num_total) {
+ if ((i < dimX) && (j < dimY)) {
U[index] = 2.0f*U[index] - U_old[index];
}
}
-__global__ void newU_kernel_ar2(float *V1, float *V2, float *V1_old, float *V2_old, int N, int M, int num_total)
+__global__ void newU_kernel_ar2(float *V1, float *V2, float *V1_old, float *V2_old, long dimX, long dimY)
{
- int xIndex = blockDim.x * blockIdx.x + threadIdx.x;
- int yIndex = blockDim.y * blockIdx.y + threadIdx.y;
-
- int index = xIndex + N*yIndex;
+ const long i = blockDim.x * blockIdx.x + threadIdx.x;
+ const long j = blockDim.y * blockIdx.y + threadIdx.y;
+
+ long index = i + (dimX)*j;
- if (index < num_total) {
+ if ((i < dimX) && (j < dimY)) {
V1[index] = 2.0f*V1[index] - V1_old[index];
V2[index] = 2.0f*V2[index] - V2_old[index];
}
}
+
/********************************************************************/
/***************************3D Functions*****************************/
/********************************************************************/
-__global__ void DualP_3D_kernel(float *U, float *V1, float *V2, float *V3, float *P1, float *P2, float *P3, int dimX, int dimY, int dimZ, float sigma)
+__global__ void DualP_3D_kernel(float *U, float *V1, float *V2, float *V3, float *P1, float *P2, float *P3, long dimX, long dimY, long dimZ, float sigma)
{
- int index;
- const int i = blockDim.x * blockIdx.x + threadIdx.x;
- const int j = blockDim.y * blockIdx.y + threadIdx.y;
- const int k = blockDim.z * blockIdx.z + threadIdx.z;
-
- int num_total = dimX*dimY*dimZ;
-
- index = (dimX*dimY)*k + i*dimX+j;
- if (index < num_total) {
+ long index;
+ const long i = blockDim.x * blockIdx.x + threadIdx.x;
+ const long j = blockDim.y * blockIdx.y + threadIdx.y;
+ const long k = blockDim.z * blockIdx.z + threadIdx.z;
+
+ index = (dimX*dimY)*k + i*dimX+j;
+
+ if ((i < dimX) && (j < dimY) && (k < dimZ)) {
/* symmetric boundary conditions (Neuman) */
if ((i >= 0) && (i < dimX-1)) P1[index] += sigma*((U[(dimX*dimY)*k + (i+1)*dimX+j] - U[index]) - V1[index]);
- else P1[index] += sigma*(-V1[index]);
+ else if (i == dimX-1) P1[index] -= sigma*(V1[index]);
+ else P1[index] = 0.0f;
if ((j >= 0) && (j < dimY-1)) P2[index] += sigma*((U[(dimX*dimY)*k + i*dimX+(j+1)] - U[index]) - V2[index]);
- else P2[index] += sigma*(-V2[index]);
+ else if (j == dimY-1) P2[index] -= sigma*(V2[index]);
+ else P2[index] = 0.0f;
if ((k >= 0) && (k < dimZ-1)) P3[index] += sigma*((U[(dimX*dimY)*(k+1) + i*dimX+(j)] - U[index]) - V3[index]);
- else P3[index] += sigma*(-V3[index]);
- }
+ else if (k == dimZ-1) P3[index] -= sigma*(V3[index]);
+ else P3[index] = 0.0f;
+ }
return;
-}
+}
-__global__ void ProjP_3D_kernel(float *P1, float *P2, float *P3, int dimX, int dimY, int dimZ, float alpha1)
+__global__ void ProjP_3D_kernel(float *P1, float *P2, float *P3, long dimX, long dimY, long dimZ, float alpha1)
{
float grad_magn;
- int index;
- int num_total = dimX*dimY*dimZ;
-
- int i = blockDim.x * blockIdx.x + threadIdx.x;
- int j = blockDim.y * blockIdx.y + threadIdx.y;
- int k = blockDim.z * blockIdx.z + threadIdx.z;
+ long index;
+ const long i = blockDim.x * blockIdx.x + threadIdx.x;
+ const long j = blockDim.y * blockIdx.y + threadIdx.y;
+ const long k = blockDim.z * blockIdx.z + threadIdx.z;
index = (dimX*dimY)*k + i*dimX+j;
- if (index < num_total) {
- grad_magn = (sqrtf(pow(P1[index],2) + pow(P2[index],2) + pow(P3[index],2)))/alpha1;
+ if ((i < dimX) && (j < dimY) && (k < dimZ)) {
+ grad_magn = (sqrtf(powf(P1[index],2) + powf(P2[index],2) + powf(P3[index],2)))/alpha1;
if (grad_magn > 1.0f) {
P1[index] /= grad_magn;
P2[index] /= grad_magn;
@@ -319,23 +317,22 @@ __global__ void ProjP_3D_kernel(float *P1, float *P2, float *P3, int dimX, int d
return;
}
-__global__ void DualQ_3D_kernel(float *V1, float *V2, float *V3, float *Q1, float *Q2, float *Q3, float *Q4, float *Q5, float *Q6, int dimX, int dimY, int dimZ, float sigma)
+__global__ void DualQ_3D_kernel(float *V1, float *V2, float *V3, float *Q1, float *Q2, float *Q3, float *Q4, float *Q5, float *Q6, long dimX, long dimY, long dimZ, float sigma)
{
- int index;
- float q1, q2, q3, q11, q22, q33, q44, q55, q66;
- int num_total = dimX*dimY*dimZ;
-
- int i = blockDim.x * blockIdx.x + threadIdx.x;
- int j = blockDim.y * blockIdx.y + threadIdx.y;
- int k = blockDim.z * blockIdx.z + threadIdx.z;
+ float q1, q2, q3, q11, q22, q33, q44, q55, q66;
+ long index;
+
+ const long i = blockDim.x * blockIdx.x + threadIdx.x;
+ const long j = blockDim.y * blockIdx.y + threadIdx.y;
+ const long k = blockDim.z * blockIdx.z + threadIdx.z;
index = (dimX*dimY)*k + i*dimX+j;
- int i1 = (dimX*dimY)*k + (i+1)*dimX+j;
- int j1 = (dimX*dimY)*k + (i)*dimX+(j+1);
- int k1 = (dimX*dimY)*(k+1) + (i)*dimX+(j);
+ long i1 = (dimX*dimY)*k + (i+1)*dimX+j;
+ long j1 = (dimX*dimY)*k + (i)*dimX+(j+1);
+ long k1 = (dimX*dimY)*(k+1) + (i)*dimX+(j);
- if (index < num_total) {
+ if ((i < dimX) && (j < dimY) && (k < dimZ)) {
q1 = 0.0f; q11 = 0.0f; q33 = 0.0f; q2 = 0.0f; q22 = 0.0f; q55 = 0.0f; q3 = 0.0f; q44 = 0.0f; q66 = 0.0f;
/* boundary conditions (Neuman) */
@@ -362,20 +359,18 @@ __global__ void DualQ_3D_kernel(float *V1, float *V2, float *V3, float *Q1, floa
return;
}
-__global__ void ProjQ_3D_kernel(float *Q1, float *Q2, float *Q3, float *Q4, float *Q5, float *Q6, int dimX, int dimY, int dimZ, float alpha0)
+__global__ void ProjQ_3D_kernel(float *Q1, float *Q2, float *Q3, float *Q4, float *Q5, float *Q6, long dimX, long dimY, long dimZ, float alpha0)
{
float grad_magn;
- int index;
- int num_total = dimX*dimY*dimZ;
-
- int i = blockDim.x * blockIdx.x + threadIdx.x;
- int j = blockDim.y * blockIdx.y + threadIdx.y;
- int k = blockDim.z * blockIdx.z + threadIdx.z;
+ long index;
+ const long i = blockDim.x * blockIdx.x + threadIdx.x;
+ const long j = blockDim.y * blockIdx.y + threadIdx.y;
+ const long k = blockDim.z * blockIdx.z + threadIdx.z;
index = (dimX*dimY)*k + i*dimX+j;
- if (index < num_total) {
- grad_magn = sqrtf(pow(Q1[index],2) + pow(Q2[index],2) + pow(Q3[index],2) + 2.0f*pow(Q4[index],2) + 2.0f*pow(Q5[index],2) + 2.0f*pow(Q6[index],2));
+ if ((i < dimX) && (j < dimY) && (k < dimZ)) {
+ grad_magn = sqrtf(powf(Q1[index],2) + powf(Q2[index],2) + powf(Q3[index],2) + 2.0f*powf(Q4[index],2) + 2.0f*powf(Q5[index],2) + 2.0f*powf(Q6[index],2));
grad_magn = grad_magn/alpha0;
if (grad_magn > 1.0f) {
Q1[index] /= grad_magn;
@@ -388,60 +383,56 @@ __global__ void ProjQ_3D_kernel(float *Q1, float *Q2, float *Q3, float *Q4, floa
}
return;
}
-__global__ void DivProjP_3D_kernel(float *U, float *U0, float *P1, float *P2, float *P3, int dimX, int dimY, int dimZ, float lambda, float tau)
+__global__ void DivProjP_3D_kernel(float *U, float *U0, float *P1, float *P2, float *P3, long dimX, long dimY, long dimZ, float lambda, float tau)
{
float P_v1, P_v2, P_v3, div;
- int index;
- int num_total = dimX*dimY*dimZ;
-
-
- int i = blockDim.x * blockIdx.x + threadIdx.x;
- int j = blockDim.y * blockIdx.y + threadIdx.y;
- int k = blockDim.z * blockIdx.z + threadIdx.z;
+ long index;
+ const long i = blockDim.x * blockIdx.x + threadIdx.x;
