/* the metapopulation. */
struct sample * draw_sample(struct metapopulation *in, int n, struct param *par){
int i, j, *nIsolatesPerPop, count;
double *nAvailPerPop;
struct pathogen *ppat;
/* create pointer to pathogens */
struct sample *out=create_sample(n);
/* escape if no isolate available */
if(get_total_ninf(in) + get_total_nexp(in) < 1){
printf("\nMetapopulation without infections - sample will be empty.\n");
out->n = 0;
out->pathogens = NULL;
out->popid = NULL;
return out;
}
/* get nb of isolates available in each population */
nAvailPerPop = (double *) calloc(get_npop(in), sizeof(double));
if(nAvailPerPop == NULL){
fprintf(stderr, "\n[in: population.c->draw_sample]\nNo memory left to isolate available pathogens. Exiting.\n");
exit(1);
}
for(j=0;j<get_npop(in);j++){
nAvailPerPop[j] = (double) get_nexp(get_populations(in)[j]) + get_ninf(get_populations(in)[j]);
}
/* get nb of isolates sampled in each population*/
nIsolatesPerPop = (int *) calloc(get_npop(in), sizeof(int));
if(nIsolatesPerPop == NULL){
fprintf(stderr, "\n[in: population.c->draw_sample]\nNo memory left to isolate available pathogens. Exiting.\n");
exit(1);
}
gsl_ran_multinomial(par->rng, get_npop(in), n, nAvailPerPop, (unsigned int *) nIsolatesPerPop);
/* fill in the sample pathogens */
count = 0;
for(j=0;j<get_npop(in);j++){ /* for each population */
for(i=0;i<nIsolatesPerPop[j];i++){
ppat = select_random_pathogen(get_populations(in)[j], par);
/* free_pathogen(out->pathogens[count]); */
out->pathogens[count] = reconstruct_genome(ppat);
out->popid[count++] = j;
}
}
/* free local pointers */
free(nAvailPerPop);
free(nIsolatesPerPop);
return out;
} /* end draw_sample */
int get_total_ninfcum(struct metapopulation *in){
int i, k=get_npop(in), out=0;
for(i=0;i<k;i++) {
out += get_ninfcum(get_populations(in)[i]);
}
return out;
}
/* Print metapopulation content */
void print_metapopulation(struct metapopulation *in, bool showGen){
int i, k, K=get_npop(in);
struct population *curPop;
struct pathogen * curpat;
/* display general info */
printf("\nnb of populations: %d", K);
printf("\ntotal nb susceptible: %d", get_total_nsus(in));
printf("\ntotal nb infected: %d", get_total_ninf(in));
printf("\ntotal nb recovered: %d", get_total_nrec(in));
printf("\ntotal population size: %d\n", get_total_nsus(in)+get_total_ninf(in)+get_total_nrec(in));
/* display populations */
for(k=0;k<K;k++){
curPop = get_populations(in)[k];
printf("\npopulation %d", k);
print_population(curPop);
if(showGen){
for(i=0;i<get_maxnpat(in);i++){
curpat = get_pathogens(in)[i];
if(!isNULL_pathogen(curpat) && get_popid(curpat)==k) print_pathogen(curpat);
}
printf("\n");
}
}
}
/* Function to be called from R */
void R_epidemics(int *seqLength, double *mutRate, int *npop, int *nHostPerPop, double *beta, int *nStart, int *t1, int *t2, int *Nsample, int *Tsample, int *duration, int *nbnb, int *listnb, double *pdisp){
int i, nstep, counter_sample = 0, tabidx;
/* Initialize random number generator */
int j;
time_t t;
t = time(NULL); // time in seconds, used to change the seed of the random generator
gsl_rng * rng;
const gsl_rng_type *typ;
gsl_rng_env_setup();
typ=gsl_rng_default;
rng=gsl_rng_alloc(typ);
gsl_rng_set(rng,t); // changes the seed of the random generator
