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formatted main branch

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mazzol_a
2025-06-10 16:09:06 +02:00
parent efd2338f54
commit f9751902a2
87 changed files with 1710 additions and 1639 deletions
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238
src/Fit.cpp
View File

@ -1,13 +1,12 @@
#include "aare/Fit.hpp"
#include "aare/utils/task.hpp"
#include "aare/utils/par.hpp"
#include "aare/utils/task.hpp"
#include <lmcurve2.h>
#include <lmfit.hpp>
#include <thread>
#include <array>
namespace aare {
namespace func {
@ -34,8 +33,10 @@ NDArray<double, 1> pol1(NDView<double, 1> x, NDView<double, 1> par) {
return y;
}
double scurve(const double x, const double * par) {
return (par[0] + par[1] * x) + 0.5 * (1 + erf((x - par[2]) / (sqrt(2) * par[3]))) * (par[4] + par[5] * (x - par[2]));
double scurve(const double x, const double *par) {
return (par[0] + par[1] * x) +
0.5 * (1 + erf((x - par[2]) / (sqrt(2) * par[3]))) *
(par[4] + par[5] * (x - par[2]));
}
NDArray<double, 1> scurve(NDView<double, 1> x, NDView<double, 1> par) {
@ -46,8 +47,10 @@ NDArray<double, 1> scurve(NDView<double, 1> x, NDView<double, 1> par) {
return y;
}
double scurve2(const double x, const double * par) {
return (par[0] + par[1] * x) + 0.5 * (1 - erf((x - par[2]) / (sqrt(2) * par[3]))) * (par[4] + par[5] * (x - par[2]));
double scurve2(const double x, const double *par) {
return (par[0] + par[1] * x) +
0.5 * (1 - erf((x - par[2]) / (sqrt(2) * par[3]))) *
(par[4] + par[5] * (x - par[2]));
}
NDArray<double, 1> scurve2(NDView<double, 1> x, NDView<double, 1> par) {
@ -91,7 +94,8 @@ NDArray<double, 3> fit_gaus(NDView<double, 1> x, NDView<double, 3> y,
return result;
}
std::array<double, 3> gaus_init_par(const NDView<double, 1> x, const NDView<double, 1> y) {
std::array<double, 3> gaus_init_par(const NDView<double, 1> x,
const NDView<double, 1> y) {
std::array<double, 3> start_par{0, 0, 0};
auto e = std::max_element(y.begin(), y.end());
auto idx = std::distance(y.begin(), e);
@ -103,31 +107,29 @@ std::array<double, 3> gaus_init_par(const NDView<double, 1> x, const NDView<doub
// For sigma we estimate the fwhm and divide by 2.35
// assuming equally spaced x values
auto delta = x[1] - x[0];
start_par[2] =
std::count_if(y.begin(), y.end(),
[e](double val) { return val > *e / 2; }) *
delta / 2.35;
start_par[2] = std::count_if(y.begin(), y.end(),
[e](double val) { return val > *e / 2; }) *
delta / 2.35;
return start_par;
}
std::array<double, 2> pol1_init_par(const NDView<double, 1> x,
const NDView<double, 1> y) {
// Estimate the initial parameters for the fit
std::array<double, 2> start_par{0, 0};
std::array<double, 2> pol1_init_par(const NDView<double, 1> x, const NDView<double, 1> y){
// Estimate the initial parameters for the fit
std::array<double, 2> start_par{0, 0};
auto y2 = std::max_element(y.begin(), y.end());
auto x2 = x[std::distance(y.begin(), y2)];
auto y1 = std::min_element(y.begin(), y.end());
auto x1 = x[std::distance(y.begin(), y1)];
auto y2 = std::max_element(y.begin(), y.end());
auto x2 = x[std::distance(y.begin(), y2)];
auto y1 = std::min_element(y.begin(), y.end());
auto x1 = x[std::distance(y.begin(), y1)];
start_par[0] =
(*y2 - *y1) / (x2 - x1); // For amplitude we use the maximum value
start_par[1] =
*y1 - ((*y2 - *y1) / (x2 - x1)) *
x1; // For the mean we use the x value of the maximum value
return start_par;
start_par[0] =
(*y2 - *y1) / (x2 - x1); // For amplitude we use the maximum value
start_par[1] =
*y1 - ((*y2 - *y1) / (x2 - x1)) *
x1; // For the mean we use the x value of the maximum value
return start_par;
}
void fit_gaus(NDView<double, 1> x, NDView<double, 1> y, NDView<double, 1> y_err,
