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https://github.com/slsdetectorgroup/aare.git
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Added fitting, fixed roi etc (#129)
Co-authored-by: Patrick <patrick.sieberer@psi.ch> Co-authored-by: JulianHeymes <julian.heymes@psi.ch>
This commit is contained in:
Erik Fröjdh
300
src/Fit.cpp
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300
src/Fit.cpp
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#include "aare/Fit.hpp"
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#include "aare/utils/task.hpp"
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#include <lmcurve2.h>
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#include <lmfit.hpp>
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#include <thread>
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namespace aare {
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namespace func {
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double gaus(const double x, const double *par) {
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return par[0] * exp(-pow(x - par[1], 2) / (2 * pow(par[2], 2)));
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}
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NDArray<double, 1> gaus(NDView<double, 1> x, NDView<double, 1> par) {
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NDArray<double, 1> y({x.shape(0)}, 0);
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for (size_t i = 0; i < x.size(); i++) {
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y(i) = gaus(x(i), par.data());
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}
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return y;
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}
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double pol1(const double x, const double *par) { return par[0] * x + par[1]; }
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NDArray<double, 1> pol1(NDView<double, 1> x, NDView<double, 1> par) {
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NDArray<double, 1> y({x.shape()}, 0);
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for (size_t i = 0; i < x.size(); i++) {
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y(i) = pol1(x(i), par.data());
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}
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return y;
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}
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} // namespace func
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NDArray<double, 1> fit_gaus(NDView<double, 1> x, NDView<double, 1> y) {
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NDArray<double, 1> result({3}, 0);
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lm_control_struct control = lm_control_double;
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// Estimate the initial parameters for the fit
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std::vector<double> start_par{0, 0, 0};
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auto e = std::max_element(y.begin(), y.end());
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auto idx = std::distance(y.begin(), e);
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start_par[0] = *e; // For amplitude we use the maximum value
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start_par[1] =
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x[idx]; // For the mean we use the x value of the maximum value
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// For sigma we estimate the fwhm and divide by 2.35
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// assuming equally spaced x values
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auto delta = x[1] - x[0];
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start_par[2] =
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std::count_if(y.begin(), y.end(),
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[e, delta](double val) { return val > *e / 2; }) *
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delta / 2.35;
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lmfit::result_t res(start_par);
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lmcurve(res.par.size(), res.par.data(), x.size(), x.data(), y.data(),
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aare::func::gaus, &control, &res.status);
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result(0) = res.par[0];
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result(1) = res.par[1];
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result(2) = res.par[2];
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return result;
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}
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NDArray<double, 3> fit_gaus(NDView<double, 1> x, NDView<double, 3> y,
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int n_threads) {
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NDArray<double, 3> result({y.shape(0), y.shape(1), 3}, 0);
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auto process = [&x, &y, &result](ssize_t first_row, ssize_t last_row) {
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for (ssize_t row = first_row; row < last_row; row++) {
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for (ssize_t col = 0; col < y.shape(1); col++) {
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NDView<double, 1> values(&y(row, col, 0), {y.shape(2)});
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auto res = fit_gaus(x, values);
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result(row, col, 0) = res(0);
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result(row, col, 1) = res(1);
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result(row, col, 2) = res(2);
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}
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}
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};
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auto tasks = split_task(0, y.shape(0), n_threads);
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std::vector<std::thread> threads;
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for (auto &task : tasks) {
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threads.push_back(std::thread(process, task.first, task.second));
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}
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for (auto &thread : threads) {
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thread.join();
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}
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return result;
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}
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void fit_gaus(NDView<double, 1> x, NDView<double, 3> y, NDView<double, 3> y_err,
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NDView<double, 3> par_out, NDView<double, 3> par_err_out,
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int n_threads) {
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auto process = [&](ssize_t first_row, ssize_t last_row) {
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for (ssize_t row = first_row; row < last_row; row++) {
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for (ssize_t col = 0; col < y.shape(1); col++) {
