#include <memory_resource>
#include <nk/value/value.hpp>
#include <string>
#include <tuple>

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Functions
Value	nk::signal::conv (const Value &a, const Value &b, const std::string &shape="full", std::pmr::memory_resource *mr=nullptr)
	1-D convolution of two real vectors.
std::tuple< Value, Value >	nk::signal::deconv (const Value &b, const Value &a, std::pmr::memory_resource *mr=nullptr)
	Polynomial long-division: b = conv(a, q) + r.
std::tuple< Value, Value >	nk::signal::xcorr (const Value &x, const Value &y, std::pmr::memory_resource *mr=nullptr)
	Cross-correlation of two real vectors.
std::tuple< Value, Value >	nk::signal::xcorr (const Value &x, std::pmr::memory_resource *mr=nullptr)
	Auto-correlation — equivalent to xcorr(x, x).
std::tuple< Value, Value >	nk::signal::xcov (const Value &x, const Value &y, int maxlag, const std::string &scaleopt, std::pmr::memory_resource *mr=nullptr)
	Cross-covariance of two real vectors.
std::tuple< Value, Value >	nk::signal::xcov (const Value &x, const Value &y, std::pmr::memory_resource *mr=nullptr)
	Cross-covariance with full lags and "none" scaling.
std::tuple< Value, Value >	nk::signal::xcov (const Value &x, std::pmr::memory_resource *mr=nullptr)
	Auto-covariance — equivalent to xcov(x, x).
Value	nk::signal::conv2 (const Value &A, const Value &B, const std::string &shape="full", std::pmr::memory_resource *mr=nullptr)
	2-D convolution of two real matrices.
Value	nk::signal::filter2 (const Value &h, const Value &X, const std::string &shape="same", std::pmr::memory_resource *mr=nullptr)
	2-D filter — filter2(h, X[, shape]).
Value	nk::signal::convn (const Value &A, const Value &B, const std::string &shape="full", std::pmr::memory_resource *mr=nullptr)
	N-D convolution.

Functions