leonarski_fandClaude Opus 5
164f15c903
Four of the seven ceres::Solve calls in image_analysis obtained a Solver::Summary and never looked at it, so a solve that failed numerically had its parameters written back and was reported as success. StillsPartialityRefine and both PostRefine solves already gated on IsSolutionUsable(); this brings the rest to the same contract. IsSolutionUsable() is the right test rather than checking for CONVERGENCE: it accepts a solve that ran out of iterations or wall-clock time but still descended, which is exactly what the real-time callers depend on when they set max_solver_time instead of max_num_iterations. Only FAILURE and USER_FAILURE are rejected. XtalOptimizer checks before the write-back, so a failed refinement now leaves the caller's geom and latt untouched instead of half-updated. GeometryRefiner folds it into result.ok, which previously reported success from spot and frame counts alone. RingOptimizer returns a geometry by value that both callers assign straight back over their input, so it hands back the unchanged reference rather than a diverged beam centre. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
549 lines
24 KiB
C++
549 lines
24 KiB
C++
// SPDX-FileCopyrightText: 2025 Filip Leonarski, Paul Scherrer Institute <filip.leonarski@psi.ch>
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// SPDX-License-Identifier: GPL-3.0-only
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#include "../../common/JFJochMath.h"
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#include <algorithm>
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#include <numeric>
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#include <Eigen/Dense>
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#include "XtalOptimizer.h"
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#include "XtalResidual.h"
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#include "ceres/ceres.h"
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#include "ceres/rotation.h"
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#include "LatticeReduction.h"
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// Soft header prior on ONE beam-centre component (the spindle-parallel, gauge-weak one). Residual = w*(b - b0);
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// the caller sets w so the prior behaves like a sigma-pixel restraint that competes with the (unit-weight)
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// positional residuals - strong enough to pin the gauge direction, negligible in the well-constrained one.
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struct BeamComponentPrior {
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BeamComponentPrior(int component, double b0, double weight)
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: component(component), b0(b0), weight(weight) {}
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template<typename T>
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bool operator()(const T *const beam, T *residual) const {
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residual[0] = T(weight) * (beam[component] - T(b0));
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return true;
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}
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int component;
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double b0, weight;
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};
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struct XtalResidualRotationOnlyPrecomp {
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XtalResidualRotationOnlyPrecomp(const Coord &recip_obs,
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const CrystalLattice &latt,
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double h, double k, double l)
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: s_obs(recip_obs),
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astar(latt.Astar()), bstar(latt.Bstar()), cstar(latt.Cstar()),
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h(h), k(k), l(l) {
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}
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template<typename T>
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bool operator()(const T *const rot_aa, T *residual) const {
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const T astar_unrot[3] = {T(astar.x), T(astar.y), T(astar.z)};
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const T bstar_unrot[3] = {T(bstar.x), T(bstar.y), T(bstar.z)};
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const T cstar_unrot[3] = {T(cstar.x), T(cstar.y), T(cstar.z)};
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T astar_rot[3], bstar_rot[3], cstar_rot[3];
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ceres::AngleAxisRotatePoint(rot_aa, astar_unrot, astar_rot);
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ceres::AngleAxisRotatePoint(rot_aa, bstar_unrot, bstar_rot);
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ceres::AngleAxisRotatePoint(rot_aa, cstar_unrot, cstar_rot);
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const Eigen::Matrix<T, 3, 1> s_pred(T(h) * astar_rot[0] + T(k) * bstar_rot[0] + T(l) * cstar_rot[0],
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T(h) * astar_rot[1] + T(k) * bstar_rot[1] + T(l) * cstar_rot[1],
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T(h) * astar_rot[2] + T(k) * bstar_rot[2] + T(l) * cstar_rot[2]
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);
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// Residual in reciprocal space
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residual[0] = T(s_obs.x) - s_pred[0];
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residual[1] = T(s_obs.y) - s_pred[1];
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residual[2] = T(s_obs.z) - s_pred[2];
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return true;
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}
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const Coord s_obs;
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const Coord astar, bstar, cstar;
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const double h, k, l;
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};
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// Regularizer: penalises ||rot_aa|| to prefer the smallest rotation that
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// explains the data. Weight should be chosen in the same units as the
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// reciprocal-space residuals (Å⁻¹ per radian). A value of ~0.01–0.1 is
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// typically enough to break degeneracy without biasing the solution.