@ -141,7 +143,6 @@ void fit_gaus(NDView<double, 1> x, NDView<double, 1> y, NDView<double, 1> y_err,
"and par_out, par_err_out must have size 3");
}
// /* Collection of output parameters for status info. */
// typedef struct {
// double fnorm; /* norm of the residue vector fvec. */
@ -153,23 +154,32 @@ void fit_gaus(NDView<double, 1> x, NDView<double, 1> y, NDView<double, 1> y_err,
// */
// } lm_status_struct;
lm_status_struct status;
par_out = gaus_init_par(x, y);
std::array<double, 9> cov{0, 0, 0, 0, 0, 0, 0 , 0 , 0};
std::array<double, 9> cov{0, 0, 0, 0, 0, 0, 0, 0, 0};
// void lmcurve2( const int n_par, double *par, double *parerr, double *covar, const int m_dat, const double *t, const double *y, const double *dy, double (*f)( const double ti, const double *par ), const lm_control_struct *control, lm_status_struct *status);
// n_par - Number of free variables. Length of parameter vector par.
// par - Parameter vector. On input, it must contain a reasonable guess. On output, it contains the solution found to minimize ||r||.
// parerr - Parameter uncertainties vector. Array of length n_par or NULL. On output, unless it or covar is NULL, it contains the weighted parameter uncertainties for the found parameters.
// covar - Covariance matrix. Array of length n_par * n_par or NULL. On output, unless it is NULL, it contains the covariance matrix.
// m_dat - Number of data points. Length of vectors t, y, dy. Must statisfy n_par <= m_dat.
// t - Array of length m_dat. Contains the abcissae (time, or "x") for which function f will be evaluated.
// y - Array of length m_dat. Contains the ordinate values that shall be fitted.
// dy - Array of length m_dat. Contains the standard deviations of the values y.
// f - A user-supplied parametric function f(ti;par).
// control - Parameter collection for tuning the fit procedure. In most cases, the default &lm_control_double is adequate. If f is only computed with single-precision accuracy, &lm_control_float should be used. Parameters are explained in lmmin2(3).
// status - A record used to return information about the minimization process: For details, see lmmin2(3).
// void lmcurve2( const int n_par, double *par, double *parerr, double
// *covar, const int m_dat, const double *t, const double *y, const double
// *dy, double (*f)( const double ti, const double *par ), const
// lm_control_struct *control, lm_status_struct *status); n_par - Number of
// free variables. Length of parameter vector par. par - Parameter vector.
// On input, it must contain a reasonable guess. On output, it contains the
// solution found to minimize ||r||. parerr - Parameter uncertainties
// vector. Array of length n_par or NULL. On output, unless it or covar is
// NULL, it contains the weighted parameter uncertainties for the found
// parameters. covar - Covariance matrix. Array of length n_par * n_par or
// NULL. On output, unless it is NULL, it contains the covariance matrix.
// m_dat - Number of data points. Length of vectors t, y, dy. Must statisfy
// n_par <= m_dat. t - Array of length m_dat. Contains the abcissae (time,
// or "x") for which function f will be evaluated. y - Array of length
// m_dat. Contains the ordinate values that shall be fitted. dy - Array of
// length m_dat. Contains the standard deviations of the values y. f - A
// user-supplied parametric function f(ti;par). control - Parameter
// collection for tuning the fit procedure. In most cases, the default
// &lm_control_double is adequate. If f is only computed with
// single-precision accuracy, &lm_control_float should be used. Parameters
// are explained in lmmin2(3). status - A record used to return information
// about the minimization process: For details, see lmmin2(3).
