Filip Leonarski
684c3df204
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446 lines
14 KiB
C++
446 lines
14 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 "ScaleAndMerge.h"
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#include <ceres/ceres.h>
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#include <ceres/covariance.h>
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#include <algorithm>
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#include <cmath>
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#include <limits>
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#include <stdexcept>
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#include <tuple>
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#include <unordered_map>
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#include <utility>
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#include <vector>
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namespace {
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struct HKLKey {
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int64_t h = 0;
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int64_t k = 0;
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int64_t l = 0;
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bool is_positive = true; // only relevant if opt.merge_friedel == false
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bool operator==(const HKLKey& o) const noexcept {
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return h == o.h && k == o.k && l == o.l && is_positive == o.is_positive;
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}
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};
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struct HKLKeyHash {
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size_t operator()(const HKLKey& key) const noexcept {
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auto mix = [](uint64_t x) {
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x ^= x >> 33;
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x *= 0xff51afd7ed558ccdULL;
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x ^= x >> 33;
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x *= 0xc4ceb9fe1a85ec53ULL;
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x ^= x >> 33;
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return x;
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};
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const uint64_t a = static_cast<uint64_t>(key.h);
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const uint64_t b = static_cast<uint64_t>(key.k);
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const uint64_t c = static_cast<uint64_t>(key.l);
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const uint64_t d = static_cast<uint64_t>(key.is_positive ? 1 : 0);
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return static_cast<size_t>(mix(a) ^ (mix(b) << 1) ^ (mix(c) << 2) ^ (mix(d) << 3));
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}
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};
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inline int RoundImageId(float image_number, double rounding_step) {
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if (!(rounding_step > 0.0))
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rounding_step = 1.0;
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const double x = static_cast<double>(image_number) / rounding_step;
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const double r = std::round(x) * rounding_step;
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return static_cast<int>(std::llround(r / rounding_step));
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}
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inline double SafeSigma(double s, double min_sigma) {
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if (!std::isfinite(s) || s <= 0.0)
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return min_sigma;
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return std::max(s, min_sigma);
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}
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inline double SafeD(double d) {
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if (!std::isfinite(d) || d <= 0.0)
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return std::numeric_limits<double>::quiet_NaN();
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return d;
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}
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inline int SafeToInt(int64_t x) {
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if (x < std::numeric_limits<int>::min() || x > std::numeric_limits<int>::max())
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throw std::out_of_range("HKL index out of int range for Gemmi");
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return static_cast<int>(x);
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}
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inline double SafeInv(double x, double fallback) {
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if (!std::isfinite(x) || x == 0.0)
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return fallback;
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return 1.0 / x;
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}
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inline HKLKey CanonicalizeHKLKey(const Reflection& r, const ScaleMergeOptions& opt) {
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HKLKey key{};
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key.h = r.h;
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key.k = r.k;
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key.l = r.l;
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key.is_positive = true;
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if (!opt.space_group.has_value()) {
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if (!opt.merge_friedel) {
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const HKLKey neg{-r.h, -r.k, -r.l, true};
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const bool pos = std::tie(key.h, key.k, key.l) >= std::tie(neg.h, neg.k, neg.l);
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if (!pos) {
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key.h = -key.h;
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key.k = -key.k;
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key.l = -key.l;
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key.is_positive = false;
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}
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}
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return key;
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}
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const gemmi::SpaceGroup& sg = *opt.space_group;
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const gemmi::GroupOps gops = sg.operations();
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const gemmi::ReciprocalAsu rasu(&sg);
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const gemmi::Op::Miller in{{SafeToInt(r.h), SafeToInt(r.k), SafeToInt(r.l)}};
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const auto [asu_hkl, sign_plus] = rasu.to_asu_sign(in, gops);
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key.h = asu_hkl[0];
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key.k = asu_hkl[1];
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key.l = asu_hkl[2];
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key.is_positive = opt.merge_friedel ? true : sign_plus;
