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Jungfraujoch/image_analysis/bragg_integration/BraggIntegrationEngine.cpp
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leonarski_fandClaude Opus 5.5 d6166f6fa5 Panel layout stage 2: prediction and Bragg integration through the panel table
Everything sparse that took a radial quantity from an image coordinate now takes it from the
reflection's (or spot's) position on the detector plane, through its panel (DetectorModules).
With an empty module table every changed expression reduces to the old one in the same
floating-point order, so flat detectors - every rotation set, EIGER, CCDs, assembled JUNGFRAU -
integrate bit for bit as before.

- Prediction (CPU, GPU, rotation CPU and GPU): bounds test against PlaneBounds instead of the
  image rectangle.
- IntegrateOnImage: a prediction in a gap or off every panel is dropped (PlaneToPanel), no longer
  passed through onto an unrelated stored pixel. The panel id stays local: the engine and the way
  back find it from the image position (the panel boxes tile the image), and a prediction that
  rounding to float put on another panel's edge is dropped so the two agree. Nothing is added to
  Reflection.
- Bragg engines (CPU, CUDA, Metal) take a BraggPanel per reflection (BraggStencil.h): its plane
  position, the panel's pixel axes on the plane and its box. The stencil's radial direction is
  taken on the plane and expressed in the panel's pixel axes; r0, the radial background curve,
  the Gaussian's elongation, the learned moments, tan(2theta) and Finalize's polarisation use the
  plane position; r_max / n_rad come from PlaneBounds. The neighbour mask and the owner map are
  clamped to the reflection's own panel; inside the walked box a pixel off the reflection's panel
  is read as a gap (unreadable, statically masked - not an overload), which is what the assembled
  image has there, so a disk cut by a panel edge is partial rather than silently "full".
- Risk R4: the ProfileEmpirical grid is learned and applied in the plane's axes (the panel axes
  taken to the nearest quarter turn, BraggProfileCell), so turned panels pool one shape. Without
  it the stacked layout read up to 0.3 % off the assembled one in the harness.
- SpotWidth / SpotFootprint: aperture bounds against the spot's panel box, beam distance,
  resolution and the shape's 2theta Jacobian on the plane, moments read in the panel's axes.
- GetDetectorMaxQ_recipA, the far-corner radii (first-pass tilt report, metric-symmetry arm,
  footprint table range) from PlaneBounds; pre-scan spot resolution on the plane;
  LogHeaderBeamCenter: geometric centre of the panels' plane box, and the masked-pixel check on
  the panel the beam lands on (none when it is in a gap).

Harness: PanelLayout_BraggIntegration is now a real [PanelLayout] check, over six settings
(default, bandwidth-elongated ring, radial background, overlap exclude, box sum, empirical
profile, all with polarisation), stacked vs assembled to 1e-5; PanelLayout_BraggIntegrationGPU
runs the same on the CUDA engine. Its spots avoid exact half pixels: the grid centres on the
rounded position, and a half rounds to the other neighbour on a turned panel.

The Metal engine (.metal/.mm) mirrors the CPU change exactly but is UNCOMPILED here (no macOS):
it needs a build and BraggIntegrationEngineMetal_BitIdenticalToCPU on a Mac.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_015CeEGgpKdfkCTJb3YNMJcu
2026-10-21 10:40:20 +02:00

271 lines
14 KiB
C++

// SPDX-FileCopyrightText: 2026 Filip Leonarski, Paul Scherrer Institute <filip.leonarski@psi.ch>
// SPDX-License-Identifier: GPL-3.0-only
#include "BraggIntegrationEngine.h"
#include <algorithm>
#include <cmath>
#include <map>
#include <mutex>
#include <numeric>
#include <string>
#include <tuple>
#include "../../common/JFJochMath.h" // PI (M_PI is not standard, and MSVC does not define it)
#include "../SensorAbsorption.h"
namespace {
// Radial parallax broadening as the coefficient of tan^2(2theta), i.e. Var(z)/pixel^2 [px^2].