+ const long j = blockDim.y * blockIdx.y + threadIdx.y;
+ const long k = blockDim.z * blockIdx.z + threadIdx.z;
index = (dimX*dimY)*k + i*dimX+j;
- int i1 = (dimX*dimY)*k + (i-1)*dimX+j;
- int j1 = (dimX*dimY)*k + (i)*dimX+(j-1);
- int k1 = (dimX*dimY)*(k-1) + (i)*dimX+(j);
-
- if (index < num_total) {
- P_v1 = 0.0f; P_v2 = 0.0f; P_v3 = 0.0f;
+ long i1 = (dimX*dimY)*k + (i-1)*dimX+j;
+ long j1 = (dimX*dimY)*k + (i)*dimX+(j-1);
+ long k1 = (dimX*dimY)*(k-1) + (i)*dimX+(j);
- if (i == 0) P_v1 = P1[index];
- if (i == dimX-1) P_v1 = -P1[i1];
+ if ((i < dimX) && (j < dimY) && (k < dimZ)) {
+
if ((i > 0) && (i < dimX-1)) P_v1 = P1[index] - P1[i1];
+ else if (i == dimX-1) P_v1 = -P1[i1];
+ else if (i == 0) P_v1 = P1[index];
+ else P_v1 = 0.0f;
- if (j == 0) P_v2 = P2[index];
- if (j == dimY-1) P_v2 = -P2[j1];
if ((j > 0) && (j < dimY-1)) P_v2 = P2[index] - P2[j1];
-
- if (k == 0) P_v3 = P3[index];
- if (k == dimZ-1) P_v3 = -P3[k1];
- if ((k > 0) && (k < dimZ-1)) P_v3 = P3[index] - P3[k1];
-
-
+ else if (j == dimY-1) P_v2 = -P2[j1];
+ else if (j == 0) P_v2 = P2[index];
+ else P_v2 = 0.0f;
+
+ if ((k > 0) && (k < dimZ-1)) P_v3 = P3[index] - P3[k1];
+ else if (k == dimZ-1) P_v3 = -P3[k1];
+ else if (k == 0) P_v3 = P3[index];
+ else P_v3 = 0.0f;
+
div = P_v1 + P_v2 + P_v3;
U[index] = (lambda*(U[index] + tau*div) + tau*U0[index])/(lambda + tau);
}
return;
}
-__global__ void UpdV_3D_kernel(float *V1, float *V2, float *V3, float *P1, float *P2, float *P3, float *Q1, float *Q2, float *Q3, float *Q4, float *Q5, float *Q6, int dimX, int dimY, int dimZ, float tau)
+__global__ void UpdV_3D_kernel(float *V1, float *V2, float *V3, float *P1, float *P2, float *P3, float *Q1, float *Q2, float *Q3, float *Q4, float *Q5, float *Q6, long dimX, long dimY, long dimZ, float tau)
{
float q1, q4x, q5x, q2, q4y, q6y, q6z, q5z, q3, div1, div2, div3;
- int index;
- int num_total = dimX*dimY*dimZ;
-
- int i = blockDim.x * blockIdx.x + threadIdx.x;
- int j = blockDim.y * blockIdx.y + threadIdx.y;
- int k = blockDim.z * blockIdx.z + threadIdx.z;
+ long index;
+ const long i = blockDim.x * blockIdx.x + threadIdx.x;
+ const long j = blockDim.y * blockIdx.y + threadIdx.y;
+ const long k = blockDim.z * blockIdx.z + threadIdx.z;
index = (dimX*dimY)*k + i*dimX+j;
- int i1 = (dimX*dimY)*k + (i-1)*dimX+j;
- int j1 = (dimX*dimY)*k + (i)*dimX+(j-1);
- int k1 = (dimX*dimY)*(k-1) + (i)*dimX+(j);
+ long i1 = (dimX*dimY)*k + (i-1)*dimX+j;
+ long j1 = (dimX*dimY)*k + (i)*dimX+(j-1);
+ long k1 = (dimX*dimY)*(k-1) + (i)*dimX+(j);
/* Q1 - Q11, Q2 - Q22, Q3 - Q33, Q4 - Q21/Q12, Q5 - Q31/Q13, Q6 - Q32/Q23*/
- if (index < num_total) {
+ if ((i < dimX) && (j < dimY) && (k < dimZ)) {
/* boundary conditions (Neuman) */
if ((i > 0) && (i < dimX-1)) {
@@ -507,64 +498,60 @@ __global__ void UpdV_3D_kernel(float *V1, float *V2, float *V3, float *P1, float
return;
}
-__global__ void copyIm_TGV_kernel3D(float *U, float *U_old, int dimX, int dimY, int dimZ, int num_total)
+__global__ void copyIm_TGV_kernel3D(float *U, float *U_old, long dimX, long dimY, long dimZ)
{
- int index;
-
- int i = blockDim.x * blockIdx.x + threadIdx.x;
- int j = blockDim.y * blockIdx.y + threadIdx.y;
- int k = blockDim.z * blockIdx.z + threadIdx.z;
+ long index;
+ const long i = blockDim.x * blockIdx.x + threadIdx.x;
+ const long j = blockDim.y * blockIdx.y + threadIdx.y;
+ const long k = blockDim.z * blockIdx.z + threadIdx.z;
index = (dimX*dimY)*k + j*dimX+i;
- if (index < num_total) {
+ if ((i < dimX) && (j < dimY) && (k < dimZ)) {
U_old[index] = U[index];
}
}
-__global__ void copyIm_TGV_kernel3D_ar3(float *V1, float *V2, float *V3, float *V1_old, float *V2_old, float *V3_old, int dimX, int dimY, int dimZ, int num_total)
+__global__ void copyIm_TGV_kernel3D_ar3(float *V1, float *V2, float *V3, float *V1_old, float *V2_old, float *V3_old, long dimX, long dimY, long dimZ)
{
- int index;
-
- int i = blockDim.x * blockIdx.x + threadIdx.x;
- int j = blockDim.y * blockIdx.y + threadIdx.y;
- int k = blockDim.z * blockIdx.z + threadIdx.z;
+ long index;
+ const long i = blockDim.x * blockIdx.x + threadIdx.x;
+ const long j = blockDim.y * blockIdx.y + threadIdx.y;
+ const long k = blockDim.z * blockIdx.z + threadIdx.z;
index = (dimX*dimY)*k + j*dimX+i;
- if (index < num_total) {
+ if ((i < dimX) && (j < dimY) && (k < dimZ)) {
V1_old[index] = V1[index];
V2_old[index] = V2[index];
V3_old[index] = V3[index];
}
}
-__global__ void newU_kernel3D(float *U, float *U_old, int dimX, int dimY, int dimZ, int num_total)
+__global__ void newU_kernel3D(float *U, float *U_old, int dimX, int dimY, int dimZ)
{
- int index;
-
- int i = blockDim.x * blockIdx.x + threadIdx.x;
- int j = blockDim.y * blockIdx.y + threadIdx.y;
- int k = blockDim.z * blockIdx.z + threadIdx.z;
+ long index;
+ const long i = blockDim.x * blockIdx.x + threadIdx.x;
+ const long j = blockDim.y * blockIdx.y + threadIdx.y;
+ const long k = blockDim.z * blockIdx.z + threadIdx.z;
index = (dimX*dimY)*k + j*dimX+i;
- if (index < num_total) {
+ if ((i < dimX) && (j < dimY) && (k < dimZ)) {
U[index] = 2.0f*U[index] - U_old[index];
}
}
-__global__ void newU_kernel3D_ar3(float *V1, float *V2, float *V3, float *V1_old, float *V2_old, float *V3_old, int dimX, int dimY, int dimZ, int num_total)
+__global__ void newU_kernel3D_ar3(float *V1, float *V2, float *V3, float *V1_old, float *V2_old, float *V3_old, long dimX, long dimY, long dimZ)
{
- int index;
-
- int i = blockDim.x * blockIdx.x + threadIdx.x;
- int j = blockDim.y * blockIdx.y + threadIdx.y;
- int k = blockDim.z * blockIdx.z + threadIdx.z;
+ long index;
+ const long i = blockDim.x * blockIdx.x + threadIdx.x;
+ const long j = blockDim.y * blockIdx.y + threadIdx.y;
+ const long k = blockDim.z * blockIdx.z + threadIdx.z;
index = (dimX*dimY)*k + j*dimX+i;
- if (index < num_total) {
+ if ((i < dimX) && (j < dimY) && (k < dimZ)) {
V1[index] = 2.0f*V1[index] - V1_old[index];
V2[index] = 2.0f*V2[index] - V2_old[index];
V3[index] = 2.0f*V3[index] - V3_old[index];
@@ -576,14 +563,20 @@ __global__ void newU_kernel3D_ar3(float *V1, float *V2, float *V3, float *V1_old
/*%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%*/
extern "C" int TGV_GPU_main(float *U0, float *U, float lambda, float alpha1, float alpha0, int iterationsNumb, float L2, int dimX, int dimY, int dimZ)
{
- int dimTotal, dev = 0;
- CHECK(cudaSetDevice(dev));
-
- dimTotal = dimX*dimY*dimZ;
+
+ int deviceCount = -1; // number of devices
+ cudaGetDeviceCount(&deviceCount);
+ if (deviceCount == 0) {
+ fprintf(stderr, "No CUDA devices found\n");
+ return -1;
+ }
+
+ long dimTotal = (long)(dimX*dimY*dimZ);
+
float *U_old, *d_U0, *d_U, *P1, *P2, *Q1, *Q2, *Q3, *V1, *V1_old, *V2, *V2_old, tau, sigma;
- tau = pow(L2,-0.5);
- sigma = pow(L2,-0.5);
+ tau = powf(L2,-0.5f);
+ sigma = tau;
CHECK(cudaMalloc((void**)&d_U0,dimTotal*sizeof(float)));
CHECK(cudaMalloc((void**)&d_U,dimTotal*sizeof(float)));
@@ -611,41 +604,51 @@ extern "C" int TGV_GPU_main(float *U0, float *U, float lambda, float alpha1, flo
if (dimZ == 1) {
/*2D case */