lmcurve2(par_out.size(), par_out.data(), par_err_out.data(), cov.data(),
x.size(), x.data(), y.data(), y_err.data(), aare::func::gaus,
@ -178,12 +188,14 @@ void fit_gaus(NDView<double, 1> x, NDView<double, 1> y, NDView<double, 1> y_err,
// Calculate chi2
chi2 = 0;
for (ssize_t i = 0; i < y.size(); i++) {
chi2 += std::pow((y(i) - func::gaus(x(i), par_out.data())) / y_err(i), 2);
chi2 +=
std::pow((y(i) - func::gaus(x(i), par_out.data())) / y_err(i), 2);
}
}
void fit_gaus(NDView<double, 1> x, NDView<double, 3> y, NDView<double, 3> y_err,
NDView<double, 3> par_out, NDView<double, 3> par_err_out, NDView<double, 2> chi2_out,
NDView<double, 3> par_out, NDView<double, 3> par_err_out,
NDView<double, 2> chi2_out,
int n_threads) {
@ -197,10 +209,9 @@ void fit_gaus(NDView<double, 1> x, NDView<double, 3> y, NDView<double, 3> y_err,
{par_out.shape(2)});
NDView<double, 1> par_err_out_view(&par_err_out(row, col, 0),
{par_err_out.shape(2)});
fit_gaus(x, y_view, y_err_view, par_out_view, par_err_out_view,
chi2_out(row, col));
}
}
};
@ -210,7 +221,8 @@ void fit_gaus(NDView<double, 1> x, NDView<double, 3> y, NDView<double, 3> y_err,
}
void fit_pol1(NDView<double, 1> x, NDView<double, 1> y, NDView<double, 1> y_err,
NDView<double, 1> par_out, NDView<double, 1> par_err_out, double& chi2) {
NDView<double, 1> par_out, NDView<double, 1> par_err_out,
double &chi2) {
// Check that we have the correct sizes
if (y.size() != x.size() || y.size() != y_err.size() ||
@ -230,13 +242,14 @@ void fit_pol1(NDView<double, 1> x, NDView<double, 1> y, NDView<double, 1> y_err,
// Calculate chi2
chi2 = 0;
for (ssize_t i = 0; i < y.size(); i++) {
chi2 += std::pow((y(i) - func::pol1(x(i), par_out.data())) / y_err(i), 2);
chi2 +=
std::pow((y(i) - func::pol1(x(i), par_out.data())) / y_err(i), 2);
}
}
void fit_pol1(NDView<double, 1> x, NDView<double, 3> y, NDView<double, 3> y_err,
NDView<double, 3> par_out, NDView<double, 3> par_err_out, NDView<double, 2> chi2_out,
int n_threads) {
NDView<double, 3> par_out, NDView<double, 3> par_err_out,
NDView<double, 2> chi2_out, int n_threads) {
auto process = [&](ssize_t first_row, ssize_t last_row) {
for (ssize_t row = first_row; row < last_row; row++) {
@ -249,15 +262,14 @@ void fit_pol1(NDView<double, 1> x, NDView<double, 3> y, NDView<double, 3> y_err,
NDView<double, 1> par_err_out_view(&par_err_out(row, col, 0),
{par_err_out.shape(2)});
fit_pol1(x, y_view, y_err_view, par_out_view, par_err_out_view, chi2_out(row, col));
fit_pol1(x, y_view, y_err_view, par_out_view, par_err_out_view,
chi2_out(row, col));
}
}
};
auto tasks = split_task(0, y.shape(0), n_threads);
RunInParallel(process, tasks);
}
NDArray<double, 1> fit_pol1(NDView<double, 1> x, NDView<double, 1> y) {
@ -300,27 +312,29 @@ NDArray<double, 3> fit_pol1(NDView<double, 1> x, NDView<double, 3> y,
// ~~ S-CURVES ~~
// SCURVE --
std::array<double, 6> scurve_init_par(const NDView<double, 1> x, const NDView<double, 1> y){
// Estimate the initial parameters for the fit
std::array<double, 6> start_par{0, 0, 0, 0, 0, 0};
std::array<double, 6> scurve_init_par(const NDView<double, 1> x,
const NDView<double, 1> y) {
// Estimate the initial parameters for the fit
std::array<double, 6> start_par{0, 0, 0, 0, 0, 0};
auto ymax = std::max_element(y.begin(), y.end());
auto ymin = std::min_element(y.begin(), y.end());
start_par[4] = *ymin + (*ymax - *ymin) / 2;
