int main() {
// Standard deviation of truncated normal distribution in proposal function
double proposal_stddev = 0.1;
// Side length of the box
double side_length = 15.0;
// Number of oppositely charged particle pairs
unsigned particle_num = 20;
// Thermodynamic beta
double beta = 300.0;
// Number of samples
std::size_t sample_num = 100000;
// Output
bool save_output = true;
std::string filename = "out.tsv";
// Convenience typedefs
using Ensemble = CanonicalEnsemble<Particle2D>;
using PotentialPtr = double (*)(const Particle2D &, const Particle2D &);
auto initial_state = random_state(side_length, particle_num);
auto proposal_function =
unif_proposal_function(proposal_stddev, side_length);
// Simulate samples of the canonical ensemble using the Random-Walk
// Metropolis-Algorithm
Ensemble ensemble(initial_state, beta,
static_cast<PotentialPtr>(coulomb_core),
proposal_function);
std::vector<Ensemble::State> samples;
samples.reserve(sample_num);
std::size_t accepted_cnt = 0;
for (std::size_t i = 0; i < sample_num; ++i) {
if (ensemble.step()) {
++accepted_cnt;
}
samples.push_back(ensemble.get_state());
}
std::cout << "Acceptance probability: "
<< static_cast<double>(accepted_cnt) / sample_num << std::endl;
// Save output to .tsv
if (save_output) {
std::ofstream os(filename);
for (auto it = samples.cbegin(), end = samples.cend(); os && it != end;
++it) {
for (const auto &particle : *it) {
os << particle.q << "\t" << particle.x << "\t" << particle.y
<< "\t";
}
os << "\n";
}
}
return 0;
}
void CEnsemble3dExt::Ensemble3Dvolume(const char* folder_name)
{
_init_temporal();
int k = 0, max_nodule_size = 35;
int start = seedz, z = seedz;
int org_x = seedx, org_y = seedy, org_z = seedz;
for(z=start; z>=0; z--)
{
//printf ("%s\n", slices_name[z].c_str());
cout<<z<<":"<<seedx<<','<<seedy<<endl;
aslice = _dcm.getSllice(z);
// cv::namedWindow( "Display window", cv::WINDOW_AUTOSIZE );// Create a window for display.
// cv::imshow( "Display window", cv::Mat(aslice) ); // Show our image inside it.
//
// cv::waitKey(0);
outmask = cvCreateImage(cvGetSize(aslice),8,1);
cvZero(outmask);
//cvShowImage("in", aslice);
//cvWaitKey(0);
CvPoint seed = cvPoint(seedx,seedy);
//cvSmooth(aslice, aslice);
CEnsemble ensemble(aslice, seed, outmask);
ensemble.doEnsembleSegmentation();
SaveResult(folder_name, z);
if(!UpdateSeed(z, BACKWARD))
break;
k ++;
if(k > max_nodule_size)
break;
cvReleaseImage(&outmask);
}
seedx = org_x;
seedy = org_y;
seedz = org_z;
k = 0;
for(z=start+1; z<slices_name.size(); z++)
{
////printf ("%s\n", slices_name[i].c_str());
cout<<z<<":"<<seedx<<','<<seedy<<endl;
aslice = _dcm.getSllice(z);
outmask = cvCreateImage(cvGetSize(aslice),8,1);
cvZero(outmask);
CvPoint seed = cvPoint(seedx,seedy);
//cvSmooth(aslice, aslice);
CEnsemble ensemble(aslice, seed, outmask);
ensemble.doEnsembleSegmentation();
SaveResult(folder_name, z);
if(!UpdateSeed(z, FORWARD))
break;
k ++;
if(k > max_nodule_size)
break;
//cvWaitKey(0);
cvReleaseImage(&outmask);
}
_saveToNiffty(_temporal, folder_name );
}
int main(int argc, char *argv[]) {
namespace po=boost::program_options;
po::options_description desc("Well-mixed SIR with demographics.");
int64_t individual_cnt=100000;
int64_t infected_cnt=individual_cnt*0.1;
int64_t recovered_cnt=individual_cnt*0.8;
int run_cnt=1;
size_t rand_seed=1;
// Time is in years.
