leonarski_fandClaude Opus 5.5
3c4d730f89
Each crystal's (psi_x, psi_y) tilt was refined by a Ceres problem rebuilt for every inner iteration: one auto-differentiated residual block and one Cauchy loss per reflection. With the scaling reference cleaned up it was 70% of the stills scale/merge. The same objective - Cauchy on the sigma-weighted intensity residuals plus the Gaussian prior on the tilt, the tilt bounded - is now minimised by a small Levenberg-Marquardt on the rho'-reweighted 2x2 normal equations, the tilt applied to all reflections through one rotation matrix and the Jacobian taken by central differences. It reaches the same answer to optimizer precision: on the new synthetic test (StillsPartialityRefine_RecoversTheTilt) partiality error 0.0559 -> 0.03019 with Ceres and 0.03016 with this, mean tilt 0.02065 vs 0.02067 deg; on two 25k-frame XFEL runs (--mode scale) CC1/2, I/sigma, R_pim and the CC against a deposited lysozyme model agree to 0.0005, mean |dpsi| 0.023 deg on both. Scale+merge 6.9 -> 3.0 s (--mode scale, loaded box). Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
449 lines
21 KiB
C++
449 lines
21 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 "StillsPartialityRefine.h"
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#include <algorithm>
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#include <array>
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#include <atomic>
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#include <cmath>
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#include <future>
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#include <thread>
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#include <vector>
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#include <ceres/rotation.h>
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#include "Merge.h"
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#include "../../common/ThreadAffinity.h"
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namespace {
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constexpr size_t MIN_FIT_REFLECTIONS = 20;
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constexpr double kRadToDeg = 180.0 / 3.14159265358979323846;
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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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// One accepted reflection reduced to the physical partiality fit: the base reciprocal vector q (crystal
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// frame at the stored per-image orientation), the reference full intensity, the measured intensity, the
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// reciprocal of the prescaling correction (the factor that turns a true intensity back into counts)
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// and the weight. dist_ewald / partiality are recomputed from q under the refined tilt.
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struct FitObs {
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double qx, qy, qz;
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double Iref;
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double Iobs;
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double inv_corr; // 1 / (prescaling_corr * qe_corr * flight_corr)
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double weight; // 1 / sigma
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};
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double VecLen(double x, double y, double z) { return std::sqrt(x * x + y * y + z * z); }
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// Analytic partiality for a reflection whose base reciprocal vector is q, tilted by (psi_x, psi_y).
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// Mirrors BraggPrediction: dist_ewald = |S| - 1/lambda with S = q_rot + S0, and
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// p = exp(-dist_ewald^2 / 2 sigma^2), sigma^2 = gamma0^2 + (gamma_e*d*)^2 + (bw*|q_z|)^2 - three
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// independent broadenings added in quadrature: the reciprocal-lattice point's own radius (gamma0,
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// ~1/domain size, resolution-INdependent), the mosaic/divergence spread (gamma_e*d*, proportional to
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// d*) and the bandwidth smear along the beam. In practice the fit below returns gamma_e ~ 0 and the
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// width is essentially gamma0 - see there.
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double ComputeP(double qx, double qy, double qz,
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double psi_x, double psi_y,
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double s0x, double s0y, double s0z, double inv_lambda,
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double gamma0, double gamma_e, double bw) {
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double aa[3] = {psi_x, psi_y, 0.0};
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double q[3] = {qx, qy, qz};
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double qr[3];
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ceres::AngleAxisRotatePoint(aa, q, qr);
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const double Sx = qr[0] + s0x, Sy = qr[1] + s0y, Sz = qr[2] + s0z;
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const double de = std::sqrt(Sx * Sx + Sy * Sy + Sz * Sz) - inv_lambda;
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const double dstar = std::sqrt(qr[0] * qr[0] + qr[1] * qr[1] + qr[2] * qr[2]);
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const double sig_ang = gamma_e * dstar;
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const double sbw = bw * std::fabs(qr[2]);
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const double sig2 = gamma0 * gamma0 + sig_ang * sig_ang + sbw * sbw;
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if (!(sig2 > 0.0))
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return 1.0;
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return std::exp(-0.5 * de * de / sig2);
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}
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// Robust per-crystal scale G (linear in G given the model coefficients), identical objective to
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// ScaleOnTheFly::SolveScaleIRLS: minimise sum Cauchy_k( w (G*coeff - Iobs) ) over G >= 0.
