Modelling_TwoPointCorrelation_wedges.h

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/********************************************************************
 *  Copyright (C) 2016 by Federico Marulli and Alfonso Veropalumbo  *   
 *  federico.marulli3@unibo.it                                      *
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 *  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       *
 *  License along with this program; if not, write to the Free      *
 *  Software Foundation, Inc.,                                      *
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#ifndef __MODELLINGTWOPOINTWED__
#define __MODELLINGTWOPOINTWED__
 
 
#include "Modelling_TwoPointCorrelation1D_monopole.h"
 
 
// ===================================================================================================
 
 
namespace cbl {
  
  namespace modelling {
 
    namespace twopt {
    
      class Modelling_TwoPointCorrelation_wedges : public Modelling_TwoPointCorrelation1D_monopole {
 
      protected:
 
    int m_nWedges;
 
    std::vector<std::vector<double>> m_mu_integral_limits;
    
        std::vector<bool> m_use_wedge;
 
    std::vector<int> m_wedges_order;
 
    bool m_ModelIsSet;
    
    double m_deltamu;
 
    
      public:
 
    
    Modelling_TwoPointCorrelation_wedges () = default;
 
    Modelling_TwoPointCorrelation_wedges (const std::shared_ptr<cbl::measure::twopt::TwoPointCorrelation> twop, const int nWedges=2, const std::vector<std::vector<double>> mu_integral_limits={{0., 0.5}, {0.5, 1.}});
 
    Modelling_TwoPointCorrelation_wedges (const std::shared_ptr<data::Data> twop_dataset, const int nWedges, const std::vector<std::vector<double>> mu_integral_limits={{0., 0.5}, {0.5, 1.}});
      
    virtual ~Modelling_TwoPointCorrelation_wedges () = default;
    
    
 
    
        virtual void write_model (const std::string output_dir, const std::string output_file, const std::vector<double> xx, const std::vector<double> parameter) override;
 
        void write_model_at_bestfit (const std::string output_dir, const std::string output_file, const std::vector<double> xx) override;
 
        void write_model_from_chains (const std::string output_dir, const std::string output_file, const std::vector<double> xx, const int start=0, const int thin=1) override;
 
    
 
    
    void set_fit_range (const double xmin, const double xmax, const int nWedges=-1);
 
    void set_fit_range (std::vector<std::vector<double>> fit_range);
    
    void set_fiducial_PkDM ();
    
    void set_fiducial_xiDM ();
 
    void set_model_BAO (const statistics::PriorDistribution alpha_perpendicular_prior={}, const statistics::PriorDistribution alpha_parallel_prior={}, const statistics::PriorDistribution Bperp_prior={}, const statistics::PriorDistribution Bpar_prior={}, const statistics::PriorDistribution Aperp0_prior={}, const statistics::PriorDistribution Apar0_prior={}, const statistics::PriorDistribution Aperp1_prior={}, const statistics::PriorDistribution Apar1_prior={}, const statistics::PriorDistribution Aperp2_prior={}, const statistics::PriorDistribution Apar2_prior={}, const bool compute_XiDM=true, const bool isRealSpace=false);
    
    void set_model_fullShape_DeWiggled (const statistics::PriorDistribution alpha_perpendicular_prior={}, const statistics::PriorDistribution alpha_parallel_prior={}, const statistics::PriorDistribution SigmaNL_perpendicular_prior={}, const statistics::PriorDistribution SigmaNL_parallel_prior={}, const statistics::PriorDistribution fsigma8_prior={}, const statistics::PriorDistribution bsigma8_prior={}, const statistics::PriorDistribution SigmaS_prior={}, const bool compute_PkDM=true);
 
