The class Modelling_TwoPointCorrelation2D. More...

#include <Modelling_TwoPointCorrelation2D.h>

Inheritance diagram for cbl::modelling::twopt::Modelling_TwoPointCorrelation2D:

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Collaboration diagram for cbl::modelling::twopt::Modelling_TwoPointCorrelation2D:

Public Member Functions
Constructors/destructors
	Modelling_TwoPointCorrelation2D ()=default
	default constuctor

	Modelling_TwoPointCorrelation2D (const std::shared_ptr< cbl::measure::twopt::TwoPointCorrelation > twop)
	constructor More...

	Modelling_TwoPointCorrelation2D (const std::shared_ptr< cbl::data::Data > dataset, const measure::twopt::TwoPType twoPType)
	constructor More...

virtual	~Modelling_TwoPointCorrelation2D ()=default
	default destructor

Member functions used to set the model parameters
void	set_data_model (const cbl::cosmology::Cosmology cosmology={}, const double redshift=0., const std::string method_Pk="CAMB", const double sigmaNL=0, const bool NL=true, const int FV=0, const bool store_output=true, const std::string output_root="test", const bool bias_nl=false, const double bA=-1., const bool xiType=false, const double k_star=-1., const bool xiNL=false, const double v_min=-5000., const double v_max=5000., const int step_v=500, const int norm=-1, const double r_min=0.1, const double r_max=150., const double k_min=0., const double k_max=100., const int step=200, const double aa=0., const bool GSL=true, const double prec=1.e-2, const std::string file_par=par::defaultString)
	set the parameters for the computation of the dark matter two-point correlation function More...

Public Member Functions inherited from cbl::modelling::twopt::Modelling_TwoPointCorrelation
measure::twopt::TwoPType	twoPType ()
	get the member m_twoPType More...

std::shared_ptr< modelling::twopt::STR_data_model >	data_model ()
	get the member m_data_model More...

	Modelling_TwoPointCorrelation ()=default
	default constuctor

virtual	~Modelling_TwoPointCorrelation ()=default
	default destructor

Public Member Functions inherited from cbl::modelling::Modelling
void	m_set_posterior (const int seed)
	set the interal variable m_posterior More...

	Modelling ()=default
	default constuctor

virtual	~Modelling ()=default
	default destructor

std::shared_ptr< data::Data >	data ()
	return the dataset More...

std::shared_ptr< data::Data >	data_fit ()
	return the dataset More...

std::shared_ptr< statistics::Likelihood >	likelihood ()
	return the likelihood parameters More...

std::shared_ptr< statistics::Posterior >	posterior ()
	return the posterior parameters More...

std::shared_ptr< statistics::ModelParameters >	likelihood_parameters ()
	return the likelihood parameters More...

std::shared_ptr< statistics::ModelParameters >	posterior_parameters ()
	return the posterior parameters More...

virtual void	set_parameter_from_string (const std::string parameter, const double value)
	set the value of a parameter providing its name string More...

virtual double	get_parameter_from_string (const std::string parameter) const
	get the value of a parameter providing its name string More...

std::shared_ptr< statistics::PriorDistribution >	get_prior (const int i)
	get the internal variable m_parameter_priors More...

std::shared_ptr< statistics::Model >	get_response_function ()
	return the response function used to compute the super-sample covariance More...

void	reset_fit_range ()
	reset the fit range More...

void	set_fit_range (const double xmin, const double xmax)
	set the fit range More...

void	set_fit_range (const double xmin, const double xmax, const double ymin, const double ymax)
	set the fit range More...

void	set_data (const std::shared_ptr< data::Data > dataset)
	set the dataset More...

void	set_likelihood (const statistics::LikelihoodType likelihood_type, const std::vector< size_t > x_index={0, 2}, const int w_index=-1, const double prec=1.e-10, const int Nres=-1)
	set the likelihood function More...

void	set_likelihood (const cbl::statistics::Likelihood_function log_likelihood_function)
	set the likelihood function, given a user-defined log-likelihood function More...

void	maximize_likelihood (const std::vector< double > start, const std::vector< std::vector< double >> parameter_limits, const unsigned int max_iter=10000, const double tol=1.e-6, const double epsilon=1.e-3)
	function that maximizes the posterior, finds the best-fit parameters and stores them in the model More...

void	maximize_posterior (const std::vector< double > start, const unsigned int max_iter=10000, const double tol=1.e-6, const double epsilon=1.e-3, const int seed=666)
	function that maximizes the posterior, finds the best-fit parameters and stores them in the model More...

void	sample_posterior (const int chain_size, const int nwalkers, const int seed=666, const double aa=2, const bool parallel=true)
	sample the posterior, initializing the chains by drawing from the prior distributions More...

void	sample_posterior (const int chain_size, const int nwalkers, const double radius, const std::vector< double > start, const unsigned int max_iter=10000, const double tol=1.e-6, const double epsilon=1.e-3, const int seed=666, const double aa=2, const bool parallel=true)
	sample the posterior, initializing the chains in a ball around the posterior best-fit parameters values More...