std::vector<Parameter> parameters;
parameters.emplace_back(Parameter{SIRParam::Beta0, "beta0", 400,
"main infection rate"});
parameters.emplace_back(Parameter{SIRParam::Beta1, "beta1", 0.6,
"seasonality ratio"});
parameters.emplace_back(Parameter{SIRParam::SeasonalPhase, "phase", 0,
"seasonality phase start between (0,1]"});
parameters.emplace_back(Parameter{SIRParam::Gamma, "gamma", 365/14.0,
"recovery rate"});
parameters.emplace_back(Parameter{SIRParam::Birth, "birth", 1/70.0,
"crude rate, before multiplying by number of individuals"});
parameters.emplace_back(Parameter{SIRParam::Mu, "mu", 1/70.0,
"death rate"});
double end_time=30.0;
bool exacttraj=true;
bool exactinfect=false;
int thread_cnt=1;
std::string log_level;
std::string data_file("sirexp.h5");
bool save_file=false;
std::string translation_file;
bool test=false;
desc.add_options()
("help", "show help message")
("threadcnt,j",
po::value<int>(&thread_cnt)->default_value(thread_cnt),
"number of threads")
("runcnt",
po::value<int>(&run_cnt)->default_value(run_cnt),
"number of runs")
("size,s",
po::value<int64_t>(&individual_cnt)->default_value(individual_cnt),
"size of the population")
("infected,i",
po::value<int64_t>(&infected_cnt),
"number of infected")
("recovered,r",
po::value<int64_t>(&recovered_cnt),
"number of recovered")
("seed",
po::value<size_t>(&rand_seed)->default_value(rand_seed),
"seed for random number generator")
("endtime",
po::value<double>(&end_time)->default_value(end_time),
"how many years to run")
("exacttraj",
po::value<bool>(&exacttraj)->default_value(exacttraj),
"save trajectory only when it changes by a certain amount")
("exactinfect",
po::value<bool>(&exactinfect)->default_value(exactinfect),
"set true to use exact distribution for seasonal infection")
("datafile",
po::value<std::string>(&data_file)->default_value(data_file),
"Write to this data file.")
("save",
po::value<bool>(&save_file)->default_value(save_file),
"Add data to file instead of erasing it with new data.")
("loglevel", po::value<std::string>(&log_level)->default_value("info"),
"Set the logging level to trace, debug, info, warning, error, or fatal.")
("translate",
po::value<std::string>(&translation_file)->default_value(""),
"write file relating place ids to internal ids")
("info", "show provenance of program")
;
for (auto& p : parameters) {
desc.add_options()(p.name.c_str(),
po::value<double>(&p.value)->default_value(p.value),
p.description.c_str());
}
po::variables_map vm;
auto parsed_options=po::parse_command_line(argc, argv, desc);
po::store(parsed_options, vm);
po::notify(vm);
if (vm.count("help")) {
std::cout << desc << std::endl;
return 0;
}
std::map<std::string,std::string> compile_info {
{"VERSION", VERSION}, {"COMPILETIME", COMPILETIME},
{"CONFIG", CFG}
};
if (vm.count("info")) {
for (auto& kv : compile_info) {
std::cout << kv.second << "\n\n";
}
return 0;
}
afidd::LogInit(log_level);
if (test) {
double tolerance=TestSeasonal(0.6);
std::cout << "Seasonal tolerance " << tolerance << std::endl;
}
std::map<SIRParam,double*> params;
for (auto& pm : parameters) {
params[pm.kind]=&pm.value;
}
// Birthrate is not frequency-dependent. It scales differently
// which creates a fixed point in the phase plane.
(*params[SIRParam::Birth])*=individual_cnt;
if (std::abs(*params[SIRParam::Beta1])>1) {
std::cout << "beta1 should be fractional, between 0 and 1: beta1=" <<
*params[SIRParam::Beta1] << std::endl;
return -4;
}
if (vm.count("infected") and !vm.count("recovered") ||
!vm.count("infected") and vm.count("recovered")) {
std::cout << "You have so set the total and I and R, not just some of them."