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struct RotationNormRegularizer {
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explicit RotationNormRegularizer(double weight) : weight(weight) {}
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template<typename T>
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bool operator()(const T *const rot_aa, T *residual) const {
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residual[0] = T(weight) * rot_aa[0];
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residual[1] = T(weight) * rot_aa[1];
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residual[2] = T(weight) * rot_aa[2];
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return true;
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}
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const double weight;
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};
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// Prior confidence weight per spot: how strong the spot is FOR ITS RESOLUTION. The frame's spots are
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// ordered by resolution and cut into equal-count shells, and each intensity is divided by its shell
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// median. Refinement needs the high-resolution spots (they carry the cell and distance information) and
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// those are legitimately weaker, so a raw intensity weight would suppress exactly the wrong ones; the
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// shell normalisation makes the weight resolution-neutral by construction.
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//
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// The weight enters as w^2 on the squared residual, w^2 = r/(1+r): the shell median contributes half,
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// a 4x-median spot 0.8, a quarter-median spot 0.2. Weak spots still pull, they just do not drive. Unlike
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// a robust loss this is a PRIOR - it never looks at the current residual, so it cannot mistake a genuine
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// spot for an outlier when the starting geometry is far off and leave the fit unable to move.
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static std::vector<double> SpotConfidenceWeights(const std::vector<SpotToSave> &spots) {
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constexpr size_t spots_per_shell = 32;
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std::vector<size_t> by_res(spots.size());
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std::iota(by_res.begin(), by_res.end(), 0);
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std::ranges::sort(by_res, {}, [&](size_t i) { return spots[i].d_A; });
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const size_t nshells = std::max<size_t>(1, spots.size() / spots_per_shell);
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std::vector<double> weight(spots.size());
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std::vector<float> shell_intensity;
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for (size_t s = 0; s < nshells; s++) {
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const size_t begin = s * spots.size() / nshells;
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const size_t end = (s + 1) * spots.size() / nshells;
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shell_intensity.clear();
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for (size_t i = begin; i < end; i++)
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shell_intensity.push_back(spots[by_res[i]].intensity);
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std::ranges::nth_element(shell_intensity, shell_intensity.begin() + shell_intensity.size() / 2);
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const double median = std::max(1e-3f, shell_intensity[shell_intensity.size() / 2]);
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for (size_t i = begin; i < end; i++) {
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const double r = std::max(0.0f, spots[by_res[i]].intensity) / median;
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weight[by_res[i]] = std::sqrt(r / (1.0 + r));
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}
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}
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return weight;
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}
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bool XtalOptimizerInternal(XtalOptimizerData &data,
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const std::vector<std::vector<SpotToSave>> &spots,
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const float tolerance,
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const int num_threads) {
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try {
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Coord vec0 = data.latt.Vec0();
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Coord vec1 = data.latt.Vec1();
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Coord vec2 = data.latt.Vec2();
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double beta = data.latt.GetUnitCell().beta;
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// Initial guess for the parameters
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double beam[2] = {data.geom.GetBeamX_pxl(), data.geom.GetBeamY_pxl()};
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double distance_mm = data.geom.GetDetectorDistance_mm();
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double detector_rot[2] = {data.geom.GetPoniRot1_rad(), data.geom.GetPoniRot2_rad()};
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ceres::Problem problem;
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double latt_vec0[3] = {0.0, 0.0, 0.0};
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double latt_vec1[3] = {0.0, 0.0, 0.0};
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double latt_vec2[3] = {0.0, 0.0, 0.0};
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double rot_vec[3] = {1, 0, 0};
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switch (data.crystal_system) {
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case gemmi::CrystalSystem::Orthorhombic:
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LatticeToRodriguesAndLengths_GS(data.latt, latt_vec0, latt_vec1);
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break;
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case gemmi::CrystalSystem::Tetragonal:
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LatticeToRodriguesAndLengths_GS(data.latt, latt_vec0, latt_vec1);
