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This commit is contained in:
Timur A. Fatkhullin
2026-03-23 18:06:41 +03:00
parent 48d1bf37f7
commit 9959f45b46
2 changed files with 138 additions and 84 deletions
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187
include/mcc/mcc_pcm_construct.h
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@@ -117,8 +117,11 @@ public:
MccDefaultPCMType pcm_type;
MccError error{}; // final model computation error
std::vector<double> model_colon, model_colat; // fitting model values
std::vector<double> colon_res, colat_res; // target - model
size_t max_iter{100}; // max number of iterations for robust linear regression method
size_t final_iter{}; // number of final iteration
std::vector<double> model_colon{}, model_colat{}; // fitting model values
std::vector<double> colon_res{}, colat_res{}; // target - model
#ifdef USE_BSPLINE_PCM
int bspline_fit_err{}; // bivariate B-spline fitting exit code (see FITPACK)
@@ -229,93 +232,29 @@ public:
}
// robust linear regression with Tukey's loss function
} else {
if (_table.size() < min_data_size) {
result.error = MccDefaultPCMConstructorErrorCode::ERROR_NOT_ENOUGH_DATA;
return result;
}
}
#ifdef USE_BSPLINE_PCM
if (pcm_data.type == MccDefaultPCMType::PCM_TYPE_BSPLINE ||
pcm_data.type == MccDefaultPCMType::PCM_TYPE_GEOMETRY_BSPLINE) {
if (pcm_data.bspline.knotsX.size() < 2 || pcm_data.bspline.knotsY.size() < 2) {
return MccDefaultPCMConstructorErrorCode::ERROR_INVALID_KNOTS_NUMBER;
}
if (pcm_data.type == MccDefaultPCMType::PCM_TYPE_BSPLINE) {
return bsplineFitting(pcm_data);
} else if (pcm_data.type == MccDefaultPCMType::PCM_TYPE_GEOMETRY_BSPLINE) {
// the fitting for geometrical coefficients is already done above so
// one must fit residuals by bivariate B-splines
double resi2x, resi2y; // fitting residuals
std::vector<double> tx(pcm_data.bspline.knotsX.size() + 6), ty(pcm_data.bspline.knotsY.size() + 6);
size_t Ncoeffs = (tx.size() - 4) * (ty.size() - 4);
pcm_data.bspline.coeffsX.resize(Ncoeffs);
pcm_data.bspline.coeffsY.resize(Ncoeffs);
if (pcm_data.type == MccDefaultPCMType::PCM_TYPE_BSPLINE) {
if (_table.size() < min_data_size) {
result.error = MccDefaultPCMConstructorErrorCode::ERROR_NOT_ENOUGH_DATA;
return result;
}
// here both direct and inverse coefficients will be calculated
pcm_data.inverseBspline.coeffsX.resize(Ncoeffs);
pcm_data.inverseBspline.coeffsY.resize(Ncoeffs);
// direct (celestial = encoder + pcm)
result.bspline_fit_err = bsplines::fitpack_sphere_fit(
_table.hw_colat, _table.hw_colon, _table.colon_res, 1.0, pcm_data.bspline.knotsY,
pcm_data.bspline.knotsX, pcm_data.bspline.coeffsX, resi2x);
if (result.bspline_fit_err > 0) {
result.error = MccDefaultPCMConstructorErrorCode::ERROR_BSPLINE_FIT;
return result;
}
result.bspline_fit_err = bsplines::fitpack_sphere_fit(
_table.hw_colat, _table.hw_colon, _table.colat_res, 1.0, pcm_data.bspline.knotsY,
pcm_data.bspline.knotsX, pcm_data.bspline.coeffsY, resi2y);
if (result.bspline_fit_err > 0) {
result.error = MccDefaultPCMConstructorErrorCode::ERROR_BSPLINE_FIT;
return result;
}
// inverse (encoder = celestial + pcm)
std::vector<double> colon_res = _table.colon_res;
std::vector<double> colat_res = _table.colat_res;
for (size_t i = 0; i < colat_res.size(); ++i) {
colon_res[i] = -colon_res[i];
colat_res[i] = -colat_res[i];
}
result.bspline_fit_err = bsplines::fitpack_sphere_fit(
_table.target_colon, _table.target_colat, colon_res, 1.0, pcm_data.bspline.knotsY,
pcm_data.bspline.knotsX, pcm_data.bspline.inverseCoeffsX, resi2x);
if (result.bspline_fit_err > 0) {
result.error = MccDefaultPCMConstructorErrorCode::ERROR_BSPLINE_FIT;
return result;
}
result.bspline_fit_err = bsplines::fitpack_sphere_fit(
_table.target_colon, _table.target_colat, colat_res, 1.0, pcm_data.bspline.knotsY,
pcm_data.bspline.knotsX, pcm_data.bspline.inverseCoeffsY, resi2y);
if (result.bspline_fit_err > 0) {
result.error = MccDefaultPCMConstructorErrorCode::ERROR_BSPLINE_FIT;
