jungfrauandClaude Opus 5
0d07141b5c
Four changes to the same least-squares fit, which the rotation first pass runs on every candidate lattice and the per-image path runs on every frame. The linear solver was DENSE_QR on a problem that is very tall and thin - thousands of spots against at most seventeen parameters. That is the shape QR handles worst: it copies the Jacobian out of Ceres' row-major storage into a column-major buffer on every solve, and Eigen's blocked Householder then degenerates to the unblocked path because its block size is the column count. Accumulating J^T J reads the Jacobian once instead. Both solve the same damped system, so the step is the same to round-off. Ceres sizes its dual numbers from the declared parameter blocks, not from which of them the caller then holds constant. Nothing outside a test set refine_distance_mm - the positional residual leaves the distance degenerate with the cell scale, which is why the rotation post-refinement fits it in a step of its own with the cell held fixed - so the block was declared only to be frozen, and every residual differentiated seventeen parameters to use sixteen. It is gone, along with the test that exercised distance recovery; that test seeded the distance off truth, which the cell would now absorb, so its seed moves to the true value. The post-refinement's own detector step held five of its seven blocks constant and now bakes them into the residual, leaving beam and distance. The predicted reciprocal vector was built by rotating all three direct columns and then crossing them. A rotation commutes with the cross product and leaves the triple product alone, so the same vector comes out of crossing the unrotated columns and turning the result once - three rotations become one, for every crystal system. The documentation described the arrangement before all this, and had drifted in a second way: the first-pass rotation indexing has been refining the detector tilt and the rotation axis by default, which the text said were held fixed. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
575 lines
26 KiB
C++
575 lines
26 KiB
C++
// SPDX-FileCopyrightText: 2025 Filip Leonarski, Paul Scherrer Institute <filip.leonarski@psi.ch>
|
||
// SPDX-License-Identifier: GPL-3.0-only
|
||
|
||
#include "../../common/JFJochMath.h"
|
||
#include <algorithm>
|
||
#include <numeric>
|
||
#include <Eigen/Dense>
|
||
|
||
#include "XtalOptimizer.h"
|
||
#include "XtalResidual.h"
|
||
#include "ceres/ceres.h"
|
||
#include "ceres/rotation.h"
|
||
#include "LatticeReduction.h"
|
||
|
||
// Soft header prior on ONE beam-centre component (the spindle-parallel, gauge-weak one). Residual = w*(b - b0);
|
||
// the caller sets w so the prior behaves like a sigma-pixel restraint that competes with the (unit-weight)
|
||
// positional residuals - strong enough to pin the gauge direction, negligible in the well-constrained one.
|
||
struct BeamComponentPrior {
|
||
BeamComponentPrior(int component, double b0, double weight)
|
||
: component(component), b0(b0), weight(weight) {}
|
||
template<typename T>
|
||
bool operator()(const T *const beam, T *residual) const {
|
||