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double SolveScaleIRLS(const std::vector<double> &coeff, const std::vector<double> &Iobs,
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const std::vector<double> &weight, double robust_k) {
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auto weighted_scale = [&](auto robust_weight) {
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double num = 0.0, den = 0.0;
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for (size_t i = 0; i < coeff.size(); ++i) {
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const double rw = robust_weight(i);
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const double w2 = weight[i] * weight[i];
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num += rw * w2 * coeff[i] * Iobs[i];
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den += rw * w2 * coeff[i] * coeff[i];
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}
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return den > 0.0 ? num / den : NAN;
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};
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double G = weighted_scale([](size_t) { return 1.0; });
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if (!std::isfinite(G))
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return 1.0;
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G = std::max(0.0, G);
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const double k2 = robust_k * robust_k;
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for (int iter = 0; iter < 30; ++iter) {
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const double G_prev = G;
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const double G_next = weighted_scale([&](size_t i) {
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const double res = weight[i] * (G * coeff[i] - Iobs[i]);
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return 1.0 / (1.0 + res * res / k2);
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});
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if (!std::isfinite(G_next))
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break;
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G = std::max(0.0, G_next);
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if (std::abs(G - G_prev) <= 1e-7 * std::max(G, 1.0))
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break;
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}
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return G;
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}
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// The rotation by the angle-axis vector (psi_x, psi_y, 0) as a matrix (Rodrigues), so that one tilt
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// is applied to all the reflections of a crystal by a matrix product each.
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std::array<double, 9> TiltMatrix(double psi_x, double psi_y) {
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const double theta = std::sqrt(psi_x * psi_x + psi_y * psi_y);
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if (theta < 1e-15)
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return {1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0};
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const double kx = psi_x / theta, ky = psi_y / theta;
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const double c = std::cos(theta), s = std::sin(theta), t = 1.0 - c;
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return {c + t * kx * kx, t * kx * ky, s * ky,
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t * kx * ky, c + t * ky * ky, -s * kx,
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-s * ky, s * kx, c};
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}
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// ComputeP with the tilt given as its rotation matrix.
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struct TiltModel {
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double s0x, s0y, s0z, inv_lambda;
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double gamma0, gamma_e, bw;
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double P(const std::array<double, 9> &R, const FitObs &o) const {
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const double qx = R[0] * o.qx + R[1] * o.qy + R[2] * o.qz;
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const double qy = R[3] * o.qx + R[4] * o.qy + R[5] * o.qz;
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const double qz = R[6] * o.qx + R[7] * o.qy + R[8] * o.qz;
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const double de = VecLen(qx + s0x, qy + s0y, qz + s0z) - inv_lambda;
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const double sig_ang = gamma_e * VecLen(qx, qy, qz);
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const double sbw = bw * std::fabs(qz);
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const double sig2 = gamma0 * gamma0 + sig_ang * sig_ang + sbw * sbw;
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if (!(sig2 > 0.0))
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return 1.0;
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return std::exp(-0.5 * de * de / sig2);
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}
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};
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// Refine the orientation tilt (psi_x, psi_y) holding the scale G fixed: minimise
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// sum_i rho(r_i^2) / 2 + |psi / sigma_prior|^2 / 2, r_i = weight_i (G p_i(psi) Iref_i / corr_i - Iobs_i),
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// with the Cauchy rho(s) = k^2 log(1 + s / k^2) - the intensity-space objective of ScaleOnTheFly, and
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// the Gaussian prior a proper MAP term since r_i is already in sigma units. Levenberg-Marquardt on the
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// normal equations reweighted by rho', the Jacobian by central differences, every trial tilt held inside
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// the bound. Returns false when the cost cannot be evaluated, leaving psi as it came in.