// Copied verbatim from ProfileIntegrate2D: a photon converts at a random depth z (exponential,
// attenuation length L, truncated at the sensor thickness), shifting the recorded spot radially by
// z*tan(2theta). L comes from the tabulated NIST attenuation coefficients (SensorAbsorption.h); the
// lambda^3 approximation this used before is within 0.2% for silicon above 10 keV but overstates
// the attenuation length of CdTe by up to a factor of two, and by six above the Cd K edge, which
// made this variance 1.9x too large on 750 um CdTe data.
double parallax_var_px2(const std::string &material, double thickness_um, double lambda_A, double pixel_um) {
if (!(thickness_um > 0.0) || !(pixel_um > 0.0) || !(lambda_A > 0.0))
return 0.0;
const double L = sensor_absorption::AttenuationLength_um(material, lambda_A);
return sensor_absorption::ConversionDepthVariance_um2(L, thickness_um) / (pixel_um * pixel_um);
}
// The radial-offset kernels below are a pure function of these six numbers, and one engine is built
// per worker per pass - 96 of them on a two-pass run - so the table was built 96 times over from the
// same inputs. Build it once and let the rest copy it; it is a few hundred floats. Two workers can
// still race to build the same table, which costs nothing but the second build: the values are
// identical, and emplace keeps whichever arrived first.
struct RadialKernelKey {
float r1_sq, r2, r3;
int n_kern, k_off, k_len;
bool operator<(const RadialKernelKey &o) const {
return std::tie(r1_sq, r2, r3, n_kern, k_off, k_len)
< std::tie(o.r1_sq, o.r2, o.r3, o.n_kern, o.k_off, o.k_len);
}
};
std::mutex radial_kernel_mutex;
std::map<RadialKernelKey, std::vector<float>> radial_kernel_cache;
} // namespace
BraggIntegrationEngine::BraggIntegrationEngine(const DiffractionExperiment &experiment)
: geom(experiment.GetDiffractionGeometry()), modules(experiment.GetModules()),
plane_bounds(modules.PlaneBounds(experiment.GetXPixelsNum(), experiment.GetYPixelsNum())) {
const auto settings = experiment.GetBraggIntegrationSettings();
const auto &det = experiment.GetDetectorSetup();
mode = settings.GetIntegrator();
empirical = mode == IntegratorMode::ProfileEmpirical;
// Same frame as the reflections' predicted_x/predicted_y and the ImagePreprocessorBuffer that
// feeds this engine (MXAnalysisWithoutFPGA sizes that buffer to GetPixelsNum()).
xpixel = experiment.GetXPixelsNum();
ypixel = experiment.GetYPixelsNum();
npixel = experiment.GetPixelsNum();
r1_sq = settings.GetR1() * settings.GetR1();
r2 = settings.GetR2();
r2_sq = r2 * r2;
r3 = settings.GetR3();
r3_sq = r3 * r3;
R = static_cast<int>(std::ceil(r2));
G = 2 * R + 1;
GG = G * G;
// The X-ray bandwidth enters ONE place: it smears a reflection radially by bw_sigma * Rpx, which
// the per-reflection Gaussian carries as part of its radial variance. It is not a mode switch -
// the background estimator and the parallax/capture term below are the same whatever the beam is.
bw_sigma = experiment.GetBandwidthFWHM().value_or(0.0f) / 2.3548f;
const double c_par = parallax_var_px2(det.GetSensorMaterial(), det.GetSensorThickness_um(),
geom.GetWavelength_A(), geom.GetPixelSize_mm() * 1000.0);
c_radial = c_par + bragg_engine::C_CAPTURE;
F_px = geom.GetDetectorDistance_mm() / std::max(1e-6f, geom.GetPixelSize_mm());
beam_x = geom.GetBeamX_pxl();
beam_y = geom.GetBeamY_pxl();
use_ellipse = !empirical;
// Per-reflection signal/background geometry: the ring elongated radially by k_sigma times the
// beam's own radial streak, capped. k_sigma = 0 is the fixed circular stencil, bit for bit, and
// so is any monochromatic beam, where the streak is zero.
stencil.beam_x = beam_x;
stencil.beam_y = beam_y;
stencil.r2 = r2;
stencil.r3 = r3;
stencil.bw_sigma = static_cast<float>(bw_sigma);
stencil.k_sigma = settings.GetStencilKSigma();
stencil.max_grow = bragg_engine::MAX_STENCIL_GROW_OVER_R3 * r3;
// The measured spot footprint (SpotFootprint.h). It moves the ring only where a spot outgrows the
// r1 disk, so a pattern of compact spots integrates exactly as without it.
