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Jungfraujoch/image_analysis/scale_merge/ScaleOnTheFly.cpp
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v1.0.0-rc.166 (#76)
* `rugnux --mode calibration` writes `<prefix>.json` beside the `.poni`, whose `dataset_settings` member is a `jfjoch_broker` `dataset_settings` body as it stands.
* `rugnux` and `jfjoch_viewer` read PILATUS miniCBF sweeps natively, without conversion.
* Masters written by other facilities open, including Eiger 1.x and third-party NXmx variants.
* `rugnux` measures the beam centre on every run, and indexes with it when the file's value indexes nothing.
* A detector swung out on a 2theta arm is placed where the file says it stands, and the calibration can hold the tilt fixed.
* `rugnux` writes the unmerged MTZ by default, and a P1 merge beside it, so a wrong space group can be re-merged without reprocessing.
* Significant improvements to symmetry handling in `rugnux`: the lattice, the point group, the setting and the systematic absences.
* The `rugnux` report gives the resolution the CC1/2 fit reached, beside the range the reflections were written to.
* The `rugnux` report gives the twinning statistics measured before the space group was decided, beside the ones measured after.
* The `rugnux` report gives the strong-direction diffraction limit, and warns when CC1/2 is not monotone with resolution.
* `rugnux` ranks screw axes on the evidence their absences carry, rather than on how many control reflections a candidate happens to have.
* Twinning is no longer reported when the L-test contradicts it.
* The `rugnux` report gives the detector tilt, the measured tilt and the direct beam beside the beam centre, and a post-refined beam centre is judged against the run's own measurement rather than the file's.
* `--no-refine-tilt` holds the detector tilt at the value in the file, instead of zeroing it, when the calibration starts from the spots.
* The `jfjoch_viewer` grid scan view draws the cells in the proportion of the scan steps, so the map has the shape of the scanned area.

Reviewed-on: #76
Co-authored-by: Filip Leonarski <filip.leonarski@psi.ch>
2026-09-02 21:17:31 +02:00

