Modelling_TwoPointCorrelation2D_cartesian.cpp

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/********************************************************************
 *  Copyright (C) 2016 by Federico Marulli and Alfonso Veropalumbo  *
 *  federico.marulli3@unibo.it                                      *
 *                                                                  *
 *  This program is free software; you can redistribute it and/or   * 
 *  modify it under the terms of the GNU General Public License as  *
 *  published by the Free Software Foundation; either version 2 of  *
 *  the License, or (at your option) any later version.             *
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 *  This program is distributed in the hope that it will be useful, *
 *  but WITHOUT ANY WARRANTY; without even the implied warranty of  *
 *  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the   *
 *  GNU General Public License for more details.                    *
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 *  You should have received a copy of the GNU General Public       *
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 *  Software Foundation, Inc.,                                      *
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#include "Modelling_TwoPointCorrelation2D_cartesian.h"
 
using namespace std;
 
using namespace cbl;
 
 
// ============================================================================================
 
 
void cbl::modelling::twopt::Modelling_TwoPointCorrelation2D_cartesian::set_fiducial_xiDM ()
{
  cout << endl; coutCBL << "Setting up the fiducial dark matter two-point correlation function model" << endl;
 
  const vector<double> rad = logarithmic_bin_vector(m_data_model->step, max(m_data_model->r_min, 1.e-4), min(m_data_model->r_max, 100.));
  vector<double> xiDM(m_data_model->step);
  
  if (m_data_model->sigmaNL==0) {  
    for (size_t i=0; i<(size_t)m_data_model->step; i++)
      xiDM[i] = m_data_model->cosmology->xi_matter(rad[i], m_data_model->method_Pk, m_data_model->NL, m_data_model->redshift, true, m_data_model->output_root, m_data_model->norm, m_data_model->k_min, m_data_model->k_max, m_data_model->aa, m_data_model->GSL, m_data_model->prec, m_data_model->file_par);
    if (!m_data_model->store_output)
      m_data_model->cosmology->remove_output_Pk_tables(m_data_model->method_Pk, m_data_model->NL, m_data_model->redshift, m_data_model->output_root);
  }
 
  else {
    const vector<double> kk = logarithmic_bin_vector(m_data_model->step, max(m_data_model->k_min, 1.e-4), min(m_data_model->k_max, 500.));
    vector<double> Pk = m_data_model->cosmology->Pk_matter_DeWiggled("CAMB", "EisensteinHu", kk, m_data_model->redshift, m_data_model->sigmaNL, 4, 10, 0.05, m_data_model->store_output, m_data_model->output_root, m_data_model->norm, m_data_model->prec);
    xiDM = Xi0(rad, kk, Pk);
  }
 
  m_data_model->func_xi = make_shared<glob::FuncGrid>(glob::FuncGrid(rad, xiDM, "Spline"));
 
  vector<double> xiDM_(m_data_model->step), xiDM__(m_data_model->step);
 
  auto integrand_ = [&] (const double rr) { 
    return m_data_model->func_xi->operator()(rr)*rr*rr;
  };
 
  auto integrand__ = [&] (const double rr) { 
    return m_data_model->func_xi->operator()(rr)*rr*rr*rr*rr;
  };
 
  for (size_t i=0; i<(size_t)m_data_model->step; i++) {
    xiDM_[i] = 3.*wrapper::gsl::GSL_integrate_qag(integrand_, 0., rad[i])*pow(rad[i], -3);
    xiDM__[i] = 5.*wrapper::gsl::GSL_integrate_qag(integrand__, 0., rad[i])*pow(rad[i], -5);
  }
 
  m_data_model->func_xi_ = make_shared<glob::FuncGrid>(glob::FuncGrid(rad, xiDM_, "Spline"));
  m_data_model->func_xi__ = make_shared<glob::FuncGrid>(glob::FuncGrid(rad, xiDM__, "Spline"));  
}
 
 
// ============================================================================================
 
 
void cbl::modelling::twopt::Modelling_TwoPointCorrelation2D_cartesian::set_model_dispersion (const statistics::PriorDistribution fsigma8_prior, const statistics::PriorDistribution bsigma8_prior, const statistics::PriorDistribution sigmav_prior, const statistics::PriorDistribution alpha_perp_prior, const statistics::PriorDistribution alpha_par_prior)
{
  // compute the fiducial dark matter two-point correlation function and associated quantities
  set_fiducial_xiDM();
  
  // set the model parameters
  const int nparameters = 5;
 
  vector<statistics::ParameterType> parameterType(nparameters, statistics::ParameterType::_Base_);
 
  vector<string> parameterName(nparameters);
 
  parameterName[0] = "f*sigma8";
  parameterName[1] = "b*sigma8";
  parameterName[2] = "sigmav";
  parameterName[3] = "alpha_perp";
  parameterName[4] = "alpha_par";
  
  vector<statistics::PriorDistribution> priors = {fsigma8_prior, bsigma8_prior, sigmav_prior, alpha_perp_prior, alpha_par_prior};
 
  // set the priors
  m_set_prior(priors);
 
  // construct the model
  m_model = make_shared<statistics::Model2D>(statistics::Model2D(&xi2D_dispersion, nparameters, parameterType, parameterName, m_data_model)); 
 
}