/* transfer simulation parameters */
struct param * par;
par = (struct param *) malloc(sizeof(struct param));
par->L = *seqLength;
par->mu = *mutRate;
par->muL = par->mu * par->L;
par->rng = rng;
par->npop = *npop;
par->popsizes = nHostPerPop;
par->beta = *beta;
par->nstart = *nStart;
par->t1 = *t1;
par->t2 = *t2;
par->t_sample = Tsample;
par->n_sample = *Nsample;
par->duration = *duration;
par->cn_nb_nb = nbnb;
par->cn_list_nb = listnb;
par->cn_weights = pdisp;
/* check/print parameters */
check_param(par);
print_param(par);
/* dispersal matrix */
struct network *cn = create_network(par);
/* print_network(cn, TRUE); */
/* group sizes */
struct ts_groupsizes * grpsizes = create_ts_groupsizes(par);
/* initiate population */
struct metapopulation * metapop;
metapop = create_metapopulation(par);
/* get sampling schemes (timestep+effectives) */
translate_dates(par);
struct table_int *tabdates = get_table_int(par->t_sample, par->n_sample);
printf("\n\nsampling at timesteps:");
print_table_int(tabdates);
/* create sample */
struct sample ** samplist = (struct sample **) malloc(tabdates->n * sizeof(struct sample *));
struct sample *samp;
/* MAKE METAPOPULATION EVOLVE */
nstep = 0;
while(get_total_nsus(metapop)>0 && (get_total_ninf(metapop)+get_total_nexp(metapop))>0 && nstep<par->duration){
nstep++;
/* age metapopulation */
age_metapopulation(metapop, par);
/* process infections */
for(j=0;j<get_npop(metapop);j++){
process_infections(get_populations(metapop)[j], metapop, cn, par);
}
/* draw samples */
if((tabidx = int_in_vec(nstep, tabdates->items, tabdates->n)) > -1){ /* TRUE if step must be sampled */
samplist[counter_sample++] = draw_sample(metapop, tabdates->times[tabidx], par);
}
fill_ts_groupsizes(grpsizes, metapop, nstep);
}
/* we stopped after 'nstep' steps */
if(nstep < par->duration){
printf("\nEpidemics ended at time %d, before last sampling time (%d).\n", nstep, par->duration);
} else {
/* printf("\n\n-- FINAL METAPOPULATION --"); */
/* print_metapopulation(metapop, FALSE); */
/* merge samples */
samp = merge_samples(samplist, tabdates->n, par);
/* write sample to file */
printf("\n\nWriting sample to file 'out-sample.txt'\n");
write_sample(samp);
/* free memory */
free_sample(samp);
}
/* write group sizes to file */
printf("\n\nPrinting group sizes to file 'out-popsize.txt'\n");
write_ts_groupsizes(grpsizes);
/* free memory */
free_metapopulation(metapop);
free_param(par);
for(i=0;i<counter_sample;i++) free_sample(samplist[i]);
free(samplist);
free_table_int(tabdates);
free_network(cn);
free_ts_groupsizes(grpsizes);
}
/* all-in-one function testing epidemics growth, summary statistics, etc. */
void test_epidemics(int seqLength, double mutRate, int npop, int *nHostPerPop, double beta, int nStart, int t1, int t2, int Nsample, int *Tsample, int duration, int *nbnb, int *listnb, double *pdisp){
int i, j, nstep=0, tabidx, counter_sample = 0;
/* Initialize random number generator */
time_t t;
t = time(NULL); // time in seconds, used to change the seed of the random generator
gsl_rng * rng;
const gsl_rng_type *typ;
gsl_rng_env_setup();
typ=gsl_rng_default;
rng=gsl_rng_alloc(typ);
gsl_rng_set(rng,t); // changes the seed of the random generator
/* transfer simulation parameters */
struct param * par;