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bool RefineTilt(const std::vector<FitObs> &obs, const TiltModel &model, double G, double robust_k,
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double inv_sigma_prior, double max_tilt, double psi[2]) {
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constexpr int MAX_ITERATIONS = 25;
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constexpr double STEP = 1e-7; // rad, for the central differences
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const double k2 = robust_k * robust_k;
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const double prior2 = inv_sigma_prior * inv_sigma_prior;
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auto cost = [&](const double p[2]) {
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const auto R = TiltMatrix(p[0], p[1]);
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double c = 0.5 * prior2 * (p[0] * p[0] + p[1] * p[1]);
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for (const auto &o: obs) {
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const double r = o.weight * (G * model.P(R, o) * o.inv_corr * o.Iref - o.Iobs);
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c += 0.5 * k2 * std::log1p(r * r / k2);
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}
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return c;
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};
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double current = cost(psi);
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if (!std::isfinite(current))
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return false;
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double lambda = 1e-4;
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for (int iter = 0; iter < MAX_ITERATIONS; ++iter) {
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const auto R = TiltMatrix(psi[0], psi[1]);
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const auto Rxp = TiltMatrix(psi[0] + STEP, psi[1]), Rxm = TiltMatrix(psi[0] - STEP, psi[1]);
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const auto Ryp = TiltMatrix(psi[0], psi[1] + STEP), Rym = TiltMatrix(psi[0], psi[1] - STEP);
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double A00 = prior2, A01 = 0.0, A11 = prior2;
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double b0 = prior2 * psi[0], b1 = prior2 * psi[1];
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for (const auto &o: obs) {
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const double a = o.weight * G * o.inv_corr * o.Iref;
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const double r = a * model.P(R, o) - o.weight * o.Iobs;
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const double jx = a * (model.P(Rxp, o) - model.P(Rxm, o)) / (2.0 * STEP);
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const double jy = a * (model.P(Ryp, o) - model.P(Rym, o)) / (2.0 * STEP);
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const double w = 1.0 / (1.0 + r * r / k2); // rho'
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A00 += w * jx * jx;
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A01 += w * jx * jy;
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A11 += w * jy * jy;
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b0 += w * jx * r;
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b1 += w * jy * r;
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}
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bool accepted = false;
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double trial[2] = {psi[0], psi[1]};
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double trial_cost = current;
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while (lambda < 1e10) {
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const double M00 = A00 * (1.0 + lambda), M11 = A11 * (1.0 + lambda);
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const double det = M00 * M11 - A01 * A01;
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if (!(det > 0.0))
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break;
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trial[0] = std::clamp(psi[0] - (M11 * b0 - A01 * b1) / det, -max_tilt, max_tilt);
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trial[1] = std::clamp(psi[1] - (M00 * b1 - A01 * b0) / det, -max_tilt, max_tilt);
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trial_cost = cost(trial);
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if (std::isfinite(trial_cost) && trial_cost < current) {
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accepted = true;
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lambda = std::max(lambda / 10.0, 1e-12);
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break;
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}
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lambda *= 10.0;
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}
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if (!accepted)
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break;
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const double step = std::hypot(trial[0] - psi[0], trial[1] - psi[1]);
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const double decrease = current - trial_cost;
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psi[0] = trial[0];
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psi[1] = trial[1];
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current = trial_cost;
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if (decrease <= 1e-6 * current || step <= 1e-10)
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break;
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}
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return true;
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}
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}
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StillsPartialityRefine::StillsPartialityRefine(const DiffractionExperiment &x)
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: experiment_(x),
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hkl_key_generator_(x.GetScalingSettings().GetMergeFriedel(), x.GetSpaceGroupOrP1()),
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d_min_limit_(x.GetScalingSettings().GetHighResolutionLimit_A()),
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d_max_limit_(x.GetScalingSettings().GetLowResolutionLimit_A()),
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min_partiality_(x.GetScalingSettings().GetMinPartiality()),
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bandwidth_sigma_(x.GetBandwidthFWHM().value_or(0.0f) / 2.3548f) {}
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double StillsPartialityRefine::RefineOne(IntegrationOutcome &outcome,
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const std::map<HKLKey, double> &reference) const {
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if (outcome.reflections.empty())
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return 0.0;
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const Coord Astar = outcome.latt.Astar();
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const Coord Bstar = outcome.latt.Bstar();
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const Coord Cstar = outcome.latt.Cstar();
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const Coord S0 = outcome.geom.GetScatteringVector();
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const double inv_lambda = 1.0 / outcome.geom.GetWavelength_A();
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const double bw = bandwidth_sigma_;
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auto base_q = [&](const Reflection &r) {
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return Astar * static_cast<float>(r.h) + Bstar * static_cast<float>(r.k)
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+ Cstar * static_cast<float>(r.l);
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};
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// Collect the reflections that constrain the fit (accepted, non-ice, finite, present in the reference).
