leonarski_f
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570 lines
19 KiB
C++
570 lines
19 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 <thread>
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#include <iostream>
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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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/// CrystFEL-like log-scaling residual
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///
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/// residual = w * [ ln(I_obs) - ln(G) - ln(partiality) - ln(lp) - ln(I_true) ]
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///
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/// Only observations with I_obs > 0 should be fed in (the caller skips the rest).
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/// G and I_true are constrained to be positive via Ceres lower bounds.
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struct LogIntensityResidual {
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LogIntensityResidual(const Reflection& r, double sigma_obs, double wedge_deg, bool refine_partiality)
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: log_Iobs_(std::log(std::max(static_cast<double>(r.I), 1e-30))),
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weight_(SafeInv(sigma_obs / std::max(static_cast<double>(r.I), 1e-30), 1.0)),
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delta_phi_(r.delta_phi_deg),
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log_lp_(std::log(std::max(SafeInv(static_cast<double>(r.rlp), 1.0), 1e-30))),
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half_wedge_(wedge_deg / 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 G,
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const T* const mosaicity,
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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[0] != 0.0) {
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const T arg_plus = T(delta_phi_ + half_wedge_) * T(c1_) / mosaicity[0];
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const T arg_minus = T(delta_phi_ - half_wedge_) * T(c1_) / mosaicity[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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}
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// Clamp partiality away from zero so log is safe
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const T min_p = T(1e-30);
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if (partiality < min_p)
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partiality = min_p;
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// ln(I_pred) = ln(G) + ln(partiality) + ln(lp) + ln(Itrue)
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const T log_Ipred = ceres::log(G[0]) + ceres::log(partiality) + T(log_lp_) + ceres::log(Itrue[0]);
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residual[0] = (log_Ipred - T(log_Iobs_)) * T(weight_);
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return true;
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}
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double log_Iobs_;
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double weight_; // w_i ≈ I_obs / sigma_obs (relative weight in log-space)
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double delta_phi_;
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double log_lp_;
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double half_wedge_;
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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 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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weight_(SafeInv(sigma_obs, 1.0)),
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delta_phi_(r.delta_phi_deg),
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lp_(SafeInv(static_cast<double>(r.rlp), 1.0)),
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half_wedge_(wedge_deg / 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 G,
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const T *const mosaicity,
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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[0] != 0.0) {
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const T arg_plus = T(delta_phi_ + half_wedge_) * T(c1_) / mosaicity[0];
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const T arg_minus = T(delta_phi_ - half_wedge_) * T(c1_) / mosaicity[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 = G[0] * partiality * T(lp_) * Itrue[0];
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residual[0] = (Ipred - T(Iobs_)) * T(weight_);
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return true;
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}
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double Iobs_;
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double weight_;
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double delta_phi_;
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double lp_;
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double half_wedge_;
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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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struct SmoothnessRegularizationResidual {
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explicit SmoothnessRegularizationResidual(double sigma)
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: inv_sigma_(SafeInv(sigma, 1.0)) {}