static_assert(SpotFootprint::MAX_BINS <= BRAGG_FOOTPRINT_MAX_BINS);
stencil.r1 = settings.GetR1();
const auto &fp = settings.GetFootprint();
if (!empirical && !fp.empty()) {
stencil.fp_n = static_cast<int>(fp.sigma_rad.size());
stencil.fp_bin_px = fp.bin_px;
for (int i = 0; i < stencil.fp_n; ++i) {
stencil.fp_sigma_rad[i] = fp.sigma_rad[i];
stencil.fp_sigma_tan[i] = fp.sigma_tan[i];
}
}
// Robust background ring, one estimator or the other (see BraggIntegrationSettings): a high-side
// sigma-clip (rugnux --background-clip, the default) or, when the clip is switched off, a
// symmetric trimmed mean (rugnux --background-trim). The caller owns the choice - the engine no
// longer overrides it for broadband data.
bkg_clip_nsigma = settings.GetBackgroundClipNSigma();
bkg_pixel_gain = settings.GetBackgroundPixelGain();
bkg_trim = bkg_clip_nsigma > 0.0f ? 0.0f : settings.GetBackgroundTrimFraction();
// Overlap treatment. Ownership is decided out to the fit grid's half size, which is where the
// profile fit reads pixels; beyond it a pixel that nobody claims is this reflection's own.
// Excluding the shared pixels needs a profile to renormalise, so it cannot act on a box sum -
// drop it to Off there rather than build an owner map nothing will read.
overlap = settings.GetOverlap();
if (overlap == OverlapMode::Exclude && mode == IntegratorMode::BoxSum)
overlap = OverlapMode::Off;
overlap_min_peak = settings.GetOverlapMinPeak();
claim = static_cast<float>(R);
inv_claim = 1.0f / claim;
// Radial-offset kernels for the background curvature correction. A stencil pixel at (dx, dy)
// sits at radial offset dx*cos(phi) + dy*sin(phi) from the reflection, where phi is the
// reflection's azimuth; averaging over phi makes the kernels position-independent, which is
// exact to the extent the stencil is small against the reflection's radius (r3 = 10 px vs
// hundreds). k_diff is the annulus histogram minus the disk histogram, each normalised, so
// dot(k_diff, B) is directly mean_annulus(B) - mean_disk(B).
// Unset = auto: start off, and let the analysis raise it per image where the ice score says the
// background really is radial. An engine nobody drives therefore never applies the correction.
const auto radial = settings.GetBackgroundRadialCorrection();
bkg_radial_auto = !radial.has_value();
bkg_radial = radial.value_or(false);
// The table spans zero growth up to whatever the widest reflection on this detector reaches, one
// kernel per pixel of growth; with nothing elongated a single kernel is all there is, which is
// the layout and the values of every build before the stencil existed. It is built only when the
// correction can ever run - the rows are not cheap, and nothing may read them otherwise:
// bkg_radial is raised after construction only by the auto mode (MXAnalysisWithoutFPGA), which
// requires bkg_radial_auto, and the GPU allocates its curve buffers under the same condition.
// n_kern is the largest row BraggStencilKernelIndex can select, plus one.
r_max = std::hypot(std::max<double>(beam_x - plane_bounds.x0, plane_bounds.x1 - beam_x),
std::max<double>(beam_y - plane_bounds.y0, plane_bounds.y1 - beam_y));
bkg_radial_built = bkg_radial || bkg_radial_auto;
const float grow_max = bkg_radial_built ? BraggStencilMaxGrow_px(static_cast<float>(r_max), stencil)
: 0.0f;
n_kern = static_cast<int>(std::lround(grow_max)) + 1;
// Every row must fit: the last one is built at grow = n_kern - 1, which rounding can put just
// above grow_max.
k_off = static_cast<int>(std::ceil(r3 + std::max<double>(grow_max, n_kern - 1))) + 1;
k_len = 2 * k_off + 1;
const RadialKernelKey kernel_key{r1_sq, r2, r3, n_kern, k_off, k_len};
{
const std::lock_guard lock(radial_kernel_mutex);
if (const auto it = radial_kernel_cache.find(kernel_key); it != radial_kernel_cache.end())
k_diff = it->second;
}
if (k_diff.empty()) {
k_diff.reserve(static_cast<size_t>(n_kern) * k_len);
for (int j = 0; j < n_kern; ++j)
BuildRadialKernel(static_cast<float>(j));
const std::lock_guard lock(radial_kernel_mutex);
radial_kernel_cache.emplace(kernel_key, k_diff);
}
polarization = experiment.GetPolarizationFactor();
}
void BraggIntegrationEngine::BuildRadialKernel(float grow) {
// Histogram the stencil over radial offset, averaged over azimuth so the kernel does not depend
// on where the reflection sits. The average is over the SUB-PIXEL PHASE of the detector grid
// against the radial direction, not over the stencil's own orientation: the stencil is built in
// the reflection's frame at each azimuth, so an elongated one stays aligned with the radius, as
// it is on the detector. k_diff is the annulus histogram minus the disk histogram, each
// normalised, so dot(k_diff, B) is directly mean_annulus(B) - mean_disk(B).
