int main(int argc, char *argv[])
{
pthread_t guiThread;
pthread_t controllerThread;
int ret;
mqd_t messageQueue;
struct mq_attr attr;
attr.mq_flags = 0;
attr.mq_maxmsg = 10;
attr.mq_msgsize = sizeof(actionEnum);
attr.mq_curmsgs = 0;
messageBuf message;
struct timespec wait = {0, 100000000};
messageQueue = mq_open(messageQueuePath, (int)(O_CREAT | O_RDWR), 0666, &attr);
while (mq_timedreceive(messageQueue, (char*)&message,
sizeof(message), NULL,
&wait) != -1);
GUI_Initialization(argc, argv, messageQueuePath, SIZE);
ModelSetup(messageQueuePath, SIZE);
if (messageQueue == -1)
{
perror("main: ");
}
else
{
ret |= pthread_create(&guiThread, NULL, (void *)GUI_Start, NULL);
ret |= pthread_create(&controllerThread, NULL, (void *)ReceiveMessages, NULL);
if (ret == 0)
{
pthread_join(guiThread, NULL);
pthread_join(controllerThread, NULL);
}
else
{
printf("Something went wrong. Start guessing...\n");
}
}
ret = mq_close(messageQueue);
if (ret == -1) perror("Closing mq");
ModelTakeDown();
return 0;
}
int main(int argc,char **argv)
{
Userctx user;
Vec p;
PetscScalar *x_ptr;
PetscErrorCode ierr;
PetscMPIInt size;
PetscInt i,numDataBuses;
KSP ksp;
PC pc;
Tao tao;
TaoConvergedReason reason;
Vec lowerb,upperb;
PetscViewer viewer;
PetscScalar *proj_vec;
//PetscLogDouble t0,t1;
/* time the inversion process */
//ierr = PetscGetTime(&t0);CHKERRQ(ierr);
ierr = PetscInitialize(&argc,&argv,"petscoptions",help);CHKERRQ(ierr);
PetscFunctionBeginUser;
ierr = MPI_Comm_size(PETSC_COMM_WORLD,&size);CHKERRQ(ierr);
if (size > 1) SETERRQ(PETSC_COMM_WORLD,PETSC_ERR_SUP,"Only for sequential runs");
ierr = ModelSetup(&user);CHKERRQ(ierr);
/* hard code the data projection here - for now assume data at all buses */
ierr = VecCreateSeq(PETSC_COMM_WORLD,nbus,&user.proj);CHKERRQ(ierr);
/*ierr = VecCreateSeq(PETSC_COMM_WORLD,4,&user.proj);CHKERRQ(ierr);*/
ierr = VecGetArray(user.proj,&proj_vec);CHKERRQ(ierr);
for(i=0; i<nbus; i++) {
proj_vec[i]=i;
}
srand( time(NULL) + rand () );
//VecView(user.proj, PETSC_VIEWER_STDOUT_WORLD);
/* -- 2 5 6 8 */
/* -- proj_vec[0]=1; proj_vec[1]=4; proj_vec[2]=5; proj_vec[3]=7; */
ierr = VecRestoreArray(user.proj,&proj_vec);CHKERRQ(ierr);
/* allocate/set the prior mean and its standard deviation */
ierr = PetscMalloc(3*sizeof(PetscScalar), &user.prior_mean);
ierr = PetscMalloc(3*sizeof(PetscScalar), &user.prior_stddev);
/*{23.64,6.4,3.01};*/
user.prior_mean[0] = 24.0;
user.prior_mean[1] = 6.0;
user.prior_mean[2] = 3.1;
for(i=0; i<3; i++) user.prior_stddev[i] = user.prior_mean[i]*user.prior_noise;
/* Create matrix to store solution */
if(user.saveSol) {
ierr = MatCreateSeqDense(PETSC_COMM_SELF,
user.neqs_pgrid+1,
(PetscInt) round((user.tfinal-user.t0)/user.dt+1),