residual[0] = T(weight) * (beam[component] - T(b0));
|
||
return true;
|
||
}
|
||
int component;
|
||
double b0, weight;
|
||
};
|
||
|
||
struct XtalResidualRotationOnlyPrecomp {
|
||
XtalResidualRotationOnlyPrecomp(const Coord &recip_obs,
|
||
const CrystalLattice &latt,
|
||
double h, double k, double l)
|
||
: s_obs(recip_obs),
|
||
astar(latt.Astar()), bstar(latt.Bstar()), cstar(latt.Cstar()),
|
||
h(h), k(k), l(l) {
|
||
}
|
||
|
||
template<typename T>
|
||
bool operator()(const T *const rot_aa, T *residual) const {
|
||
const T astar_unrot[3] = {T(astar.x), T(astar.y), T(astar.z)};
|
||
const T bstar_unrot[3] = {T(bstar.x), T(bstar.y), T(bstar.z)};
|
||
const T cstar_unrot[3] = {T(cstar.x), T(cstar.y), T(cstar.z)};
|
||
|
||
T astar_rot[3], bstar_rot[3], cstar_rot[3];
|
||
|
||
const AngleAxisRotator<T> rot(rot_aa);
|
||
rot.Rotate(astar_unrot, astar_rot);
|
||
rot.Rotate(bstar_unrot, bstar_rot);
|
||
rot.Rotate(cstar_unrot, cstar_rot);
|
||
|
||
const Eigen::Matrix<T, 3, 1> s_pred(T(h) * astar_rot[0] + T(k) * bstar_rot[0] + T(l) * cstar_rot[0],
|
||
T(h) * astar_rot[1] + T(k) * bstar_rot[1] + T(l) * cstar_rot[1],
|
||
T(h) * astar_rot[2] + T(k) * bstar_rot[2] + T(l) * cstar_rot[2]
|
||
);
|
||
|
||
// Residual in reciprocal space
|
||
residual[0] = T(s_obs.x) - s_pred[0];
|
||
residual[1] = T(s_obs.y) - s_pred[1];
|
||
residual[2] = T(s_obs.z) - s_pred[2];
|
||
return true;
|
||
}
|
||
|
||
const Coord s_obs;
|
||
const Coord astar, bstar, cstar;
|
||
const double h, k, l;
|
||
};
|
||
|
||
// Regularizer: penalises ||rot_aa|| to prefer the smallest rotation that
|
||
// explains the data. Weight should be chosen in the same units as the
|
||
// reciprocal-space residuals (Å⁻¹ per radian). A value of ~0.01–0.1 is
|
||
// typically enough to break degeneracy without biasing the solution.
|
||
struct RotationNormRegularizer {
|
||
explicit RotationNormRegularizer(double weight) : weight(weight) {}
|
||
|
||
template<typename T>
|
||
bool operator()(const T *const rot_aa, T *residual) const {
|
||
residual[0] = T(weight) * rot_aa[0];
|
||
residual[1] = T(weight) * rot_aa[1];
|
||
residual[2] = T(weight) * rot_aa[2];
|
||
return true;
|
||
}
|
||
|
||
const double weight;
|
||
};
|
||
|
||
// Prior confidence weight per spot: how strong the spot is FOR ITS RESOLUTION. The frame's spots are
|
||
// ordered by resolution and cut into equal-count shells, and each intensity is divided by its shell
|
||
// median. Refinement needs the high-resolution spots (they carry the cell and distance information) and
|
||
// those are legitimately weaker, so a raw intensity weight would suppress exactly the wrong ones; the
|
||
// shell normalisation makes the weight resolution-neutral by construction.
|
||
//
|
||
// The weight enters as w^2 on the squared residual, w^2 = r/(1+r): the shell median contributes half,
|
||
// a 4x-median spot 0.8, a quarter-median spot 0.2. Weak spots still pull, they just do not drive. Unlike
|
||
// a robust loss this is a PRIOR - it never looks at the current residual, so it cannot mistake a genuine
|
||
// spot for an outlier when the starting geometry is far off and leave the fit unable to move.
