The class Modelling_ThreePointCorrelation. More...

#include <Modelling_ThreePointCorrelation.h>

Inheritance diagram for cbl::modelling::threept::Modelling_ThreePointCorrelation:

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Collaboration diagram for cbl::modelling::threept::Modelling_ThreePointCorrelation:

Public Member Functions
measure::threept::ThreePType	threePType ()
	return the type of correlation function More...

void	set_data_model (const std::vector< double > Q_DM)
	set the data model for the three-point correlation function More...

void	set_data_model_zeta_RSD (const double r1, const double r2, const cbl::cosmology::Cosmology cosmology, const double redshift, const std::string method_Pk="CAMB", const bool NL=false, const int max_ll=5, const double k_min=1.e-4, const double k_max=100, const int step_k=500, const double r_min=1.e-4, const double r_max=200, const int step_r=200, const bool force_realSpace=false, const bool use_k=false, const bool store_output=true, const std::string output_root=cbl::par::defaultString, const int norm=-1, const double prec=1.e-4)
	set the data model for the three-point correlation function (see Slepian, Eisenstein 2017) More...

void	set_data_Q_nonlocal (const cosmology::Cosmology cosmology, const double r1, const double r2, const std::vector< double > theta, const std::string model, const std::vector< double > kk, const std::vector< double > Pk_matter)
	set the data model for the three-point correlation function with non-local contributions More...

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...

Protected Attributes
measure::threept::ThreePType	m_threePType
	the three-point correlation function type

modelling::threept::STR_data_model_threept	m_data_model
	the container of parameters for three-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

Constructors/destructors
	Modelling_ThreePointCorrelation ()=default
	default constuctor ThreePointCorrelation

	Modelling_ThreePointCorrelation (const std::shared_ptr< cbl::measure::threept::ThreePointCorrelation > threep)
	constuctor More...

virtual	~Modelling_ThreePointCorrelation ()=default
	default destructor

static std::shared_ptr< Modelling_ThreePointCorrelation >	Create (const std::shared_ptr< measure::threept::ThreePointCorrelation > threep)
	static factory used to construct modelling of three-point correlation functions of any type More...

static std::shared_ptr< Modelling_ThreePointCorrelation >	Create (const measure::threept::ThreePType threePType, const std::shared_ptr< data::Data > threept_dataset)
	static factory used to construct modelling of three-point correlation functions of any type More...

Additional Inherited Members
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

Detailed Description

The class Modelling_ThreePointCorrelation.

Modelling_ThreePointCorrelation.h "Headers/Modelling_ThreePointCorrelation.h"

This file defines the interface of the base class Modelling_ThreePointCorrelation, used for modelling any kind of three-point correlation function measurements

Definition at line 72 of file Modelling_ThreePointCorrelation.h.

Constructor & Destructor Documentation

◆ Modelling_ThreePointCorrelation()

cbl::modelling::threept::Modelling_ThreePointCorrelation::Modelling_ThreePointCorrelation ( const std::shared_ptr< cbl::measure::threept::ThreePointCorrelation > threep )

inline

constuctor

Parameters

threep the three-point correlation function to model _ThreePointCorrelation

Definition at line 100 of file Modelling_ThreePointCorrelation.h.

Member Function Documentation

◆ Create() [1/2]

std::shared_ptr< cbl::modelling::threept::Modelling_ThreePointCorrelation > cbl::modelling::threept::Modelling_ThreePointCorrelation::Create	(	const measure::threept::ThreePType	threePType,
		const std::shared_ptr< data::Data >	threept_dataset
	)

static

static factory used to construct modelling of three-point correlation functions of any type

Parameters

threePType	type of the three-point correlation function
threept_dataset	the dataset containing the three-point correlation function to model

Returns: a pointer to an object of class Modelling_ThreePointCorrelation of a given type

Definition at line 78 of file Modelling_ThreePointCorrelation.cpp.

◆ Create() [2/2]

std::shared_ptr< cbl::modelling::threept::Modelling_ThreePointCorrelation > cbl::modelling::threept::Modelling_ThreePointCorrelation::Create ( const std::shared_ptr< measure::threept::ThreePointCorrelation > threep )

static

static factory used to construct modelling of three-point correlation functions of any type

Parameters

threep the three-point correlation function to model

Returns: a pointer to an object of class Modelling_ThreePointCorrelation of a given type

Definition at line 55 of file Modelling_ThreePointCorrelation.cpp.