NULL,
&user.Sol);
CHKERRQ(ierr);
}
printf("Num cols=%d\n", (PetscInt) round((user.tfinal-user.t0)/user.dt+1));
/* *********************************
* Generate/load observations
**********************************/
ierr = VecGetSize(user.proj, &numDataBuses);CHKERRQ(ierr);
/* Create matrix to save solutions at each time step */
ierr = MatCreateSeqDense(PETSC_COMM_SELF,
2*numDataBuses,
//(PetscInt) round((user.tfinal-user.tdisturb)/user.data_dt)+1,
(PetscInt) round((user.tfinal-user.trestore)/user.data_dt)+1,
NULL,
&user.obs);
CHKERRQ(ierr);
ierr = InitializeData(H0, &user, user.data_noise, user.data_dt);CHKERRQ(ierr);
if(0==strlen(user.loadObsFile)) {
/* save observations */
ierr = PetscViewerBinaryOpen(PETSC_COMM_SELF,"obs-perturbed.bin",FILE_MODE_WRITE,&viewer);CHKERRQ(ierr);
ierr = MatView(user.obs,viewer);CHKERRQ(ierr);
ierr = PetscViewerDestroy(&viewer);CHKERRQ(ierr);
printf("Observations generated.\n");
}
if(user.saveSol) {
ierr = PetscViewerBinaryOpen(PETSC_COMM_SELF,"out_pert.bin",FILE_MODE_WRITE,&viewer);CHKERRQ(ierr);
ierr = MatView(user.Sol,viewer);CHKERRQ(ierr);
ierr = PetscViewerDestroy(&viewer);CHKERRQ(ierr);
ierr = MatDestroy(&user.Sol);CHKERRQ(ierr);
CHKERRQ(ierr);
}
if(user.outputCov) {
printf("The diagonal of the data noise covariance matrix (%g absolute noise) is:\n", user.data_noise);
for(i=0; i<2*numDataBuses; i++) printf("%18.12f ", user.data_stddev[i]*user.data_stddev[i]); printf("\n");
printf("The prior mean is: ");
for(i=0; i<3; i++) printf("%18.12f ", user.prior_mean[i]); printf("\n");
printf("The diagonal of the prior covariance matrix (%g relative noise) is:\n", user.prior_noise);
for(i=0; i<3; i++) printf("%18.12f ", user.prior_stddev[i]*user.prior_stddev[i]); printf("\n");
goto finalize;
}
/* ***************************************
* Optimization phase
* ***************************************/
/* Create TAO solver and set desired solution method */
ierr = TaoCreate(PETSC_COMM_WORLD,&tao);CHKERRQ(ierr);
ierr = TaoSetType(tao,TAOBLMVM);CHKERRQ(ierr);
/*
Optimization starts
*/
printf("Starting optimization...\n");
/* PetscScalar H_disturb[3]= {25.,6.4,3.01}; New inertia (after tdisturb) to be estimated */
/* Set initial solution guess */
ierr = VecCreateSeq(PETSC_COMM_WORLD,3,&p);CHKERRQ(ierr);
ierr = VecGetArray(p,&x_ptr);CHKERRQ(ierr);
//x_ptr[0] = H0[0]; x_ptr[1] = H0[1]; x_ptr[2] = H0[2];
x_ptr[0] = H0[0]*1.1; x_ptr[1] = H0[1]*1.1; x_ptr[2] = H0[2]*1.1;
ierr = VecRestoreArray(p,&x_ptr);CHKERRQ(ierr);
ierr = TaoSetInitialVector(tao,p);CHKERRQ(ierr);
/* Set routine for function and gradient evaluation */
//ierr = TaoSetObjectiveRoutine(tao,FormFunction,(void *)&user);CHKERRQ(ierr);