par = (struct param *) malloc(sizeof(struct param));
par->L = seqLength;
par->mu = mutRate;
par->muL = par->mu * par->L;
par->rng = rng;
par->npop = npop;
par->npop = npop;
par->popsizes = nHostPerPop;
par->beta = beta;
par->nstart = nStart;
par->t1 = t1;
par->t2 = t2;
par->t_sample = Tsample;
par->n_sample = Nsample;
par->duration = duration;
par->cn_nb_nb = nbnb;
par->cn_list_nb = listnb;
par->cn_weights = pdisp;
/* check/print parameters */
check_param(par);
print_param(par);
/* dispersal matrix */
struct network *cn = create_network(par);
/* group sizes */
struct ts_groupsizes * grpsizes = create_ts_groupsizes(par);
/* initiate population */
struct metapopulation * metapop;
metapop = create_metapopulation(par);
/* get sampling schemes (timestep+effectives) */
translate_dates(par);
struct table_int *tabdates = get_table_int(par->t_sample, par->n_sample);
printf("\n\nsampling at timesteps:");
print_table_int(tabdates);
/* create sample */
struct sample ** samplist = (struct sample **) malloc(tabdates->n * sizeof(struct sample *));
struct sample *samp;
/* MAKE METAPOPULATION EVOLVE */
nstep = 0;
while(get_total_nsus(metapop)>0 && (get_total_ninf(metapop)+get_total_nexp(metapop))>0 && nstep<par->duration){
nstep++;
/* age metapopulation */
age_metapopulation(metapop, par);
/* process infections */
for(j=0;j<get_npop(metapop);j++){
process_infections(get_populations(metapop)[j], metapop, cn, par);
}
/* draw samples */
if((tabidx = int_in_vec(nstep, tabdates->items, tabdates->n)) > -1){ /* TRUE if step must be sampled */
samplist[counter_sample++] = draw_sample(metapop, tabdates->times[tabidx], par);
}
fill_ts_groupsizes(grpsizes, metapop, nstep);
}
/* we stopped after 'nstep' steps */
if(nstep < par->duration){
printf("\nEpidemics ended at time %d, before last sampling time (%d).\n", nstep, par->duration);
} else {
printf("\n\n-- FINAL METAPOPULATION --");
print_metapopulation(metapop, TRUE);
/* test samples */
for(i=0;i<tabdates->n;i++) {
printf("\nsample %d\n", i);
print_sample(samplist[i], TRUE);
}
samp = merge_samples(samplist, tabdates->n, par) ;
print_sample(samp, TRUE);
/* test allele listing */
struct snplist *snpbilan;
snpbilan = list_snps(samp, par);
print_snplist(snpbilan);
/* test allele frequencies */
struct allfreq *freq;
freq = get_frequencies(samp, par);
print_allfreq(freq);
/* test Hs*/
double Hs = hs(samp,par);
printf("\nHs = %0.3f\n", Hs);
/* test Hs full genome */
Hs = hs_full_genome(samp,par);
printf("\nHs (full genome) = %0.5f\n", Hs);
/* test nb of snps */
int nball = nb_snps(samp,par);
printf("\nnumber of SNPs = %d\n", nball);
/* test mean nb of snps */
double temp = mean_nb_snps(samp);
printf("\nmean number of SNPs = %.2f\n", temp);
/* test var nb of snps */
temp = var_nb_snps(samp);
printf("\nvariance of number of alleles = %.2f\n", temp);
/* test pairwise distances */
struct distmat_int *mat = pairwise_dist(samp, par);
print_distmat_int(mat);
/* test mean pairwise distances */
temp = mean_pairwise_dist(samp,par);
printf("\nmean pairwise distance: %.2f", temp);
/* test variance of pairwise distances */
temp = var_pairwise_dist(samp,par);
printf("\nvar pairwise distance: %.2f", temp);
/* test Fst */
temp = fst(samp,par);
printf("\nfst: %.2f", temp);
printf("\n\n");
/* free memory */
