kopia lustrzana https://github.com/vsamy/DiFipp
198 wiersze
6.2 KiB
C++
198 wiersze
6.2 KiB
C++
// Copyright (c) 2019, Vincent SAMY
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// All rights reserved.
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// Redistribution and use in source and binary forms, with or without
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// modification, are permitted provided that the following conditions are met:
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// 1. Redistributions of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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// 2. Redistributions in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
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// ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
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// WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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// DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
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// ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
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// (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
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// LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
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// ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
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// SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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// The views and conclusions contained in the software and documentation are those
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// of the authors and should not be interpreted as representing official policies,
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// either expressed or implied, of the FreeBSD Project.
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#pragma once
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#include "typedefs.h"
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#include <array>
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#include <cmath>
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#include <tuple>
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template <typename T>
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using FunctionGenerator = std::tuple<difi::vectX_t<T>, difi::vectX_t<T>, difi::vectX_t<T>>;
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template <typename T>
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FunctionGenerator<T> sinGenerator(int nrSteps, T amplitude, T frequency, T dt)
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{
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using namespace difi;
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vectX_t<T> f(nrSteps);
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vectX_t<T> df(nrSteps);
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vectX_t<T> ddf(nrSteps);
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for (int i = 0; i < nrSteps; ++i) {
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// function
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f(i) = amplitude * std::sin(2 * pi<T> * frequency * i * dt);
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// derivative 1
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df(i) = 2 * pi<T> * frequency * amplitude * std::cos(2 * pi<T> * frequency * i * dt);
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// derivative 2
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ddf(i) = -4 * pi<T> * pi<T> * frequency * frequency * amplitude * std::sin(2 * pi<T> * frequency * i * dt);
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}
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return { f, df, ddf };
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}
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template <typename T, size_t N>
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FunctionGenerator<T> polyGenerator(int nrSteps, std::array<T, N> coeffs, T dt)
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{
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using namespace difi;
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static_assert(N >= 2, "N must be superior to 20");
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auto computePoly = [](const auto& coeffs, T time) {
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auto recursiveComputation = [time, &coeffs](size_t i, T result, const auto& self) -> T {
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if (i > 0)
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return self(i - 1, time * result + coeffs[i - 1], self);
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else
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return result;
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};
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return recursiveComputation(coeffs.size(), 0, recursiveComputation);
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};
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std::array<T, N - 1> dCoeffs;
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for (Eigen::Index i = 1; i < N; ++i)
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dCoeffs[i - 1] = i * coeffs[i];
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std::array<T, N - 2> ddCoeffs;
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for (Eigen::Index i = 1; i < N - 1; ++i)
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ddCoeffs[i - 1] = i * dCoeffs[i];
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vectX_t<T> f(nrSteps);
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vectX_t<T> df(nrSteps);
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vectX_t<T> ddf(nrSteps);
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for (int i = 0; i < nrSteps; ++i) {
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// function
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f(i) = computePoly(coeffs, i * dt);
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// derivative 1
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df(i) = computePoly(dCoeffs, i * dt);
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// derivative 2
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ddf(i) = computePoly(ddCoeffs, i * dt);
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}
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return { f, df, ddf };
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}
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// TV sin generator
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template <typename T>
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using TVFunctionGenerator = std::tuple<difi::vectX_t<T>, difi::vectX_t<T>, difi::vectX_t<T>, difi::vectX_t<T>>;
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template <typename T>
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TVFunctionGenerator<T> tvSinGenerator(int nrSteps, T amplitude, T frequency, T meanDt)
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{
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using namespace difi;
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vectX_t<T> t(nrSteps);
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vectX_t<T> f(nrSteps);
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vectX_t<T> df(nrSteps);
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vectX_t<T> ddf(nrSteps);
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t(0) = T(0);
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f(0) = T(0);
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df(0) = 2 * pi<T> * frequency * amplitude;
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ddf(0) = T(0);
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for (int i = 1; i < nrSteps; ++i) {
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// time
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if (i % 3 == 0)
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t(i) = t(i - 1) + 0.9 * meanDt;
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else if (i % 3 == 1)
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t(i) = t(i - 1) + meanDt;
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else
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t(i) = t(i - 1) + 1.1 * meanDt;
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// function
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f(i) = amplitude * std::sin(2 * pi<T> * frequency * t(i));
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// derivative 1
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df(i) = 2 * pi<T> * frequency * amplitude * std::cos(2 * pi<T> * frequency * t(i));
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// derivative 2
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ddf(i) = -4 * pi<T> * pi<T> * frequency * frequency * amplitude * std::sin(2 * pi<T> * frequency * t(i));
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}
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return { t, f, df, ddf };
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}
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template <typename T, size_t N>
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TVFunctionGenerator<T> tvPolyGenerator(int nrSteps, std::array<T, N> coeffs, T meanDt)
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{
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using namespace difi;
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static_assert(N >= 2, "N must be superior to 20");
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auto computePoly = [](const auto& coeffs, T time) {
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auto recursiveComputation = [time, &coeffs](size_t i, T result, const auto& self) -> T {
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if (i > 0)
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return self(i - 1, time * result + coeffs[i - 1], self);
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else
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return result;
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};
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return recursiveComputation(coeffs.size(), 0, recursiveComputation);
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};
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std::array<T, N - 1> dCoeffs;
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for (Eigen::Index i = 1; i < N; ++i)
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dCoeffs[i - 1] = i * coeffs[i];
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std::array<T, N - 2> ddCoeffs;
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for (Eigen::Index i = 1; i < N - 1; ++i)
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ddCoeffs[i - 1] = i * dCoeffs[i];
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vectX_t<T> t(nrSteps);
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vectX_t<T> f(nrSteps);
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vectX_t<T> df(nrSteps);
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vectX_t<T> ddf(nrSteps);
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t(0) = T(0);
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f(0) = computePoly(coeffs, t(0));
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df(0) = computePoly(dCoeffs, t(0));
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ddf(0) = computePoly(ddCoeffs, t(0));
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for (int i = 1; i < nrSteps; ++i) {
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// time
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if (i % 3 == 0)
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t(i) = t(i - 1) + 0.9 * meanDt;
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else if (i % 3 == 1)
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t(i) = t(i - 1) + meanDt;
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else
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t(i) = t(i - 1) + 1.1 * meanDt;
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// function
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f(i) = computePoly(coeffs, t(i));
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// derivative 1
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df(i) = computePoly(dCoeffs, t(i));
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// derivative 2
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ddf(i) = computePoly(ddCoeffs, t(i));
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}
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return { t, f, df, ddf };
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}
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