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return key;
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}
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struct IntensityResidual {
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IntensityResidual(const Reflection& r, double sigma_obs, double wedge_deg, bool refine_partiality)
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: Iobs_(static_cast<double>(r.I)),
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inv_sigma_(SafeInv(sigma_obs, 1.0)),
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delta_phi_rad_(static_cast<double>(r.delta_phi) * M_PI / 180.0),
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lp_(SafeInv(static_cast<double>(r.rlp), 1.0)),
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half_wedge_rad_(wedge_deg * M_PI / 180.0 / 2.0),
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c1_(r.zeta / std::sqrt(2.0)),
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partiality_(r.partiality),
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refine_partiality_(refine_partiality) {}
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template<typename T>
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bool operator()(const T* const k,
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const T* const mosaicity_rad,
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const T* const Itrue,
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T* residual) const {
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T partiality;
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if (refine_partiality_ && mosaicity_rad[0] != 0.0) {
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const T arg_plus = T(delta_phi_rad_ + half_wedge_rad_) * T(c1_) / mosaicity_rad[0];
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const T arg_minus = T(delta_phi_rad_ - half_wedge_rad_) * T(c1_) / mosaicity_rad[0];
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partiality = (ceres::erf(arg_plus) - ceres::erf(arg_minus)) / T(2.0);
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} else
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partiality = T(partiality_);
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const T Ipred = k[0] * partiality * T(lp_) * Itrue[0];
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residual[0] = (Ipred - T(Iobs_)) * T(inv_sigma_);
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return true;
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}
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double Iobs_;
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double inv_sigma_;
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double delta_phi_rad_;
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double lp_;
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double half_wedge_rad_;
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double c1_;
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double partiality_;
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bool refine_partiality_;
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};
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struct ScaleRegularizationResidual {
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explicit ScaleRegularizationResidual(double sigma_k)
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: inv_sigma_(SafeInv(sigma_k, 1.0)) {}
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template <typename T>
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bool operator()(const T* const k, T* residual) const {
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residual[0] = (k[0] - T(1.0)) * T(inv_sigma_);
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return true;
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}
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double inv_sigma_;
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};
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} // namespace
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ScaleMergeResult ScaleAndMergeReflectionsCeres(const std::vector<Reflection>& observations,
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const ScaleMergeOptions& opt) {
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ScaleMergeResult out;
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struct ObsRef {
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const Reflection* r = nullptr;
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int img_id = 0;
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int img_slot = -1;
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int hkl_slot = -1;
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double sigma = 0.0;
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};
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std::vector<ObsRef> obs;
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obs.reserve(observations.size());
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std::unordered_map<int, int> imgIdToSlot;
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imgIdToSlot.reserve(256);
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std::unordered_map<HKLKey, int, HKLKeyHash> hklToSlot;
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hklToSlot.reserve(observations.size());
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for (const auto& r : observations) {
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const double d = SafeD(r.d);
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if (!std::isfinite(d))
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continue;
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if (!std::isfinite(r.I))
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continue;
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if (!std::isfinite(r.zeta) || r.zeta <= 0.0f)
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continue;
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if (!std::isfinite(r.rlp) || r.rlp == 0.0f)
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continue;
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const double sigma = SafeSigma(static_cast<double>(r.sigma), opt.min_sigma);
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const int img_id = RoundImageId(r.image_number, opt.image_number_rounding);
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int img_slot;
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{
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auto it = imgIdToSlot.find(img_id);
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if (it == imgIdToSlot.end()) {
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img_slot = static_cast<int>(imgIdToSlot.size());
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imgIdToSlot.emplace(img_id, img_slot);
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} else {
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img_slot = it->second;
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}
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}
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int hkl_slot;
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try {
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const HKLKey key = CanonicalizeHKLKey(r, opt);
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auto it = hklToSlot.find(key);
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if (it == hklToSlot.end()) {
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hkl_slot = static_cast<int>(hklToSlot.size());
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hklToSlot.emplace(key, hkl_slot);
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} else {