    void set_model_fullShape_ModeCoupling (const statistics::PriorDistribution alpha_perpendicular_prior={}, const statistics::PriorDistribution alpha_parallel_prior={}, const statistics::PriorDistribution fsigma8_prior={}, const statistics::PriorDistribution bsigma8_prior={}, const statistics::PriorDistribution SigmaV_prior={}, const statistics::PriorDistribution AMC_prior={}, const bool compute_PkDM=true);
    
    void set_model_dispersion (const statistics::PriorDistribution fsigma8_prior={}, const statistics::PriorDistribution bsigma8_prior={}, const statistics::PriorDistribution sigmav_prior={}, const statistics::PriorDistribution alpha_perpendicular_prior={cbl::glob::DistributionType::_Constant_, 1.}, const statistics::PriorDistribution alpha_parallel_prior={cbl::glob::DistributionType::_Constant_, 1.}, const bool DFoG=true, const bool compute_PkDM=true);
    
    void set_model_Scoccimarro (const statistics::PriorDistribution fsigma8_prior={}, const statistics::PriorDistribution bsigma8_prior={}, const statistics::PriorDistribution sigmav_prior={}, const statistics::PriorDistribution alpha_perpendicular_prior={cbl::glob::DistributionType::_Constant_, 1.}, const statistics::PriorDistribution alpha_parallel_prior={cbl::glob::DistributionType::_Constant_, 1.}, const bool DFoG=true, const bool compute_PkDM=true);
 
    void set_model_Scoccimarro_fitPezzotta (const statistics::PriorDistribution fsigma8_prior={}, const statistics::PriorDistribution bsigma8_prior={}, const statistics::PriorDistribution sigmav_prior={}, const statistics::PriorDistribution kd_prior={}, const statistics::PriorDistribution kt_prior={}, const statistics::PriorDistribution alpha_perpendicular_prior={cbl::glob::DistributionType::_Constant_, 1.}, const statistics::PriorDistribution alpha_parallel_prior={cbl::glob::DistributionType::_Constant_, 1.}, const bool DFoG=true, const bool compute_PkDM=true); 
 
    void set_model_Scoccimarro_fitBel (const statistics::PriorDistribution fsigma8_prior={}, const statistics::PriorDistribution bsigma8_prior={}, const statistics::PriorDistribution sigmav_prior={}, const statistics::PriorDistribution kd_prior={}, const statistics::PriorDistribution bb_prior={}, const statistics::PriorDistribution a1_prior={}, const statistics::PriorDistribution a2_prior={}, const statistics::PriorDistribution a3_prior={}, const statistics::PriorDistribution alpha_perpendicular_prior={cbl::glob::DistributionType::_Constant_, 1.}, const statistics::PriorDistribution alpha_parallel_prior={cbl::glob::DistributionType::_Constant_, 1.}, const bool DFoG=true, const bool compute_PkDM=true);
 
    void set_model_TNS (const statistics::PriorDistribution fsigma8_prior={}, const statistics::PriorDistribution bsigma8_prior={}, const statistics::PriorDistribution sigmav_prior={}, const statistics::PriorDistribution alpha_perpendicular_prior={cbl::glob::DistributionType::_Constant_, 1.}, const statistics::PriorDistribution alpha_parallel_prior={cbl::glob::DistributionType::_Constant_, 1.}, const bool DFoG=true, const bool compute_PkDM=true);
 
    void set_model_eTNS (const statistics::PriorDistribution fsigma8_prior={}, const statistics::PriorDistribution b1sigma8_prior={}, const statistics::PriorDistribution b2sigma8_prior={}, const statistics::PriorDistribution sigmav_prior={}, const statistics::PriorDistribution alpha_perpendicular_prior={cbl::glob::DistributionType::_Constant_, 1.}, const statistics::PriorDistribution alpha_parallel_prior={cbl::glob::DistributionType::_Constant_, 1.}, const statistics::PriorDistribution N_prior={cbl::glob::DistributionType::_Constant_, 0.}, const bool DFoG=true, const bool compute_PkDM=true);
        
      };
    }
  }
}
 
#endif