- dim3 dimBlock(BLKXSIZE2D,BLKYSIZE2D);
- dim3 dimGrid(idivup(dimX,BLKXSIZE2D), idivup(dimY,BLKYSIZE2D));
+ dim3 dimBlock(BLKXSIZE2D,BLKYSIZE2D);
+ dim3 dimGrid(idivup(dimX,BLKXSIZE2D), idivup(dimY,BLKYSIZE2D));
for(int n=0; n < iterationsNumb; n++) {
/* Calculate Dual Variable P */
- DualP_2D_kernel<<<dimGrid,dimBlock>>>(d_U, V1, V2, P1, P2, dimX, dimY, sigma);
- CHECK(cudaDeviceSynchronize());
+ DualP_2D_kernel<<<dimGrid,dimBlock>>>(d_U, V1, V2, P1, P2, (long)(dimX), (long)(dimY), sigma);
+ checkCudaErrors( cudaDeviceSynchronize() );
+ checkCudaErrors(cudaPeekAtLastError() );
/*Projection onto convex set for P*/
- ProjP_2D_kernel<<<dimGrid,dimBlock>>>(P1, P2, dimX, dimY, alpha1);
- CHECK(cudaDeviceSynchronize());
+ ProjP_2D_kernel<<<dimGrid,dimBlock>>>(P1, P2, (long)(dimX), (long)(dimY), alpha1);
+ checkCudaErrors( cudaDeviceSynchronize() );
+ checkCudaErrors(cudaPeekAtLastError() );
/* Calculate Dual Variable Q */
- DualQ_2D_kernel<<<dimGrid,dimBlock>>>(V1, V2, Q1, Q2, Q3, dimX, dimY, sigma);
- CHECK(cudaDeviceSynchronize());
+ DualQ_2D_kernel<<<dimGrid,dimBlock>>>(V1, V2, Q1, Q2, Q3, (long)(dimX), (long)(dimY), sigma);
+ checkCudaErrors( cudaDeviceSynchronize() );
+ checkCudaErrors(cudaPeekAtLastError() );
/*Projection onto convex set for Q*/
- ProjQ_2D_kernel<<<dimGrid,dimBlock>>>(Q1, Q2, Q3, dimX, dimY, alpha0);
- CHECK(cudaDeviceSynchronize());
+ ProjQ_2D_kernel<<<dimGrid,dimBlock>>>(Q1, Q2, Q3, (long)(dimX), (long)(dimY), alpha0);
+ checkCudaErrors( cudaDeviceSynchronize() );
+ checkCudaErrors(cudaPeekAtLastError() );
/*saving U into U_old*/
- copyIm_TGV_kernel<<<dimGrid,dimBlock>>>(d_U, U_old, dimX, dimY, dimTotal);
- CHECK(cudaDeviceSynchronize());
+ copyIm_TGV_kernel<<<dimGrid,dimBlock>>>(d_U, U_old, (long)(dimX), (long)(dimY));
+ checkCudaErrors( cudaDeviceSynchronize() );
+ checkCudaErrors(cudaPeekAtLastError() );
/*adjoint operation -> divergence and projection of P*/
- DivProjP_2D_kernel<<<dimGrid,dimBlock>>>(d_U, d_U0, P1, P2, dimX, dimY, lambda, tau);
- CHECK(cudaDeviceSynchronize());
+ DivProjP_2D_kernel<<<dimGrid,dimBlock>>>(d_U, d_U0, P1, P2, (long)(dimX), (long)(dimY), lambda, tau);
+ checkCudaErrors( cudaDeviceSynchronize() );
+ checkCudaErrors(cudaPeekAtLastError() );
/*get updated solution U*/
- newU_kernel<<<dimGrid,dimBlock>>>(d_U, U_old, dimX, dimY, dimTotal);
- CHECK(cudaDeviceSynchronize());
+ newU_kernel<<<dimGrid,dimBlock>>>(d_U, U_old, (long)(dimX), (long)(dimY));
+ checkCudaErrors( cudaDeviceSynchronize() );
+ checkCudaErrors(cudaPeekAtLastError() );
/*saving V into V_old*/
- copyIm_TGV_kernel_ar2<<<dimGrid,dimBlock>>>(V1, V2, V1_old, V2_old, dimX, dimY, dimTotal);
- CHECK(cudaDeviceSynchronize());
+ copyIm_TGV_kernel_ar2<<<dimGrid,dimBlock>>>(V1, V2, V1_old, V2_old, (long)(dimX), (long)(dimY));
+ checkCudaErrors( cudaDeviceSynchronize() );
+ checkCudaErrors(cudaPeekAtLastError() );
/* upd V*/
- UpdV_2D_kernel<<<dimGrid,dimBlock>>>(V1, V2, P1, P2, Q1, Q2, Q3, dimX, dimY, tau);
- CHECK(cudaDeviceSynchronize());
+ UpdV_2D_kernel<<<dimGrid,dimBlock>>>(V1, V2, P1, P2, Q1, Q2, Q3, (long)(dimX), (long)(dimY), tau);
+ checkCudaErrors( cudaDeviceSynchronize() );
+ checkCudaErrors(cudaPeekAtLastError() );
/*get new V*/
- newU_kernel_ar2<<<dimGrid,dimBlock>>>(V1, V2, V1_old, V2_old, dimX, dimY, dimTotal);
- CHECK(cudaDeviceSynchronize());
+ newU_kernel_ar2<<<dimGrid,dimBlock>>>(V1, V2, V1_old, V2_old, (long)(dimX), (long)(dimY));
+ checkCudaErrors( cudaDeviceSynchronize() );
+ checkCudaErrors(cudaPeekAtLastError() );
}
}
else {
@@ -671,35 +674,45 @@ extern "C" int TGV_GPU_main(float *U0, float *U, float lambda, float alpha1, flo
for(int n=0; n < iterationsNumb; n++) {
/* Calculate Dual Variable P */
- DualP_3D_kernel<<<dimGrid,dimBlock>>>(d_U, V1, V2, V3, P1, P2, P3, dimX, dimY, dimZ, sigma);
- CHECK(cudaDeviceSynchronize());
+ DualP_3D_kernel<<<dimGrid,dimBlock>>>(d_U, V1, V2, V3, P1, P2, P3, (long)(dimX), (long)(dimY), (long)(dimZ), sigma);
+ checkCudaErrors( cudaDeviceSynchronize() );
+ checkCudaErrors(cudaPeekAtLastError() );
/*Projection onto convex set for P*/
- ProjP_3D_kernel<<<dimGrid,dimBlock>>>(P1, P2, P3, dimX, dimY, dimZ, alpha1);
- CHECK(cudaDeviceSynchronize());
+ ProjP_3D_kernel<<<dimGrid,dimBlock>>>(P1, P2, P3, (long)(dimX), (long)(dimY), (long)(dimZ), alpha1);
+ checkCudaErrors( cudaDeviceSynchronize() );
+ checkCudaErrors(cudaPeekAtLastError() );
/* Calculate Dual Variable Q */
- DualQ_3D_kernel<<<dimGrid,dimBlock>>>(V1, V2, V3, Q1, Q2, Q3, Q4, Q5, Q6, dimX, dimY, dimZ, sigma);
- CHECK(cudaDeviceSynchronize());
+ DualQ_3D_kernel<<<dimGrid,dimBlock>>>(V1, V2, V3, Q1, Q2, Q3, Q4, Q5, Q6, (long)(dimX), (long)(dimY), (long)(dimZ), sigma);
+ checkCudaErrors( cudaDeviceSynchronize() );
+ checkCudaErrors(cudaPeekAtLastError() );
/*Projection onto convex set for Q*/
- ProjQ_3D_kernel<<<dimGrid,dimBlock>>>(Q1, Q2, Q3, Q4, Q5, Q6, dimX, dimY, dimZ, alpha0);
- CHECK(cudaDeviceSynchronize());
+ ProjQ_3D_kernel<<<dimGrid,dimBlock>>>(Q1, Q2, Q3, Q4, Q5, Q6, (long)(dimX), (long)(dimY), (long)(dimZ), alpha0);
+ checkCudaErrors( cudaDeviceSynchronize() );
+ checkCudaErrors(cudaPeekAtLastError() );
/*saving U into U_old*/
- copyIm_TGV_kernel3D<<<dimGrid,dimBlock>>>(d_U, U_old, dimX, dimY, dimZ, dimTotal);
- CHECK(cudaDeviceSynchronize());
+ copyIm_TGV_kernel3D<<<dimGrid,dimBlock>>>(d_U, U_old, (long)(dimX), (long)(dimY), (long)(dimZ));
+ checkCudaErrors( cudaDeviceSynchronize() );
+ checkCudaErrors(cudaPeekAtLastError() );
/*adjoint operation -> divergence and projection of P*/
- DivProjP_3D_kernel<<<dimGrid,dimBlock>>>(d_U, d_U0, P1, P2, P3, dimX, dimY, dimZ, lambda, tau);
- CHECK(cudaDeviceSynchronize());
+ DivProjP_3D_kernel<<<dimGrid,dimBlock>>>(d_U, d_U0, P1, P2, P3, (long)(dimX), (long)(dimY), (long)(dimZ), lambda, tau);
+ checkCudaErrors( cudaDeviceSynchronize() );
+ checkCudaErrors(cudaPeekAtLastError() );
/*get updated solution U*/
- newU_kernel3D<<<dimGrid,dimBlock>>>(d_U, U_old, dimX, dimY, dimZ, dimTotal);
- CHECK(cudaDeviceSynchronize());
+ newU_kernel3D<<<dimGrid,dimBlock>>>(d_U, U_old, (long)(dimX), (long)(dimY), (long)(dimZ));
+ checkCudaErrors( cudaDeviceSynchronize() );
+ checkCudaErrors(cudaPeekAtLastError() );
/*saving V into V_old*/
- copyIm_TGV_kernel3D_ar3<<<dimGrid,dimBlock>>>(V1, V2, V3, V1_old, V2_old, V3_old, dimX, dimY, dimZ, dimTotal);
- CHECK(cudaDeviceSynchronize());
+ copyIm_TGV_kernel3D_ar3<<<dimGrid,dimBlock>>>(V1, V2, V3, V1_old, V2_old, V3_old, (long)(dimX), (long)(dimY), (long)(dimZ));
+ checkCudaErrors( cudaDeviceSynchronize() );
+ checkCudaErrors(cudaPeekAtLastError() );
/* upd V*/
- UpdV_3D_kernel<<<dimGrid,dimBlock>>>(V1, V2, V3, P1, P2, P3, Q1, Q2, Q3, Q4, Q5, Q6, dimX, dimY, dimZ, tau);
- CHECK(cudaDeviceSynchronize());
+ UpdV_3D_kernel<<<dimGrid,dimBlock>>>(V1, V2, V3, P1, P2, P3, Q1, Q2, Q3, Q4, Q5, Q6, (long)(dimX), (long)(dimY), (long)(dimZ), tau);
+ checkCudaErrors( cudaDeviceSynchronize() );
+ checkCudaErrors(cudaPeekAtLastError() );
/*get new V*/
- newU_kernel3D_ar3<<<dimGrid,dimBlock>>>(V1, V2, V3, V1_old, V2_old, V3_old, dimX, dimY, dimZ, dimTotal);