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hkl_slot = it->second;
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}
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} catch (...) {
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continue;
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}
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ObsRef o;
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o.r = &r;
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o.img_id = img_id;
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o.img_slot = img_slot;
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o.hkl_slot = hkl_slot;
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o.sigma = sigma;
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obs.push_back(o);
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}
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const int nimg = static_cast<int>(imgIdToSlot.size());
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const int nhkl = static_cast<int>(hklToSlot.size());
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out.image_scale_k.assign(nimg, 1.0);
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out.image_ids.assign(nimg, 0);
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for (const auto& kv : imgIdToSlot) {
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out.image_ids[kv.second] = kv.first;
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}
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std::vector<double> k(nimg, 1.0);
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std::vector<double> Itrue(nhkl, 0.0);
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auto deg2rad = [](double deg) { return deg * M_PI / 180.0; };
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// Mosaicity: always per-image
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std::vector<double> mosaicity_rad(nimg, deg2rad(opt.mosaicity_init_deg));
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// Initialize Itrue from per-HKL median of observed intensities
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{
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std::vector<std::vector<double>> per_hkl_I(nhkl);
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for (const auto& o : obs) {
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per_hkl_I[o.hkl_slot].push_back(static_cast<double>(o.r->I));
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}
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for (int h = 0; h < nhkl; ++h) {
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auto& v = per_hkl_I[h];
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if (v.empty()) {
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Itrue[h] = std::max(opt.min_sigma, 1e-6);
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continue;
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}
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std::nth_element(v.begin(), v.begin() + static_cast<long>(v.size() / 2), v.end());
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double med = v[v.size() / 2];
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if (!std::isfinite(med) || med <= opt.min_sigma)
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med = opt.min_sigma;
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Itrue[h] = med;
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}
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}
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ceres::Problem problem;
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const bool refine_partiality = opt.wedge_deg > 0.0;
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for (const auto& o : obs) {
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auto* cost = new ceres::AutoDiffCostFunction<IntensityResidual, 1, 1, 1, 1>(
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new IntensityResidual(*o.r, o.sigma, opt.wedge_deg, refine_partiality));
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problem.AddResidualBlock(cost,
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nullptr, // no loss function
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&k[o.img_slot],
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&mosaicity_rad[o.img_slot],
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&Itrue[o.hkl_slot]);
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}
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// Optional Kabsch-like regularization for k
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if (opt.regularize_scale_to_one) {
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for (int i = 0; i < nimg; ++i) {
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auto* rcost = new ceres::AutoDiffCostFunction<ScaleRegularizationResidual, 1, 1>(
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new ScaleRegularizationResidual(opt.scale_regularization_sigma));
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problem.AddResidualBlock(rcost, nullptr, &k[i]);
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}
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}
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// Fix gauge freedom
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if (opt.fix_first_image_scale && nimg > 0) {
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k[0] = 1.0;
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problem.SetParameterBlockConstant(&k[0]);
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}
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// Mosaicity refinement + bounds
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if (!opt.refine_mosaicity) {
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for (int i = 0; i < nimg; ++i)
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problem.SetParameterBlockConstant(&mosaicity_rad[i]);
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} else {
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const double min_rad = deg2rad(opt.mosaicity_min_deg);
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const double max_rad = deg2rad(opt.mosaicity_max_deg);
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for (int i = 0; i < nimg; ++i) {
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problem.SetParameterLowerBound(&mosaicity_rad[i], 0, min_rad);
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problem.SetParameterUpperBound(&mosaicity_rad[i], 0, max_rad);
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}
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}
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// Force k[i] > 0 for all images (scale factors must be positive)
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{
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constexpr double k_floor = 1e-8;
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for (int i = 0; i < nimg; ++i) {
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if (!(opt.fix_first_image_scale && i == 0)) {
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problem.SetParameterLowerBound(&k[i], 0, k_floor);
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}
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}
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}
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ceres::Solver::Options options;
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options.linear_solver_type = ceres::SPARSE_NORMAL_CHOLESKY;
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options.minimizer_progress_to_stdout = false;
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options.logging_type = ceres::LoggingType::SILENT;
