Jungfraujoch/image_analysis/bragg_integration/BraggIntegrationEngineCPU.cpp

// SPDX-FileCopyrightText: 2026 Filip Leonarski, Paul Scherrer Institute <filip.leonarski@psi.ch>
// SPDX-License-Identifier: GPL-3.0-only

#include "BraggIntegrationEngineCPU.h"

#include <algorithm>
#include <cmath>
#include <cstdint>
#include <limits>
#include <vector>

#include "../../common/CompressedImage.h"
#include "../../common/JFJochException.h"

using namespace bragg_engine;

namespace {

// The engine reads pixels in the INT32_MIN(masked)/INT32_MAX(saturated) convention.
inline bool valid(int32_t v) { return v != INT32_MIN && v != INT32_MAX; }

// Identity sampler over the preprocessed int32 buffer (already in that convention).
struct BufferSampler {
    const int32_t *p;
    int32_t operator[](size_t i) const { return p[i]; }
};

// Sampler over a raw detector image of pixel type T: masked pixels carry the type minimum, saturated
// the type maximum (the FPGA image has no lossy-codec +/-1 band). Only the pixels actually read - the
// reflection disks - are converted, so there is no whole-image pass.
template <class T>
struct ImageSampler {
    const T *p;
    int64_t special_value;
    int64_t saturation;
    int32_t operator[](size_t i) const {
        const int64_t v = p[i];
        if (v == special_value) return INT32_MIN;
        if (v == saturation) return INT32_MAX;
        return static_cast<int32_t>(v);
    }
};

}   // namespace

BraggIntegrationEngineCPU::BraggIntegrationEngineCPU(const DiffractionExperiment &experiment)
    : BraggIntegrationEngine(experiment) {}

template <class Sampler>
std::vector<Reflection> BraggIntegrationEngineCPU::RunImpl(const Sampler &img,
                                                           const std::vector<Reflection> &predicted,
                                                           size_t npredicted, int64_t image_number) {
    std::vector<BraggFitResult> results(npredicted);
    if (npredicted == 0)
        return Finalize(predicted, npredicted, results, image_number);

    const int W = static_cast<int>(xpixel), H = static_cast<int>(ypixel);
    const bool do_clip = bkg_clip_nsigma > 0.0f && mode != IntegratorMode::BoxSum;

    // Symmetric trimmed-mean background fraction (BraggIntegrationSettings, rugnux --background-trim):
    // replaces the r2..r3 ring MEAN with an f-trimmed mean, robust to the high-side contamination
    // (neighbour wings, tails) that biases the plain mean up and makes it over-subtract weak high-angle
    // reflections. The base ctor has already forced it to 0 whenever the clip below is in force, which
    // is the default - the two are alternatives.
    const double bkg_trim_frac = bkg_trim;

    auto grid_idx = [this](int dx, int dy) { return (dy + R) * G + (dx + R); };

    // --- Reflection mask: mark the r2 signal region of every predicted reflection so a neighbour's
    //     signal is excluded from this reflection's r2..r3 background ring. The region is the INNER
    //     stencil ellipse, taken in each neighbour's OWN frame - an elongated neighbour whose streak
    //     is still marked as a disk would leak its tails into this reflection's ring. ---
    std::vector<uint8_t> refl_mask(npixel, 0);
    for (size_t i = 0; i < npredicted; ++i) {
        const auto &r = predicted[i];
        const BraggStencil st = MakeBraggStencil(r.predicted_x, r.predicted_y, stencil);
        const int x0 = std::max(0, static_cast<int>(std::floor(r.predicted_x - st.ex_in - 1.0f)));
        const int x1 = std::min(W - 1, static_cast<int>(std::ceil(r.predicted_x + st.ex_in + 1.0f)));
        const int y0 = std::max(0, static_cast<int>(std::floor(r.predicted_y - st.ey_in - 1.0f)));
        const int y1 = std::min(H - 1, static_cast<int>(std::ceil(r.predicted_y + st.ey_in + 1.0f)));
        for (int y = y0; y <= y1; ++y)
            for (int x = x0; x <= x1; ++x) {
                const auto d = BraggStencilDistances(st, x - r.predicted_x, y - r.predicted_y);
                if (d.inner < r2_sq) refl_mask[y * W + x] = 1;
            }
    }