free_sample(samp);
free_snplist(snpbilan);
free_allfreq(freq);
free_distmat_int(mat);
}
/* write group sizes to file */
printf("\n\nPrinting group sizes to file 'out-popsize.txt'");
write_ts_groupsizes(grpsizes);
/* free memory */
free_metapopulation(metapop);
free_param(par);
for(i=0;i<counter_sample;i++) free_sample(samplist[i]);
free(samplist);
free_table_int(tabdates);
free_network(cn);
free_ts_groupsizes(grpsizes);
}
/* Function to be called from R */
void R_monitor_epidemics(int *seqLength, double *mutRate, int *npop, int *nHostPerPop, double *beta, int *nStart, int *t1, int *t2, int *Nsample, int *Tsample, int *duration, int *nbnb, int *listnb, double *pdisp, int *minSize){
int nstep;
/* Initialize random number generator */
int j;
time_t t;
t = time(NULL); // time in seconds, used to change the seed of the random generator
gsl_rng * rng;
const gsl_rng_type *typ;
gsl_rng_env_setup();
typ=gsl_rng_default;
rng=gsl_rng_alloc(typ);
gsl_rng_set(rng,t); // changes the seed of the random generator
/* transfer simulation parameters */
struct param * par;
par = (struct param *) malloc(sizeof(struct param));
par->L = *seqLength;
par->mu = *mutRate;
par->muL = par->mu * par->L;
par->rng = rng;
par->npop = *npop;
par->popsizes = nHostPerPop;
par->beta = *beta;
par->nstart = *nStart;
par->t1 = *t1;
par->t2 = *t2;
par->t_sample = Tsample;
par->n_sample = *Nsample;
par->duration = *duration;
par->cn_nb_nb = nbnb;
par->cn_list_nb = listnb;
par->cn_weights = pdisp;
/* check/print parameters */
check_param(par);
print_param(par);
/* dispersal matrix */
struct network *cn = create_network(par);
/* print_network(cn, TRUE); */
/* group sizes */
struct ts_groupsizes * grpsizes = create_ts_groupsizes(par);
struct ts_sumstat * sumstats = create_ts_sumstat(par);
/* initiate population */
struct metapopulation * metapop;
metapop = create_metapopulation(par);
/* get sampling schemes (timestep+effectives) */
translate_dates(par);
struct table_int *tabdates = get_table_int(par->t_sample, par->n_sample);
printf("\n\nsampling at timesteps:");
print_table_int(tabdates);
/* create sample */
struct sample *samp;
/* MAKE METAPOPULATION EVOLVE */
nstep = 0;
while(get_total_nsus(metapop)>0 && (get_total_ninf(metapop)+get_total_nexp(metapop))>0 && nstep<par->duration){
nstep++;
/* age metapopulation */
age_metapopulation(metapop, par);
/* process infections */
for(j=0;j<get_npop(metapop);j++){
process_infections(get_populations(metapop)[j], metapop, cn, par);
}
/* draw sample */
samp = draw_sample(metapop, par->n_sample, par);
/* compute statistics */
if(get_total_ninf(metapop)> *minSize) fill_ts_sumstat(sumstats, samp, nstep, par);
/* get group sizes */
fill_ts_groupsizes(grpsizes, metapop, nstep);
}
/* write group sizes to file */
printf("\n\nWriting results to file...");
write_ts_groupsizes(grpsizes);
write_ts_sumstat(sumstats);
printf("done.\n\n");
/* free memory */
free_sample(samp);
free_metapopulation(metapop);
free_param(par);
free_network(cn);
free_ts_groupsizes(grpsizes);
free_ts_sumstat(sumstats);
}
/*
* steady_state solves for the steady_state of the system
* that was previously setup using the add_to_ham and add_lin
* routines. Solver selection and parameterscan be controlled via PETSc
* command line options.