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NDView<double, 1> y_view(&y(row, col, 0), {y.shape(2)});
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NDView<double, 1> y_err_view(&y_err(row, col, 0),
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{y_err.shape(2)});
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NDView<double, 1> par_out_view(&par_out(row, col, 0),
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{par_out.shape(2)});
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NDView<double, 1> par_err_out_view(&par_err_out(row, col, 0),
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{par_err_out.shape(2)});
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fit_gaus(x, y_view, y_err_view, par_out_view, par_err_out_view);
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}
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}
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};
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auto tasks = split_task(0, y.shape(0), n_threads);
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std::vector<std::thread> threads;
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for (auto &task : tasks) {
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threads.push_back(std::thread(process, task.first, task.second));
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}
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for (auto &thread : threads) {
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thread.join();
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}
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}
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void fit_gaus(NDView<double, 1> x, NDView<double, 1> y, NDView<double, 1> y_err,
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NDView<double, 1> par_out, NDView<double, 1> par_err_out) {
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// Check that we have the correct sizes
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if (y.size() != x.size() || y.size() != y_err.size() ||
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par_out.size() != 3 || par_err_out.size() != 3) {
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throw std::runtime_error("Data, x, data_err must have the same size "
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"and par_out, par_err_out must have size 3");
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}
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lm_control_struct control = lm_control_double;
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// Estimate the initial parameters for the fit
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std::vector<double> start_par{0, 0, 0};
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std::vector<double> start_par_err{0, 0, 0};
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std::vector<double> start_cov{0, 0, 0, 0, 0, 0, 0, 0, 0};
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auto e = std::max_element(y.begin(), y.end());
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auto idx = std::distance(y.begin(), e);
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start_par[0] = *e; // For amplitude we use the maximum value
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start_par[1] =
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x[idx]; // For the mean we use the x value of the maximum value
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// For sigma we estimate the fwhm and divide by 2.35
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// assuming equally spaced x values
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auto delta = x[1] - x[0];
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start_par[2] =
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std::count_if(y.begin(), y.end(),
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[e, delta](double val) { return val > *e / 2; }) *
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delta / 2.35;
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lmfit::result_t res(start_par);
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lmfit::result_t res_err(start_par_err);
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lmfit::result_t cov(start_cov);
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// TODO can we make lmcurve write the result directly where is should be?
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lmcurve2(res.par.size(), res.par.data(), res_err.par.data(), cov.par.data(),
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x.size(), x.data(), y.data(), y_err.data(), aare::func::gaus,
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&control, &res.status);
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par_out(0) = res.par[0];
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par_out(1) = res.par[1];
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par_out(2) = res.par[2];
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par_err_out(0) = res_err.par[0];
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par_err_out(1) = res_err.par[1];
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par_err_out(2) = res_err.par[2];
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}
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void fit_pol1(NDView<double, 1> x, NDView<double, 1> y, NDView<double, 1> y_err,
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NDView<double, 1> par_out, NDView<double, 1> par_err_out) {
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// Check that we have the correct sizes
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if (y.size() != x.size() || y.size() != y_err.size() ||
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par_out.size() != 2 || par_err_out.size() != 2) {
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throw std::runtime_error("Data, x, data_err must have the same size "
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"and par_out, par_err_out must have size 2");
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}
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lm_control_struct control = lm_control_double;
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// Estimate the initial parameters for the fit
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std::vector<double> start_par{0, 0};
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std::vector<double> start_par_err{0, 0};
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std::vector<double> start_cov{0, 0, 0, 0};
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auto y2 = std::max_element(y.begin(), y.end());
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auto x2 = x[std::distance(y.begin(), y2)];
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auto y1 = std::min_element(y.begin(), y.end());
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auto x1 = x[std::distance(y.begin(), y1)];
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start_par[0] =
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(*y2 - *y1) / (x2 - x1); // For amplitude we use the maximum value
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start_par[1] =
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*y1 - ((*y2 - *y1) / (x2 - x1)) *
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x1; // For the mean we use the x value of the maximum value
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lmfit::result_t res(start_par);