- CHECK(cudaDeviceSynchronize());
+ newU_kernel3D_ar3<<<dimGrid,dimBlock>>>(V1, V2, V3, V1_old, V2_old, V3_old, (long)(dimX), (long)(dimY), (long)(dimZ));
+ checkCudaErrors( cudaDeviceSynchronize() );
+ checkCudaErrors(cudaPeekAtLastError() );
}
CHECK(cudaFree(Q4));
@@ -724,5 +737,7 @@ extern "C" int TGV_GPU_main(float *U0, float *U, float lambda, float alpha1, flo
CHECK(cudaFree(V2));
CHECK(cudaFree(V1_old));
CHECK(cudaFree(V2_old));
+
+ cudaDeviceReset();
return 0;
}
diff --git a/Core/regularisers_GPU/TGV_GPU_core.h b/Core/regularisers_GPU/TGV_GPU_core.h
index 9f73d1c..e8f9c6e 100644
--- a/Core/regularisers_GPU/TGV_GPU_core.h
+++ b/Core/regularisers_GPU/TGV_GPU_core.h
@@ -1,6 +1,8 @@
#ifndef __TGV_GPU_H__
#define __TGV_GPU_H__
+
#include "CCPiDefines.h"
+#include <memory.h>
#include <stdio.h>
extern "C" CCPI_EXPORT int TGV_GPU_main(float *U0, float *U, float lambda, float alpha1, float alpha0, int iterationsNumb, float L2, int dimX, int dimY, int dimZ);
diff --git a/Wrappers/Python/demos/SoftwareX_supp/Demo_RealData_Recon_SX.py b/Wrappers/Python/demos/SoftwareX_supp/Demo_RealData_Recon_SX.py
new file mode 100644
index 0000000..01491d9
--- /dev/null
+++ b/Wrappers/Python/demos/SoftwareX_supp/Demo_RealData_Recon_SX.py
@@ -0,0 +1,231 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+This demo scripts support the following publication:
+"CCPi-Regularisation Toolkit for computed tomographic image reconstruction with
+proximal splitting algorithms" by Daniil Kazantsev, Edoardo Pasca, Martin J. Turner,
+ Philip J. Withers; Software X, 2019
+____________________________________________________________________________
+* Reads real tomographic data (stored at Zenodo)
+--- https://doi.org/10.5281/zenodo.2578893
+* Reconstructs using TomoRec software
+* Saves reconstructed images
+____________________________________________________________________________
+>>>>> Dependencies: <<<<<
+1. ASTRA toolbox: conda install -c astra-toolbox astra-toolbox
+2. TomoRec: conda install -c dkazanc tomorec
+or install from https://github.com/dkazanc/TomoRec
+3. libtiff if one needs to save tiff images:
+ install pip install libtiff
+
+@author: Daniil Kazantsev, e:mail daniil.kazantsev@diamond.ac.uk
+GPLv3 license (ASTRA toolbox)
+"""
+import numpy as np
+import matplotlib.pyplot as plt
+import h5py
+from tomorec.supp.suppTools import normaliser
+import time
+
+# load dendritic projection data
+h5f = h5py.File('data/DendrData_3D.h5','r')
+dataRaw = h5f['dataRaw'][:]
+flats = h5f['flats'][:]
+darks = h5f['darks'][:]
+angles_rad = h5f['angles_rad'][:]
+h5f.close()
+#%%
+# normalise the data [detectorsVert, Projections, detectorsHoriz]
+data_norm = normaliser(dataRaw, flats, darks, log='log')
+del dataRaw, darks, flats
+
+intens_max = 2.3
+plt.figure()
+plt.subplot(131)
+plt.imshow(data_norm[:,150,:],vmin=0, vmax=intens_max)
+plt.title('2D Projection (analytical)')
+plt.subplot(132)
+plt.imshow(data_norm[300,:,:],vmin=0, vmax=intens_max)
+plt.title('Sinogram view')
+plt.subplot(133)
+plt.imshow(data_norm[:,:,600],vmin=0, vmax=intens_max)
+plt.title('Tangentogram view')
+plt.show()
+
+detectorHoriz = np.size(data_norm,2)
+det_y_crop = [i for i in range(0,detectorHoriz-22)]
+N_size = 950 # reconstruction domain
+time_label = int(time.time())
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("%%%%%%%%%%%%Reconstructing with FBP method %%%%%%%%%%%%%%%%%")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+from tomorec.methodsDIR import RecToolsDIR
+
+RectoolsDIR = RecToolsDIR(DetectorsDimH = np.size(det_y_crop), # DetectorsDimH # detector dimension (horizontal)
+ DetectorsDimV = 100, # DetectorsDimV # detector dimension (vertical) for 3D case only
+ AnglesVec = angles_rad, # array of angles in radians
+ ObjSize = N_size, # a scalar to define reconstructed object dimensions
+ device='gpu')
+
+FBPrec = RectoolsDIR.FBP(data_norm[0:100,:,det_y_crop])
+
+sliceSel = 50
+max_val = 0.003
+plt.figure()
+plt.subplot(131)
+plt.imshow(FBPrec[sliceSel,:,:],vmin=0, vmax=max_val, cmap="gray")
+plt.title('FBP Reconstruction, axial view')
+
+plt.subplot(132)
+plt.imshow(FBPrec[:,sliceSel,:],vmin=0, vmax=max_val, cmap="gray")
+plt.title('FBP Reconstruction, coronal view')
+
+plt.subplot(133)
+plt.imshow(FBPrec[:,:,sliceSel],vmin=0, vmax=max_val, cmap="gray")
+plt.title('FBP Reconstruction, sagittal view')
+plt.show()
+
+# saving to tiffs (16bit)
+"""
+from libtiff import TIFF
+FBPrec += np.abs(np.min(FBPrec))
+multiplier = (int)(65535/(np.max(FBPrec)))
+
+# saving to tiffs (16bit)
+for i in range(0,np.size(FBPrec,0)):
+ tiff = TIFF.open('Dendr_FBP'+'_'+str(i)+'.tiff', mode='w')
+ tiff.write_image(np.uint16(FBPrec[i,:,:]*multiplier))
+ tiff.close()
+"""
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("Reconstructing with ADMM method using TomoRec software")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+# initialise TomoRec ITERATIVE reconstruction class ONCE
+from tomorec.methodsIR import RecToolsIR
+RectoolsIR = RecToolsIR(DetectorsDimH = np.size(det_y_crop), # DetectorsDimH # detector dimension (horizontal)
+ DetectorsDimV = 100, # DetectorsDimV # detector dimension (vertical) for 3D case only
+ AnglesVec = angles_rad, # array of angles in radians
+ ObjSize = N_size, # a scalar to define reconstructed object dimensions
+ datafidelity='LS',# data fidelity, choose LS, PWLS (wip), GH (wip), Student (wip)
+ nonnegativity='ENABLE', # enable nonnegativity constraint (set to 'ENABLE')
+ OS_number = None, # the number of subsets, NONE/(or > 1) ~ classical / ordered subsets
+ tolerance = 1e-08, # tolerance to stop outer iterations earlier
+ device='gpu')
+#%%
+print ("Reconstructing with ADMM method using SB-TV penalty")
+RecADMM_reg_sbtv = RectoolsIR.ADMM(data_norm[0:100,:,det_y_crop],
+ rho_const = 2000.0, \
+ iterationsADMM = 15, \
+ regularisation = 'SB_TV', \
+ regularisation_parameter = 0.00085,\
+ regularisation_iterations = 50)
+
+sliceSel = 50
+max_val = 0.003
+plt.figure()
+plt.subplot(131)
+plt.imshow(RecADMM_reg_sbtv[sliceSel,:,:],vmin=0, vmax=max_val, cmap="gray")
+plt.title('3D ADMM-SB-TV Reconstruction, axial view')
+
+plt.subplot(132)
+plt.imshow(RecADMM_reg_sbtv[:,sliceSel,:],vmin=0, vmax=max_val, cmap="gray")
+plt.title('3D ADMM-SB-TV Reconstruction, coronal view')
+
+plt.subplot(133)
+plt.imshow(RecADMM_reg_sbtv[:,:,sliceSel],vmin=0, vmax=max_val, cmap="gray")
+plt.title('3D ADMM-SB-TV Reconstruction, sagittal view')
+plt.show()
+
+
+# saving to tiffs (16bit)
+"""
+from libtiff import TIFF
+multiplier = (int)(65535/(np.max(RecADMM_reg_sbtv)))
+for i in range(0,np.size(RecADMM_reg_sbtv,0)):
+ tiff = TIFF.open('Dendr_ADMM_SBTV'+'_'+str(i)+'.tiff', mode='w')
+ tiff.write_image(np.uint16(RecADMM_reg_sbtv[i,:,:]*multiplier))
+ tiff.close()
+"""
+# Saving recpnstructed data with a unique time label
+np.save('Dendr_ADMM_SBTV'+str(time_label)+'.npy', RecADMM_reg_sbtv)
+del RecADMM_reg_sbtv
+#%%
+print ("Reconstructing with ADMM method using ROF-LLT penalty")