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latt_vec1[0] = (latt_vec1[0] + latt_vec1[1]) / 2.0;
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break;
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case gemmi::CrystalSystem::Cubic:
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LatticeToRodriguesAndLengths_GS(data.latt, latt_vec0, latt_vec1);
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latt_vec1[0] = (latt_vec1[0] + latt_vec1[1] + latt_vec1[2]) / 3.0;
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break;
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case gemmi::CrystalSystem::Hexagonal:
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LatticeToRodriguesAndLengths_Hex(data.latt, latt_vec0, latt_vec1);
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break;
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case gemmi::CrystalSystem::Monoclinic:
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LatticeToRodriguesLengthsBeta_Mono(data.latt, latt_vec0, latt_vec1, beta);
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latt_vec2[0] = beta;
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latt_vec2[1] = 0.0;
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latt_vec2[2] = 0.0;
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break;
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default:
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// Triclinic: initialize a,b,c and α,β,γ from current unit cell
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LatticeToRodriguesAndLengths_GS(data.latt, latt_vec0, latt_vec1);
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auto uc = data.latt.GetUnitCell();
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latt_vec2[0] = uc.alpha * PI / 180.0;
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latt_vec2[1] = uc.beta * PI / 180.0;
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latt_vec2[2] = uc.gamma * PI / 180.0;
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break;
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}
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if (data.axis) {
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rot_vec[0] = data.axis->GetAxis().x;
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rot_vec[1] = data.axis->GetAxis().y;
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rot_vec[2] = data.axis->GetAxis().z;
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}
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const float tolerance_sq = tolerance * tolerance;
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// Sum of w^2 over the spots that entered - the beam prior below is scaled by it so that its
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// strength relative to the data is the same weighted or not. Equals the residual block count
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// when the spots are unweighted.
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double effective_spots = 0.0;
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for (int i = 0; i < spots.size(); i++) {
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if (spots[i].empty())
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continue;
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std::vector<double> weight; // empty = unweighted
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if (data.weight_spots_by_confidence)
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weight = SpotConfidenceWeights(spots[i]);
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double angle_rad = 0.0;
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std::optional<RotMatrix> rot_matr;
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if (data.axis) {
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const float angle_deg = data.axis->GetAngle_deg(i) + data.axis->GetWedge_deg() / 2.0;
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angle_rad = angle_deg * PI / 180.0;
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rot_matr = data.axis->GetTransformationAngle(angle_deg);
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}
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// Add residuals for each point
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for (size_t j = 0; j < spots[i].size(); j++) {
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const auto &pt = spots[i][j];
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if (!data.index_ice_rings && pt.ice_ring)
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continue;
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Coord recip = pt.ReciprocalCoord(data.geom);
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if (rot_matr)
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recip = rot_matr.value() * recip;
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double h_fp = recip * vec0;
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double k_fp = recip * vec1;
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double l_fp = recip * vec2;
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double h = std::round(h_fp);
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double k = std::round(k_fp);
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double l = std::round(l_fp);
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double norm_sq = (h - h_fp) * (h - h_fp) + (k - k_fp) * (k - k_fp) + (l - l_fp) * (l - l_fp);
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if (norm_sq > tolerance_sq)
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continue;
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const double weight_sq = weight.empty() ? 1.0 : weight[j] * weight[j];
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effective_spots += weight_sq;
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problem.AddResidualBlock(
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new ceres::AutoDiffCostFunction<XtalResidual, 3, 2, 1, 2, 3, 3, 3, 3>(
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new XtalResidual(pt.x, pt.y,
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data.geom.GetWavelength_A(),
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data.geom.GetPixelSize_mm(),
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data.geom.GetPoniRot3_rad(),
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angle_rad,
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h, k, l,
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data.crystal_system)),
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// Ceres has no per-residual weight; ScaledLoss(nullptr, a) multiplies the squared
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// residual by the constant a, i.e. it applies a weight of sqrt(a) to the residual.