|
||
static std::vector<double> SpotConfidenceWeights(const std::vector<SpotToSave> &spots) {
|
||
constexpr size_t spots_per_shell = 32;
|
||
|
||
std::vector<size_t> by_res(spots.size());
|
||
std::iota(by_res.begin(), by_res.end(), 0);
|
||
std::ranges::sort(by_res, {}, [&](size_t i) { return spots[i].d_A; });
|
||
|
||
const size_t nshells = std::max<size_t>(1, spots.size() / spots_per_shell);
|
||
std::vector<double> weight(spots.size());
|
||
std::vector<float> shell_intensity;
|
||
|
||
for (size_t s = 0; s < nshells; s++) {
|
||
const size_t begin = s * spots.size() / nshells;
|
||
const size_t end = (s + 1) * spots.size() / nshells;
|
||
|
||
shell_intensity.clear();
|
||
for (size_t i = begin; i < end; i++)
|
||
shell_intensity.push_back(spots[by_res[i]].intensity);
|
||
std::ranges::nth_element(shell_intensity, shell_intensity.begin() + shell_intensity.size() / 2);
|
||
const double median = std::max(1e-3f, shell_intensity[shell_intensity.size() / 2]);
|
||
|
||
for (size_t i = begin; i < end; i++) {
|
||
const double r = std::max(0.0f, spots[by_res[i]].intensity) / median;
|
||
weight[by_res[i]] = std::sqrt(r / (1.0 + r));
|
||
}
|
||
}
|
||
return weight;
|
||
}
|
||
|
||
bool XtalOptimizerInternal(XtalOptimizerData &data,
|
||
const std::vector<std::vector<SpotToSave>> &spots,
|
||
const float tolerance,
|
||
const int num_threads) {
|
||
try {
|
||
Coord vec0 = data.latt.Vec0();
|
||
Coord vec1 = data.latt.Vec1();
|
||
Coord vec2 = data.latt.Vec2();
|
||
double beta = data.latt.GetUnitCell().beta;
|
||
|
||
// Initial guess for the parameters
|
||
double beam[2] = {data.geom.GetBeamX_pxl(), data.geom.GetBeamY_pxl()};
|
||
double distance_mm = data.geom.GetDetectorDistance_mm();
|
||
|
||
double detector_rot[2] = {data.geom.GetPoniRot1_rad(), data.geom.GetPoniRot2_rad()};
|
||
|
||
ceres::Problem problem;
|
||
|
||
double latt_vec0[3] = {0.0, 0.0, 0.0};
|
||
double latt_vec1[3] = {0.0, 0.0, 0.0};
|
||
double latt_vec2[3] = {0.0, 0.0, 0.0};
|
||
|
||
double rot_vec[3] = {1, 0, 0};
|
||
|
||
switch (data.crystal_system) {
|
||
case gemmi::CrystalSystem::Orthorhombic:
|
||
LatticeToRodriguesAndLengths_GS(data.latt, latt_vec0, latt_vec1);
|
||
break;
|
||
case gemmi::CrystalSystem::Tetragonal:
|
||
LatticeToRodriguesAndLengths_GS(data.latt, latt_vec0, latt_vec1);
|
||
latt_vec1[0] = (latt_vec1[0] + latt_vec1[1]) / 2.0;
|
||
break;
|
||
case gemmi::CrystalSystem::Cubic:
|
||
LatticeToRodriguesAndLengths_GS(data.latt, latt_vec0, latt_vec1);
|
||
latt_vec1[0] = (latt_vec1[0] + latt_vec1[1] + latt_vec1[2]) / 3.0;
|
||
break;
|
||
case gemmi::CrystalSystem::Hexagonal:
|
||
LatticeToRodriguesAndLengths_Hex(data.latt, latt_vec0, latt_vec1);
|
||
break;
|
||
case gemmi::CrystalSystem::Monoclinic:
|
||
LatticeToRodriguesLengthsBeta_Mono(data.latt, latt_vec0, latt_vec1, beta);
|
||
latt_vec2[0] = beta;
|
||
latt_vec2[1] = 0.0;
|
||
latt_vec2[2] = 0.0;
|
||
break;
|
||
default:
|
||
// Triclinic: initialize a,b,c and α,β,γ from current unit cell
|
||
LatticeToRodriguesAndLengths_GS(data.latt, latt_vec0, latt_vec1);
|
||
auto uc = data.latt.GetUnitCell();
|
||
latt_vec2[0] = uc.alpha * PI / 180.0;
|
||
latt_vec2[1] = uc.beta * PI / 180.0;
|
||
latt_vec2[2] = uc.gamma * PI / 180.0;
|
||
break;
|
||
}
|
||
|
||
if (data.axis) {
|
||
rot_vec[0] = data.axis->GetAxis().x;
|
||
rot_vec[1] = data.axis->GetAxis().y;
|
||
rot_vec[2] = data.axis->GetAxis().z;
|
||
}
|
||
|
||
const float tolerance_sq = tolerance * tolerance;
|
||
|
||
// Per-image rotation refinement frees only the beam and the orientation and holds the other five
|
||
// blocks constant, so the seven-block residual makes Ceres differentiate 17 parameters to use 5.
