A time delay estimator based on cross-correlation. More...

#include <time_delay_estimator_many.hpp>

Public Types
Type traits
using	array_type = array_t
	The input timeseries type. More...

using	element_type = typename jb::detail::array_traits< array_type >::element_type
	The values stored in the input timeseries. More...

using	precision_type = typename jb::extract_value_type< element_type >::precision
	Extract T out of std::complex<T>, otherwise simply T. More...

using	complex_type = std::complex< precision_type >
	complex_type use by FFT plans frequency array type More...

using	frequency_array_type = typename jb::detail::aligned_container< complex_type, array_type >::array_type
	The type used to store the DFT of the input timeseries. More...

using	output_array_type = typename jb::detail::aligned_container< precision_type, array_type >::array_type
	The type used to store the inverse of the DFT. More...

using	dplan = jb::fftw::plan< array_type, frequency_array_type >
	The execution plan to apply the (forward) DFT. More...

using	iplan = jb::fftw::plan< frequency_array_type, output_array_type >
	The execution plan to apply the inverse (aka backward) DFT. More...

using	confidence_type = jb::fftw::tde_result< array_t, precision_type >
	The type used to store the TDE confidence between two timeseries. More...

using	estimated_delay_type = jb::fftw::tde_result< array_t, std::size_t >
	The type used to store the estimated_delay between two timeseries. More...

using	sum2_type = jb::fftw::tde_result< array_t, precision_type >
	The sqr sum of a timeseries. More...

Public Member Functions
	time_delay_estimator_many (array_type &a, array_type &b)
	Constructs a time delay estimator using a and b as prototypes for the arguments. More...

void	estimate_delay (confidence_type &confidence, estimated_delay_type &estimated_delay, array_type &a, array_type &b, sum2_type const &sum2)
	Compute the time-delay estimate between two timeseries a and b. More...

Static Private Member Functions
static int	planning_flags ()
	Determine the correct FFTW planning flags given the inputs. More...

Private Attributes
frequency_array_type	tmpa_
	tmpa_ : timeseries to store the result of FFT(a) More...

frequency_array_type	tmpb_
	tmpb_ : timeseries to store the result of FFT(b) More...

dplan	a2tmpa_
	a2tmpa_ : fftw plan to execute FFT(a) More...

dplan	b2tmpb_
	b2tmpb_ : fftw plan to execute FFT(b) More...

output_array_type	out_
	out_ : timeseries to store the result of inverse FFT More...

iplan	tmpa2out_
	tmpa2out : fftw plan to execute inverse FFT of a timeseries More...

std::size_t	nsamples_
	nsamples : num samples of timeseries More...

std::size_t	num_timeseries_
	num_timeseries : num of timeseries contained in a and b More...

Detailed Description

template<typename array_t>
class jb::fftw::time_delay_estimator_many< array_t >

A time delay estimator based on cross-correlation.

Timeseries are implemented as standard containers (e.g. vector<>), as well as boost multi arrays of N dimensions

Template Parameters

array_t timeseries array type

Definition at line 25 of file time_delay_estimator_many.hpp.

Member Typedef Documentation

◆ array_type

template<typename array_t >

using jb::fftw::time_delay_estimator_many< array_t >::array_type = array_t

The input timeseries type.

Definition at line 33 of file time_delay_estimator_many.hpp.

◆ complex_type

template<typename array_t >

using jb::fftw::time_delay_estimator_many< array_t >::complex_type = std::complex<precision_type>

complex_type use by FFT plans frequency array type

Definition at line 44 of file time_delay_estimator_many.hpp.

◆ confidence_type

template<typename array_t >

using jb::fftw::time_delay_estimator_many< array_t >::confidence_type = jb::fftw::tde_result<array_t, precision_type>

The type used to store the TDE confidence between two timeseries.

Definition at line 61 of file time_delay_estimator_many.hpp.

◆ dplan

template<typename array_t >

using jb::fftw::time_delay_estimator_many< array_t >::dplan = jb::fftw::plan<array_type, frequency_array_type>

The execution plan to apply the (forward) DFT.

Definition at line 55 of file time_delay_estimator_many.hpp.

◆ element_type

template<typename array_t >

using jb::fftw::time_delay_estimator_many< array_t >::element_type = typename jb::detail::array_traits<array_type>::element_type

The values stored in the input timeseries.

Definition at line 37 of file time_delay_estimator_many.hpp.

◆ estimated_delay_type

template<typename array_t >

using jb::fftw::time_delay_estimator_many< array_t >::estimated_delay_type = jb::fftw::tde_result<array_t, std::size_t>

The type used to store the estimated_delay between two timeseries.

Definition at line 64 of file time_delay_estimator_many.hpp.

◆ frequency_array_type

template<typename array_t >

using jb::fftw::time_delay_estimator_many< array_t >::frequency_array_type = typename jb::detail::aligned_container< complex_type, array_type>::array_type

The type used to store the DFT of the input timeseries.

Definition at line 48 of file time_delay_estimator_many.hpp.

◆ iplan

template<typename array_t >

using jb::fftw::time_delay_estimator_many< array_t >::iplan = jb::fftw::plan<frequency_array_type, output_array_type>

The execution plan to apply the inverse (aka backward) DFT.

Definition at line 58 of file time_delay_estimator_many.hpp.

◆ output_array_type

template<typename array_t >

using jb::fftw::time_delay_estimator_many< array_t >::output_array_type = typename jb::detail::aligned_container< precision_type, array_type>::array_type

The type used to store the inverse of the DFT.

Definition at line 52 of file time_delay_estimator_many.hpp.

◆ precision_type

template<typename array_t >

using jb::fftw::time_delay_estimator_many< array_t >::precision_type = typename jb::extract_value_type<element_type>::precision

Extract T out of std::complex<T>, otherwise simply T.

Definition at line 41 of file time_delay_estimator_many.hpp.

◆ sum2_type

template<typename array_t >

using jb::fftw::time_delay_estimator_many< array_t >::sum2_type = jb::fftw::tde_result<array_t, precision_type>

The sqr sum of a timeseries.

Definition at line 67 of file time_delay_estimator_many.hpp.

Constructor & Destructor Documentation

◆ time_delay_estimator_many()

template<typename array_t >

jb::fftw::time_delay_estimator_many< array_t >::time_delay_estimator_many	(	array_type &	a,
		array_type &	b
	)

inline

Constructs a time delay estimator using a and b as prototypes for the arguments.

The optimal algorithm to compute the FFTs used in the cross correlation depends on the size of the input parameters and their memory alignment.

The FFTW library modifies the arguments to compute the optimal execution plan, do not assume the values are unmodified.

Parameters

a	multi array timeseries
b	multi array timeseries

Definition at line 84 of file time_delay_estimator_many.hpp.

References jb::detail::array_shape().

Member Function Documentation

◆ estimate_delay()

template<typename array_t >

void jb::fftw::time_delay_estimator_many< array_t >::estimate_delay	(	confidence_type &	confidence,
		estimated_delay_type &	estimated_delay,
		array_type &	a,
		array_type &	b,
		sum2_type const &	sum2
	)

inline

Compute the time-delay estimate between two timeseries a and b.

Parameters

confidence	to return the TDE(a,b) confidence
estimated_delay	to return the result of TDE(a,b)
a	input timeseries, FFTW library might modify their values
b	input timeseries, FFTW library might modify their values
sum2	contains sqr sum of one of the timeseries (a or b)