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weight.empty()
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? nullptr
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: new ceres::ScaledLoss(nullptr, weight_sq, ceres::TAKE_OWNERSHIP),
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beam,
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&distance_mm,
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detector_rot,
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rot_vec,
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latt_vec0,
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latt_vec1,
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latt_vec2
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);
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}
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}
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if (problem.NumResidualBlocks() < data.min_spots)
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return false;
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if (!data.refine_distance_mm)
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problem.SetParameterBlockConstant(&distance_mm);
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else {
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const double dist_range = 0.1;
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problem.SetParameterLowerBound(&distance_mm, 0, distance_mm * (1.0 - dist_range));
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problem.SetParameterUpperBound(&distance_mm, 0, distance_mm * (1.0 + dist_range));
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}
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if (!data.refine_beam_center)
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problem.SetParameterBlockConstant(beam);
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else if (data.axis) {
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// Gauge handling (single-axis rotation): rotating the whole experiment about the spindle leaves every
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// spot position unchanged, so the beam-centre component PARALLEL to the spindle is a null/gauge-weak
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// direction. Refining it freely lets it wander (~+3 px) and absorb centroid systematics into a wrong
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// beam that the co-refined orientation keeps position-consistent. Rather than freeze it (the beam
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// does drift - it is only LaB6-monitored to ~a few px), RESTRAIN it toward the header with a soft
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// prior: the gauge direction has ~zero data sensitivity so the prior pins it near the header, while a
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// real, well-supported drift can still overcome it. The spindle is along a detector axis in standard
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// geometry, so restrain the dominant of X / Y.
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const Coord spindle = data.axis->GetAxis();
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const int parallel = (std::fabs(spindle.x) >= std::fabs(spindle.y)) ? 0 : 1;
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// Weight so the prior is a sigma_px-pixel restraint that competes with the positional residuals.
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// k = d|recip|/d(beam_px) ~ pixel/(distance*lambda) [A^-1/px]; scaling by sqrt(#residuals) makes the
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// prior's curvature ~ (1/9) of the well-constrained-data curvature at sigma_px=3, i.e. data wins the
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// perpendicular direction, the prior wins the gauge one.
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constexpr double sigma_px = 3.0;
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const double k = data.geom.GetPixelSize_mm() / (distance_mm * data.geom.GetWavelength_A());
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const double w = k * std::sqrt(effective_spots) / sigma_px;
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problem.AddResidualBlock(
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new ceres::AutoDiffCostFunction<BeamComponentPrior, 1, 2>(
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new BeamComponentPrior(parallel, beam[parallel], w)),
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nullptr, beam);
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}
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if (!data.refine_detector_angles) {
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problem.SetParameterBlockConstant(detector_rot);
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} else {
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const double rot_range = 3.0 / 180.0 * PI;
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for (int i = 0; i < 2; ++i) {
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problem.SetParameterLowerBound(detector_rot, i, detector_rot[i] - rot_range);
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problem.SetParameterUpperBound(detector_rot, i, detector_rot[i] + rot_range);
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}
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}
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if (!data.refine_rotation_axis) {
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problem.SetParameterBlockConstant(rot_vec);
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}
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if (!data.refine_unit_cell) {
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problem.SetParameterBlockConstant(latt_vec1);
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problem.SetParameterBlockConstant(latt_vec2);
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} else {
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// Parameter bounds
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// Lengths
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for (int i = 0; i < 3; ++i) {
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problem.SetParameterLowerBound(latt_vec1, i, data.min_length_A);
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problem.SetParameterUpperBound(latt_vec1, i, data.max_length_A);
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}
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if (data.crystal_system == gemmi::CrystalSystem::Monoclinic) {
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const double beta_lo = std::max(1e-6, PI * (data.min_angle_deg / 180.0));
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const double beta_hi = std::min(PI - 1e-6, PI * (data.max_angle_deg / 180.0));
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problem.SetParameterLowerBound(latt_vec2, 0, beta_lo);
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problem.SetParameterUpperBound(latt_vec2, 0, beta_hi);
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} else if (data.crystal_system == gemmi::CrystalSystem::Triclinic) {
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// α, β, γ bounds (radians)
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const double alo = PI * (data.min_angle_deg / 180.0);
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const double ahi = PI * (data.max_angle_deg / 180.0);
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for (int i = 0; i < 3; ++i) {
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problem.SetParameterLowerBound(latt_vec2, i, alo);
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problem.SetParameterUpperBound(latt_vec2, i, ahi);
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}
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} else {
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// Orthorhombic / Tetragonal / Cubic / Hexagonal:
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// latt_vec2 has no meaning for these systems — always freeze it.