    // --- Signal-region ownership: a pixel inside two reflections' signal regions belongs to the
    //     NEARER predicted centre. The union mask above cannot answer that - it also marks a
    //     reflection's own core - so ownership gets its own map, one (distance, reflection) key per
    //     pixel (BraggStencil.h). Built only when an overlap treatment is asked for. ---
    // A box sum has no profile to renormalise a disk it has taken pixels out of, so it never excludes.
    const bool exclude = overlap == OverlapMode::Exclude && mode != IntegratorMode::BoxSum;
    std::vector<uint32_t> owner;
    if (overlap != OverlapMode::Off) {
        owner.assign(npixel, BRAGG_OWNER_NONE);
        const float claim_sq = claim * claim;
        for (size_t i = 0; i < npredicted; ++i) {
            const auto &r = predicted[i];
            const int x0 = std::max(0, static_cast<int>(std::floor(r.predicted_x - claim)));
            const int x1 = std::min(W - 1, static_cast<int>(std::ceil(r.predicted_x + claim)));
            const int y0 = std::max(0, static_cast<int>(std::floor(r.predicted_y - claim)));
            const int y1 = std::min(H - 1, static_cast<int>(std::ceil(r.predicted_y + claim)));
            for (int y = y0; y <= y1; ++y)
                for (int x = x0; x <= x1; ++x) {
                    const float dx = x - r.predicted_x, dy = y - r.predicted_y;
                    const float d2 = dx * dx + dy * dy;
                    if (d2 >= claim_sq) continue;
                    uint32_t &o = owner[y * W + x];
                    o = std::min(o, BraggOwnerKey(std::sqrt(d2), inv_claim, static_cast<int>(i)));
                }
        }
    }
    auto clean = [&](int x, int y, size_t i) {
        return owner.empty()
               || BraggOwnedBy(owner[static_cast<size_t>(y) * W + x], static_cast<int>(i));
    };

    // --- Pass A: box-sum every reflection (rough I, background, centroid, strong flag). ---
    struct Rough {
        double I = 0.0, sigma = NAN, bkg = 0.0, obs_x = 0.0, obs_y = 0.0;
        double bkg_var = 0.0;   // variance of the background ESTIMATE itself, bkg / n_bkg
        double var_bkg = 0.0;   // total non-signal variance carried to the merge
        int64_t I_sum = 0;      // kept so I can be rebuilt after the radial background correction
        int n_inner = 0;
        int n_disk = 0, n_own = 0;   // signal-disk pixels, and how many of them are this reflection's
        int r_bin = 0;          // rounded distance from the beam centre, indexes the radial curve
        int k_bin = 0;          // which radial-background kernel this reflection's stencil needs
        int cx = 0, cy = 0, shell = -1;
        bool ok = false, strong = false, has_obs = false;
        bool full = false;      // every pixel of the signal disk was readable
    };
    std::vector<Rough> rough(npredicted);
    double inv_d2_min = std::numeric_limits<double>::max(), inv_d2_max = 0.0;
    std::vector<int32_t> bkg_vals;   // reused per reflection for the trimmed-mean background (idea 1)

    // Radial background curve, accumulated from the annulus pixels this pass already reads. A pixel's
    // radius is the reflection's radius plus the pixel's projection on the beam->reflection direction,
    // so no per-pixel sqrt is needed.
    const int n_rad = static_cast<int>(std::ceil(std::hypot(std::max<double>(beam_x, W - beam_x),
                                                            std::max<double>(beam_y, H - beam_y)))) + 2;
    std::vector<double> rad_sum;
    std::vector<int> rad_cnt;
    if (bkg_radial) {
        rad_sum.assign(n_rad, 0.0);
        rad_cnt.assign(n_rad, 0);
    }

    for (size_t i = 0; i < npredicted; ++i) {
        const auto &r = predicted[i];
        Rough out;
        // This reflection's stencil: the r1 signal disk, and the r2..r3 ring elongated radially by
        // the analytic smear. The bounding box spans the OUTER ellipse, tightly - taking the largest
        // semi-axis in both directions instead would read up to 60% more pixels for nothing.
        const BraggStencil st = MakeBraggStencil(r.predicted_x, r.predicted_y, stencil);
        const int x0 = std::max(0, static_cast<int>(std::floor(r.predicted_x - st.ex_out - 1.0f)));
        const int x1 = std::min(W - 1, static_cast<int>(std::ceil(r.predicted_x + st.ex_out + 1.0f)));
        const int y0 = std::max(0, static_cast<int>(std::floor(r.predicted_y - st.ey_out - 1.0f)));
        const int y1 = std::min(H - 1, static_cast<int>(std::ceil(r.predicted_y + st.ey_out + 1.0f)));