// Find the first x where the corresponding y value is above the threshold (start_par[4])
for (ssize_t i = 0; i < y.size(); ++i) {
if (y[i] >= start_par[4]) {
start_par[2] = x[i];
break; // Exit the loop after finding the first valid x
}
auto ymax = std::max_element(y.begin(), y.end());
auto ymin = std::min_element(y.begin(), y.end());
start_par[4] = *ymin + (*ymax - *ymin) / 2;
// Find the first x where the corresponding y value is above the threshold
// (start_par[4])
for (ssize_t i = 0; i < y.size(); ++i) {
if (y[i] >= start_par[4]) {
start_par[2] = x[i];
break; // Exit the loop after finding the first valid x
}
}
start_par[3] = 2 * sqrt(start_par[2]);
start_par[0] = 100;
start_par[1] = 0.25;
start_par[5] = 1;
return start_par;
start_par[3] = 2 * sqrt(start_par[2]);
start_par[0] = 100;
start_par[1] = 0.25;
start_par[5] = 1;
return start_par;
}
// - No error
@ -334,7 +348,8 @@ NDArray<double, 1> fit_scurve(NDView<double, 1> x, NDView<double, 1> y) {
return result;
}
NDArray<double, 3> fit_scurve(NDView<double, 1> x, NDView<double, 3> y, int n_threads) {
NDArray<double, 3> fit_scurve(NDView<double, 1> x, NDView<double, 3> y,
int n_threads) {
NDArray<double, 3> result({y.shape(0), y.shape(1), 6}, 0);
auto process = [&x, &y, &result](ssize_t first_row, ssize_t last_row) {
@ -358,8 +373,9 @@ NDArray<double, 3> fit_scurve(NDView<double, 1> x, NDView<double, 3> y, int n_th
}
// - Error
void fit_scurve(NDView<double, 1> x, NDView<double, 1> y, NDView<double, 1> y_err,
NDView<double, 1> par_out, NDView<double, 1> par_err_out, double& chi2) {
void fit_scurve(NDView<double, 1> x, NDView<double, 1> y,
NDView<double, 1> y_err, NDView<double, 1> par_out,
NDView<double, 1> par_err_out, double &chi2) {
// Check that we have the correct sizes
if (y.size() != x.size() || y.size() != y_err.size() ||
@ -380,13 +396,15 @@ void fit_scurve(NDView<double, 1> x, NDView<double, 1> y, NDView<double, 1> y_er
// Calculate chi2
chi2 = 0;
for (ssize_t i = 0; i < y.size(); i++) {
chi2 += std::pow((y(i) - func::pol1(x(i), par_out.data())) / y_err(i), 2);
chi2 +=
std::pow((y(i) - func::pol1(x(i), par_out.data())) / y_err(i), 2);
}
}
void fit_scurve(NDView<double, 1> x, NDView<double, 3> y, NDView<double, 3> y_err,
NDView<double, 3> par_out, NDView<double, 3> par_err_out, NDView<double, 2> chi2_out,
int n_threads) {
void fit_scurve(NDView<double, 1> x, NDView<double, 3> y,
NDView<double, 3> y_err, NDView<double, 3> par_out,
NDView<double, 3> par_err_out, NDView<double, 2> chi2_out,
int n_threads) {
auto process = [&](ssize_t first_row, ssize_t last_row) {
for (ssize_t row = first_row; row < last_row; row++) {
@ -399,40 +417,41 @@ void fit_scurve(NDView<double, 1> x, NDView<double, 3> y, NDView<double, 3> y_er
NDView<double, 1> par_err_out_view(&par_err_out(row, col, 0),
{par_err_out.shape(2)});
fit_scurve(x, y_view, y_err_view, par_out_view, par_err_out_view, chi2_out(row, col));
fit_scurve(x, y_view, y_err_view, par_out_view,
par_err_out_view, chi2_out(row, col));
}
}
};
auto tasks = split_task(0, y.shape(0), n_threads);
RunInParallel(process, tasks);
}
// SCURVE2 ---
std::array<double, 6> scurve2_init_par(const NDView<double, 1> x, const NDView<double, 1> y){
// Estimate the initial parameters for the fit
std::array<double, 6> start_par{0, 0, 0, 0, 0, 0};
std::array<double, 6> scurve2_init_par(const NDView<double, 1> x,
const NDView<double, 1> y) {