//ierr = TaoSetGradientRoutine(tao,TaoDefaultComputeGradient,(void *)&user);CHKERRQ(ierr);
/* Sets the cost and gradient evaluation routine for minimization */
ierr = TaoSetObjectiveAndGradientRoutine(tao,FormFunctionGradient,&user);CHKERRQ(ierr);
/* Set bounds for the optimization */
ierr = VecDuplicate(p,&lowerb);CHKERRQ(ierr);
ierr = VecDuplicate(p,&upperb);CHKERRQ(ierr);
ierr = VecGetArray(lowerb,&x_ptr);CHKERRQ(ierr);
x_ptr[0] = 20.64; x_ptr[1] = 5.4; x_ptr[2] = 2.01;
ierr = VecRestoreArray(lowerb,&x_ptr);CHKERRQ(ierr);
ierr = VecGetArray(upperb,&x_ptr);CHKERRQ(ierr);
x_ptr[0] = 25.64; x_ptr[1] = 7.4; x_ptr[2] = 4.01;
ierr = VecRestoreArray(upperb,&x_ptr);CHKERRQ(ierr);
ierr = TaoSetVariableBounds(tao,lowerb,upperb);
/* Check for any TAO command line options */
ierr = TaoSetFromOptions(tao);CHKERRQ(ierr);
ierr = TaoGetKSP(tao,&ksp);CHKERRQ(ierr);
if (ksp) {
ierr = KSPGetPC(ksp,&pc);CHKERRQ(ierr);
ierr = PCSetType(pc,PCNONE);CHKERRQ(ierr);
}
//ierr = TaoSetTolerances(tao,1e-8,1e-6,1e-8,1e-6,1e-4);
ierr = TaoSetTolerances(tao,1e-8,1e-8,1e-8,1e-8,1e-6);
//ierr = TaoSetGradientTolerances(tao,1e-8, 1e-6, 1e-6);
/* SOLVE the estimation problem */
ierr = TaoSolve(tao); CHKERRQ(ierr);
/* Get information on termination */
printf("--- optimization done\n");
/* time the inversion process */
//ierr = PetscGetTime(&t1);CHKERRQ(ierr);
//printf("elapsed_time %f seconds\n", t1 - t0);
ierr = TaoGetConvergedReason(tao,&reason);CHKERRQ(ierr);
if (reason <= 0){
ierr=PetscPrintf(MPI_COMM_WORLD, "Try another method! \n");CHKERRQ(ierr);
}
/*ierr = VecView(p,PETSC_VIEWER_STDOUT_WORLD);CHKERRQ(ierr);*/
ierr = VecGetArray(p,&x_ptr);CHKERRQ(ierr);
printf("inertia-out: %.12f %.12f %.12f\n", x_ptr[0], x_ptr[1], x_ptr[2]);
ierr = VecRestoreArray(p,&x_ptr);CHKERRQ(ierr);
//ierr = EvaluateHessianFD(tao, p, &user);CHKERRQ(ierr);
/* Free TAO data structures */
ierr = TaoDestroy(&tao);CHKERRQ(ierr);
ierr = VecDestroy(&lowerb);CHKERRQ(ierr);
ierr = VecDestroy(&upperb);CHKERRQ(ierr);
finalize:
ierr = MatDestroy(&user.obs);CHKERRQ(ierr);
ierr = VecDestroy(&user.X0_disturb);CHKERRQ(ierr);
ierr = PetscFree(user.data_stddev);CHKERRQ(ierr);
PetscFree(user.prior_mean);
PetscFree(user.prior_stddev);
ierr = DMDestroy(&user.dmgen);CHKERRQ(ierr);
ierr = DMDestroy(&user.dmnet);CHKERRQ(ierr);
ierr = DMDestroy(&user.dmpgrid);CHKERRQ(ierr);
ierr = ISDestroy(&user.is_diff);CHKERRQ(ierr);
ierr = ISDestroy(&user.is_alg);CHKERRQ(ierr);
ierr = MatDestroy(&user.J);CHKERRQ(ierr);
ierr = MatDestroy(&user.Jacp);CHKERRQ(ierr);
ierr = MatDestroy(&user.Ybus);CHKERRQ(ierr);
ierr = VecDestroy(&user.V0);CHKERRQ(ierr);
ierr = VecDestroy(&p);CHKERRQ(ierr);
ierr = PetscFinalize();
return(0);
}