221 lines
9.0 KiB
C++

// SPDX-FileCopyrightText: 2025 Filip Leonarski, Paul Scherrer Institute <filip.leonarski@psi.ch>
// SPDX-License-Identifier: GPL-3.0-only
#include "ScaleOnTheFly.h"
#include "../../common/Logger.h"
#include <algorithm>
#include <chrono>
#include <cmath>
#include <future>
#include <vector>
namespace {
// Robust loss scale (in sigma units) for the per-image scale fit: a few outlier reflections
// (zingers, overlaps, a mis-predicted spot) must not drag a frame's G/B into a bad optimum -
// that is the stochastic per-frame mis-scaling that elevates R-meas and collapses CC1/2 at low
// symmetry. Cauchy down-weights residuals beyond ~this many sigma without a hard cut.
constexpr double SCALE_ROBUST_K = 3.0;
// Smallest per-image scale, relative to the run's median, that is still believable as a scale
// rather than a failed fit. The same ratio guards the rotation path's per-frame scales.
constexpr double MIN_CREDIBLE_SCALE_RATIO = 0.02;
double SafeInv(double x, double fallback) {
if (!std::isfinite(x) || x == 0.0)
return fallback;
return 1.0 / x;
}
// One reflection reduced to the 1-D scale fit: predicted intensity is G * coeff (coeff is constant
// while B is fixed), measured is Iobs, weighted by 1/sigma.
struct ScaleObs {
double coeff;
double Iobs;
double weight;
};
// Robust per-image scale: minimise sum_i Cauchy_k( weight_i (G*coeff_i - Iobs_i) ) over G >= 0. The
// model is linear in G, so this M-estimate is a few reweighted-least-squares steps (each a closed-form
// weighted ratio) - the same objective the Ceres path solves, without a per-image problem/autodiff/
// trust-region. Seeded from the plain weighted-LS solution; Cauchy weight is 1/(1 + (res/k)^2).
double SolveScaleIRLS(const std::vector<ScaleObs> &obs, double robust_k) {
auto weighted_scale = [&obs](auto robust_weight) {
double num = 0.0, den = 0.0;
for (const auto &o: obs) {
const double rw = robust_weight(o);
const double w2 = o.weight * o.weight;
num += rw * w2 * o.coeff * o.Iobs;
den += rw * w2 * o.coeff * o.coeff;
}
return den > 0.0 ? num / den : NAN;
};
double G = weighted_scale([](const ScaleObs &) { return 1.0; });
if (!std::isfinite(G))
return 1.0;
G = std::max(0.0, G);
const double k2 = robust_k * robust_k;
for (int iter = 0; iter < 30; ++iter) {
const double G_prev = G;
const double G_next = weighted_scale([&](const ScaleObs &o) {
const double res = o.weight * (G * o.coeff - o.Iobs);
return 1.0 / (1.0 + res * res / k2);
});
if (!std::isfinite(G_next))
break;
G = std::max(0.0, G_next);
if (std::abs(G - G_prev) <= 1e-7 * std::max(G, 1.0))
break;
}
return G;
}
}
ScaleOnTheFly::ScaleOnTheFly(const DiffractionExperiment &x, const std::vector<MergedReflection> &ref)
: s(x.GetScalingSettings()),
hkl_key_generator(s.GetMergeFriedel(), x.GetSpaceGroupOrP1()) {
for (const auto &r: ref) {
const auto key = hkl_key_generator(r);
reference_data[key] = r.I;
}
}
bool ScaleOnTheFly::Accept(const Reflection &r) const {
if (r.on_ice_ring) // ice-contaminated intensity would drag the per-image scale; keep it out of the fit
return false;
return AcceptReflection(r, s.GetHighResolutionLimit_A(), s.GetLowResolutionLimit_A());
}
void ScaleOnTheFly::Scale(IntegrationOutcome &integration_outcome) const {
if (integration_outcome.reflections.empty())
return;
ScaleOnTheFlyResult result{ .G = 1.0 };
auto clear_scale = [&]() {
integration_outcome.image_scale_cc.reset();
integration_outcome.image_scale_cc_n.reset();
integration_outcome.image_scale_g.reset();
};
// The fixed-partiality model G * coeff is linear in G, so the robust per-image scale is a 1-D
// M-estimate solved directly (IRLS) rather than a Ceres problem per image.
{
std::vector<ScaleObs> obs;
obs.reserve(integration_outcome.reflections.size());
for (const auto &r: integration_outcome.reflections) {
if (!Accept(r))
continue;
const auto it = reference_data.find(hkl_key_generator(r));
if (it == reference_data.end())
continue;
const double coeff = r.partiality * SafeInv(r.rlp, 1.0) * it->second;
obs.push_back({coeff, static_cast<double>(r.I), SafeInv(r.sigma, 1.0)});
}
if (obs.size() < MIN_REFLECTIONS) {
clear_scale();
return;
}
result.G = SolveScaleIRLS(obs, SCALE_ROBUST_K);
}
for (auto &r: integration_outcome.reflections) {
const double denom = r.partiality * result.G;
r.image_scale_corr = (std::isfinite(r.rlp) && std::isfinite(denom) && denom > 0.0)
? static_cast<float>(r.rlp / denom)
: NAN;
}
const auto [cc, cc_n] = ImageReferenceCC(integration_outcome.reflections, reference_data,
hkl_key_generator, s.GetHighResolutionLimit_A(),
s.GetLowResolutionLimit_A(), s.GetMinPartiality());
result.cc = cc;
result.cc_n = cc_n;
integration_outcome.image_scale_cc = cc;
integration_outcome.image_scale_cc_n = cc_n;
integration_outcome.image_scale_g = result.G;
integration_outcome.image_scale_wedge_deg.reset();
}
// A per-image scale that has collapsed toward zero multiplies that image's intensities by 1/G - and its
// sigmas by the same factor, so nothing downstream can recognise it: the merge's n-sigma outlier test
// scales with the very number that is wrong. The rotation path already refuses a per-frame scale this
// far below its neighbours; the stills path had no such guard.
//
// Such an image is DROPPED from the merge, not merged unscaled. Substituting G = 1 looks conservative
// but is the more damaging of the two errors: if the collapsed value was a failed fit, the image goes in
// mis-scaled by an unknown factor, and if it was a real scale (per-crystal scales on serial stills
// genuinely span orders of magnitude, unlike frames of one rotation sweep) then G = 1 divides both its
// intensities AND its sigmas by 1/G, so it enters at 1/G^2 times the weight it deserves - the merge
// cannot down-weight it, because the number that is wrong is the same number the weight is built from.
// An image whose scale is not believable has no usable scale, so it contributes nothing instead.
void ScaleOnTheFly::RejectCollapsedScales(std::vector<IntegrationOutcome> &integration) {
std::vector<double> fitted;
fitted.reserve(integration.size());
for (const auto &i: integration)
if (i.image_scale_g && std::isfinite(*i.image_scale_g) && *i.image_scale_g > 0.0)
fitted.push_back(*i.image_scale_g);
if (fitted.size() < 2)
return;
const size_t mid = fitted.size() / 2;
std::nth_element(fitted.begin(), fitted.begin() + mid, fitted.end());
const double g_floor = fitted[mid] * MIN_CREDIBLE_SCALE_RATIO;
int64_t n_rejected = 0;
for (auto &i: integration) {
if (!i.image_scale_g || !std::isfinite(*i.image_scale_g) || *i.image_scale_g >= g_floor)
continue;
// A non-finite correction is what every merge path already skips on (Merge.cpp), so this drops
// the image consistently from the merged intensities, the error model and the statistics.
for (auto &r: i.reflections)
r.image_scale_corr = NAN;
i.image_scale_cc.reset();
i.image_scale_cc_n.reset();
i.image_scale_g.reset();
++n_rejected;
}
if (n_rejected > 0)
Logger("ScaleOnTheFly").Warning(
"Dropped {} image(s) from the merge: their per-image scale collapsed more than {:.0f}x below "
"the run median, so it is not a scale the intensities can be put on",
n_rejected, 1.0 / MIN_CREDIBLE_SCALE_RATIO);
}
void ScaleOnTheFly::Scale(std::vector<IntegrationOutcome> &integration, size_t nthreads) const {
if (nthreads == 0)
nthreads = std::thread::hardware_concurrency();
if (nthreads <= 1) {
for (auto & i : integration)
Scale(i);
} else {
auto local_nthreads = std::min(nthreads, integration.size());
std::vector<std::future<void>> futures;
futures.reserve(local_nthreads);
std::atomic<size_t> curr_image = 0;
for (size_t t = 0; t < local_nthreads; ++t)
futures.emplace_back(std::async(std::launch::async, [&] {
size_t i = curr_image.fetch_add(1);
while (i < integration.size()) {
Scale(integration[i]);
i = curr_image.fetch_add(1);
}
}));
for (auto &f: futures)
f.get();
}
RejectCollapsedScales(integration);
}