Add xtensor fft implementation

docs/source/xfft.rst

@@ -0,0 +1,17 @@
+.. Copyright (c) 2016, Johan Mabille, Sylvain Corlay and Wolf Vollprecht
+   Distributed under the terms of the BSD 3-Clause License.
+   The full license is in the file LICENSE, distributed with this software.
+xfft
+====
+
+Defined in ``xtensor/xfft.hpp``
+
+.. doxygenclass:: xt::fft_convolve
+   :project: xtensor
+   :members:
+
+.. doxygentypedef:: xt::fft
+   :project: xtensor
+
+.. doxygentypedef:: xt::ifft
+   :project: xtensor

include/xtensor/xfft.hpp

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+#ifdef XTENSOR_USE_TBB
+#include <oneapi/tbb.h>
+#endif
+#include <stdexcept>
+
+#include <xtl/xcomplex.hpp>
+
+#include <xtensor/xarray.hpp>
+#include <xtensor/xaxis_slice_iterator.hpp>
+#include <xtensor/xbuilder.hpp>
+#include <xtensor/xcomplex.hpp>
+#include <xtensor/xmath.hpp>
+#include <xtensor/xnoalias.hpp>
+#include <xtensor/xview.hpp>
+
+namespace xt
+{
+    namespace fft
+    {
+        namespace detail
+        {
+            template <
+                class E,
+                typename std::enable_if<xtl::is_complex<typename std::decay<E>::type::value_type>::value, bool>::type = true>
+            inline auto radix2(E&& e)
+            {
+                using namespace xt::placeholders;
+                using namespace std::complex_literals;
+                using value_type = typename std::decay_t<E>::value_type;
+                using precision = typename value_type::value_type;
+                auto N = e.size();
+                const bool powerOfTwo = !(N == 0) && !(N & (N - 1));
+                // check for power of 2
+                if (!powerOfTwo || N == 0)
+                {
+                    // TODO: Replace implementation with dft
+                    XTENSOR_THROW(std::runtime_error, "FFT Implementation requires power of 2");
+                }
+                auto pi = xt::numeric_constants<precision>::PI;
+                xt::xtensor<value_type, 1> ev = e;
+                if (N <= 1)
+                {
+                    return ev;
+                }
+                else
+                {
+#ifdef XTENSOR_USE_TBB
+                    xt::xtensor<value_type, 1> even;
+                    xt::xtensor<value_type, 1> odd;
+                    oneapi::tbb::parallel_invoke(
+                        [&]
+                        {
+                            even = radix2(xt::view(ev, xt::range(0, _, 2)));
+                        },
+                        [&]
+                        {
+                            odd = radix2(xt::view(ev, xt::range(1, _, 2)));
+                        }
+                    );
+#else
+                    auto even = radix2(xt::view(ev, xt::range(0, _, 2)));
+                    auto odd = radix2(xt::view(ev, xt::range(1, _, 2)));
+#endif
+
+                    auto range = xt::arange<double>(N / 2);
+                    auto exp = xt::exp(static_cast<value_type>(-2i) * pi * range / N);
+                    auto t = exp * odd;
+                    auto first_half = even + t;
+                    auto second_half = even - t;
+                    // TODO: should be a call to stack if performance was improved
+                    auto spectrum = xt::xtensor<value_type, 1>::from_shape({N});
+                    xt::view(spectrum, xt::range(0, N / 2)) = first_half;
+                    xt::view(spectrum, xt::range(N / 2, N)) = second_half;
+                    return spectrum;
+                }
+            }
+
+            template <typename E>
+            auto transform_bluestein(E&& data)
+            {
+                using value_type = typename std::decay_t<E>::value_type;
+                using precision = typename value_type::value_type;
+
+                // Find a power-of-2 convolution length m such that m >= n * 2 + 1
+                const std::size_t n = data.size();
+                size_t m = std::ceil(std::log2(n * 2 + 1));
+                m = std::pow(2, m);
+
+                // Trignometric table
+                auto exp_table = xt::xtensor<std::complex<precision>, 1>::from_shape({n});
+                xt::xtensor<std::size_t, 1> i = xt::pow(xt::linspace<std::size_t>(0, n - 1, n), 2);
+                i %= (n * 2);
+
+                auto angles = xt::eval(precision{3.141592653589793238463} * i / n);
+                auto j = std::complex<precision>(0, 1);
+                exp_table = xt::exp(-angles * j);
+
+                // Temporary vectors and preprocessing
+                auto av = xt::empty<std::complex<precision>>({m});
+                xt::view(av, xt::range(0, n)) = data * exp_table;
+
+
+                auto bv = xt::empty<std::complex<precision>>({m});
+                xt::view(bv, xt::range(0, n)) = ::xt::conj(exp_table);
+                xt::view(bv, xt::range(-n + 1, xt::placeholders::_)) = xt::view(
+                    ::xt::conj(xt::flip(exp_table)),
+                    xt::range(xt::placeholders::_, -1)
+                );
+
+                // Convolution
+                auto xv = radix2(av);
+                auto yv = radix2(bv);