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lmfit::result_t res_err(start_par_err);
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lmfit::result_t cov(start_cov);
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lmcurve2(res.par.size(), res.par.data(), res_err.par.data(), cov.par.data(),
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x.size(), x.data(), y.data(), y_err.data(), aare::func::pol1,
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&control, &res.status);
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par_out(0) = res.par[0];
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par_out(1) = res.par[1];
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par_err_out(0) = res_err.par[0];
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par_err_out(1) = res_err.par[1];
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}
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void fit_pol1(NDView<double, 1> x, NDView<double, 3> y, NDView<double, 3> y_err,
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NDView<double, 3> par_out, NDView<double, 3> par_err_out,
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int n_threads) {
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auto process = [&](ssize_t first_row, ssize_t last_row) {
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for (ssize_t row = first_row; row < last_row; row++) {
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for (ssize_t col = 0; col < y.shape(1); col++) {
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NDView<double, 1> y_view(&y(row, col, 0), {y.shape(2)});
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NDView<double, 1> y_err_view(&y_err(row, col, 0),
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{y_err.shape(2)});
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NDView<double, 1> par_out_view(&par_out(row, col, 0),
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{par_out.shape(2)});
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NDView<double, 1> par_err_out_view(&par_err_out(row, col, 0),
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{par_err_out.shape(2)});
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fit_pol1(x, y_view, y_err_view, par_out_view, par_err_out_view);
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}
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}
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};
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auto tasks = split_task(0, y.shape(0), n_threads);
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std::vector<std::thread> threads;
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for (auto &task : tasks) {
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threads.push_back(std::thread(process, task.first, task.second));
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}
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for (auto &thread : threads) {
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thread.join();
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}
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}
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NDArray<double, 1> fit_pol1(NDView<double, 1> x, NDView<double, 1> y) {
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// // Check that we have the correct sizes
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// if (y.size() != x.size() || y.size() != y_err.size() ||
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// par_out.size() != 2 || par_err_out.size() != 2) {
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// throw std::runtime_error("Data, x, data_err must have the same size "
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// "and par_out, par_err_out must have size 2");
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// }
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NDArray<double, 1> par({2}, 0);
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lm_control_struct control = lm_control_double;
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// Estimate the initial parameters for the fit
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std::vector<double> start_par{0, 0};
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auto y2 = std::max_element(y.begin(), y.end());
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auto x2 = x[std::distance(y.begin(), y2)];
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auto y1 = std::min_element(y.begin(), y.end());
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auto x1 = x[std::distance(y.begin(), y1)];
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start_par[0] = (*y2 - *y1) / (x2 - x1);
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start_par[1] = *y1 - ((*y2 - *y1) / (x2 - x1)) * x1;
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lmfit::result_t res(start_par);
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lmcurve(res.par.size(), res.par.data(), x.size(), x.data(), y.data(),
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aare::func::pol1, &control, &res.status);
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par(0) = res.par[0];
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par(1) = res.par[1];
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return par;
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}
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NDArray<double, 3> fit_pol1(NDView<double, 1> x, NDView<double, 3> y,
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int n_threads) {
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NDArray<double, 3> result({y.shape(0), y.shape(1), 2}, 0);
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auto process = [&](ssize_t first_row, ssize_t last_row) {
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for (ssize_t row = first_row; row < last_row; row++) {
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for (ssize_t col = 0; col < y.shape(1); col++) {
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NDView<double, 1> values(&y(row, col, 0), {y.shape(2)});
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auto res = fit_pol1(x, values);
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result(row, col, 0) = res(0);
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result(row, col, 1) = res(1);
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}
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}
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};
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auto tasks = split_task(0, y.shape(0), n_threads);
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std::vector<std::thread> threads;
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for (auto &task : tasks) {
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threads.push_back(std::thread(process, task.first, task.second));
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}
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for (auto &thread : threads) {
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thread.join();
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}
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return result;
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}
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} // namespace aare
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