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std::vector<FitObs> obs;
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obs.reserve(outcome.reflections.size());
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// Moments of the excitation error against resolution: de^2 ~ gamma0^2 + gamma_e^2 * d*^2, fitted per
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// crystal by ordinary least squares on (d*^2, de^2). Both components come out of the data.
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double m_n = 0.0, m_x = 0.0, m_xx = 0.0, m_y = 0.0, m_xy = 0.0;
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size_t n_de = 0;
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for (const Reflection &r: outcome.reflections) {
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if (r.on_ice_ring || !AcceptReflection(r, d_min_limit_, d_max_limit_))
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continue;
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if (!std::isfinite(r.I) || !std::isfinite(r.sigma) || r.sigma <= 0.0f)
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continue;
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const auto it = reference.find(hkl_key_generator_(r));
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if (it == reference.end() || !std::isfinite(it->second))
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continue;
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const Coord q = base_q(r);
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obs.push_back(FitObs{
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.qx = q.x, .qy = q.y, .qz = q.z,
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.Iref = it->second,
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.Iobs = static_cast<double>(r.I),
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.inv_corr = SafeInv(r.prescaling_corr * r.qe_corr * r.flight_corr, 1.0),
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.weight = SafeInv(r.sigma, 1.0),
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});
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// Excitation error at the stored orientation (psi = 0), collected as the moments of de^2 against
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// d*^2, so BOTH width components are fitted rather than one being forced to zero. Forcing the
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// width to be purely angular (gamma0 = 0) pins it to the high-resolution edge - it is fitted over
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// a d*^2-dense population - and it then collapses at low d*, giving p ~ 0 for reflections that
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// were plainly recorded, which inflated the merged low-resolution intensity scale ~3.6x.
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const double dstar = VecLen(q.x, q.y, q.z);
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const double de0 = VecLen(q.x + S0.x, q.y + S0.y, q.z + S0.z) - inv_lambda;
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if (dstar > 1e-9) {
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const double x = dstar * dstar, y = de0 * de0;
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m_n += 1.0;
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m_x += x;
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m_xx += x * x;
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m_y += y;
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m_xy += x * y;
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++n_de;
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}
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}
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if (obs.size() < MIN_FIT_REFLECTIONS || n_de == 0)
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return 0.0;
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// Solve the 2x2 normal equations for de^2 = A + B d*^2. A degenerate spread in d* (all reflections in
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// one shell) leaves B undetermined, so fall back to the pure angular width there; a negative fitted
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// component is unphysical and is clamped to zero, which reduces to the previous model.
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//
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// Measured outcome, worth knowing before touching this: the fit does NOT split the width between the
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// two terms - it returns gamma0 ~ 4e-4 1/A and gamma_e ~ 0 (their cross-over sits at d = 0.3 A, far
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// outside any measured range), i.e. a width constant in the LINEAR Ewald distance. That is
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// structural, not a fluke: prediction accepts reflections on a fixed linear |dist_ewald| cutoff, so
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// the accepted population's de^2 is flat in d*^2 by construction and the slope is genuinely ~0. The
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// truncated population cannot constrain an angular term; the resolution-independent one is what the
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// data actually support.