*/
void steady_state(Vec x){
PetscViewer mat_view;
PC pc;
Vec b;
KSP ksp; /* linear solver context */
PetscInt row,col,its,j,i,Istart,Iend;
PetscScalar mat_tmp;
long dim;
int num_pop;
double *populations;
Mat solve_A;
if (_lindblad_terms) {
dim = total_levels*total_levels;
solve_A = full_A;
if (nid==0) {
printf("Lindblad terms found, using Lindblad solver.");
}
} else {
if (nid==0) {
printf("Warning! Steady state not supported for Schrodinger.\n");
printf(" Defaulting to (less efficient) Lindblad Solver\n");
exit(0);
}
dim = total_levels*total_levels;
solve_A = ham_A;
}
if (!stab_added){
if (nid==0) printf("Adding stabilization...\n");
/*
* Add elements to the matrix to make the normalization work
* I have no idea why this works, I am copying it from qutip
* We add 1.0 in the 0th spot and every n+1 after
*/
if (nid==0) {
row = 0;
for (i=0;i<total_levels;i++){
col = i*(total_levels+1);
mat_tmp = 1.0 + 0.*PETSC_i;
MatSetValue(full_A,row,col,mat_tmp,ADD_VALUES);
}
/* Print dense ham, if it was asked for */
if (_print_dense_ham){
FILE *fp_ham;
fp_ham = fopen("ham","w");
if (nid==0){
for (i=0;i<total_levels;i++){
for (j=0;j<total_levels;j++){
fprintf(fp_ham,"%e %e ",PetscRealPart(_hamiltonian[i][j]),PetscImaginaryPart(_hamiltonian[i][j]));
}
fprintf(fp_ham,"\n");
}
}
fclose(fp_ham);
for (i=0;i<total_levels;i++){
free(_hamiltonian[i]);
}
free(_hamiltonian);
_print_dense_ham = 0;
}
}
stab_added = 1;
}
// if (!matrix_assembled) {
MatGetOwnershipRange(full_A,&Istart,&Iend);
/*
* Explicitly add 0.0 to all diagonal elements;
* this fixes a 'matrix in wrong state' message that PETSc
* gives if the diagonal was never initialized.
*/
if (nid==0) printf("Adding 0 to diagonal elements...\n");
for (i=Istart;i<Iend;i++){
mat_tmp = 0 + 0.*PETSC_i;
MatSetValue(full_A,i,i,mat_tmp,ADD_VALUES);
}
/* Tell PETSc to assemble the matrix */
MatAssemblyBegin(full_A,MAT_FINAL_ASSEMBLY);
MatAssemblyEnd(full_A,MAT_FINAL_ASSEMBLY);
if (nid==0) printf("Matrix Assembled.\n");
matrix_assembled = 1;
// }
/* Print information about the matrix. */
PetscViewerASCIIOpen(PETSC_COMM_WORLD,NULL,&mat_view);
PetscViewerPushFormat(mat_view,PETSC_VIEWER_ASCII_INFO);
MatView(full_A,mat_view);
PetscViewerPopFormat(mat_view);
PetscViewerDestroy(&mat_view);
/*
* Create parallel vectors.
* - When using VecCreate(), VecSetSizes() and VecSetFromOptions(),
* we specify only the vector's global
* dimension; the parallel partitioning is determined at runtime.
* - Note: We form 1 vector from scratch and then duplicate as needed.
*/
VecCreate(PETSC_COMM_WORLD,&b);
VecSetSizes(b,PETSC_DECIDE,dim);
VecSetFromOptions(b);
// VecDuplicate(b,&x); Assume x is passed in
/*
* Set rhs, b, and solution, x to 1.0 in the first
* element, 0.0 elsewhere.
*/
VecSet(b,0.0);
VecSet(x,0.0);
if(nid==0) {
row = 0;
mat_tmp = 1.0 + 0.0*PETSC_i;
VecSetValue(x,row,mat_tmp,INSERT_VALUES);
VecSetValue(b,row,mat_tmp,INSERT_VALUES);
}
/* Assemble x and b */
VecAssemblyBegin(x);
VecAssemblyEnd(x);
VecAssemblyBegin(b);
VecAssemblyEnd(b);
/* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -*
* Create the linear solver and set various options *
*- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - */
/*
* Create linear solver context
*/
KSPCreate(PETSC_COMM_WORLD,&ksp);
/*
* Set operators. Here the matrix that defines the linear system
* also serves as the preconditioning matrix.