+RecADMM_reg_rofllt = RectoolsIR.ADMM(data_norm[0:100,:,det_y_crop],
+ rho_const = 2000.0, \
+ iterationsADMM = 15, \
+ regularisation = 'LLT_ROF', \
+ regularisation_parameter = 0.0009,\
+ regularisation_parameter2 = 0.0007,\
+ time_marching_parameter = 0.001,\
+ regularisation_iterations = 550)
+
+sliceSel = 50
+max_val = 0.003
+plt.figure()
+plt.subplot(131)
+plt.imshow(RecADMM_reg_rofllt[sliceSel,:,:],vmin=0, vmax=max_val)
+plt.title('3D ADMM-ROFLLT Reconstruction, axial view')
+
+plt.subplot(132)
+plt.imshow(RecADMM_reg_rofllt[:,sliceSel,:],vmin=0, vmax=max_val)
+plt.title('3D ADMM-ROFLLT Reconstruction, coronal view')
+
+plt.subplot(133)
+plt.imshow(RecADMM_reg_rofllt[:,:,sliceSel],vmin=0, vmax=max_val)
+plt.title('3D ADMM-ROFLLT Reconstruction, sagittal view')
+plt.show()
+
+# saving to tiffs (16bit)
+"""
+from libtiff import TIFF
+multiplier = (int)(65535/(np.max(RecADMM_reg_rofllt)))
+for i in range(0,np.size(RecADMM_reg_rofllt,0)):
+ tiff = TIFF.open('Dendr_ADMM_ROFLLT'+'_'+str(i)+'.tiff', mode='w')
+ tiff.write_image(np.uint16(RecADMM_reg_rofllt[i,:,:]*multiplier))
+ tiff.close()
+"""
+
+# Saving recpnstructed data with a unique time label
+np.save('Dendr_ADMM_ROFLLT'+str(time_label)+'.npy', RecADMM_reg_rofllt)
+del RecADMM_reg_rofllt
+#%%
+print ("Reconstructing with ADMM method using TGV penalty")
+RecADMM_reg_tgv = RectoolsIR.ADMM(data_norm[0:100,:,det_y_crop],
+ rho_const = 2000.0, \
+ iterationsADMM = 15, \
+ regularisation = 'TGV', \
+ regularisation_parameter = 0.01,\
+ regularisation_iterations = 500)
+
+sliceSel = 50
+max_val = 0.003
+plt.figure()
+plt.subplot(131)
+plt.imshow(RecADMM_reg_tgv[sliceSel,:,:],vmin=0, vmax=max_val)
+plt.title('3D ADMM-TGV Reconstruction, axial view')
+
+plt.subplot(132)
+plt.imshow(RecADMM_reg_tgv[:,sliceSel,:],vmin=0, vmax=max_val)
+plt.title('3D ADMM-TGV Reconstruction, coronal view')
+
+plt.subplot(133)
+plt.imshow(RecADMM_reg_tgv[:,:,sliceSel],vmin=0, vmax=max_val)
+plt.title('3D ADMM-TGV Reconstruction, sagittal view')
+plt.show()
+
+# saving to tiffs (16bit)
+"""
+from libtiff import TIFF
+multiplier = (int)(65535/(np.max(RecADMM_reg_tgv)))
+for i in range(0,np.size(RecADMM_reg_tgv,0)):
+ tiff = TIFF.open('Dendr_ADMM_TGV'+'_'+str(i)+'.tiff', mode='w')
+ tiff.write_image(np.uint16(RecADMM_reg_tgv[i,:,:]*multiplier))
+ tiff.close()
+"""
+# Saving recpnstructed data with a unique time label
+np.save('Dendr_ADMM_TGV'+str(time_label)+'.npy', RecADMM_reg_tgv)
+del RecADMM_reg_tgv
+#%% \ No newline at end of file
diff --git a/Wrappers/Python/demos/SoftwareX_supp/Demo_SimulData_ParOptimis_SX.py b/Wrappers/Python/demos/SoftwareX_supp/Demo_SimulData_ParOptimis_SX.py
new file mode 100644
index 0000000..59ffc0e
--- /dev/null
+++ b/Wrappers/Python/demos/SoftwareX_supp/Demo_SimulData_ParOptimis_SX.py
@@ -0,0 +1,161 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+This demo scripts support the following publication:
+"CCPi-Regularisation Toolkit for computed tomographic image reconstruction with
+proximal splitting algorithms" by Daniil Kazantsev, Edoardo Pasca, Martin J. Turner,
+ Philip J. Withers; Software X, 2019
+____________________________________________________________________________
+* Reads data which is previosly generated by TomoPhantom software (Zenodo link)
+--- https://doi.org/10.5281/zenodo.2578893
+* Optimises for the regularisation parameters which later used in the script:
+Demo_SimulData_Recon_SX.py
+____________________________________________________________________________
+>>>>> Dependencies: <<<<<
+>>>>> Dependencies: <<<<<
+1. ASTRA toolbox: conda install -c astra-toolbox astra-toolbox
+2. TomoRec: conda install -c dkazanc tomorec
+or install from https://github.com/dkazanc/TomoRec
+
+@author: Daniil Kazantsev, e:mail daniil.kazantsev@diamond.ac.uk
+GPLv3 license (ASTRA toolbox)
+"""
+#import timeit
+import matplotlib.pyplot as plt
+import numpy as np
+import h5py
+from ccpi.supp.qualitymetrics import QualityTools
+
+# loading the data
+h5f = h5py.File('data/TomoSim_data1550671417.h5','r')
+phantom = h5f['phantom'][:]
+projdata_norm = h5f['projdata_norm'][:]
+proj_angles = h5f['proj_angles'][:]
+h5f.close()
+
+[Vert_det, AnglesNum, Horiz_det] = np.shape(projdata_norm)
+N_size = Vert_det
+
+sliceSel = 128
+#plt.gray()
+plt.figure()
+plt.subplot(131)
+plt.imshow(phantom[sliceSel,:,:],vmin=0, vmax=1)
+plt.title('3D Phantom, axial view')
+
+plt.subplot(132)
+plt.imshow(phantom[:,sliceSel,:],vmin=0, vmax=1)
+plt.title('3D Phantom, coronal view')
+
+plt.subplot(133)
+plt.imshow(phantom[:,:,sliceSel],vmin=0, vmax=1)
+plt.title('3D Phantom, sagittal view')
+plt.show()
+
+intens_max = 240
+plt.figure()
+plt.subplot(131)
+plt.imshow(projdata_norm[:,sliceSel,:],vmin=0, vmax=intens_max)
+plt.title('2D Projection (erroneous)')
+plt.subplot(132)
+plt.imshow(projdata_norm[sliceSel,:,:],vmin=0, vmax=intens_max)
+plt.title('Sinogram view')
+plt.subplot(133)
+plt.imshow(projdata_norm[:,:,sliceSel],vmin=0, vmax=intens_max)
+plt.title('Tangentogram view')
+plt.show()
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("Reconstructing with ADMM method using TomoRec software")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+# initialise TomoRec ITERATIVE reconstruction class ONCE
+from tomorec.methodsIR import RecToolsIR
+RectoolsIR = RecToolsIR(DetectorsDimH = Horiz_det, # DetectorsDimH # detector dimension (horizontal)
+ DetectorsDimV = Vert_det, # DetectorsDimV # detector dimension (vertical) for 3D case only
+ AnglesVec = proj_angles, # array of angles in radians
+ ObjSize = N_size, # a scalar to define reconstructed object dimensions
+ datafidelity='LS',# data fidelity, choose LS, PWLS (wip), GH (wip), Student (wip)
+ nonnegativity='ENABLE', # enable nonnegativity constraint (set to 'ENABLE')
+ OS_number = None, # the number of subsets, NONE/(or > 1) ~ classical / ordered subsets
+ tolerance = 1e-08, # tolerance to stop outer iterations earlier
+ device='gpu')
+#%%
+param_space = 30
+reg_param_sb_vec = np.linspace(0.03,0.15,param_space,dtype='float32') # a vector of parameters
+erros_vec_sbtv = np.zeros((param_space)) # a vector of errors
+
+print ("Reconstructing with ADMM method using SB-TV penalty")
+for i in range(0,param_space):
+ RecADMM_reg_sbtv = RectoolsIR.ADMM(projdata_norm,
+ rho_const = 2000.0, \
+ iterationsADMM = 15, \
+ regularisation = 'SB_TV', \
+ regularisation_parameter = reg_param_sb_vec[i],\
+ regularisation_iterations = 50)
+ # calculate errors
+ Qtools = QualityTools(phantom, RecADMM_reg_sbtv)
+ erros_vec_sbtv[i] = Qtools.rmse()
+ print("RMSE for regularisation parameter {} for ADMM-SB-TV is {}".format(reg_param_sb_vec[i],erros_vec_sbtv[i]))
+
+plt.figure()
+plt.plot(erros_vec_sbtv)
+
+# Saving generated data with a unique time label
+h5f = h5py.File('Optim_admm_sbtv.h5', 'w')
+h5f.create_dataset('reg_param_sb_vec', data=reg_param_sb_vec)
+h5f.create_dataset('erros_vec_sbtv', data=erros_vec_sbtv)
+h5f.close()
+#%%
+param_space = 30
+reg_param_rofllt_vec = np.linspace(0.03,0.15,param_space,dtype='float32') # a vector of parameters