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const double det = m_n * m_xx - m_x * m_x;
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double A = 0.0, B = 0.0;
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if (std::fabs(det) > 1e-30) {
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A = (m_xx * m_y - m_x * m_xy) / det;
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B = (m_n * m_xy - m_x * m_y) / det;
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} else {
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B = m_x > 0.0 ? m_y / m_x : 0.0;
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}
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const double gamma0 = std::sqrt(std::max(0.0, A));
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const double gamma_e_fit = std::max(std::sqrt(std::max(0.0, B)), 1e-9);
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const double gamma_e = settings_.gamma_e > 0.0 ? settings_.gamma_e : gamma_e_fit;
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double psi[2] = {0.0, 0.0};
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double G = 1.0;
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const TiltModel model{.s0x = S0.x, .s0y = S0.y, .s0z = S0.z, .inv_lambda = inv_lambda,
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.gamma0 = gamma0, .gamma_e = gamma_e, .bw = bw};
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const bool refine_tilt = obs.size() >= settings_.min_reflections;
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const int inner = refine_tilt ? settings_.inner_iterations : 1;
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for (int it = 0; it < inner; ++it) {
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// (1) Solve G given the current partialities.
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std::vector<double> coeff(obs.size()), Iobs(obs.size()), weight(obs.size());
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for (size_t j = 0; j < obs.size(); ++j) {
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const double p = ComputeP(obs[j].qx, obs[j].qy, obs[j].qz, psi[0], psi[1],
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S0.x, S0.y, S0.z, inv_lambda, gamma0, gamma_e, bw);
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coeff[j] = p * obs[j].inv_corr * obs[j].Iref;
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Iobs[j] = obs[j].Iobs;
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weight[j] = obs[j].weight;
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}
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G = SolveScaleIRLS(coeff, Iobs, weight, settings_.robust_k);
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if (!(G > 0.0) || !std::isfinite(G))
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return 0.0;
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if (!refine_tilt)
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break;
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// (2) Refine the tilt holding G fixed. A cost that cannot be evaluated keeps the tilt this crystal
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// came in with and stops refining it - G alone is still a usable model.
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const double inv_sigma_prior = settings_.prior_sigma_deg > 0.0 ? kRadToDeg / settings_.prior_sigma_deg : 0.0;
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if (!RefineTilt(obs, model, G, settings_.robust_k, inv_sigma_prior, settings_.max_tilt_rad, psi))
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break;
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}
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// Keep what the crystal came in with. This refinement is on by default, so a crystal the tilt model
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// happens to suit WORSE than the fixed partiality it replaces must not be made worse by it - and
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// whether it suits is only known once the corrections are written and the CC re-measured.
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const auto cc_before = outcome.image_scale_cc;
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const auto g_before = outcome.image_scale_g;
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std::vector<std::pair<float, float>> before; // partiality, image_scale_corr
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before.reserve(outcome.reflections.size());
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for (const auto &r: outcome.reflections)
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before.emplace_back(r.partiality, r.image_scale_corr);
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// Write the refined partiality + scale correction onto every reflection of the crystal (not only the
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// fit subset), so the merge sees a consistent model. image_scale_corr = prescaling_corr * qe_corr * flight_corr /
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// (partiality * G), the same composition ScaleOnTheFly writes.
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for (auto &r: outcome.reflections) {
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const Coord q = base_q(r);
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|
const double p = ComputeP(q.x, q.y, q.z, psi[0], psi[1], S0.x, S0.y, S0.z, inv_lambda,
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gamma0, gamma_e, bw);
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r.partiality = static_cast<float>(p);
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const float corr = r.prescaling_corr * r.qe_corr * r.flight_corr;
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const double denom = p * G;
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r.image_scale_corr = (std::isfinite(corr) && std::isfinite(denom) && denom > 0.0)
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? static_cast<float>(corr / denom)
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: NAN;
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|
}
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|
outcome.image_scale_g = static_cast<float>(G);
|
|
|
|
// The corrections just changed, so the CC that ScaleOnTheFly measured no longer describes them.
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|
// Refresh it here: it is reported per image and --min-image-cc drops images by it, so it has to be
|
|
// the CC of the data that is actually merged.