*/
KSPSetOperators(ksp,full_A,full_A);
/*
* Set good default options for solver
*/
/* relative tolerance */
KSPSetTolerances(ksp,default_rtol,PETSC_DEFAULT,PETSC_DEFAULT,PETSC_DEFAULT);
/* bjacobi preconditioner */
KSPGetPC(ksp,&pc);
PCSetType(pc,PCASM);
/* gmres solver with 100 restart*/
KSPSetType(ksp,KSPGMRES);
KSPGMRESSetRestart(ksp,default_restart);
/*
* Set runtime options, e.g.,
* -ksp_type <type> -pc_type <type> -ksp_monitor -ksp_rtol <rtol>
*/
KSPSetFromOptions(ksp);
/* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Solve the linear system
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - */
if (nid==0) printf("KSP set. Solving for steady state...\n");
KSPSolve(ksp,b,x);
num_pop = get_num_populations();
populations = malloc(num_pop*sizeof(double));
get_populations(x,&populations);
if(nid==0){
printf("Final populations: ");
for(i=0;i<num_pop;i++){
printf(" %e ",populations[i]);
}
printf("\n");
}
KSPGetIterationNumber(ksp,&its);
PetscPrintf(PETSC_COMM_WORLD,"Iterations %D\n",its);
/* Free work space */
KSPDestroy(&ksp);
// VecDestroy(&x);
VecDestroy(&b);
return;
}
/*
* time_step solves for the time_dependence of the system
* that was previously setup using the add_to_ham and add_lin
* routines. Solver selection and parameters can be controlled via PETSc
* command line options. Default solver is TSRK3BS
*
* Inputs:
* Vec x: The density matrix, with appropriate inital conditions
* double dt: initial timestep. For certain explicit methods, this timestep
* can be changed, as those methods have adaptive time steps
* double time_max: the maximum time to integrate to
* int steps_max: max number of steps to take
*/
void time_step(Vec x, PetscReal init_time, PetscReal time_max,PetscReal dt,PetscInt steps_max){
PetscViewer mat_view;
TS ts; /* timestepping context */
PetscInt i,j,Istart,Iend,steps,row,col;
PetscScalar mat_tmp;
PetscReal tmp_real;
Mat AA;
PetscInt nevents,direction;
PetscBool terminate;
operator op;
int num_pop;
double *populations;
Mat solve_A,solve_stiff_A;
PetscLogStagePop();
PetscLogStagePush(solve_stage);
if (_lindblad_terms) {
if (nid==0) {
printf("Lindblad terms found, using Lindblad solver.\n");
}
solve_A = full_A;
if (_stiff_solver) {
if(nid==0) printf("ERROR! Lindblad-stiff solver untested.");
exit(0);
}
} else {
if (nid==0) {
printf("No Lindblad terms found, using (more efficient) Schrodinger solver.\n");
}
solve_A = ham_A;
solve_stiff_A = ham_stiff_A;
if (_num_time_dep&&_stiff_solver) {
if(nid==0) printf("ERROR! Schrodinger-stiff + timedep solver untested.");
exit(0);
}
}
/* Possibly print dense ham. No stabilization is needed? */
if (nid==0) {
/* Print dense ham, if it was asked for */
if (_print_dense_ham){
FILE *fp_ham;
fp_ham = fopen("ham","w");
if (nid==0){
for (i=0;i<total_levels;i++){
for (j=0;j<total_levels;j++){
fprintf(fp_ham,"%e %e ",PetscRealPart(_hamiltonian[i][j]),PetscImaginaryPart(_hamiltonian[i][j]));
}
fprintf(fp_ham,"\n");
}
}
fclose(fp_ham);
for (i=0;i<total_levels;i++){
free(_hamiltonian[i]);
}
free(_hamiltonian);
_print_dense_ham = 0;
}
}
/* Remove stabilization if it was previously added */
if (stab_added){
if (nid==0) printf("Removing stabilization...\n");
/*
* We add 1.0 in the 0th spot and every n+1 after
*/
if (nid==0) {
row = 0;
for (i=0;i<total_levels;i++){
col = i*(total_levels+1);
mat_tmp = -1.0 + 0.*PETSC_i;
MatSetValue(full_A,row,col,mat_tmp,ADD_VALUES);
}
}
}
MatGetOwnershipRange(solve_A,&Istart,&Iend);
/*
* Explicitly add 0.0 to all diagonal elements;
* this fixes a 'matrix in wrong state' message that PETSc
* gives if the diagonal was never initialized.
