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Jungfraujoch/image_analysis/scale_merge/ScaleOnTheFly.cpp
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v1.0.0.rc-161 (#71)
This is an UNSTABLE release. It includes many experimental features, as well as many AI generated fixes. We recommend using rc.152 for production use.

* **rugnux: significantly better quality of results, and faster.** A large rework of integration, scaling, merging, geometry refinement and space-group determination, together with measurements the program previously made no attempt at - the direct beam before indexing, the beam stop, the goniometer rotation scale, and the stretches of a sweep the crystal did not deliver. A rotation dataset typically gains observations at better <I/sigma> and R_meas, and every `mx` and `scale` run writes a `<prefix>_report.txt` results report modelled on XDS's `CORRECT.LP`. Many defaults moved with it: spot detection is self-calibrating, beam-stop detection and rotation geometry post-refinement are on, resolution limits default to as far as the detector reaches, and ice-ring handling engages only where the crystal is measured to have ice.
* **jfjoch_viewer:** the beam-stop shadow, the detector calibration and the beam-centre measurement are reachable from "Analyze dataset"; the settings panel reports how the sample moved and how polarized the beam was; image rendering and interaction are faster.
* **Performance:** bitshuffle+LZ4 images are decoded on the GPU rather than on the host, with the bitshuffle inverse fused into preprocessing so the decompressed frame is never held in device memory.
* **Broker, writer, packaging and build:** image-slot lifetime and locking fixes, per-image datasets sized by the images actually written, the Debian/Ubuntu broker package renamed to `jfjoch`, and `image_analysis` compiling under MSVC again.

**Breaking change to the rugnux command line:**
* `--azint-only` and `--scale` are **removed**, replaced by `--mode azint` and `--mode scale`; the full pipeline is `--mode mx` and remains the default. A script passing the old flags now fails with the list of valid modes rather than silently running the wrong one.
* `-t`/`--stride` is **refused on rotation data**: skipping frames cuts every reflection's rocking curve, so the combined fulls and their partiality would be measured over frames the sweep never recorded. Select a contiguous range with `-s`/`-e` instead. `--mode azint` and `--force-still` still take a stride.

**Breaking changes to OpenAPI** - regenerate the client (`jfjoch-client` 1.0.0-rc.161, `frontend/src/client`) or read the affected fields as optional:
* `image_scale_b` is removed from the `plot_type` enum, so a client requesting that plot now gets an error rather than a curve.
* `azim_int_settings.high_q_recipA`, `spot_finding_settings.high_resolution_limit` and `spot_finding_settings.low_resolution_limit` are no longer `required`. All three mean "no limit at that end" when unset and are omitted from the response instead of carrying a placeholder value, which raises in a client generated from an rc.160-or-earlier spec. A value of 0 is still accepted and means the same thing.

**Breaking changes to the stored formats** - a consumer reading these fields must treat them as optional:
* The per-image image-scale B factor is no longer computed, so `/entry/MX/imageScaleBFactor` is absent from newly written HDF5 files and the corresponding key is absent from the CBOR DataMessage and END blocks. Files written by rc.160 and earlier still contain it and still open; nothing in the pipeline reads it any more.
* `_reflns.jfjoch_diffrn_ISa` now carries the whole-range `1/sqrt(a*b)` that XDS's ISa denotes, and the error-model `a` and `b` are reported in XDS's convention; the strong-reflection asymptote moves to `_reflns.jfjoch_diffrn_ISa_asymptotic`. **A file written by an earlier version carries the asymptote under the plain `ISa` name.**

Reviewed-on: #71
Co-authored-by: Filip Leonarski <filip.leonarski@psi.ch>
2026-08-13 17:03:10 +02:00

221 lines
9.1 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.GetSpaceGroupNumber().value_or(1)) {
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);
}