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#include "quetzal/quetzal.h"
#include <boost/math/special_functions/binomial.hpp> // binomial coefficient
#include <random>
#include <algorithm>
#include <cassert>
#include <vector>
int main()
{
/******************************
* Geospatial dataset
*****************************/
using time_type = unsigned int;
using landscape_type = quetzal::geography::DiscreteLandscape<std::string,time_type>;
using coord_type = landscape_type::coord_type;
landscape_type env( {{"rain", "europe_precipitation.tif"}}, {time_type(0)} );
/******************************
* Simulator configuration
*****************************/
using strategy_type = quetzal::demography::strategy::mass_based;
using simulator_type = quetzal::simulators::SpatiallyExplicitCoalescenceSimulator<coord_type, time_type, strategy_type>;
coord_type x_0(40.0, -3.0);
x_0 = env.reproject_to_centroid(x_0);
time_type t_0 = 2000;
strategy_type::value_type N_0 = 100;
simulator_type simulator(x_0, t_0, N_0);
/******************************
* Niche and growth functions
*****************************/
using quetzal::expressive::literal_factory;
using quetzal::expressive::use;
literal_factory<coord_type, time_type> lit;
auto rain = env["rain"];
auto s = [rain](coord_type x, time_type){return rain(x, 0);};
unsigned int factor = 5;
auto K = use(s)*lit(factor);
auto r = lit(5);
auto pop_sizes = simulator.pop_size_history();
auto N = use( [pop_sizes](coord_type x, time_type t){ return pop_sizes(x,t);} );
auto g = N * ( lit(1) + r ) / ( lit(1) + ( (r * N)/K ));
auto sim_children = [g](auto& gen, coord_type const&x, time_type t){
std::poisson_distribution<unsigned int> poisson(g(x,t));
return poisson(gen);
};
std::random_device rd;
std::mt19937 gen(rd());
/******************
* Dispersal
******************/
auto const& space = env.geographic_definition_space();
coord_type::km radius = 60.0;
using quetzal::geography::memoized_great_circle_distance;
auto distance = [](coord_type const& a, coord_type const& b) -> double {return memoized_great_circle_distance(a,b);};
auto weight = [radius](coord_type::km d) -> double { if(d < radius){return 1.0;} else {return 0.0;} ;};
auto f = [weight, distance](coord_type const& a, coord_type const& b){
if(a == b){
return 500.0;
}else{
return weight(distance(a,b));
}
};
auto dispersal = strategy_type::make_sparse_distance_based_dispersal(space, f);
/**************************
* Demographic expansion
**************************/
unsigned int t_sampling = 4000;
simulator.expand_demography(t_sampling, sim_children, dispersal, gen);
/**********************
* Sampling schemes
**********************/
std::vector<coord_type> distribution_area = pop_sizes.get().definition_space(t_sampling);
unsigned int n = 200;
auto last_N = [N, t_sampling](coord_type const& x){return N(x, t_sampling);};
// Uniform sampling
auto unif_sampler = quetzal::sampling_scheme::make_unif_constrained_sampler(distribution_area, last_N, n);
auto global_sample = unif_sampler(gen);
// Clustered sampling
// Radius sampling in Spain
coord_type::km radius_1 = 200.0;
auto weight_1 = [radius_1](coord_type::km d) -> double { if(d < radius_1){return 1.0;} else {return 0.0;} ;};
auto f1 = [weight_1, distance, x_0](coord_type const& x){return weight_1(distance(x_0, x));};
auto spain_sampler = quetzal::sampling_scheme::make_constrained_sampler(distribution_area, f1, last_N, n/2);
auto spain_sample = spain_sampler(gen);
// Radius sampling around Sicilia
coord_type sicilia = env.reproject_to_centroid(coord_type(37.3, 15.0));
coord_type::km radius_2 = 200.0;
auto weight_2 = [radius_2](coord_type::km d) -> double { if(d < radius_2){return 1.0;} else {return 0.0;} ;};
auto f2 = [weight_2, distance, sicilia](coord_type const& x){return weight_2(distance(sicilia, x));};
auto sicilia_sampler = quetzal::sampling_scheme::make_constrained_sampler(distribution_area, f2, last_N, n/2);
auto sicilia_sample = sicilia_sampler(gen);
auto merged_sample = spain_sample;
for(auto const& it: sicilia_sample){
merged_sample[it.first] += it.second;
}
/**********************
* Data visualization
**********************/
std::string N_filename = "/home/me/dev/sampling_project/output/N.tif";
env.export_to_geotiff(N, t_0, t_sampling, [&pop_sizes](time_type const& t){return pop_sizes.get().definition_space(t);}, N_filename);
env.export_to_shapefile(global_sample, "output/uniform.shp");
env.export_to_shapefile(spain_sample, "output/spain.shp" );
env.export_to_shapefile(sicilia_sample, "output/sicilia.shp");
/**********************
* Coalescence
**********************/
unsigned int WF_N = 30;
//std::cout << simulate_newick(simulator, global_sample, t_sampling, t_0, WF_N, gen) << std::endl;
//std::cout << simulate_newick(simulator, spain_sample, t_sampling, t_0, WF_N, gen) << std::endl;
//std::cout << simulate_newick(simulator, sicilia_sample, t_sampling, t_0, WF_N, gen) << std::endl;
auto get_name = [&sicilia_sample, &spain_sample](coord_type const&x, time_type){
if ( sicilia_sample.find(x) == sicilia_sample.end() ) {
return "Spain";
} else {
return "Sicilia";
}
};
std::cout << simulate_newick_leaf_name(simulator, merged_sample, t_sampling, t_0, WF_N, get_name, gen) << std::endl;
return 0;
} |
Seabirds 
Configuration, cycle de vie et release d'un petit programme
)
). Bon le code est évidemment loin d’être parfait mais en vrai c'est plutôt cool a utiliser et perso moi ça me suffit dans mes projets.
Répondre avec citation
, c'était le Fortran ou Matlab, et "R" semble bien mieux implanté dans des domaines comme la statistique.
Envoyé par bacelar
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