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problem.SetParameterBlockConstant(latt_vec2);
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}
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}
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// Configure solver
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ceres::Solver::Options options;
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options.linear_solver_type = ceres::DENSE_QR;
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options.minimizer_progress_to_stdout = false;
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if (data.max_iterations > 0)
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options.max_num_iterations = data.max_iterations;
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else
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options.max_solver_time_in_seconds = data.max_time;
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options.logging_type = ceres::LoggingType::SILENT;
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options.num_threads = num_threads; // usually 1 (called from many threads); caller may raise it
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ceres::Solver::Summary summary;
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// Run optimization
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ceres::Solve(options, &problem, &summary);
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// Only a genuine numerical failure is rejected here: a solve that ran out of iterations or
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// out of time but still descended counts as usable, which is what the real-time caller
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// relies on when it sets max_solver_time. Checked before anything is written back, so a
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// failed refinement leaves data untouched rather than committing half a fit.
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if (!summary.IsSolutionUsable())
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return false;
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if (data.refine_beam_center) {
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data.beam_corr_x = data.geom.GetBeamX_pxl() - beam[0];
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data.beam_corr_y = data.geom.GetBeamY_pxl() - beam[1];
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data.geom.BeamX_pxl(beam[0]).BeamY_pxl(beam[1]);
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}
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if (data.refine_distance_mm)
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data.geom.DetectorDistance_mm(distance_mm);
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if (data.refine_detector_angles)
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data.geom.PoniRot1_rad(detector_rot[0]).PoniRot2_rad(detector_rot[1]);
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if (data.axis && data.refine_rotation_axis)
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data.axis.value().Axis(Coord(rot_vec[0], rot_vec[1], rot_vec[2]));
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if (data.crystal_system == gemmi::CrystalSystem::Orthorhombic)
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data.latt = AngleAxisAndCellToLattice(latt_vec0, latt_vec1, PI / 2.0, PI / 2.0, PI / 2.0);
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else if (data.crystal_system == gemmi::CrystalSystem::Tetragonal) {
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latt_vec1[1] = latt_vec1[0];
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data.latt = AngleAxisAndCellToLattice(latt_vec0, latt_vec1, PI / 2.0, PI / 2.0, PI / 2.0);