+erros_vec_rofllt = np.zeros((param_space)) # a vector of errors
+
+print ("Reconstructing with ADMM method using ROF-LLT penalty")
+for i in range(0,param_space):
+ RecADMM_reg_rofllt = RectoolsIR.ADMM(projdata_norm,
+ rho_const = 2000.0, \
+ iterationsADMM = 15, \
+ regularisation = 'LLT_ROF', \
+ regularisation_parameter = reg_param_rofllt_vec[i],\
+ regularisation_parameter2 = 0.005,\
+ regularisation_iterations = 600)
+ # calculate errors
+ Qtools = QualityTools(phantom, RecADMM_reg_rofllt)
+ erros_vec_rofllt[i] = Qtools.rmse()
+ print("RMSE for regularisation parameter {} for ADMM-ROF-LLT is {}".format(reg_param_rofllt_vec[i],erros_vec_rofllt[i]))
+
+plt.figure()
+plt.plot(erros_vec_rofllt)
+
+# Saving generated data with a unique time label
+h5f = h5py.File('Optim_admm_rofllt.h5', 'w')
+h5f.create_dataset('reg_param_rofllt_vec', data=reg_param_rofllt_vec)
+h5f.create_dataset('erros_vec_rofllt', data=erros_vec_rofllt)
+h5f.close()
+#%%
+param_space = 30
+reg_param_tgv_vec = np.linspace(0.03,0.15,param_space,dtype='float32') # a vector of parameters
+erros_vec_tgv = np.zeros((param_space)) # a vector of errors
+
+print ("Reconstructing with ADMM method using TGV penalty")
+for i in range(0,param_space):
+ RecADMM_reg_tgv = RectoolsIR.ADMM(projdata_norm,
+ rho_const = 2000.0, \
+ iterationsADMM = 15, \
+ regularisation = 'TGV', \
+ regularisation_parameter = reg_param_tgv_vec[i],\
+ regularisation_iterations = 600)
+ # calculate errors
+ Qtools = QualityTools(phantom, RecADMM_reg_tgv)
+ erros_vec_tgv[i] = Qtools.rmse()
+ print("RMSE for regularisation parameter {} for ADMM-TGV is {}".format(reg_param_tgv_vec[i],erros_vec_tgv[i]))
+
+plt.figure()
+plt.plot(erros_vec_tgv)
+
+# Saving generated data with a unique time label
+h5f = h5py.File('Optim_admm_tgv.h5', 'w')
+h5f.create_dataset('reg_param_tgv_vec', data=reg_param_tgv_vec)
+h5f.create_dataset('erros_vec_tgv', data=erros_vec_tgv)
+h5f.close()
+#%% \ No newline at end of file
diff --git a/Wrappers/Python/demos/SoftwareX_supp/Demo_SimulData_Recon_SX.py b/Wrappers/Python/demos/SoftwareX_supp/Demo_SimulData_Recon_SX.py
new file mode 100644
index 0000000..93b0cef
--- /dev/null
+++ b/Wrappers/Python/demos/SoftwareX_supp/Demo_SimulData_Recon_SX.py
@@ -0,0 +1,309 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+This demo scripts support the following publication:
+"CCPi-Regularisation Toolkit for computed tomographic image reconstruction with
+proximal splitting algorithms" by Daniil Kazantsev, Edoardo Pasca, Martin J. Turner,
+ Philip J. Withers; Software X, 2019
+____________________________________________________________________________
+* Reads data which is previously generated by TomoPhantom software (Zenodo link)
+--- https://doi.org/10.5281/zenodo.2578893
+* Reconstruct using optimised regularisation parameters (see Demo_SimulData_ParOptimis_SX.py)
+____________________________________________________________________________
+>>>>> Dependencies: <<<<<
+1. ASTRA toolbox: conda install -c astra-toolbox astra-toolbox
+2. TomoRec: conda install -c dkazanc tomorec
+or install from https://github.com/dkazanc/TomoRec
+
+@author: Daniil Kazantsev, e:mail daniil.kazantsev@diamond.ac.uk
+GPLv3 license (ASTRA toolbox)
+"""
+#import timeit
+import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+import numpy as np
+import h5py
+from ccpi.supp.qualitymetrics import QualityTools
+from scipy.signal import gaussian
+
+# loading the data
+h5f = h5py.File('data/TomoSim_data1550671417.h5','r')
+phantom = h5f['phantom'][:]
+projdata_norm = h5f['projdata_norm'][:]
+proj_angles = h5f['proj_angles'][:]
+h5f.close()
+
+[Vert_det, AnglesNum, Horiz_det] = np.shape(projdata_norm)
+N_size = Vert_det
+
+# loading optmisation parameters (the result of running Demo_SimulData_ParOptimis_SX)
+h5f = h5py.File('optim_param/Optim_admm_sbtv.h5','r')
+reg_param_sb_vec = h5f['reg_param_sb_vec'][:]
+erros_vec_sbtv = h5f['erros_vec_sbtv'][:]
+h5f.close()
+
+h5f = h5py.File('optim_param/Optim_admm_rofllt.h5','r')
+reg_param_rofllt_vec = h5f['reg_param_rofllt_vec'][:]
+erros_vec_rofllt = h5f['erros_vec_rofllt'][:]
+h5f.close()
+
+h5f = h5py.File('optim_param/Optim_admm_tgv.h5','r')
+reg_param_tgv_vec = h5f['reg_param_tgv_vec'][:]
+erros_vec_tgv = h5f['erros_vec_tgv'][:]
+h5f.close()
+
+index_minSBTV = min(xrange(len(erros_vec_sbtv)), key=erros_vec_sbtv.__getitem__)
+index_minROFLLT = min(xrange(len(erros_vec_rofllt)), key=erros_vec_rofllt.__getitem__)
+index_minTGV = min(xrange(len(erros_vec_tgv)), key=erros_vec_tgv.__getitem__)
+# assign optimal regularisation parameters:
+optimReg_sbtv = reg_param_sb_vec[index_minSBTV]
+optimReg_rofllt = reg_param_rofllt_vec[index_minROFLLT]
+optimReg_tgv = reg_param_tgv_vec[index_minTGV]
+#%%
+# plot loaded data
+sliceSel = 128
+#plt.figure()
+fig, (ax1, ax2) = plt.subplots(figsize=(15, 5), ncols=2)
+plt.rcParams.update({'xtick.labelsize': 'x-small'})
+plt.rcParams.update({'ytick.labelsize':'x-small'})
+plt.subplot(121)
+one = plt.imshow(phantom[sliceSel,:,:],vmin=0, vmax=1, interpolation='none', cmap="PuOr")
+fig.colorbar(one, ax=ax1)
+plt.title('3D Phantom, axial (X-Y) view')
+plt.subplot(122)
+two = plt.imshow(phantom[:,sliceSel,:],vmin=0, vmax=1,interpolation='none', cmap="PuOr")
+fig.colorbar(two, ax=ax2)
+plt.title('3D Phantom, coronal (Y-Z) view')
+"""
+plt.subplot(133)
+plt.imshow(phantom[:,:,sliceSel],vmin=0, vmax=1, cmap="PuOr")
+plt.title('3D Phantom, sagittal view')
+
+"""
+plt.show()
+#%%
+intens_max = 220
+plt.figure()
+plt.rcParams.update({'xtick.labelsize': 'x-small'})
+plt.rcParams.update({'ytick.labelsize':'x-small'})
+plt.subplot(131)
+plt.imshow(projdata_norm[:,sliceSel,:],vmin=0, vmax=intens_max, cmap="PuOr")
+plt.xlabel('X-detector', fontsize=16)
+plt.ylabel('Z-detector', fontsize=16)
+plt.title('2D Projection (X-Z) view', fontsize=19)
+plt.subplot(132)
+plt.imshow(projdata_norm[sliceSel,:,:],vmin=0, vmax=intens_max, cmap="PuOr")
+plt.xlabel('X-detector', fontsize=16)
+plt.ylabel('Projection angle', fontsize=16)
+plt.title('Sinogram (X-Y) view', fontsize=19)
+plt.subplot(133)
+plt.imshow(projdata_norm[:,:,sliceSel],vmin=0, vmax=intens_max, cmap="PuOr")
+plt.xlabel('Projection angle', fontsize=16)
+plt.ylabel('Z-detector', fontsize=16)
+plt.title('Vertical (Y-Z) view', fontsize=19)
+plt.show()
+#plt.savefig('projdata.pdf', format='pdf', dpi=1200)
+#%%
+# initialise TomoRec DIRECT reconstruction class ONCE
+from tomorec.methodsDIR import RecToolsDIR
+RectoolsDIR = RecToolsDIR(DetectorsDimH = Horiz_det, # DetectorsDimH # detector dimension (horizontal)
+ DetectorsDimV = Vert_det, # DetectorsDimV # detector dimension (vertical) for 3D case only
+ AnglesVec = proj_angles, # array of angles in radians
+ ObjSize = N_size, # a scalar to define reconstructed object dimensions
+ device = 'gpu')
+#%%
+print ("Reconstruction using FBP from TomoRec")
+recFBP= RectoolsDIR.FBP(projdata_norm) # FBP reconstruction
+#%%
+x0, y0 = 0, 127 # These are in _pixel_ coordinates!!