+                auto spectrum_k = xv * yv;
+                auto complex_args = xt::conj(spectrum_k);
+                auto fft_res = radix2(complex_args);
+                auto cv = xt::conj(fft_res) / m;
+
+                return xt::eval(xt::view(cv, xt::range(0, n)) * exp_table);
+            }
+        }  // namespace detail
+
+        /**
+         * @brief 1D FFT of an Nd array along a specified axis
+         * @param e an Nd expression to be transformed to the fourier domain
+         * @param axis the axis along which to perform the 1D FFT
+         * @return a transformed xarray of the specified precision
+         */
+        template <
+            class E,
+            typename std::enable_if<xtl::is_complex<typename std::decay<E>::type::value_type>::value, bool>::type = true>
+        inline auto fft(E&& e, std::ptrdiff_t axis = -1)
+        {
+            using value_type = typename std::decay_t<E>::value_type;
+            using precision = typename value_type::value_type;
+            const auto saxis = xt::normalize_axis(e.dimension(), axis);
+            const size_t N = e.shape(saxis);
+            const bool powerOfTwo = !(N == 0) && !(N & (N - 1));
+            xt::xarray<std::complex<precision>> out = xt::eval(e);
+            auto begin = xt::axis_slice_begin(out, saxis);
+            auto end = xt::axis_slice_end(out, saxis);
+            for (auto iter = begin; iter != end; iter++)
+            {
+                if (powerOfTwo)
+                {
+                    xt::noalias(*iter) = detail::radix2(*iter);
+                }
+                else
+                {
+                    xt::noalias(*iter) = detail::transform_bluestein(*iter);
+                }
+            }
+            return out;
+        }
+
+        /**
+         * @breif 1D FFT of an Nd array along a specified axis
+         * @param e an Nd expression to be transformed to the fourier domain
+         * @param axis the axis along which to perform the 1D FFT
+         * @return a transformed xarray of the specified precision
+         */
+        template <
+            class E,
+            typename std::enable_if<!xtl::is_complex<typename std::decay<E>::type::value_type>::value, bool>::type = true>
+        inline auto fft(E&& e, std::ptrdiff_t axis = -1)
+        {
+            using value_type = typename std::decay<E>::type::value_type;
+            return fft(xt::cast<std::complex<value_type>>(e), axis);
+        }
+
+        template <
+            class E,
+            typename std::enable_if<xtl::is_complex<typename std::decay<E>::type::value_type>::value, bool>::type = true>
+        auto ifft(E&& e, std::ptrdiff_t axis = -1)
+        {
+            // check the length of the data on that axis
+            const std::size_t n = e.shape(axis);
+            if (n == 0)
+            {
+                XTENSOR_THROW(std::runtime_error, "Cannot take the iFFT along an empty dimention");
+            }
+            auto complex_args = xt::conj(e);
+            auto fft_res = xt::fft::fft(complex_args, axis);
+            fft_res = xt::conj(fft_res);
+            return fft_res;
+        }
+
+        template <
+            class E,
+            typename std::enable_if<!xtl::is_complex<typename std::decay<E>::type::value_type>::value, bool>::type = true>
+        inline auto ifft(E&& e, std::ptrdiff_t axis = -1)
+        {
+            using value_type = typename std::decay<E>::type::value_type;
+            return ifft(xt::cast<std::complex<value_type>>(e), axis);
+        }
+
+        /*
+         * @brief performs a circular fft convolution xvec and yvec must
+         *        be the same shape.
+         * @param xvec first array of the convolution
+         * @param yvec second array of the convolution
+         * @param axis axis along which to perform the convolution
+         */
+        template <typename E1, typename E2>
+        auto convolve(E1&& xvec, E2&& yvec, std::ptrdiff_t axis = -1)
+        {
+            // we could broadcast but that could get complicated???
+            if (xvec.dimension() != yvec.dimension())
+            {
+                XTENSOR_THROW(std::runtime_error, "Mismatched dimentions");
+            }
+
+            auto saxis = xt::normalize_axis(xvec.dimension(), axis);
+            if (xvec.shape(saxis) != yvec.shape(saxis))
+            {
+                XTENSOR_THROW(std::runtime_error, "Mismatched lengths along slice axis");
+            }
+
+            const std::size_t n = xvec.shape(saxis);
+
+            auto xv = fft(xvec, axis);
+            auto yv = fft(yvec, axis);
+
+            auto begin_x = xt::axis_slice_begin(xv, saxis);
+            auto end_x = xt::axis_slice_end(xv, saxis);
+            auto iter_y = xt::axis_slice_begin(yv, saxis);
+
+            for (auto iter = begin_x; iter != end_x; iter++)
+            {
+                (*iter) = (*iter_y++) * (*iter);
+            }
+
+            auto outvec = ifft(xv, axis);
+
+            // Scaling (because this FFT implementation omits it)
+            outvec = outvec / n;
+
+            return outvec;
+        }
+
+    }
+}  // namespace xt::fft