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} else if (data.crystal_system == gemmi::CrystalSystem::Cubic) {
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latt_vec1[1] = latt_vec1[0];
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latt_vec1[2] = latt_vec1[0];
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data.latt = AngleAxisAndCellToLattice(latt_vec0, latt_vec1, PI / 2.0, PI / 2.0, PI / 2.0);
|
||
} else if (data.crystal_system == gemmi::CrystalSystem::Hexagonal) {
|
||
latt_vec1[1] = latt_vec1[0];
|
||
data.latt = AngleAxisAndCellToLattice(latt_vec0, latt_vec1,PI / 2.0, PI / 2.0, 2.0 * PI / 3.0);
|
||
} else if (data.crystal_system == gemmi::CrystalSystem::Monoclinic) {
|
||
data.latt = AngleAxisAndCellToLattice(latt_vec0, latt_vec1, PI / 2.0, latt_vec2[0], PI / 2.0);
|
||
} else {
|
||
// Triclinic via the same generic builder
|
||
data.latt = AngleAxisAndCellToLattice(latt_vec0, latt_vec1, latt_vec2[0], latt_vec2[1], latt_vec2[2]);
|
||
}
|
||
return true;
|
||
} catch (...) {
|
||
// Convergence problems, likely not updated
|
||
return false;
|
||
}
|
||
}
|
||
|
||
bool XtalOptimizer(XtalOptimizerData &data, const std::vector<std::vector<SpotToSave>> &spots,
|
||
int num_threads) {
|
||
if (!XtalOptimizerInternal(data, spots, 0.3, num_threads))
|
||
return false;
|
||
XtalOptimizerInternal(data, spots, 0.2, num_threads);
|
||
return XtalOptimizerInternal(data, spots, 0.1, num_threads);
|
||
}
|
||
|
||
bool XtalOptimizerRotationOnly(XtalOptimizerData &data,
|
||
const std::vector<SpotToSave> &spots,
|
||
const float tolerance) {
|
||
try {
|
||
// Parameter: angle-axis for the extra rotation. Identity == {0,0,0}.
|
||
double rot_aa[3] = {0.0, 0.0, 0.0};
|
||
|
||
// Spot selection by current indexing (same approach as XtalOptimizerInternal)
|
||
const Coord a0 = data.latt.Vec0();
|
||
const Coord b0 = data.latt.Vec1();
|
||
const Coord c0 = data.latt.Vec2();
|
||
|
||
const float tol_sq = tolerance * tolerance;
|
||
|
||
ceres::Problem problem;
|
||
|
||
for (const auto &pt : spots) {
|
||
if (!data.index_ice_rings && pt.ice_ring)
|
||
continue;
|
||
|
||
// Compute fractional HKL using the CURRENT lattice
|
||
Coord recip_index = pt.ReciprocalCoord(data.geom);
|
||
if (data.axis.has_value())
|
||
recip_index = data.axis->GetTransformationAngle(pt.phi) * recip_index;
|
||
|
||
const double h_fp = static_cast<double>(recip_index * a0);
|
||
const double k_fp = static_cast<double>(recip_index * b0);
|
||
const double l_fp = static_cast<double>(recip_index * c0);
|
||
|
||
const double h = std::round(h_fp);
|
||
const double k = std::round(k_fp);
|
||
const double l = std::round(l_fp);
|
||
|
||
const double norm_sq =
|
||
(h - h_fp) * (h - h_fp) +
|
||
(k - k_fp) * (k - k_fp) +
|
||
(l - l_fp) * (l - l_fp);
|
||
|
||
if (norm_sq > static_cast<double>(tol_sq))
|
||
continue;
|
||
|
||
// s_obs must be in the same reference frame as the
|
||
// predicted reciprocal vector (h·a* + k·b* + l·c*), which is the
|
||
// phi=0 crystal frame. Apply the same goniometer back-rotation
|
||
// that was used above for the HKL assignment.
|
||
Coord s_obs = data.geom.DetectorToRecip(pt.x, pt.y);
|
||
if (data.axis.has_value())
|
||
s_obs = data.axis->GetTransformationAngle(pt.phi) * s_obs;
|
||
|
||
auto *cost =
|
||
new ceres::AutoDiffCostFunction<XtalResidualRotationOnlyPrecomp, 3, 3>(
|
||
new XtalResidualRotationOnlyPrecomp(s_obs, data.latt, h, k, l)
|
||
);
|
||
|
||
problem.AddResidualBlock(cost, nullptr, rot_aa);
|
||
}
|
||
|
||
if (problem.NumResidualBlocks() < data.min_spots)
|
||
return false;
|
||
|
||
// Regularization: prefer the smallest rotation correction that fits the
|
||
// data. This is essential when spots are nearly coplanar in reciprocal
|
||
// space (e.g. still images), where the rotation component perpendicular
|
||
// to the scattering plane is otherwise underdetermined.