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template <typename T>
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bool operator()(const T* const k0,
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const T* const k1,
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const T* const k2,
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T* residual) const {
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residual[0] = (ceres::log(k0[0]) + ceres::log(k2[0]) - T(2.0) * ceres::log(k1[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_g.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> g(nimg, 1.0);
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std::vector<double> Itrue(nhkl, 0.0);
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// Mosaicity: always per-image
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std::vector<double> mosaicity(nimg, opt.mosaicity_init_deg);
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for (int i = 0; i < nimg && i < opt.mosaicity_init_deg_vec.size(); ++i) {
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if (opt.mosaicity_init_deg_vec[i] > 0.0)
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mosaicity[i] = opt.mosaicity_init_deg_vec[i];
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}
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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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std::vector<bool> is_valid_hkl_slot(nhkl, false);
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for (const auto& o : obs) {
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size_t mos_slot = opt.per_image_mosaicity ? o.img_slot : 0;
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if (opt.log_scaling_residual) {
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// Log residual requires positive I_obs
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if (o.r->I <= 0.0f)
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continue;
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auto* cost = new ceres::AutoDiffCostFunction<LogIntensityResidual, 1, 1, 1, 1>(
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new LogIntensityResidual(*o.r, o.sigma, opt.wedge_deg, refine_partiality));
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problem.AddResidualBlock(cost,
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nullptr,
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&g[o.img_slot],
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&mosaicity[mos_slot],
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&Itrue[o.hkl_slot]);
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is_valid_hkl_slot[o.hkl_slot] = true;
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} else {
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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,
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&g[o.img_slot],
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&mosaicity[mos_slot],
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&Itrue[o.hkl_slot]);
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is_valid_hkl_slot[o.hkl_slot] = true;
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}
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}
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if (opt.smoothen_g) {
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for (int i = 0; i < nimg - 2; ++i) {
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auto *cost = new ceres::AutoDiffCostFunction<SmoothnessRegularizationResidual, 1, 1, 1, 1>(
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new SmoothnessRegularizationResidual(0.05));
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problem.AddResidualBlock(cost, nullptr,
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&g[i],
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&g[i + 1],
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&g[i + 2]);
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}
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}
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if (opt.smoothen_mos && opt.per_image_mosaicity) {
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for (int i = 0; i < nimg - 2; ++i) {
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auto *cost = new ceres::AutoDiffCostFunction<SmoothnessRegularizationResidual, 1, 1, 1, 1>(
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new SmoothnessRegularizationResidual(0.05));
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problem.AddResidualBlock(cost, nullptr,
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&mosaicity[i],
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&mosaicity[i + 1],
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&mosaicity[i + 2]);
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}
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}
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// Scaling factors must be always positive
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for (int i = 0; i < nimg; ++i)
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problem.SetParameterLowerBound(&g[i], 0, 1e-12);
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// For log residual, Itrue must stay positive
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if (opt.log_scaling_residual) {
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for (int h = 0; h < nhkl; ++h) {
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if (is_valid_hkl_slot[h])