test/test_xfft.cpp

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+#include "xtensor/xarray.hpp"
+#include "xtensor/xfft.hpp"
+
+#include "test_common_macros.hpp"
+
+namespace xt
+{
+    TEST(xfft, fft_power_2)
+    {
+        size_t k = 2;
+        size_t n = 8192;
+        size_t A = 10;
+        auto x = xt::linspace<float>(0, static_cast<float>(n - 1), n);
+        xt::xarray<float> y = A * xt::sin(2 * xt::numeric_constants<float>::PI * x * k / n);
+        auto res = xt::fft::fft(y) / (n / 2);
+        REQUIRE(A == doctest::Approx(std::abs(res(k))).epsilon(.0001));
+    }
+
+    TEST(xfft, ifft_power_2)
+    {
+        size_t k = 2;
+        size_t n = 8;
+        size_t A = 10;
+        auto x = xt::linspace<float>(0, static_cast<float>(n - 1), n);
+        xt::xarray<float> y = A * xt::sin(2 * xt::numeric_constants<float>::PI * x * k / n);
+        auto res = xt::fft::ifft(y) / (n / 2);
+        REQUIRE(A == doctest::Approx(std::abs(res(k))).epsilon(.0001));
+    }
+
+    TEST(xfft, convolve_power_2)
+    {
+        xt::xarray<float> x = {1.0, 1.0, 1.0, 5.0};
+        xt::xarray<float> y = {5.0, 1.0, 1.0, 1.0};
+        xt::xarray<float> expected = {12, 12, 12, 28};
+
+        auto result = xt::fft::convolve(x, y);
+
+        for (size_t i = 0; i < x.size(); i++)
+        {
+            REQUIRE(expected(i) == doctest::Approx(std::abs(result(i))).epsilon(.0001));
+        }
+    }
+
+    TEST(xfft, fft_n_0_axis)
+    {
+        size_t k = 2;
+        size_t n = 10;
+        size_t A = 1;
+        size_t dim = 10;
+        auto x = xt::linspace<float>(0, n - 1, n) * xt::ones<float>({dim, n});
+        xt::xarray<float> y = A * xt::sin(2 * xt::numeric_constants<float>::PI * x * k / n);
+        y = xt::transpose(y);
+        auto res = xt::fft::fft(y, 0) / (n / 2.0);
+        REQUIRE(A == doctest::Approx(std::abs(res(k, 0))).epsilon(.0001));
+        REQUIRE(A == doctest::Approx(std::abs(res(k, 1))).epsilon(.0001));
+    }
+
+    TEST(xfft, fft_n_1_axis)
+    {
+        size_t k = 2;
+        size_t n = 15;
+        size_t A = 1;
+        size_t dim = 2;
+        auto x = xt::linspace<float>(0, n - 1, n) * xt::ones<float>({dim, n});
+        xt::xarray<float> y = A * xt::sin(2 * xt::numeric_constants<float>::PI * x * k / n);
+        auto res = xt::fft::fft(y) / (n / 2.0);
+        REQUIRE(A == doctest::Approx(std::abs(res(0, k))).epsilon(.0001));
+        REQUIRE(A == doctest::Approx(std::abs(res(1, k))).epsilon(.0001));
+    }
+
+    TEST(xfft, convolve_n)
+    {
+        xt::xarray<float> x = {1.0, 1.0, 1.0, 5.0, 1.0};
+        xt::xarray<float> y = {5.0, 1.0, 1.0, 1.0, 1.0};
+        xt::xarray<size_t> expected = {13, 13, 13, 29, 13};
+
+        auto result = xt::fft::convolve(x, y);
+
+        xt::xarray<float> abs = xt::abs(result);
+
+        for (size_t i = 0; i < abs.size(); i++)
+        {
+            REQUIRE(expected(i) == doctest::Approx(abs(i)).epsilon(.0001));
+        }
+    }
+}