*/
//if (nid==0) printf("Adding 0 to diagonal elements...\n");
for (i=Istart;i<Iend;i++){
mat_tmp = 0 + 0.*PETSC_i;
MatSetValue(solve_A,i,i,mat_tmp,ADD_VALUES);
}
if(_stiff_solver){
MatGetOwnershipRange(solve_stiff_A,&Istart,&Iend);
for (i=Istart;i<Iend;i++){
mat_tmp = 0 + 0.*PETSC_i;
MatSetValue(solve_stiff_A,i,i,mat_tmp,ADD_VALUES);
}
}
/* - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -*
* Create the timestepping solver and set various options *
*- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - */
/*
* Create timestepping solver context
*/
TSCreate(PETSC_COMM_WORLD,&ts);
TSSetProblemType(ts,TS_LINEAR);
/*
* Set function to get information at every timestep
*/
if (_ts_monitor!=NULL){
TSMonitorSet(ts,_ts_monitor,_tsctx,NULL);
}
/*
* Set up ODE system
*/
TSSetRHSFunction(ts,NULL,TSComputeRHSFunctionLinear,NULL);
if(_stiff_solver) {
/* TSSetIFunction(ts,NULL,TSComputeRHSFunctionLinear,NULL); */
if (nid==0) {
printf("Stiff solver not implemented!\n");
exit(0);
}
if(nid==0) printf("Using stiff solver - TSROSW\n");
}
if(_num_time_dep+_num_time_dep_lin) {
for(i=0;i<_num_time_dep;i++){
tmp_real = 0.0;
_add_ops_to_mat_ham(tmp_real,solve_A,_time_dep_list[i].num_ops,_time_dep_list[i].ops);
}
for(i=0;i<_num_time_dep_lin;i++){
tmp_real = 0.0;
_add_ops_to_mat_lin(tmp_real,solve_A,_time_dep_list_lin[i].num_ops,_time_dep_list_lin[i].ops);
}
/* Tell PETSc to assemble the matrix */
MatAssemblyBegin(solve_A,MAT_FINAL_ASSEMBLY);
MatAssemblyEnd(solve_A,MAT_FINAL_ASSEMBLY);
if (nid==0) printf("Matrix Assembled.\n");
MatDuplicate(solve_A,MAT_COPY_VALUES,&AA);
MatAssemblyBegin(AA,MAT_FINAL_ASSEMBLY);
MatAssemblyEnd(AA,MAT_FINAL_ASSEMBLY);
TSSetRHSJacobian(ts,AA,AA,_RHS_time_dep_ham_p,NULL);
} else {
/* Tell PETSc to assemble the matrix */
MatAssemblyBegin(solve_A,MAT_FINAL_ASSEMBLY);
MatAssemblyEnd(solve_A,MAT_FINAL_ASSEMBLY);
if (_stiff_solver){
MatAssemblyBegin(solve_stiff_A,MAT_FINAL_ASSEMBLY);
MatAssemblyEnd(solve_stiff_A,MAT_FINAL_ASSEMBLY);
/* TSSetIJacobian(ts,solve_stiff_A,solve_stiff_A,TSComputeRHSJacobianConstant,NULL); */
if (nid==0) {
printf("Stiff solver not implemented!\n");
exit(0);
}
}
if (nid==0) printf("Matrix Assembled.\n");
TSSetRHSJacobian(ts,solve_A,solve_A,TSComputeRHSJacobianConstant,NULL);
}
/* Print information about the matrix. */
PetscViewerASCIIOpen(PETSC_COMM_WORLD,NULL,&mat_view);
PetscViewerPushFormat(mat_view,PETSC_VIEWER_ASCII_INFO);
/* PetscViewerPushFormat(mat_view,PETSC_VIEWER_ASCII_MATLAB); */
/* MatView(solve_A,mat_view); */
/* PetscInt ncols; */
/* const PetscInt *cols; */
/* const PetscScalar *vals; */
/* for(i=0;i<total_levels*total_levels;i++){ */
/* MatGetRow(solve_A,i,&ncols,&cols,&vals); */
/* for (j=0;j<ncols;j++){ */
/* if(PetscAbsComplex(vals[j])>1e-5){ */
/* printf("%d %d %lf %lf\n",i,cols[j],vals[j]); */
/* } */
/* } */
/* MatRestoreRow(solve_A,i,&ncols,&cols,&vals); */
/* } */
if(_stiff_solver){
MatView(solve_stiff_A,mat_view);
}
PetscViewerPopFormat(mat_view);