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problem.SetParameterLowerBound(&Itrue[h], 0, 1e-12);
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}
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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 < (opt.per_image_mosaicity ? nimg : 1); ++i)
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problem.SetParameterBlockConstant(&mosaicity[i]);
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} else {
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for (int i = 0; i < (opt.per_image_mosaicity ? nimg : 1); ++i) {
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problem.SetParameterLowerBound(&mosaicity[i], 0, opt.mosaicity_min_deg);
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problem.SetParameterUpperBound(&mosaicity[i], 0, opt.mosaicity_max_deg);
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}
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}
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// use all available threads
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unsigned int hw = std::thread::hardware_concurrency();
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if (hw == 0)
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hw = 1; // fallback
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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 = true;
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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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options.num_threads = static_cast<int>(hw);
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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_g[i] = g[i];
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out.mosaicity_deg.resize(nimg);
|
||
for (int i = 0; i < nimg; ++i)
|
||
out.mosaicity_deg[i] = opt.per_image_mosaicity ? mosaicity[i] : mosaicity[0];
|
||
|
||
std::vector<HKLKey> slotToHKL(nhkl);
|
||
for (const auto& kv : hklToSlot) {
|
||
slotToHKL[kv.second] = kv.first;
|
||
}
|
||
|
||
out.merged.resize(nhkl);
|
||
for (int h = 0; h < nhkl; ++h) {
|
||
out.merged[h].h = slotToHKL[h].h;
|
||
out.merged[h].k = slotToHKL[h].k;
|
||
out.merged[h].l = slotToHKL[h].l;
|
||
out.merged[h].I = Itrue[h];
|
||
out.merged[h].sigma = 0.0;
|
||
out.merged[h].d = 0.0;
|
||
}
|
||
|
||
// Populate d from median of observations per HKL
|
||
{
|
||
std::vector<std::vector<double>> per_hkl_d(nhkl);
|
||
for (const auto& o : obs) {
|
||
const double d_val = static_cast<double>(o.r->d);
|
||
if (std::isfinite(d_val) && d_val > 0.0)
|
||
per_hkl_d[o.hkl_slot].push_back(d_val);
|
||
}
|
||
for (int h = 0; h < nhkl; ++h) {
|
||
auto& v = per_hkl_d[h];
|
||
if (!v.empty()) {
|
||
std::nth_element(v.begin(), v.begin() + static_cast<long>(v.size() / 2), v.end());
|
||
out.merged[h].d = v[v.size() / 2];
|
||
}
|
||
}
|
||
}
|
||
|
||
std::cout << summary.FullReport() << std::endl;
|
||
|
||
// Merge (XDS/XSCALE style: inverse-variance weighted mean)
|
||
{
|
||
struct HKLAccum {
|
||
double sum_wI = 0.0; // Σ(w_i * I_i)
|
||
double sum_w = 0.0; // Σ(w_i)
|
||
double sum_wI2 = 0.0; // Σ(w_i * I_i²) for internal variance
|
||
int count = 0;
|
||
};
|
||
std::vector<HKLAccum> accum(nhkl);
|
||
|
||
const double half_wedge = opt.wedge_deg / 2.0;
|
||
|
||
for (const auto& o : obs) {
|
||
const Reflection& r = *o.r;
|
||
const double lp = SafeInv(static_cast<double>(r.rlp), 1.0);
|
||
const double G_i = g[o.img_slot];
|
||
|
||
// Compute partiality with refined mosaicity
|
||
double partiality;
|
||
size_t mos_slot = opt.per_image_mosaicity ? o.img_slot : 0;
|
||
if (refine_partiality && mosaicity[mos_slot] > 0.0) {
|
||
double c1 = r.zeta / std::sqrt(2.0);
|
||
double arg_plus = (r.delta_phi_deg + half_wedge) * c1 / mosaicity[mos_slot];
|
||
double arg_minus = (r.delta_phi_deg - half_wedge) * c1 / mosaicity[mos_slot];
|
||
partiality = (std::erf(arg_plus) - std::erf(arg_minus)) / 2.0;
|
||
} else {
|
||
partiality = r.partiality;
|
||
}
|
||
|
||
if (partiality > opt.min_partiality_for_merge) {
|
||
const double correction = G_i * partiality * lp;
|
||
if (correction <= 0.0) continue;
|
||
|
||
// Corrected intensity and propagated sigma
|
||
const double I_corr = static_cast<double>(r.I) / correction;
|
||
const double sigma_corr = o.sigma / correction;
|
||
|
||
// XDS/XSCALE style: weight = 1/σ²
|
||
const double w = 1.0 / (sigma_corr * sigma_corr);
|
||
|
||
auto& a = accum[o.hkl_slot];
|
||
a.sum_wI += w * I_corr;
|
||
a.sum_w += w;
|
||
a.sum_wI2 += w * I_corr * I_corr;
|
||
a.count++;
|
||
}
|
||
}
|
||
|
||
// Populate merged reflections
|
||
for (int h = 0; h < nhkl; ++h) {
|
||
const auto& a = accum[h];
|
||
|
||
if (a.sum_w > 0.0) {
|
||
// XDS/XSCALE style: weighted mean
|
||
out.merged[h].I = a.sum_wI / a.sum_w;
|
||
|
||
// Standard error of weighted mean: σ = 1/√(Σw)
|
||
double sigma_stat = std::sqrt(1.0 / a.sum_w);
|
||
|
||
if (a.count >= 2) {
|
||
// XDS/XSCALE style: use internal consistency to estimate sigma
|
||
// σ² = (1/(n-1)) * Σw_i(I_i - <I>)² / Σw_i
|
||
const double var_internal = a.sum_wI2 - (a.sum_wI * a.sum_wI) / a.sum_w;
|
||
const double sigma_int = std::sqrt(var_internal / (a.sum_w * (a.count - 1)));
|
||
|
||
// Take the larger of statistical and internal sigma
|
||
// (XDS approach: if data is worse than expected, use internal estimate)
|
||
out.merged[h].sigma = std::max(sigma_stat, sigma_int);
|
||
} else {
|
||
// Single observation: use propagated sigma
|
||
out.merged[h].sigma = sigma_stat;
|
||
}
|
||
}
|
||
// else: I and sigma stay at initialized values (Itrue[h] and 0.0)
|
||
}
|
||
}
|
||
|
||
return out;
|
||
}
|