        // Both from the stencil's own radius, so nothing downstream is derived from a second one.
        out.r_bin = std::clamp(static_cast<int>(std::lround(st.r0)), 0, n_rad - 1);
        out.k_bin = BraggStencilKernelIndex(st, n_kern);

        int64_t I_sum = 0, I_sum_x = 0, I_sum_y = 0, n_inner = 0, n_inner_valid = 0;
        int n_disk = 0, n_own = 0;   // pixels in the signal disk, and how many are this reflection's
        double bkg_sum = 0.0;
        int n_bkg = 0;
        bkg_vals.clear();
        for (int y = y0; y <= y1; ++y)
            for (int x = x0; x <= x1; ++x) {
                const auto d = BraggStencilDistances(st, x - r.predicted_x, y - r.predicted_y);
                const int32_t px = img[y * W + x];
                if (d.signal < r1_sq) {
                    // A pixel a nearer neighbour owns carries that neighbour's flux, so in Exclude
                    // mode it leaves the disk entirely - the sum, the pixel count the background is
                    // subtracted with, and the all-or-nothing validity gate alike. The box sum only
                    // counts how many were lost, which is all it can act on.
                    ++n_disk;
                    if (overlap != OverlapMode::Off) {
                        if (clean(x, y, i)) ++n_own;
                        else if (exclude) continue;
                    }
                    ++n_inner;
                    if (!valid(px)) continue;
                    I_sum += px;
                    I_sum_x += static_cast<int64_t>(x) * px;
                    I_sum_y += static_cast<int64_t>(y) * px;
                    ++n_inner_valid;
                } else if (d.inner >= r2_sq && d.outer < r3_sq) {
                    if (refl_mask[y * W + x]) continue;
                    if (!valid(px)) continue;
                    bkg_sum += static_cast<double>(px);
                    if (bkg_trim_frac > 0.0) bkg_vals.push_back(px);
                    ++n_bkg;
                }
            }