// Estimate the initial parameters for the fit
std::array<double, 6> start_par{0, 0, 0, 0, 0, 0};
auto ymax = std::max_element(y.begin(), y.end());
auto ymin = std::min_element(y.begin(), y.end());
start_par[4] = *ymin + (*ymax - *ymin) / 2;
// Find the first x where the corresponding y value is above the threshold (start_par[4])
for (ssize_t i = 0; i < y.size(); ++i) {
if (y[i] <= start_par[4]) {
start_par[2] = x[i];
break; // Exit the loop after finding the first valid x
}
auto ymax = std::max_element(y.begin(), y.end());
auto ymin = std::min_element(y.begin(), y.end());
start_par[4] = *ymin + (*ymax - *ymin) / 2;
// Find the first x where the corresponding y value is above the threshold
// (start_par[4])
for (ssize_t i = 0; i < y.size(); ++i) {
if (y[i] <= start_par[4]) {
start_par[2] = x[i];
break; // Exit the loop after finding the first valid x
}
}
start_par[3] = 2 * sqrt(start_par[2]);
start_par[0] = 100;
start_par[1] = 0.25;
start_par[5] = -1;
return start_par;
start_par[3] = 2 * sqrt(start_par[2]);
start_par[0] = 100;
start_par[1] = 0.25;
start_par[5] = -1;
return start_par;
}
// - No error
@ -446,7 +465,8 @@ NDArray<double, 1> fit_scurve2(NDView<double, 1> x, NDView<double, 1> y) {
return result;
}
NDArray<double, 3> fit_scurve2(NDView<double, 1> x, NDView<double, 3> y, int n_threads) {
NDArray<double, 3> fit_scurve2(NDView<double, 1> x, NDView<double, 3> y,
int n_threads) {
NDArray<double, 3> result({y.shape(0), y.shape(1), 6}, 0);
auto process = [&x, &y, &result](ssize_t first_row, ssize_t last_row) {
@ -470,8 +490,9 @@ NDArray<double, 3> fit_scurve2(NDView<double, 1> x, NDView<double, 3> y, int n_t
}
// - Error
void fit_scurve2(NDView<double, 1> x, NDView<double, 1> y, NDView<double, 1> y_err,
NDView<double, 1> par_out, NDView<double, 1> par_err_out, double& chi2) {
void fit_scurve2(NDView<double, 1> x, NDView<double, 1> y,
NDView<double, 1> y_err, NDView<double, 1> par_out,
NDView<double, 1> par_err_out, double &chi2) {
// Check that we have the correct sizes
if (y.size() != x.size() || y.size() != y_err.size() ||
@ -492,13 +513,15 @@ void fit_scurve2(NDView<double, 1> x, NDView<double, 1> y, NDView<double, 1> y_e
// Calculate chi2
chi2 = 0;
for (ssize_t i = 0; i < y.size(); i++) {
chi2 += std::pow((y(i) - func::pol1(x(i), par_out.data())) / y_err(i), 2);
chi2 +=
std::pow((y(i) - func::pol1(x(i), par_out.data())) / y_err(i), 2);
}
}
void fit_scurve2(NDView<double, 1> x, NDView<double, 3> y, NDView<double, 3> y_err,
NDView<double, 3> par_out, NDView<double, 3> par_err_out, NDView<double, 2> chi2_out,
int n_threads) {
void fit_scurve2(NDView<double, 1> x, NDView<double, 3> y,
NDView<double, 3> y_err, NDView<double, 3> par_out,
NDView<double, 3> par_err_out, NDView<double, 2> chi2_out,
int n_threads) {
auto process = [&](ssize_t first_row, ssize_t last_row) {
for (ssize_t row = first_row; row < last_row; row++) {
@ -511,15 +534,14 @@ void fit_scurve2(NDView<double, 1> x, NDView<double, 3> y, NDView<double, 3> y_e
NDView<double, 1> par_err_out_view(&par_err_out(row, col, 0),
{par_err_out.shape(2)});
fit_scurve2(x, y_view, y_err_view, par_out_view, par_err_out_view, chi2_out(row, col));
fit_scurve2(x, y_view, y_err_view, par_out_view,
par_err_out_view, chi2_out(row, col));
}
}
};
auto tasks = split_task(0, y.shape(0), n_threads);
RunInParallel(process, tasks);
}
} // namespace aare