|
||
// Where that is the configuration, use the reduced residual instead - identical fit, Jet<5>
|
||
// autodiff. Any other combination (stills also free the cell, the offline refiner frees distance
|
||
// and detector angles) keeps the general form below.
|
||
const bool beam_and_orientation_only = data.refine_beam_center
|
||
&& !data.refine_detector_angles
|
||
&& !data.refine_rotation_axis
|
||
&& !data.refine_unit_cell;
|
||
|
||
// Sum of w^2 over the spots that entered - the beam prior below is scaled by it so that its
|
||
// strength relative to the data is the same weighted or not. Equals the residual block count
|
||
// when the spots are unweighted.
|
||
double effective_spots = 0.0;
|
||
|
||
for (int i = 0; i < spots.size(); i++) {
|
||
if (spots[i].empty())
|
||
continue;
|
||
|
||
std::vector<double> weight; // empty = unweighted
|
||
if (data.weight_spots_by_confidence)
|
||
weight = SpotConfidenceWeights(spots[i]);
|
||
|
||
double angle_rad = 0.0;
|
||
std::optional<RotMatrix> rot_matr;
|
||
|
||
if (data.axis) {
|
||
const float angle_deg = data.axis->GetAngle_deg(i) + data.axis->GetWedge_deg() / 2.0;
|
||
angle_rad = angle_deg * PI / 180.0;
|
||
rot_matr = data.axis->GetTransformationAngle(angle_deg);
|
||
}
|
||
|
||
// Add residuals for each point
|
||
for (size_t j = 0; j < spots[i].size(); j++) {
|
||
const auto &pt = spots[i][j];
|
||
if (!data.index_ice_rings && pt.ice_ring)
|
||
continue;
|
||
|
||
Coord recip = pt.ReciprocalCoord(data.geom);
|
||
|
||
if (rot_matr)
|
||
recip = rot_matr.value() * recip;
|
||
|
||
double h_fp = recip * vec0;
|
||
double k_fp = recip * vec1;
|
||
double l_fp = recip * vec2;
|
||
|
||
double h = std::round(h_fp);
|
||
double k = std::round(k_fp);
|
||
double l = std::round(l_fp);
|
||
|
||
double norm_sq = (h - h_fp) * (h - h_fp) + (k - k_fp) * (k - k_fp) + (l - l_fp) * (l - l_fp);
|
||
|
||
if (norm_sq > tolerance_sq)
|
||
continue;
|
||
|
||
const double weight_sq = weight.empty() ? 1.0 : weight[j] * weight[j];
|
||
effective_spots += weight_sq;
|
||
|
||
const XtalResidual residual(pt.x, pt.y,
|
||
data.geom.GetWavelength_A(),
|
||
data.geom.GetPixelSize_mm(),
|
||
data.geom.GetPoniRot3_rad(),
|
||
angle_rad,
|
||
h, k, l,
|
||
data.crystal_system);
|
||
|
||
// Ceres has no per-residual weight; ScaledLoss(nullptr, a) multiplies the squared
|
||
// residual by the constant a, i.e. it applies a weight of sqrt(a) to the residual.