        int n_bkg_used = n_bkg;   // pixels behind the FINAL background value (trim/clip shrink it)
        // A masked, untrusted, gapped or overloaded pixel inside the signal disk used to discard the
        // reflection outright. A profile fit does not need it to: the fit is the amplitude of a
        // NORMALISED profile, so leaving pixels out renormalises the estimator by construction and
        // widens sigma by the information they carried - the same argument Exclude makes for a
        // neighbour's pixels. Pass B keeps the reflection only while enough of the expected profile
        // survived to constrain the amplitude (XDS's MINPK, dials' valid_foreground_threshold). A box
        // sum has no profile to renormalise with, so there it stays all or nothing.
        const bool full = n_inner_valid == n_inner;
        if ((full || mode != IntegratorMode::BoxSum) && n_bkg > 5) {
            out.bkg = bkg_sum / n_bkg;
            if (bkg_trim_frac > 0.0 && bkg_vals.size() > 5
                && bkg_vals.size() <= static_cast<size_t>(bragg_engine::BKG_TRIM_MAX)) {
                // Symmetric trimmed mean over the background ring (idea 1): drop the lowest and highest
                // bkg_trim_frac of the pixels, average the rest. Robust to the high-side contamination
                // that biases the plain ring mean and makes it over-subtract at high resolution.
                std::sort(bkg_vals.begin(), bkg_vals.end());
                const size_t lo = static_cast<size_t>(bkg_vals.size() * bkg_trim_frac);
                const size_t hi = bkg_vals.size() - lo;
                if (hi > lo) {
                    double s = 0.0;
                    for (size_t t = lo; t < hi; ++t) s += bkg_vals[t];
                    out.bkg = s / static_cast<double>(hi - lo);
                    n_bkg_used = static_cast<int>(hi - lo);
                }
            } else if (do_clip) {
                // One high-outlier sigma-clip pass on the background ring: reject pixels above
                // mean + n*sqrt(mean) to strip a neighbour core or a zinger that biases the mean.
                const double thr = out.bkg + bkg_clip_nsigma * std::sqrt(std::max(out.bkg, 1.0));
                double s = 0.0;
                int n = 0;
                for (int y = y0; y <= y1; ++y)
                    for (int x = x0; x <= x1; ++x) {
                        const auto d = BraggStencilDistances(st, x - r.predicted_x, y - r.predicted_y);
                        if (!(d.inner >= r2_sq && d.outer < r3_sq)) continue;
                        if (refl_mask[y * W + x]) continue;
                        const int32_t px = img[y * W + x];
                        if (!valid(px)) continue;
                        if (static_cast<double>(px) <= thr) {
                            s += px;
                            ++n;
                            if (bkg_radial) {
                                // The radial curve is binned on the TRUE detector radius, so the
                                // offset here is the unshrunk radial projection.
                                const int b = std::clamp(static_cast<int>(std::lround(st.r0 + d.rad)), 0, n_rad - 1);
                                rad_sum[b] += static_cast<double>(px);
                                ++rad_cnt[b];
                            }
                        }
                    }
                if (n > 5) { out.bkg = s / n; n_bkg_used = n; }
            }
            // The sum is over the pixels actually READ, so that is the count the background is
            // subtracted with; with nothing missing it is the whole disk, exactly as before.
            out.I = static_cast<double>(I_sum) - static_cast<double>(n_inner_valid) * out.bkg;
            // I = I_sum - n_inner*bkg, and bkg is itself estimated from n_bkg_used pixels, so its
            // error enters n_inner times over: var(I) = I_sum + n_inner^2 * bkg/n_bkg_used. Leaving
            // the second term out understates sigma by sqrt(1 + n_inner/n_bkg) - 1.109x at the
            // default r1=4/r2=6/r3=10 stencil, on every reflection of every dataset.
            out.bkg_var = out.bkg / n_bkg_used;
            out.var_bkg = static_cast<double>(n_inner_valid) * out.bkg
                          + static_cast<double>(n_inner_valid) * n_inner_valid * out.bkg_var;
            out.I_sum = I_sum;
            out.n_inner = static_cast<int>(n_inner_valid);
            out.n_disk = n_disk;
            out.n_own = n_own;
            const double var_bkg_term = static_cast<double>(n_inner_valid) * n_inner_valid * out.bkg_var;
            out.sigma = 1.0;
            if (I_sum > 0) {
                out.sigma = std::max(out.sigma, std::sqrt(static_cast<double>(I_sum) + var_bkg_term));
                out.obs_x = static_cast<double>(I_sum_x) / static_cast<double>(I_sum);
                out.obs_y = static_cast<double>(I_sum_y) / static_cast<double>(I_sum);
                // A disk with a hole in it gives a centroid pulled away from the hole, and the hole
                // sits at a fixed place on the detector - post-refinement would read that as geometry.
                out.has_obs = full;
            }
            out.cx = static_cast<int>(std::lround(r.predicted_x));
            out.cy = static_cast<int>(std::lround(r.predicted_y));
            out.ok = true;
            out.full = full;
            // The profile, its resolution shells and their widths are learned from COMPLETE
            // reflections: a partial one has no intensity to normalise its grid by and a hole to
            // stack into it. So the learning sees exactly what it saw before this rescue existed.
            out.strong = full && out.sigma > 0.0 && out.I / out.sigma >= STRONG_I_OVER_SIGMA;
            if (full && r.d > 0.0f) {
                const double inv_d2 = 1.0 / (static_cast<double>(r.d) * r.d);
                inv_d2_min = std::min(inv_d2_min, inv_d2);
                inv_d2_max = std::max(inv_d2_max, inv_d2);
            }
        }
        rough[i] = out;
    }

    // --- Radial background curvature correction. The annulus mean is blind to the curvature of the
    //     radial background (a linear background cancels between the concentric disk and annulus), so
    //     correct it by mean_annulus(B) - mean_disk(B) taken from the curve just accumulated. Reads no
    //     pixels: one short dot product per reflection. ---
    if (bkg_radial) {
        for (size_t i = 0; i < npredicted; ++i) {
            auto &rh = rough[i];
            if (!rh.ok) continue;
            // An empty bin contributes the reflection's own background, so a fully empty
            // neighbourhood gives corr == 0 exactly (the kernel weights sum to zero).
            const float *kern = k_diff.data() + static_cast<size_t>(rh.k_bin) * k_len;
            double corr = 0.0;
            for (int k = 0; k < k_len; ++k) {
                const int b = std::clamp(rh.r_bin + k - k_off, 0, n_rad - 1);
                const double v = rad_cnt[b] > 0 ? rad_sum[b] / rad_cnt[b] : rh.bkg;
                corr += static_cast<double>(kern[k]) * v;
            }
            rh.bkg -= corr;                       // annulus mean -> mean over the signal disk
            rh.I = static_cast<double>(rh.I_sum) - static_cast<double>(rh.n_inner) * rh.bkg;
        }
    }