// The signal disk is a circle whatever the ring does, so its histogram is the same for every
// kernel in the table - build it once.
const bool first = hist_disk.empty();
if (first)
hist_disk.assign(k_len, 0.0);
std::vector<double> hist_ann(k_len, 0.0);
constexpr int n_phi = 512;
const int span = static_cast<int>(std::ceil(r3 + grow)) + 1;
const float si = r2 / (r2 + grow), so = r3 / (r3 + grow);
const double q_in = 1.0 - static_cast<double>(si) * si;
const double q_out = 1.0 - static_cast<double>(so) * so;
for (int p = 0; p < n_phi; ++p) {
const double phi = 2.0 * PI * p / n_phi, cp = std::cos(phi), sp = std::sin(phi);
for (int dy = -span; dy <= span; ++dy)
for (int dx = -span; dx <= span; ++dx) {
const double d2 = static_cast<double>(dx) * dx + static_cast<double>(dy) * dy;
const double rad = dx * cp + dy * sp;
const int k = k_off + static_cast<int>(std::lround(rad));
if (k < 0 || k >= k_len)
continue;
const double rad2 = rad * rad;
if (d2 < r1_sq) {
if (first) hist_disk[k] += 1.0;
} else if (d2 - q_in * rad2 >= r2_sq && d2 - q_out * rad2 < r3_sq) {
hist_ann[k] += 1.0;
}
}
}
if (first) sum_disk = std::accumulate(hist_disk.begin(), hist_disk.end(), 0.0);
const double sd = sum_disk;
const double sa = std::accumulate(hist_ann.begin(), hist_ann.end(), 0.0);
for (int k = 0; k < k_len; ++k)
k_diff.push_back(static_cast<float>(hist_ann[k] / sa - hist_disk[k] / sd));
}
std::vector<BraggPanel> BraggIntegrationEngine::Panels(const std::vector<Reflection> &predicted,
size_t npredicted) const {
std::vector<BraggPanel> out(npredicted);
for (size_t i = 0; i < npredicted; ++i) {
const float x = predicted[i].predicted_x, y = predicted[i].predicted_y;
out[i] = MakeBraggPanel(modules, modules.ModuleOfImagePixel(x, y), static_cast<int64_t>(xpixel),
static_cast<int64_t>(ypixel), x, y);
}
return out;
}
std::vector<Reflection> BraggIntegrationEngine::Finalize(const std::vector<Reflection> &predicted,
size_t npredicted,
const std::vector<BraggPanel> &panels,
const std::vector<BraggFitResult> &results,
int64_t image_number) const {
// Sized to what is kept, not to what was predicted: this vector is the one every image holds to the
// end of the pass, and a background ring lost to the neighbours drops a reflection here - on a
// dense pattern more than half of them, which a reserve of npredicted would carry as dead capacity.
size_t n_ok = 0;
for (size_t i = 0; i < npredicted; ++i)
n_ok += results[i].ok ? 1 : 0;
std::vector<Reflection> out;
out.reserve(n_ok);
for (size_t i = 0; i < npredicted; ++i) {
const auto &fr = results[i];
if (!fr.ok)
continue;
Reflection refl = predicted[i];
refl.I = fr.I;
refl.sigma = fr.sigma;
refl.bkg = fr.bkg;
refl.var_bkg = fr.var_bkg;
if (fr.has_observed) {
refl.observed_x = fr.observed_x;
refl.observed_y = fr.observed_y;
}
// has_observed is set only for a signal disk every pixel of which was readable.
refl.clipped = !fr.has_observed;
refl.overloaded = fr.overloaded;
refl.observed = true;
if (polarization)
refl.prescaling_corr /= geom.CalcAzIntPolarizationCorr(panels[i].plane_x, panels[i].plane_y,
polarization.value());
refl.image_scale_corr = refl.prescaling_corr * refl.qe_corr * refl.flight_corr / refl.partiality;
refl.image_number = static_cast<float>(image_number);
out.push_back(refl);
}
return out;
}