<< std::endl;
return -3;
} else if (!vm.count("infected") && !vm.count("recovered")) {
double b=*params[SIRParam::Beta0];
double m=*params[SIRParam::Mu];
double g=*params[SIRParam::Gamma];
double B=*params[SIRParam::Birth];
// Long-time averages for fixed forcing for ODE model.
int64_t susceptible_start=std::floor((m+g)*individual_cnt/b);
infected_cnt=std::floor(individual_cnt*(b-m-g)*m/(b*(m+g)));
recovered_cnt=individual_cnt-(susceptible_start+infected_cnt);
}
int64_t susceptible_cnt=individual_cnt-(infected_cnt+recovered_cnt);
assert(susceptible_cnt>0);
if (susceptible_cnt<0) {
BOOST_LOG_TRIVIAL(error)<<"Number of susceptibles is "<<susceptible_cnt;
return -2;
}
std::vector<int64_t> sir_init{susceptible_cnt, infected_cnt, recovered_cnt};
BOOST_LOG_TRIVIAL(info)<<"Starting with sir="<<sir_init[0]<<" "<<sir_init[1]
<<" "<<sir_init[2];
for (auto& showp : parameters) {
BOOST_LOG_TRIVIAL(info)<<showp.name<<" "<<showp.value;
}
HDFFile file(data_file);
if (!file.Open(!save_file)) {
BOOST_LOG_TRIVIAL(error)<<"could not open output file: "<<data_file;
return -1;
}
file.WriteExecutableData(compile_info, parsed_options, sir_init);
auto runnable=[=](RandGen& rng, size_t single_seed, size_t idx)->void {
std::shared_ptr<TrajectoryObserver> observer=0;
if (exacttraj) {
observer=std::make_shared<TrajectorySave>();
} else {
observer=std::make_shared<PercentTrajectorySave>();
}
SIR_run(end_time, sir_init, parameters, *observer, rng, exactinfect);
file.SaveTrajectory(parameters, single_seed, idx, observer->Trajectory());
};
Ensemble<decltype(runnable)> ensemble(runnable, thread_cnt, run_cnt,
rand_seed);
ensemble.Run();
BOOST_LOG_TRIVIAL(debug)<<"Finished running ensemble.";
file.Close();
return 0;
}
/* main routine */
int main(int argc, char *argv[]){
FILELog::ReportingLevel() = logINFO;
VARIABLES vars;
vars = commandline_input(argc, argv);
clock_t init, final;
int number_of_particles = vars.num;
double timestep = vars.dt;
bool use_T2 = vars.use_T2; // = false (no T2 decay), = true (T2 decay)
int num_of_repeat = vars.num_of_repeat; //Number of times to repeat simulation. For every repeat all data is flushed and we start the simulation again.
int number_of_timesteps; number_of_timesteps = (int)ceil(vars.gs/timestep);
double permeability = 0.0;
double radius = .00535;
double d = sqrt( 2.0*PI*radius*radius/( sqrt(3.0)*.79 ) );
//double lattice_size = 0.00601922026995422789215053328651;
double grad_duration = 4.5;
double D_extra = 2.5E-6;
double D_intra = 1.0E-6;
double T2_e = 200;
double T2_i = 200;
FILE_LOG(logINFO) << "f = " << 2.0*PI*radius*radius/(sqrt(3.0)*d*d) << std::endl;
Vector3 xhat(1.0,0.0,0.0);
Vector3 yhat(0.0,1.0,0.0);
Vector3 zhat(0.0,0.0,1.0);
Lattice<Cylinder_XY> lattice(D_extra, T2_e, permeability);
lattice.setLatticeVectors(d,2.0*d*0.86602540378443864676372317075294,d,xhat,yhat,zhat);
lattice.addBasis(Cylinder_XY(d/2.0, 0.0, radius, T2_i, D_intra, 1));
lattice.addBasis(Cylinder_XY(0.0, d*0.86602540378443864676372317075294, radius, T2_i, D_intra, 2));
lattice.addBasis(Cylinder_XY(d/2.0, 2.0*d*0.86602540378443864676372317075294, radius, T2_i, D_intra, 3));
lattice.addBasis(Cylinder_XY(d, d*0.86602540378443864676372317075294, radius, T2_i, D_intra, 4));