PetscViewerDestroy(&mat_view);
TSSetTimeStep(ts,dt);
/*
* Set default options, can be changed at runtime
*/
TSSetMaxSteps(ts,steps_max);
TSSetMaxTime(ts,time_max);
TSSetTime(ts,init_time);
TSSetExactFinalTime(ts,TS_EXACTFINALTIME_STEPOVER);
if (_stiff_solver) {
TSSetType(ts,TSROSW);
} else {
TSSetType(ts,TSRK);
TSRKSetType(ts,TSRK3BS);
}
/* If we have gates to apply, set up the event handler. */
if (_num_quantum_gates > 0) {
nevents = 1; //Only one event for now (did we cross a gate?)
direction = -1; //We only want to count an event if we go from positive to negative
terminate = PETSC_FALSE; //Keep time stepping after we passed our event
/* Arguments are: ts context, nevents, direction of zero crossing, whether to terminate,
* a function to check event status, a function to apply events, private data context.
*/
TSSetEventHandler(ts,nevents,&direction,&terminate,_QG_EventFunction,_QG_PostEventFunction,NULL);
}
if (_num_circuits > 0) {
nevents = 1; //Only one event for now (did we cross a gate?)
direction = -1; //We only want to count an event if we go from positive to negative
terminate = PETSC_FALSE; //Keep time stepping after we passed our event
/* Arguments are: ts context, nevents, direction of zero crossing, whether to terminate,
* a function to check event status, a function to apply events, private data context.
*/
TSSetEventHandler(ts,nevents,&direction,&terminate,_QC_EventFunction,_QC_PostEventFunction,NULL);
}
if (_discrete_ec > 0) {
nevents = 1; //Only one event for now (did we cross an ec step?)
direction = -1; //We only want to count an event if we go from positive to negative
terminate = PETSC_FALSE; //Keep time stepping after we passed our event
/* Arguments are: ts context, nevents, direction of zero crossing, whether to terminate,
* a function to check event status, a function to apply events, private data context.
*/
TSSetEventHandler(ts,nevents,&direction,&terminate,_DQEC_EventFunction,_DQEC_PostEventFunction,NULL);
}
/* if (_lindblad_terms) { */
/* nevents = 1; //Only one event for now (did we cross a gate?) */
/* direction = 0; //We only want to count an event if we go from positive to negative */
/* terminate = PETSC_FALSE; //Keep time stepping after we passed our event */
/* TSSetEventHandler(ts,nevents,&direction,&terminate,_Normalize_EventFunction,_Normalize_PostEventFunction,NULL); */
/* } */
TSSetFromOptions(ts);
TSSolve(ts,x);
TSGetStepNumber(ts,&steps);
num_pop = get_num_populations();
populations = malloc(num_pop*sizeof(double));
get_populations(x,&populations);
/* if(nid==0){ */
/* printf("Final populations: "); */
/* for(i=0;i<num_pop;i++){ */
/* printf(" %e ",populations[i]); */
/* } */
/* printf("\n"); */
/* } */
/* PetscPrintf(PETSC_COMM_WORLD,"Steps %D\n",steps); */
/* Free work space */
TSDestroy(&ts);
if(_num_time_dep+_num_time_dep_lin){
MatDestroy(&AA);
}
free(populations);
PetscLogStagePop();
PetscLogStagePush(post_solve_stage);
return;
}