+x1, y1 = 255, 127
+
+sliceSel = int(0.5*N_size)
+max_val = 1
+plt.figure(figsize = (20,5))
+gs1 = gridspec.GridSpec(1, 3)
+gs1.update(wspace=0.1, hspace=0.05) # set the spacing between axes.
+ax1 = plt.subplot(gs1[0])
+plt.imshow(recFBP[sliceSel,:,:],vmin=0, vmax=max_val, cmap="PuOr")
+ax1.plot([x0, x1], [y0, y1], 'ko-', linestyle='--')
+plt.colorbar(ax=ax1)
+plt.title('FBP Reconstruction, axial (X-Y) view', fontsize=19)
+ax1.set_aspect('equal')
+ax3 = plt.subplot(gs1[1])
+plt.plot(phantom[sliceSel,sliceSel,0:N_size],color='k',linewidth=2)
+plt.plot(recFBP[sliceSel,sliceSel,0:N_size],linestyle='--',color='g')
+plt.title('Profile', fontsize=19)
+ax2 = plt.subplot(gs1[2])
+plt.imshow(recFBP[:,sliceSel,:],vmin=0, vmax=max_val, cmap="PuOr")
+plt.title('FBP Reconstruction, coronal (Y-Z) view', fontsize=19)
+ax2.set_aspect('equal')
+plt.show()
+#plt.savefig('FBP_phantom.pdf', format='pdf', dpi=1600)
+
+# calculate errors
+Qtools = QualityTools(phantom, recFBP)
+RMSE_fbp = Qtools.rmse()
+print("Root Mean Square Error for FBP is {}".format(RMSE_fbp))
+
+# SSIM measure
+Qtools = QualityTools(phantom[128,:,:]*255, recFBP[128,:,:]*235)
+win = np.array([gaussian(11, 1.5)])
+win2d = win * (win.T)
+ssim_fbp = Qtools.ssim(win2d)
+print("Mean SSIM for FBP is {}".format(ssim_fbp[0]))
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("Reconstructing with ADMM method using TomoRec software")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+# initialise TomoRec ITERATIVE reconstruction class ONCE
+from tomorec.methodsIR import RecToolsIR
+RectoolsIR = RecToolsIR(DetectorsDimH = Horiz_det, # DetectorsDimH # detector dimension (horizontal)
+ DetectorsDimV = Vert_det, # DetectorsDimV # detector dimension (vertical) for 3D case only
+ AnglesVec = proj_angles, # array of angles in radians
+ ObjSize = N_size, # a scalar to define reconstructed object dimensions
+ datafidelity='LS',# data fidelity, choose LS, PWLS (wip), GH (wip), Student (wip)
+ nonnegativity='ENABLE', # enable nonnegativity constraint (set to 'ENABLE')
+ OS_number = None, # the number of subsets, NONE/(or > 1) ~ classical / ordered subsets
+ tolerance = 1e-08, # tolerance to stop outer iterations earlier
+ device='gpu')
+#%%
+print ("Reconstructing with ADMM method using SB-TV penalty")
+RecADMM_reg_sbtv = RectoolsIR.ADMM(projdata_norm,
+ rho_const = 2000.0, \
+ iterationsADMM = 25, \
+ regularisation = 'SB_TV', \
+ regularisation_parameter = optimReg_sbtv,\
+ regularisation_iterations = 50)
+
+sliceSel = int(0.5*N_size)
+max_val = 1
+plt.figure(figsize = (20,3))
+gs1 = gridspec.GridSpec(1, 4)
+gs1.update(wspace=0.02, hspace=0.01) # set the spacing between axes.
+ax1 = plt.subplot(gs1[0])
+plt.plot(reg_param_sb_vec, erros_vec_sbtv, color='k',linewidth=2)
+plt.xlabel('Regularisation parameter', fontsize=16)
+plt.ylabel('RMSE value', fontsize=16)
+plt.title('Regularisation selection', fontsize=19)
+ax2 = plt.subplot(gs1[1])
+plt.imshow(RecADMM_reg_sbtv[sliceSel,:,:],vmin=0, vmax=max_val, cmap="PuOr")
+ax2.plot([x0, x1], [y0, y1], 'ko-', linestyle='--')
+plt.title('ADMM-SBTV (X-Y) view', fontsize=19)
+#ax2.set_aspect('equal')
+ax3 = plt.subplot(gs1[2])
+plt.plot(phantom[sliceSel,sliceSel,0:N_size],color='k',linewidth=2)
+plt.plot(RecADMM_reg_sbtv[sliceSel,sliceSel,0:N_size],linestyle='--',color='g')
+plt.title('Profile', fontsize=19)
+ax4 = plt.subplot(gs1[3])
+plt.imshow(RecADMM_reg_sbtv[:,sliceSel,:],vmin=0, vmax=max_val, cmap="PuOr")
+plt.title('ADMM-SBTV (Y-Z) view', fontsize=19)
+plt.colorbar(ax=ax4)
+plt.show()
+plt.savefig('SBTV_phantom.pdf', format='pdf', dpi=1600)
+
+# calculate errors
+Qtools = QualityTools(phantom, RecADMM_reg_sbtv)
+RMSE_admm_sbtv = Qtools.rmse()
+print("Root Mean Square Error for ADMM-SB-TV is {}".format(RMSE_admm_sbtv))
+
+# SSIM measure
+Qtools = QualityTools(phantom[128,:,:]*255, RecADMM_reg_sbtv[128,:,:]*235)
+win = np.array([gaussian(11, 1.5)])
+win2d = win * (win.T)
+ssim_admm_sbtv = Qtools.ssim(win2d)
+print("Mean SSIM ADMM-SBTV is {}".format(ssim_admm_sbtv[0]))
+#%%
+print ("Reconstructing with ADMM method using ROFLLT penalty")
+RecADMM_reg_rofllt = RectoolsIR.ADMM(projdata_norm,
+ rho_const = 2000.0, \
+ iterationsADMM = 25, \
+ regularisation = 'LLT_ROF', \
+ regularisation_parameter = optimReg_rofllt,\
+ regularisation_parameter2 = 0.0085,\
+ regularisation_iterations = 600)
+
+sliceSel = int(0.5*N_size)
+max_val = 1
+plt.figure(figsize = (20,3))
+gs1 = gridspec.GridSpec(1, 4)
+gs1.update(wspace=0.02, hspace=0.01) # set the spacing between axes.
+ax1 = plt.subplot(gs1[0])
+plt.plot(reg_param_rofllt_vec, erros_vec_rofllt, color='k',linewidth=2)
+plt.xlabel('Regularisation parameter', fontsize=16)
+plt.ylabel('RMSE value', fontsize=16)
+plt.title('Regularisation selection', fontsize=19)
+ax2 = plt.subplot(gs1[1])
+plt.imshow(RecADMM_reg_rofllt[sliceSel,:,:],vmin=0, vmax=max_val, cmap="PuOr")
+ax2.plot([x0, x1], [y0, y1], 'ko-', linestyle='--')
+plt.title('ADMM-ROFLLT (X-Y) view', fontsize=19)
+#ax2.set_aspect('equal')
+ax3 = plt.subplot(gs1[2])
+plt.plot(phantom[sliceSel,sliceSel,0:N_size],color='k',linewidth=2)
+plt.plot(RecADMM_reg_rofllt[sliceSel,sliceSel,0:N_size],linestyle='--',color='g')
+plt.title('Profile', fontsize=19)
+ax4 = plt.subplot(gs1[3])
+plt.imshow(RecADMM_reg_rofllt[:,sliceSel,:],vmin=0, vmax=max_val, cmap="PuOr")
+plt.title('ADMM-ROFLLT (Y-Z) view', fontsize=19)
+plt.colorbar(ax=ax4)
+plt.show()
+#plt.savefig('ROFLLT_phantom.pdf', format='pdf', dpi=1600)
+
+# calculate errors
+Qtools = QualityTools(phantom, RecADMM_reg_rofllt)
+RMSE_admm_rofllt = Qtools.rmse()
+print("Root Mean Square Error for ADMM-ROF-LLT is {}".format(RMSE_admm_rofllt))
+
+# SSIM measure
+Qtools = QualityTools(phantom[128,:,:]*255, RecADMM_reg_rofllt[128,:,:]*235)
+win = np.array([gaussian(11, 1.5)])
+win2d = win * (win.T)
+ssim_admm_rifllt = Qtools.ssim(win2d)
+print("Mean SSIM ADMM-ROFLLT is {}".format(ssim_admm_rifllt[0]))
+#%%
+print ("Reconstructing with ADMM method using TGV penalty")
+RecADMM_reg_tgv = RectoolsIR.ADMM(projdata_norm,
+ rho_const = 2000.0, \
+ iterationsADMM = 25, \
+ regularisation = 'TGV', \
+ regularisation_parameter = optimReg_tgv,\
+ regularisation_iterations = 600)
+#%%
+sliceSel = int(0.5*N_size)
+max_val = 1
+plt.figure(figsize = (20,3))
+gs1 = gridspec.GridSpec(1, 4)
+gs1.update(wspace=0.02, hspace=0.01) # set the spacing between axes.