|
||
ceres::LossFunction *loss = weight.empty()
|
||
? nullptr
|
||
: new ceres::ScaledLoss(nullptr, weight_sq,
|
||
ceres::TAKE_OWNERSHIP);
|
||
|
||
if (beam_and_orientation_only)
|
||
problem.AddResidualBlock(
|
||
new ceres::AutoDiffCostFunction<XtalResidualBeamOrientation, 3, 2, 3>(
|
||
new XtalResidualBeamOrientation(residual, distance_mm, detector_rot,
|
||
rot_vec, latt_vec1, latt_vec2)),
|
||
loss,
|
||
beam,
|
||
latt_vec0
|
||
);
|
||
else
|
||
problem.AddResidualBlock(
|
||
new ceres::AutoDiffCostFunction<XtalResidualFixedDistance, 3, 2, 2, 3, 3, 3, 3>(
|
||
new XtalResidualFixedDistance(residual, distance_mm)),
|
||
loss,
|
||
beam,
|
||
detector_rot,
|
||
rot_vec,
|
||
latt_vec0,
|
||
latt_vec1,
|
||
latt_vec2
|
||
);
|
||
}
|
||
}
|
||
|
||
if (problem.NumResidualBlocks() < data.min_spots)
|
||
return false;
|
||
|
||
if (!data.refine_beam_center)
|
||
problem.SetParameterBlockConstant(beam);
|
||
else if (data.axis) {
|
||
// Gauge handling (single-axis rotation): rotating the whole experiment about the spindle leaves every
|
||
// spot position unchanged, so the beam-centre component PARALLEL to the spindle is a null/gauge-weak
|
||
// direction. Refining it freely lets it wander (~+3 px) and absorb centroid systematics into a wrong
|
||
// beam that the co-refined orientation keeps position-consistent. Rather than freeze it (the beam
|
||
// does drift - it is only LaB6-monitored to ~a few px), RESTRAIN it toward the header with a soft
|
||
// prior: the gauge direction has ~zero data sensitivity so the prior pins it near the header, while a
|
||
// real, well-supported drift can still overcome it. The spindle is along a detector axis in standard
|
||
// geometry, so restrain the dominant of X / Y.
|
||
const Coord spindle = data.axis->GetAxis();
|
||
const int parallel = (std::fabs(spindle.x) >= std::fabs(spindle.y)) ? 0 : 1;
|
||
// Weight so the prior is a sigma_px-pixel restraint that competes with the positional residuals.
|
||
// k = d|recip|/d(beam_px) ~ pixel/(distance*lambda) [A^-1/px]; scaling by sqrt(#residuals) makes the
|
||
// prior's curvature ~ (1/9) of the well-constrained-data curvature at sigma_px=3, i.e. data wins the
|
||
// perpendicular direction, the prior wins the gauge one.
|
||
constexpr double sigma_px = 3.0;
|
||
const double k = data.geom.GetPixelSize_mm() / (distance_mm * data.geom.GetWavelength_A());
|
||
const double w = k * std::sqrt(effective_spots) / sigma_px;
|
||
problem.AddResidualBlock(
|
||
new ceres::AutoDiffCostFunction<BeamComponentPrior, 1, 2>(
|
||
new BeamComponentPrior(parallel, beam[parallel], w)),
|
||
nullptr, beam);
|
||
}
|
||
|
||
// Distance, detector angles, rotation axis and cell are parameter blocks only in the general
|
||
// seven-block residual; the reduced one bakes them in, so there is nothing left to configure.
|
||
if (!beam_and_orientation_only) {
|
||
if (!data.refine_detector_angles) {
|
||
problem.SetParameterBlockConstant(detector_rot);
|
||
} else {
|
||
const double rot_range = 3.0 / 180.0 * PI;
|
||
for (int i = 0; i < 2; ++i) {
|
||
problem.SetParameterLowerBound(detector_rot, i, detector_rot[i] - rot_range);
|
||
problem.SetParameterUpperBound(detector_rot, i, detector_rot[i] + rot_range);
|
||
}
|
||
}
|
||
|
||
if (!data.refine_rotation_axis) {
|
||
problem.SetParameterBlockConstant(rot_vec);
|
||
}
|
||
|
||
if (!data.refine_unit_cell) {
|
||
problem.SetParameterBlockConstant(latt_vec1);
|
||
problem.SetParameterBlockConstant(latt_vec2);
|
||
} else {
|
||
// Parameter bounds
|
||
// Lengths
|
||
for (int i = 0; i < 3; ++i) {
|
||
problem.SetParameterLowerBound(latt_vec1, i, data.min_length_A);
|
||
problem.SetParameterUpperBound(latt_vec1, i, data.max_length_A);
|
||
}
|
||
|
||
if (data.crystal_system == gemmi::CrystalSystem::Monoclinic) {
|
||
const double beta_lo = std::max(1e-6, PI * (data.min_angle_deg / 180.0));
|
||
const double beta_hi = std::min(PI - 1e-6, PI * (data.max_angle_deg / 180.0));
|
||
problem.SetParameterLowerBound(latt_vec2, 0, beta_lo);
|
||
problem.SetParameterUpperBound(latt_vec2, 0, beta_hi);
|
||
} else if (data.crystal_system == gemmi::CrystalSystem::Triclinic) {
|
||
// α, β, γ bounds (radians)
|
||
const double alo = PI * (data.min_angle_deg / 180.0);
|
||
const double ahi = PI * (data.max_angle_deg / 180.0);
|
||
for (int i = 0; i < 3; ++i) {
|
||
problem.SetParameterLowerBound(latt_vec2, i, alo);
|
||
problem.SetParameterUpperBound(latt_vec2, i, ahi);
|
||
}
|
||
} else {
|
||
// Orthorhombic / Tetragonal / Cubic / Hexagonal:
|
||
// latt_vec2 has no meaning for these systems — always freeze it.