|
||
// The weight is in Å⁻¹ rad⁻¹; tune relative to your typical residual.
|
||
{
|
||
const double reg_weight = 0.05; // e.g. 0.05
|
||
problem.AddResidualBlock(
|
||
new ceres::AutoDiffCostFunction<RotationNormRegularizer, 3, 3>(
|
||
new RotationNormRegularizer(reg_weight)),
|
||
nullptr, rot_aa);
|
||
}
|
||
|
||
ceres::Solver::Options options;
|
||
options.linear_solver_type = ceres::DENSE_QR;
|
||
options.minimizer_progress_to_stdout = false;
|
||
if (data.max_iterations > 0)
|
||
options.max_num_iterations = data.max_iterations;
|
||
else
|
||
options.max_solver_time_in_seconds = data.max_time;
|
||
options.logging_type = ceres::LoggingType::SILENT;
|
||
options.num_threads = 1;
|
||
|
||
ceres::Solver::Summary summary;
|
||
ceres::Solve(options, &problem, &summary);
|
||
|
||
if (!summary.IsSolutionUsable())
|
||
return false;
|
||
|
||
// Apply rotation to direct-lattice vectors.
|
||
// ceres::AngleAxisToRotationMatrix writes a **row-major** 3×3 matrix,
|
||
// and Eigen's << operator also fills row-by-row, so the assignment
|
||
// below is correct without any transposing.
|
||
//
|
||
// Note: for a pure orthogonal rotation R, R⁻ᵀ = R, so rotating the
|
||
// direct-lattice vectors (A, B, C) by R is exactly equivalent to
|
||
// rotating the reciprocal vectors (a*, b*, c*) by the same R. No
|
||
// transpose or inversion of R is needed here.
|
||
double R_raw[9];
|
||
ceres::AngleAxisToRotationMatrix(rot_aa, R_raw); // row-major 3x3
|
||
|
||
Eigen::Matrix3d R;
|
||
R << R_raw[0], R_raw[3], R_raw[6],
|
||
R_raw[1], R_raw[4], R_raw[7],
|
||
R_raw[2], R_raw[5], R_raw[8];
|
||
|
||
const Eigen::Vector3d A(a0.x, a0.y, a0.z);
|
||
const Eigen::Vector3d B(b0.x, b0.y, b0.z);
|
||
const Eigen::Vector3d C(c0.x, c0.y, c0.z);
|
||
|
||
const Eigen::Vector3d A2 = R * A;
|
||
const Eigen::Vector3d B2 = R * B;
|
||
const Eigen::Vector3d C2 = R * C;
|
||
|
||
data.latt = CrystalLattice(
|
||
Coord(static_cast<float>(A2.x()), static_cast<float>(A2.y()), static_cast<float>(A2.z())),
|
||
Coord(static_cast<float>(B2.x()), static_cast<float>(B2.y()), static_cast<float>(B2.z())),
|
||
Coord(static_cast<float>(C2.x()), static_cast<float>(C2.y()), static_cast<float>(C2.z()))
|
||
);
|
||
|
||
double theta = std::sqrt(rot_aa[0] * rot_aa[0] + rot_aa[1] * rot_aa[1] + rot_aa[2] * rot_aa[2]);
|
||
data.angle_corr = theta;
|
||
if (theta > 1e-6) {
|
||
Coord rot;
|
||
rot.x = rot_aa[0] / theta;
|
||
rot.y = rot_aa[1] / theta;
|
||
rot.z = rot_aa[2] / theta;
|
||
data.angle_axis = rot;
|
||
} else
|
||
data.angle_axis.reset();
|
||
|
||
return true;
|
||
} catch (...) {
|
||
return false;
|
||
}
|
||
} |