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|
const auto [cc, cc_n] = ImageReferenceCC(outcome.reflections, reference, hkl_key_generator_,
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|
d_min_limit_, d_max_limit_, min_partiality_);
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|
|
|
// Adopt the refined model only if it correlates with the reference at least as well as the model it
|
|
// replaces. Rejecting puts the crystal back exactly as it arrived, which is the same state a
|
|
// crystal with too few reflections to fit ends in. A CC that cannot be measured at all counts as
|
|
// worse, not as no opinion: ImageReferenceCC returns NaN when too few reflections clear the
|
|
// partiality cut, which is precisely what a refinement that collapsed the partialities produces -
|
|
// and adopting it would leave image_scale_cc NaN, on which --min-image-cc drops the image outright.
|
|
if (cc_before.has_value() && std::isfinite(*cc_before) && !(std::isfinite(cc) && cc >= *cc_before)) {
|
|
for (size_t i = 0; i < outcome.reflections.size(); ++i) {
|
|
outcome.reflections[i].partiality = before[i].first;
|
|
outcome.reflections[i].image_scale_corr = before[i].second;
|
|
}
|
|
outcome.image_scale_g = g_before;
|
|
return 0.0; // nothing adopted, so no tilt to report
|
|
}
|
|
outcome.image_scale_cc = cc;
|
|
outcome.image_scale_cc_n = cc_n;
|
|
|
|
const double tilt_deg = std::sqrt(psi[0] * psi[0] + psi[1] * psi[1]) * kRadToDeg;
|
|
return tilt_deg;
|
|
}
|
|
|
|
double StillsPartialityRefine::Run(std::vector<IntegrationOutcome> &outcomes, size_t nthreads,
|
|
const std::optional<UnitCell> &reference_cell) const {
|
|
const auto &indexing = experiment_.GetIndexingSettings();
|
|
auto merged_crystal = [&](const IntegrationOutcome &o) {
|
|
return !reference_cell || o.latt.GetUnitCell().is_close(*reference_cell,
|
|
indexing.GetUnitCellDistTolerance(),
|
|
indexing.GetUnitCellAngleTolerance_deg());
|
|
};
|
|
if (nthreads == 0)
|
|
nthreads = AvailableCpus();
|
|
nthreads = std::max<size_t>(1, nthreads);
|
|
|
|
double last_mean_tilt = 0.0;
|
|
|
|
for (int outer = 0; outer < settings_.outer_iterations; ++outer) {
|
|
// Reference full intensities from the current corrections.
|
|
const std::vector<MergedReflection> merged = MergeAll(experiment_, outcomes, nthreads, reference_cell);
|
|
std::map<HKLKey, double> reference;
|
|
for (const auto &m: merged)
|
|
reference[hkl_key_generator_(m)] = m.I;
|
|
|
|
std::atomic<double> tilt_sum{0.0};
|
|
std::atomic<size_t> tilt_n{0};
|
|
std::atomic<size_t> next{0};
|
|
|
|
auto worker = [&]() {
|
|
size_t i = next.fetch_add(1);
|
|
while (i < outcomes.size()) {
|
|
const double t = merged_crystal(outcomes[i]) ? RefineOne(outcomes[i], reference) : 0.0;
|
|
if (t > 0.0) {
|
|
double prev = tilt_sum.load();
|
|
while (!tilt_sum.compare_exchange_weak(prev, prev + t)) {}
|
|
tilt_n.fetch_add(1);
|
|
}
|
|
i = next.fetch_add(1);
|
|
}
|
|
};
|
|
|
|
const size_t nt = std::min(nthreads, std::max<size_t>(1, outcomes.size()));
|
|
if (nt <= 1) {
|
|
worker();
|
|
} else {
|
|
std::vector<std::future<void>> futures;
|
|
futures.reserve(nt);
|
|
for (size_t t = 0; t < nt; ++t)
|
|
futures.emplace_back(std::async(std::launch::async, worker));
|
|
for (auto &f: futures)
|
|
f.get();
|
|
}
|
|
|
|
last_mean_tilt = tilt_n > 0 ? tilt_sum.load() / static_cast<double>(tilt_n.load()) : 0.0;
|
|
}
|
|
|
|
return last_mean_tilt;
|
|
}
|