+ax1 = plt.subplot(gs1[0])
+plt.plot(reg_param_tgv_vec, erros_vec_tgv, color='k',linewidth=2)
+plt.xlabel('Regularisation parameter', fontsize=16)
+plt.ylabel('RMSE value', fontsize=16)
+plt.title('Regularisation selection', fontsize=19)
+ax2 = plt.subplot(gs1[1])
+plt.imshow(RecADMM_reg_tgv[sliceSel,:,:],vmin=0, vmax=max_val, cmap="PuOr")
+ax2.plot([x0, x1], [y0, y1], 'ko-', linestyle='--')
+plt.title('ADMM-TGV (X-Y) view', fontsize=19)
+#ax2.set_aspect('equal')
+ax3 = plt.subplot(gs1[2])
+plt.plot(phantom[sliceSel,sliceSel,0:N_size],color='k',linewidth=2)
+plt.plot(RecADMM_reg_tgv[sliceSel,sliceSel,0:N_size],linestyle='--',color='g')
+plt.title('Profile', fontsize=19)
+ax4 = plt.subplot(gs1[3])
+plt.imshow(RecADMM_reg_tgv[:,sliceSel,:],vmin=0, vmax=max_val, cmap="PuOr")
+plt.title('ADMM-TGV (Y-Z) view', fontsize=19)
+plt.colorbar(ax=ax4)
+plt.show()
+#plt.savefig('TGV_phantom.pdf', format='pdf', dpi=1600)
+
+# calculate errors
+Qtools = QualityTools(phantom, RecADMM_reg_tgv)
+RMSE_admm_tgv = Qtools.rmse()
+print("Root Mean Square Error for ADMM-TGV is {}".format(RMSE_admm_tgv))
+
+# SSIM measure
+#Create a 2d gaussian for the window parameter
+Qtools = QualityTools(phantom[128,:,:]*255, RecADMM_reg_tgv[128,:,:]*235)
+win = np.array([gaussian(11, 1.5)])
+win2d = win * (win.T)
+ssim_admm_tgv = Qtools.ssim(win2d)
+print("Mean SSIM ADMM-TGV is {}".format(ssim_admm_tgv[0]))
+#%% \ No newline at end of file
diff --git a/Wrappers/Python/demos/SoftwareX_supp/Demo_SimulData_SX.py b/Wrappers/Python/demos/SoftwareX_supp/Demo_SimulData_SX.py
new file mode 100644
index 0000000..cdf4325
--- /dev/null
+++ b/Wrappers/Python/demos/SoftwareX_supp/Demo_SimulData_SX.py
@@ -0,0 +1,117 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+This demo scripts support the following publication:
+"CCPi-Regularisation Toolkit for computed tomographic image reconstruction with
+proximal splitting algorithms" by Daniil Kazantsev, Edoardo Pasca, Martin J. Turner,
+ Philip J. Withers; Software X, 2019
+____________________________________________________________________________
+* Runs TomoPhantom software to simulate tomographic projection data with
+some imaging errors and noise
+* Saves the data into hdf file to be uploaded in reconstruction scripts
+__________________________________________________________________________
+
+>>>>> Dependencies: <<<<<
+1. TomoPhantom software for phantom and data generation
+
+@author: Daniil Kazantsev, e:mail daniil.kazantsev@diamond.ac.uk
+Apache 2.0 license
+"""
+import timeit
+import os
+import matplotlib.pyplot as plt
+import numpy as np
+import tomophantom
+from tomophantom import TomoP3D
+from tomophantom.supp.flatsgen import flats
+from tomophantom.supp.normraw import normaliser_sim
+
+print ("Building 3D phantom using TomoPhantom software")
+tic=timeit.default_timer()
+model = 16 # select a model number from the library
+N_size = 256 # Define phantom dimensions using a scalar value (cubic phantom)
+path = os.path.dirname(tomophantom.__file__)
+path_library3D = os.path.join(path, "Phantom3DLibrary.dat")
+#This will generate a N_size x N_size x N_size phantom (3D)
+phantom_tm = TomoP3D.Model(model, N_size, path_library3D)
+toc=timeit.default_timer()
+Run_time = toc - tic
+print("Phantom has been built in {} seconds".format(Run_time))
+
+sliceSel = int(0.5*N_size)
+#plt.gray()
+plt.figure()
+plt.subplot(131)
+plt.imshow(phantom_tm[sliceSel,:,:],vmin=0, vmax=1)
+plt.title('3D Phantom, axial view')
+
+plt.subplot(132)
+plt.imshow(phantom_tm[:,sliceSel,:],vmin=0, vmax=1)
+plt.title('3D Phantom, coronal view')
+
+plt.subplot(133)
+plt.imshow(phantom_tm[:,:,sliceSel],vmin=0, vmax=1)
+plt.title('3D Phantom, sagittal view')
+plt.show()
+
+# Projection geometry related parameters:
+Horiz_det = int(np.sqrt(2)*N_size) # detector column count (horizontal)
+Vert_det = N_size # detector row count (vertical) (no reason for it to be > N)
+angles_num = int(0.35*np.pi*N_size); # angles number
+angles = np.linspace(0.0,179.9,angles_num,dtype='float32') # in degrees
+angles_rad = angles*(np.pi/180.0)
+#%%
+print ("Building 3D analytical projection data with TomoPhantom")
+projData3D_analyt= TomoP3D.ModelSino(model, N_size, Horiz_det, Vert_det, angles, path_library3D)
+
+intens_max = N_size
+sliceSel = int(0.5*N_size)
+plt.figure()
+plt.subplot(131)
+plt.imshow(projData3D_analyt[:,sliceSel,:],vmin=0, vmax=intens_max)
+plt.title('2D Projection (analytical)')
+plt.subplot(132)
+plt.imshow(projData3D_analyt[sliceSel,:,:],vmin=0, vmax=intens_max)
+plt.title('Sinogram view')
+plt.subplot(133)
+plt.imshow(projData3D_analyt[:,:,sliceSel],vmin=0, vmax=intens_max)
+plt.title('Tangentogram view')
+plt.show()
+#%%
+print ("Simulate flat fields, add noise and normalise projections...")
+flatsnum = 20 # generate 20 flat fields
+flatsSIM = flats(Vert_det, Horiz_det, maxheight = 0.1, maxthickness = 3, sigma_noise = 0.2, sigmasmooth = 3, flatsnum=flatsnum)
+
+plt.figure()
+plt.imshow(flatsSIM[0,:,:],vmin=0, vmax=1)
+plt.title('A selected simulated flat-field')
+#%%
+# Apply normalisation of data and add noise
+flux_intensity = 60000 # controls the level of noise
+sigma_flats = 0.01 # contro the level of noise in flats (higher creates more ring artifacts)
+projData3D_norm = normaliser_sim(projData3D_analyt, flatsSIM, sigma_flats, flux_intensity)
+
+intens_max = N_size
+sliceSel = int(0.5*N_size)
+plt.figure()
+plt.subplot(131)
+plt.imshow(projData3D_norm[:,sliceSel,:],vmin=0, vmax=intens_max)
+plt.title('2D Projection (erroneous)')
+plt.subplot(132)
+plt.imshow(projData3D_norm[sliceSel,:,:],vmin=0, vmax=intens_max)
+plt.title('Sinogram view')
+plt.subplot(133)
+plt.imshow(projData3D_norm[:,:,sliceSel],vmin=0, vmax=intens_max)
+plt.title('Tangentogram view')
+plt.show()
+#%%
+import h5py
+import time
+time_label = int(time.time())
+# Saving generated data with a unique time label
+h5f = h5py.File('TomoSim_data'+str(time_label)+'.h5', 'w')
+h5f.create_dataset('phantom', data=phantom_tm)
+h5f.create_dataset('projdata_norm', data=projData3D_norm)
+h5f.create_dataset('proj_angles', data=angles_rad)
+h5f.close()
+#%% \ No newline at end of file
diff --git a/Wrappers/Python/demos/SoftwareX_supp/Readme.md b/Wrappers/Python/demos/SoftwareX_supp/Readme.md
new file mode 100644
index 0000000..54e83f1
--- /dev/null
+++ b/Wrappers/Python/demos/SoftwareX_supp/Readme.md
@@ -0,0 +1,26 @@
+
+# SoftwareX publication [1] supporting files
+
+## Decription:
+The scripts here support publication in SoftwareX journal [1] to ensure reproducibility of the research. The scripts linked with data shared at Zenodo.
+
+## Data:
+Data is shared at Zenodo [here](https://doi.org/10.5281/zenodo.2578893)
+
+## Dependencies:
+1. [ASTRA toolbox](https://github.com/astra-toolbox/astra-toolbox): `conda install -c astra-toolbox astra-toolbox`
+2. [TomoRec](https://github.com/dkazanc/TomoRec): `conda install -c dkazanc tomorec`
+3. [Tomophantom](https://github.com/dkazanc/TomoPhantom): `conda install tomophantom -c ccpi`
+
+## Files description:
+- `Demo_SimulData_SX.py` - simulates 3D projection data using [Tomophantom](https://github.com/dkazanc/TomoPhantom) software. One can skip this module if the data is taken from [Zenodo](https://doi.org/10.5281/zenodo.2578893)
+- `Demo_SimulData_ParOptimis_SX.py` - runs computationally extensive calculations for optimal regularisation parameters, the result are saved into directory `optim_param`. This script can be also skipped.
+- `Demo_SimulData_Recon_SX.py` - using established regularisation parameters, one runs iterative reconstruction
+- `Demo_RealData_Recon_SX.py` - runs real data reconstructions. Can be quite intense on memory so reduce the size of the reconstructed volume if needed.
+
+### References:
+[1] "CCPi-Regularisation Toolkit for computed tomographic image reconstruction with proximal splitting algorithms" by Daniil Kazantsev, Edoardo Pasca, Martin J. Turner and Philip J. Withers; SoftwareX, 2019.
+
+### Acknowledgments:
+CCPi-RGL software is a product of the [CCPi](https://www.ccpi.ac.uk/) group, STFC SCD software developers and Diamond Light Source (DLS). Any relevant questions/comments can be e-mailed to Daniil Kazantsev at dkazanc@hotmail.com
+
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