void	sample_posterior (const int chain_size, const int nwalkers, std::vector< double > &value, const double radius, const int seed=666, const double aa=2, const bool parallel=true)
	sample the posterior, initializing the chains by drawing from the prior distributions More...

void	sample_posterior (const int chain_size, const std::vector< std::vector< double >> chain_value, const int seed=666, const double aa=2, const bool parallel=true)
	sample the posterior, initializing the chains with input values More...

void	sample_posterior (const int chain_size, const int nwalkers, const std::string input_dir, const std::string input_file, const int seed=666, const double aa=2, const bool parallel=true)
	sample the posterior, initializing the chains reading the input values from an input file More...

void	importance_sampling (const std::string input_dir, const std::string input_file, const int seed=666, const std::vector< size_t > column={}, const int header_lines_to_skip=1, const bool is_FITS_format=false, const bool apply_to_likelihood=false)
	perform importance sampling More...

void	write_chain (const std::string output_dir, const std::string output_file, const int start=0, const int thin=1, const bool is_FITS_format=false, const int prec=5, const int ww=14)
	write the chains obtained after the MCMC sampling More...

void	read_chain (const std::string input_dir, const std::string input_file, const int nwalkers, const std::vector< size_t > columns={}, const int skip_header=1, const bool fits=false)
	read the chains More...

void	show_results (const int start=0, const int thin=1, const int nbins=50, const bool show_mode=false, const int ns=-1)
	show the results of the MCMC sampling on screen More...

void	write_results (const std::string output_dir, const std::string root_file, const int start=0, const int thin=1, const int nbins=50, const bool fits=false, const bool compute_mode=false, const int ns=-1)
	write the results of the MCMC sampling to file More...

virtual void	write_model (const std::string output_dir, const std::string output_file, const std::vector< double > xx, const std::vector< double > parameters)
	write the model at xx for given parameters More...

virtual void	write_model (const std::string output_dir, const std::string output_file, const std::vector< double > xx, const std::vector< double > yy, const std::vector< double > parameters)
	write the model at xx, yy for given parameters More...

virtual void	write_model_at_bestfit (const std::string output_dir, const std::string output_file, const std::vector< double > xx)
	write the model at xx with best-fit parameters obtained from posterior maximization More...

virtual void	write_model_at_bestfit (const std::string output_dir, const std::string output_file, const std::vector< double > xx, const std::vector< double > yy)
	write the model at xx, yy with best-fit parameters obtained from likelihood maximization More...

virtual 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)
	write the model at xx computing 16th, 50th and 84th percentiles from the chains More...

virtual void	write_model_from_chains (const std::string output_dir, const std::string output_file, const std::vector< double > xx, const std::vector< double > yy, const int start=0, const int thin=1)
	write the model at xx, yy computing 16th, 50th and 84th percentiles from the chains More...

double	reduced_chi2 (const std::vector< double > parameter={})
	the reduced \(\chi^2\) More...

Additional Inherited Members
Static Public Member Functions inherited from cbl::modelling::twopt::Modelling_TwoPointCorrelation
static std::shared_ptr< Modelling_TwoPointCorrelation >	Create (const std::shared_ptr< measure::twopt::TwoPointCorrelation > twop)
	static factory used to construct modelling of two-point correlation functions of any type More...

static std::shared_ptr< Modelling_TwoPointCorrelation >	Create (const measure::twopt::TwoPType twoPType, const std::shared_ptr< data::Data > twop_dataset)
	static factory used to construct modelling of two-point correlation functions of any type More...

Protected Member Functions inherited from cbl::modelling::Modelling
void	m_set_prior (std::vector< statistics::PriorDistribution > prior_distribution)
	set the internal variable m_parameter_priors More...

void	m_isSet_response ()
	check if the response function used to compute the super-sample covariance is set

Protected Attributes inherited from cbl::modelling::twopt::Modelling_TwoPointCorrelation
measure::twopt::TwoPType	m_twoPType
	the two-point correlation function type

std::shared_ptr< modelling::twopt::STR_data_model >	m_data_model
	the container of parameters for two-point correlation function model computation

Protected Attributes inherited from cbl::modelling::Modelling
std::shared_ptr< data::Data >	m_data = NULL
	input data to be modelled

bool	m_fit_range = false
	check if fit range has been set

std::shared_ptr< data::Data >	m_data_fit
	input data restricted to the range used for the fit

std::shared_ptr< statistics::Model >	m_model = NULL
	input model

std::shared_ptr< statistics::Model >	m_response_func = NULL
	response function for the computation of the super-sample covariance

std::shared_ptr< statistics::Likelihood >	m_likelihood = NULL
	likelihood

std::vector< std::shared_ptr< statistics::PriorDistribution > >	m_parameter_priors
	prior

std::shared_ptr< statistics::Posterior >	m_posterior = NULL
	posterior

Detailed Description

The class Modelling_TwoPointCorrelation2D.