◆ set_data_model()

void cbl::modelling::threept::Modelling_ThreePointCorrelation::set_data_model ( const std::vector< double > Q_DM )

set the data model for the three-point correlation function

Parameters

Q_DM	vector contaning the DM reduced three-point correlation function

Examples: model_3pt.cpp.

Definition at line 100 of file Modelling_ThreePointCorrelation.cpp.

◆ set_data_model_zeta_RSD()

void cbl::modelling::threept::Modelling_ThreePointCorrelation::set_data_model_zeta_RSD	(	const double	r1,
		const double	r2,
		const cbl::cosmology::Cosmology	cosmology,
		const double	redshift,
		const std::string	method_Pk = `"CAMB"`,
		const bool	NL = `false`,
		const int	max_ll = `5`,
		const double	k_min = `1.e-4`,
		const double	k_max = `100`,
		const int	step_k = `500`,
		const double	r_min = `1.e-4`,
		const double	r_max = `200`,
		const int	step_r = `200`,
		const bool	force_realSpace = `false`,
		const bool	use_k = `false`,
		const bool	store_output = `true`,
		const std::string	output_root = `cbl::par::defaultString`,
		const int	norm = `-1`,
		const double	prec = `1.e-4`
	)

set the data model for the three-point correlation function (see Slepian, Eisenstein 2017)

Parameters

r1	the first triangle side
r2	the second triangle side
cosmology	the fiducial cosmology
redshift	the redshift
method_Pk	method used to compute the power spectrum; 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]
NL	0 → linear power spectrum; 1 → non-linear power spectrum
max_ll	the maximum legendre multipole in 3pcf model
k_min	minimum wave vector module up to which the binned dark matter power spectrum is computed
k_max	maximum wave vector module up to which the binned dark matter power spectrum is computed
step_k	number of steps used to compute the binned dark matter correlation function
r_min	minimum separation up to which the integrals of the tree-level 3pcf are computed
r_max	maximum separation up to which the integrals of the tree-level 3pcf are computed
step_r	number of steps used to compute the integrals of the tree-level 3pcf
force_realSpace	\( \rightarrow \) redshift-space model; 1 \( \rightarrow \) real-space model
use_k	false \( \rightarrow \) do not compute k-integrals; true \( \rightarrow \) compute k-integrals @param use_k → do not compute the k-integrals; 1 → compute the k-integrals
store_output	if true the output files created by the Boltmann solver are stored; if false the output files are removed
output_root	the output file root
norm	0 → don't normalize the power spectrum; 1 → normalize the power spectrum
prec	accuracy of the GSL integration

Definition at line 124 of file Modelling_ThreePointCorrelation.cpp.

◆ set_data_Q_nonlocal()

void cbl::modelling::threept::Modelling_ThreePointCorrelation::set_data_Q_nonlocal	(	const cosmology::Cosmology	cosmology,
		const double	r1,
		const double	r2,
		const std::vector< double >	theta,
		const std::string	model,
		const std::vector< double >	kk,
		const std::vector< double >	Pk_matter
	)

set the data model for the three-point correlation function with non-local contributions

Parameters

cosmology	the fiducial cosmology
r1	the first triangle side
r2	the second triangle side
theta	vector of theta
model	method used to compute the 3pcf
kk	vector containing wavevector moduls
Pk_matter	vector containing the theoretical dark matter power-spectrum

Definition at line 109 of file Modelling_ThreePointCorrelation.cpp.

◆ threePType()

measure::threept::ThreePType cbl::modelling::threept::Modelling_ThreePointCorrelation::threePType ( )

inline

return the type of correlation function

Returns: the type of correlation function

Definition at line 143 of file Modelling_ThreePointCorrelation.h.

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

Headers/Modelling_ThreePointCorrelation.h
Modelling/ThreePointCorrelation/Modelling_ThreePointCorrelation.cpp

Public Member Functions

Protected Attributes

Constructors/destructors

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

◆ Modelling_ThreePointCorrelation()

Member Function Documentation

◆ Create() [1/2]

◆ Create() [2/2]

◆ set_data_model()

◆ set_data_model_zeta_RSD()

◆ set_data_Q_nonlocal()

◆ threePType()