double gspacings [] = { 8.0 , 10.5 , 14.0 , 18.5 , 24.5 , 32.5 , 42.5 , 56.5 };
double bvals [] = {108780, 154720, 219040, 301730, 411980, 558990, 742420, 1000000 };
double G [9];
for (int kk = 0; kk < num_of_repeat; kk++) {
vector<PGSE> measurements_x;
vector<PGSE> measurements_y;
vector<PGSE> measurements_z;
for (int i = 0; i < 8; i++){
double echo_time = 2.0*grad_duration + gspacings[i];
G[0] = 0.0;
G[i+1] = sqrt(bvals[i]/(GAMMA*GAMMA*grad_duration*grad_duration*(grad_duration + gspacings[i] - (grad_duration/3.0))));
measurements_x.push_back(PGSE(grad_duration,gspacings[i], timestep, G[0], echo_time, number_of_particles, xhat));
measurements_y.push_back(PGSE(grad_duration,gspacings[i], timestep, G[0], echo_time, number_of_particles, yhat));
measurements_z.push_back(PGSE(grad_duration,gspacings[i], timestep, G[0], echo_time, number_of_particles, zhat));
measurements_x.push_back(PGSE(grad_duration,gspacings[i], timestep, G[i+1], echo_time, number_of_particles, xhat));
measurements_y.push_back(PGSE(grad_duration,gspacings[i], timestep, G[i+1], echo_time, number_of_particles, yhat));
measurements_z.push_back(PGSE(grad_duration,gspacings[i], timestep, G[i+1], echo_time, number_of_particles, zhat));
}
vector<double> lnsignal(2);
vector<double> b(2);
cout << " trial = " << kk << endl;
Particles ensemble(number_of_particles,timestep, use_T2);
lattice.initializeUniformly(ensemble.getGenerator() , ensemble.getEnsemble() );
for (int k = 0; k < measurements_x.size();k++){
measurements_x[k].updatePhase(ensemble.getEnsemble(), 0.0);
measurements_y[k].updatePhase(ensemble.getEnsemble(), 0.0);
measurements_z[k].updatePhase(ensemble.getEnsemble(), 0.0);
}
for (int i = 1; i <= number_of_timesteps; i++){
ensemble.updateposition(lattice);
for (int k = 0; k < measurements_x.size();k++){
measurements_x[k].updatePhase(ensemble.getEnsemble(), i*timestep);
measurements_y[k].updatePhase(ensemble.getEnsemble(), i*timestep);
measurements_z[k].updatePhase(ensemble.getEnsemble(), i*timestep);
}
}
for (int i = 0; i < 8*2; i+=2){
double ADCx, ADCy, ADCz, diff_time;
lnsignal[0] = log(measurements_x[i].get_signal());
b[0] = measurements_x[i].get_b();
lnsignal[1] = log(measurements_x[i+1].get_signal());
b[1] = measurements_x[i+1].get_b();
ADCx = -1.0*linear_regression(lnsignal,b);
//std::cout << b[0] << " " << lnsignal[0] << " " << b[1] << " " << lnsignal[1] << " ";
lnsignal[0] = log(measurements_y[i].get_signal());
b[0] = measurements_y[i].get_b();
lnsignal[1] = log(measurements_y[i+1].get_signal());
b[1] = measurements_y[i+1].get_b();
ADCy = -1.0*linear_regression(lnsignal,b);
//std::cout << b[0] << " " << lnsignal[0] << " " << b[1] << " " << lnsignal[1] << " ";
lnsignal[0] = log(measurements_z[i].get_signal());
b[0] = measurements_z[i].get_b();
lnsignal[1] = log(measurements_z[i+1].get_signal());
b[1] = measurements_z[i+1].get_b();
ADCz = -1.0*linear_regression(lnsignal,b);
//std::cout << b[0] << " " << lnsignal[0] << " " << b[1] << " " << lnsignal[1] << std::endl;
diff_time = measurements_x[i].get_DT();
std::cout << i << " " << diff_time << " " << ADCx << " " << ADCy << " " << ADCz << " " << b[1] << " " << G[1] << std::endl;
}
}
final= clock()-init; //final time - intial time