Modelling_TwoPointCorrelation2D.h "Headers/Modelling_TwoPointCorrelation2D.h"

This file defines the interface of the base class Modelling_TwoPointCorrelation2D, used for modelling the 2D two-point correlation function in cartesian coordinates

Definition at line 62 of file Modelling_TwoPointCorrelation2D.h.

Constructor & Destructor Documentation

◆ Modelling_TwoPointCorrelation2D() [1/2]

cbl::modelling::twopt::Modelling_TwoPointCorrelation2D::Modelling_TwoPointCorrelation2D ( const std::shared_ptr< cbl::measure::twopt::TwoPointCorrelation > twop )

constructor

Parameters

twop	the two-point correlation function to model

Definition at line 47 of file Modelling_TwoPointCorrelation2D.cpp.

◆ Modelling_TwoPointCorrelation2D() [2/2]

cbl::modelling::twopt::Modelling_TwoPointCorrelation2D::Modelling_TwoPointCorrelation2D	(	const std::shared_ptr< cbl::data::Data >	dataset,
		const measure::twopt::TwoPType	twoPType
	)

constructor

Parameters

dataset	the two-point correlation dataset
twoPType	the two-point correlation type

Definition at line 57 of file Modelling_TwoPointCorrelation2D.cpp.

Member Function Documentation

◆ set_data_model()

void cbl::modelling::twopt::Modelling_TwoPointCorrelation2D::set_data_model	(	const cbl::cosmology::Cosmology	cosmology = `{}`,
		const double	redshift = `0.`,
		const std::string	method_Pk = `"CAMB"`,
		const double	sigmaNL = `0`,
		const bool	NL = `true`,
		const int	FV = `0`,
		const bool	store_output = `true`,
		const std::string	output_root = `"test"`,
		const bool	bias_nl = `false`,
		const double	bA = `-1.`,
		const bool	xiType = `false`,
		const double	k_star = `-1.`,
		const bool	xiNL = `false`,
		const double	v_min = `-5000.`,
		const double	v_max = `5000.`,
		const int	step_v = `500`,
		const int	norm = `-1`,
		const double	r_min = `0.1`,
		const double	r_max = `150.`,
		const double	k_min = `0.`,
		const double	k_max = `100.`,
		const int	step = `200`,
		const double	aa = `0.`,
		const bool	GSL = `true`,
		const double	prec = `1.e-2`,
		const std::string	file_par = `par::defaultString`
	)

set the parameters for the computation of the dark matter two-point correlation function

Parameters

cosmology	the cosmology used
redshift	redshift
method_Pk	method used to compute the power spectrum and σ(mass) (i.e. the Boltzmann solver); valid choices for method_Pk are: CAMB [http://camb.info/], CLASS [http://class-code.net/], MPTbreeze-v1 [http://arxiv.org/abs/1207.1465], EisensteinHu [http://background.uchicago.edu/~whu/transfer/transferpage.html]
sigmaNL	damping of the wiggles in the linear power spectrum
NL	0 → linear power spectrum; 1 → non-linear power spectrum
FV	0 → exponential form for f(v); 1 → Gaussian form for f(v); where f(v) is the velocity distribution function
store_output	if true the output files created by the Boltzmann solver are stored; if false the output files are removed
output_root	output_root of the parameter file used to compute the power spectrum and σ(mass); it can be any name
bias_nl	0 → linear bias; 1 → non-linear bias
bA	b_a non-linear bias parameter
xiType	0 → standard; 1 → Chuang & Wang model
k_star	k_* of the Chuang & Wang model
xiNL	0 → linear power spectrum; 1 → non-linear power spectrum
v_min	minimum velocity used in the convolution of the correlation function
v_max	maximum velocity used in the convolution of the correlation function
step_v	number of steps used in the convolution of the correlation function
norm	0 → don't normalize the power spectrum; 1 → normalize the power spectrum
r_min	minimum separation up to which the binned dark matter correlation function is computed
r_max	maximum separation up to which the binned dark matter correlation function is computed
k_min	minimum wave vector module up to which the binned power spectrum is computed
k_max	maximum wave vector module up to which the binned power spectrum is computed
step	number of steps used to compute the binned dark matter correlation function
aa	parameter a of Eq. 24 of Anderson et al. 2012
GSL	0 → the Numerical libraries are used; 1 → the GSL libraries are used
prec	accuracy of the GSL integration
file_par	name of the parameter file; if a parameter file is provided (i.e. file_par!=NULL), it will be used, ignoring the cosmological parameters of the object

Examples: model_2pt_2D.cpp.

Definition at line 67 of file Modelling_TwoPointCorrelation2D.cpp.

The documentation for this class was generated from the following files:

Headers/Modelling_TwoPointCorrelation2D.h
Modelling/TwoPointCorrelation/Modelling_TwoPointCorrelation2D.cpp

Public Member Functions

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

◆ Modelling_TwoPointCorrelation2D() [1/2]

◆ Modelling_TwoPointCorrelation2D() [2/2]

Member Function Documentation

◆ set_data_model()