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Merge branch 'main' into dev/strixels/remap_simple

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hinger_v
2026-05-22 18:12:05 +02:00
parent 69da26c3f3 41c47cb54b
commit 47ebbd2267
64 changed files with 7087 additions and 1994 deletions
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src/Fit.cpp
+12 -99
View File
@@ -1,12 +1,14 @@
// SPDX-License-Identifier: MPL-2.0
#include "aare/Fit.hpp"
#include "aare/Chi2.hpp"
#include "aare/Models.hpp"
#include "aare/utils/par.hpp"
#include "aare/utils/task.hpp"
#include <array>
#include <lmcurve2.h>
#include <lmfit.hpp>
#include <thread>
#include <array>
#include <type_traits>
namespace aare {
@@ -65,7 +67,7 @@ NDArray<double, 1> scurve2(NDView<double, 1> x, NDView<double, 1> par) {
} // namespace func
NDArray<double, 1> fit_gaus(NDView<double, 1> x, NDView<double, 1> y) {
NDArray<double, 1> result = gaus_init_par(x, y);
NDArray<double, 1> result = model::Gaussian::estimate_par(x, y);
lm_status_struct status;
lmcurve(result.size(), result.data(), x.size(), x.data(), y.data(),
@@ -95,44 +97,6 @@ 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> start_par{0, 0, 0};
auto e = std::max_element(y.begin(), y.end());
auto idx = std::distance(y.begin(), e);
start_par[0] = *e; // For amplitude we use the maximum value
start_par[1] =
x[idx]; // For the mean we use the x value of the maximum value
// 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;
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};
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;
}
void fit_gaus(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) {
@@ -156,7 +120,7 @@ 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);
par_out = model::Gaussian::estimate_par(x, y);
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
@@ -233,7 +197,7 @@ void fit_pol1(NDView<double, 1> x, NDView<double, 1> y, NDView<double, 1> y_err,
}
lm_status_struct status;
par_out = pol1_init_par(x, y);
par_out = model::Pol1::estimate_par(x, y);
std::array<double, 4> cov{0, 0, 0, 0};
lmcurve2(par_out.size(), par_out.data(), par_err_out.data(), cov.data(),
@@ -280,7 +244,7 @@ NDArray<double, 1> fit_pol1(NDView<double, 1> x, NDView<double, 1> y) {
// throw std::runtime_error("Data, x, data_err must have the same size "
// "and par_out, par_err_out must have size 2");
// }
NDArray<double, 1> par = pol1_init_par(x, y);
NDArray<double, 1> par = model::Pol1::estimate_par(x, y);
lm_status_struct status;
lmcurve(par.size(), par.data(), x.size(), x.data(), y.data(),
@@ -312,35 +276,9 @@ 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};
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;
}
// - No error
NDArray<double, 1> fit_scurve(NDView<double, 1> x, NDView<double, 1> y) {
NDArray<double, 1> result = scurve_init_par(x, y);
NDArray<double, 1> result = model::RisingScurve::estimate_par(x, y);
lm_status_struct status;
lmcurve(result.size(), result.data(), x.size(), x.data(), y.data(),
@@ -386,7 +324,7 @@ void fit_scurve(NDView<double, 1> x, NDView<double, 1> y,
}
lm_status_struct status;
par_out = scurve_init_par(x, y);
par_out = model::RisingScurve::estimate_par(x, y);
std::array<double, 36> cov = {0}; // size 6x6
// std::array<double, 4> cov{0, 0, 0, 0};
@@ -430,34 +368,9 @@ void fit_scurve(NDView<double, 1> x, NDView<double, 3> y,
// 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};
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;
}
// - No error
NDArray<double, 1> fit_scurve2(NDView<double, 1> x, NDView<double, 1> y) {
NDArray<double, 1> result = scurve2_init_par(x, y);
NDArray<double, 1> result = model::FallingScurve::estimate_par(x, y);
lm_status_struct status;
lmcurve(result.size(), result.data(), x.size(), x.data(), y.data(),
@@ -503,7 +416,7 @@ void fit_scurve2(NDView<double, 1> x, NDView<double, 1> y,
}
lm_status_struct status;
par_out = scurve2_init_par(x, y);
par_out = model::FallingScurve::estimate_par(x, y);
std::array<double, 36> cov = {0}; // size 6x6
// std::array<double, 4> cov{0, 0, 0, 0};
src/RawMasterFile.cpp
+1 -2
View File
@@ -199,8 +199,7 @@ std::optional<ROI> RawMasterFile::roi() const {
}
if (m_rois->empty()) {
throw std::runtime_error(LOCATION +
"Zero ROIs in metadata.");
throw std::runtime_error(LOCATION + "Zero ROIs in metadata.");
}
if (m_rois.value().size() > 1) {
src/utils/task.cpp
+7 -7
View File
@@ -8,19 +8,19 @@ std::vector<std::pair<int, int>> split_task(int first, int last,
std::vector<std::pair<int, int>> vec;
vec.reserve(n_threads);
int n_frames = last - first;
int n_items = last - first;
if (n_threads >= n_frames) {
for (int i = 0; i != n_frames; ++i) {
vec.push_back({i, i + 1});
if (n_threads >= n_items) {
for (int i = 0; i != n_items; ++i) {
vec.push_back({first + i, first + i + 1});
}
return vec;
}
int step = (n_frames) / n_threads;
int step = n_items / n_threads;
for (int i = 0; i != n_threads; ++i) {
int start = step * i;
int stop = step * (i + 1);
int start = first + step * i;
int stop = first + step * (i + 1);
if (i == n_threads - 1)
stop = last;
vec.push_back({start, stop});