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options.max_num_iterations = opt.max_num_iterations;
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options.max_solver_time_in_seconds = opt.max_solver_time_s;
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ceres::Solver::Summary summary;
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ceres::Solve(options, &problem, &summary);
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// --- Export per-image results ---
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for (int i = 0; i < nimg; ++i)
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out.image_scale_k[i] = k[i];
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out.mosaicity_deg.resize(nimg);
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for (int i = 0; i < nimg; ++i)
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out.mosaicity_deg[i] = mosaicity_rad[i] * 180.0 / M_PI;
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// --- Compute goodness-of-fit (reduced chi-squared) ---
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const int n_obs = static_cast<int>(obs.size());
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// Count free parameters: nhkl Itrue + per-image (k + mosaicity) minus fixed ones
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int n_params = nhkl;
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for (int i = 0; i < nimg; ++i) {
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if (!(opt.fix_first_image_scale && i == 0))
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n_params += 1; // k
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if (opt.refine_mosaicity)
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n_params += 1; // mosaicity
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}
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const double half_wedge_rad = opt.wedge_deg * M_PI / 180.0 / 2.0;
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double sum_r2 = 0.0;
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for (const auto& o : obs) {
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const int i = o.img_slot;
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const int h = o.hkl_slot;
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const double ki = k[i];
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const double mosa_i = mosaicity_rad[i];
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const double c2 = o.r->zeta / (std::sqrt(2.0) * mosa_i);
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const double delta_phi_rad = static_cast<double>(o.r->delta_phi) * M_PI / 180.0;
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const double partiality = refine_partiality
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? (std::erf((delta_phi_rad + half_wedge_rad) * c2)
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- std::erf((delta_phi_rad - half_wedge_rad) * c2)) / 2.0
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: o.r->partiality;
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const double lp = SafeInv(o.r->rlp, 1.0);
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const double Ipred = ki * partiality * lp * Itrue[h];
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const double resid = (Ipred - static_cast<double>(o.r->I)) / o.sigma;
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sum_r2 += resid * resid;
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}
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const double gof2 = (n_obs > n_params) ? sum_r2 / static_cast<double>(n_obs - n_params) : 1.0;
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out.gof2 = gof2;
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// --- Covariance: σ(I_true) from (J^T W J)^{-1} scaled by GoF² ---
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std::vector<HKLKey> slotToHKL(nhkl);
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for (const auto& kv : hklToSlot) {
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slotToHKL[kv.second] = kv.first;
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}
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out.merged.resize(nhkl);
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for (int h = 0; h < nhkl; ++h) {
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out.merged[h].h = slotToHKL[h].h;
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out.merged[h].k = slotToHKL[h].k;
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out.merged[h].l = slotToHKL[h].l;
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out.merged[h].I = Itrue[h];
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out.merged[h].sigma = std::numeric_limits<double>::quiet_NaN();
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out.merged[h].d = 0.0;
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}
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// Populate d from median of observations per HKL
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{
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std::vector<std::vector<double>> per_hkl_d(nhkl);
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for (const auto& o : obs) {
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const double d_val = static_cast<double>(o.r->d);
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if (std::isfinite(d_val) && d_val > 0.0)
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per_hkl_d[o.hkl_slot].push_back(d_val);
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}
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for (int h = 0; h < nhkl; ++h) {
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auto& v = per_hkl_d[h];
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if (!v.empty()) {
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std::nth_element(v.begin(), v.begin() + static_cast<long>(v.size() / 2), v.end());
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out.merged[h].d = v[v.size() / 2];
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}
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}
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}
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ceres::Covariance::Options cov_options;
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cov_options.algorithm_type = ceres::SPARSE_QR;
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ceres::Covariance covariance(cov_options);
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std::vector<std::pair<const double*, const double*>> covariance_blocks;
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covariance_blocks.reserve(nhkl);
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for (int h = 0; h < nhkl; ++h) {
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covariance_blocks.emplace_back(&Itrue[h], &Itrue[h]);
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}
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if (covariance.Compute(covariance_blocks, &problem)) {
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for (int h = 0; h < nhkl; ++h) {
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double var_I = 0.0;
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covariance.GetCovarianceBlock(&Itrue[h], &Itrue[h], &var_I);
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// σ(I) = I * sqrt( var(I) * GoF² )
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out.merged[h].sigma = std::sqrt(var_I * gof2);
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}
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}
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return out;
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}
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