|
||
problem.SetParameterBlockConstant(latt_vec2);
|
||
}
|
||
}
|
||
}
|
||
|
||
// Configure solver
|
||
ceres::Solver::Options options;
|
||
// Normal equations, not QR. The problem is very tall and thin - thousands of spots against at
|
||
// most 17 parameters - and that is the shape DENSE_QR handles worst: it copies the Jacobian out
|
||
// of Ceres' row-major storage into a column-major buffer on every solve, and Eigen's blocked
|
||
// Householder then degenerates to the unblocked path because its block size is min(48, columns).
|
||
// Accumulating J^T J reads the Jacobian once instead. Both solve the same damped system, so the
|
||
// step is the same to round-off; the column scaling Ceres applies by default and the LM diagonal
|
||
// keep the squared condition number in hand.
|
||
options.linear_solver_type = ceres::DENSE_NORMAL_CHOLESKY;
|
||
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 = num_threads; // usually 1 (called from many threads); caller may raise it
|
||
ceres::Solver::Summary summary;
|
||
|
||
// Run optimization
|
||
ceres::Solve(options, &problem, &summary);
|
||
|
||
// Only a genuine numerical failure is rejected here: a solve that ran out of iterations or
|
||
// out of time but still descended counts as usable, which is what the real-time caller
|
||
// relies on when it sets max_solver_time. Checked before anything is written back, so a
|
||
// failed refinement leaves data untouched rather than committing half a fit.
|
||
if (!summary.IsSolutionUsable())
|
||
return false;
|
||
|
||
if (data.refine_beam_center) {
|
||
data.beam_corr_x = data.geom.GetBeamX_pxl() - beam[0];
|
||
data.beam_corr_y = data.geom.GetBeamY_pxl() - beam[1];
|
||
data.geom.BeamX_pxl(beam[0]).BeamY_pxl(beam[1]);
|
||
}
|
||
|
||
|
||
if (data.refine_detector_angles)
|
||
data.geom.PoniRot1_rad(detector_rot[0]).PoniRot2_rad(detector_rot[1]);
|
||
|
||
if (data.axis && data.refine_rotation_axis)
|
||
data.axis.value().Axis(Coord(rot_vec[0], rot_vec[1], rot_vec[2]));
|
||
|
||
if (data.crystal_system == gemmi::CrystalSystem::Orthorhombic)
|
||
data.latt = AngleAxisAndCellToLattice(latt_vec0, latt_vec1, PI / 2.0, PI / 2.0, PI / 2.0);
|
||
else if (data.crystal_system == gemmi::CrystalSystem::Tetragonal) {
|
||
latt_vec1[1] = latt_vec1[0];
|
||
data.latt = AngleAxisAndCellToLattice(latt_vec0, latt_vec1, PI / 2.0, PI / 2.0, PI / 2.0);
|
||
} else if (data.crystal_system == gemmi::CrystalSystem::Cubic) {
|
||
latt_vec1[1] = latt_vec1[0];
|
||
latt_vec1[2] = latt_vec1[0];
|
||
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_NORMAL_CHOLESKY; // tall and thin, as above
|
||
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;
|
||
}
|
||
} |