return result;
}
} else { // geometry + B-spline
// the fitting for geometrical coefficients is already done above so
// one must fit residuals by bivariate B-splines
std::vector<double> xres(numberOfPoints()), yres(numberOfPoints());
// for (size_t i = 0; i < _table.size(); ++i) {
// xres = _table[i].target_colon;
// yres = _table[i].target_colat;
// }
for (size_t i = 0; i < numberOfPoints(); ++i) {
xres = _table.target_colon[i] - result.;
yres = _table.target_colat[i];
}
std::vector<double> xres(numberOfPoints()), yres(numberOfPoints());
for (size_t i = 0; i < numberOfPoints(); ++i) {
xres = _table.target_colon[i] - result.model_colon;
yres = _table.target_colat[i] - result.model_colat;
}
}
}
#endif
}
protected:
// std::vector<table_elem_t> _table;
@@ -327,10 +266,12 @@ protected:
compute_result_t result{.pcm_type = pcm_data.type, .error = MccDefaultPCMConstructorErrorCode::ERROR_OK};
if (pcm_data.bspline.knotsX.size() < 2 || pcm_data.bspline.knotsY.size() < 2) {
return MccDefaultPCMConstructorErrorCode::ERROR_INVALID_KNOTS_NUMBER;
result.error = MccDefaultPCMConstructorErrorCode::ERROR_INVALID_KNOTS_NUMBER;
return result;
}
double resi2x, resi2y; // fitting residuals
double resi2x, resi2y; // sum of fitting residuals squares
std::vector<double> tx(pcm_data.bspline.knotsX.size() + 6), ty(pcm_data.bspline.knotsY.size() + 6);
@@ -432,7 +373,85 @@ protected:
}
void robustLinearRegress() {}
compute_result_t robustLinearRegress(MccDefaultPCM<MOUNT_TYPE>::pcm_data_t& pcm_data, size_t max_iter = 100)
{
auto tukey_weight = [](Eigen::VectorXd const& resi, double tukey_c, Eigen::VectorXd& tukey_weight) {
Eigen::ArrayXd u = (resi / tukey_c).cwiseAbs();
auto mask = (u < 1.0);
Eigen::VectorXd r = mask.select(1.0 - u * u, 0.0);
tukey_weight = r * r;
};
auto gastwirth_est = [](Eigen::VectorXd const& resi) {
// Gastwirth’s estimation: 0.3*Q(1/3) + 0.4*Q(1/2) + 0.3*Q(2/3)
Eigen::ArrayXd arr = resi.cwiseAbs();
std::nth_element(arr.begin(), arr.begin() + 2 * arr.size() / 3, arr.end());
// add 1.0E-8 to avoid 0.0
return (0.3 * arr(arr.size() / 3) + 0.4 * arr(arr.size() / 2) + 0.3 * arr(2 * arr.size() / 3)) / 0.6745 +
1.0E-8;
};
compute_result_t result{
.pcm_type = pcm_data.type, .error = MccDefaultPCMConstructorErrorCode::ERROR_OK, .max_iter = max_iter};
const size_t n_coeffs = pcm_data.type == MOUNT_TYPE == MccMountType::FORK_TYPE ? 9 : 8;
Eigen::MatrixXd X(2 * numberOfPoints(), n_coeffs);
Eigen::VectorXd y(2 * numberOfPoints()), beta(n_coeffs);
Eigen::RowVectorXd row(n_coeffs);
double tgY, cosY, rcosY, cosX, sinX, cosPhi = std::cos(pcm_data.siteLatitude),
sinPhi = std::sin(pcm_data.siteLatitude);
// system matrix and right-hand vector
for (size_t i = 0; i < numberOfPoints(); ++i) {
cosY = std::cos(_table.target_colat[i]);
rcosY = 1.0 / cosY;
tgY = std::tan(_table.target_colat[i]);
cosX = std::cos(_table.target_colon[i]);
sinX = std::sin(_table.target_colon[i]);
// [X0, Y0, collim, nonperp, misal1, misal2, tubefl, DEcaxfl, forkfl] or
// [X0, Y0, collim, nonperp, misal1, misal2, tubefl, DEcaxfl]
if constexpr (MOUNT_TYPE == MccMountType::FORK_TYPE) {
row << 1.0, 0.0, rcosY, tgY, -cosX * tgY, sinX * tgY, cosPhi * sinX * rcosY,
-(cosPhi * cosX + sinPhi * tgY), 0.0;
} else {
row << 1.0, 0.0, rcosY, tgY, -cosX * tgY, sinX * tgY, cosPhi * sinX * rcosY,
-(cosPhi * cosX + sinPhi * tgY);
}
X.row(i) = row;
if constexpr (MOUNT_TYPE == MccMountType::FORK_TYPE) {
row << 0.0, 1.0, 0.0, 0.0, sinX, cosX, cosPhi * cosX * std::sin(_table.target_colat[i]) - sinPhi * cosY,
0.0, 1.0 / cosX;
} else {
row << 0.0, 1.0, 0.0, 0.0, sinX, cosX, cosPhi * cosX * std::sin(_table.target_colat[i]) - sinPhi * cosY,
0.0;
}
X.row(i + numberOfPoints()) = row;
y(i) = _table.colon_res[i];
y(i + numberOfPoints()) = _table.colat_res[i];
}
Eigen::VectorXd resi = y;
Eigen::VectorXd weights(2 * numberOfPoints());
double scale;
for (result.final_iter = 1; result.final_iter <= max_iter; ++result.final_iter) {
scale = gastwirth_est(resi);
}
}
};
} // namespace mcc::impl