    // --- BoxSum mode is BraggIntegrate2D: emit the rough result directly. ---
    if (mode == IntegratorMode::BoxSum) {
        for (size_t i = 0; i < npredicted; ++i) {
            const auto &rh = rough[i];
            if (!rh.ok) continue;
            // A box sum measures what is in the disk with no model of what should be there, so it can
            // neither renormalise nor tell a neighbour's photon from its own: dropping the reflection
            // is the only treatment it has, and it is applied only where that is what was ASKED for.
            // Exclude means "leave the shared pixels out of the fit", and a box sum has no fit, so it
            // is a no-op here rather than a rejection the caller never requested. The fraction is by
            // AREA, not by profile mass, so the same threshold cuts harder here than in the profile
            // modes.
            if (overlap == OverlapMode::Reject && rh.n_own < overlap_min_peak * rh.n_disk) continue;
            results[i] = {static_cast<float>(rh.I), static_cast<float>(rh.sigma), static_cast<float>(rh.bkg),
                          static_cast<float>(rh.obs_x), static_cast<float>(rh.obs_y),
                          static_cast<float>(rh.var_bkg), true, rh.has_obs};
        }
        return Finalize(predicted, npredicted, results, image_number);
    }

    auto shell_of = [&](float d) {
        if (!(d > 0.0f) || inv_d2_max <= inv_d2_min) return 0;
        const double t = (1.0 / (static_cast<double>(d) * d) - inv_d2_min) / (inv_d2_max - inv_d2_min);
        return std::clamp(static_cast<int>(t * N_SHELL), 0, N_SHELL - 1);
    };
    for (size_t i = 0; i < npredicted; ++i)
        if (rough[i].ok) rough[i].shell = shell_of(predicted[i].d);

    // --- Learn the profile per shell (+ global) from the strong spots. ---
    // Two things are learned. The empirical profile is the average grid in the DETECTOR frame, which is
    // where it is applied. The width is a pair of second moments taken in each spot's OWN radial /
    // tangential frame: a grid stacked in the detector frame is azimuthally averaged, so its <r^2> is
    // sigma_r^2 + sigma_t^2 with no way back, and a radially smeared spot reads as a wide TANGENTIAL
    // one. Rotating each contribution into the spot's frame keeps the two apart.
    struct Moments { double rad = 0.0, tan = 0.0, w = 0.0; };
    struct Sigma2 { double rad = 1.0, tan = 1.0; };
    std::vector<std::vector<double>> shell_grid(N_SHELL, std::vector<double>(GG, 0.0));
    std::vector<Moments> shell_mom(N_SHELL);
    std::vector<int> shell_n(N_SHELL, 0);
    std::vector<double> global_grid(GG, 0.0);
    Moments global_mom;
    int global_n = 0;
    for (size_t i = 0; i < npredicted; ++i) {
        const auto &rh = rough[i];
        if (!rh.ok || !rh.strong || rh.I <= 0.0) continue;
        const double rx = predicted[i].predicted_x - beam_x, ry = predicted[i].predicted_y - beam_y;
        const double Rpx = std::hypot(rx, ry);
        const double ux = Rpx > 1e-6 ? rx / Rpx : 1.0, uy = Rpx > 1e-6 ? ry / Rpx : 0.0;
        for (int dy = -R; dy <= R; ++dy)
            for (int dx = -R; dx <= R; ++dx) {
                const int x = rh.cx + dx, y = rh.cy + dy;
                if (x < 0 || y < 0 || x >= W || y >= H) continue;
                const int32_t px = img[y * W + x];
                if (!valid(px)) continue;
                if (exclude && !clean(x, y, i)) continue;
                const double v = (static_cast<double>(px) - rh.bkg) / rh.I;
                shell_grid[rh.shell][grid_idx(dx, dy)] += v;
                global_grid[grid_idx(dx, dy)] += v;
                if (dx * dx + dy * dy >= r1_sq) continue;
                const double rad = dx * ux + dy * uy, tn = -dx * uy + dy * ux;
                shell_mom[rh.shell].rad += v * rad * rad;
                shell_mom[rh.shell].tan += v * tn * tn;
                shell_mom[rh.shell].w += v;
                global_mom.rad += v * rad * rad;
                global_mom.tan += v * tn * tn;
                global_mom.w += v;
            }
        ++shell_n[rh.shell];
        ++global_n;
    }

    // Radial and tangential variances from the moments. The domain is the r1 disk, which is
    // azimuthally symmetric and so adds no anisotropy of its own. The cells are SIGNED: away from the
    // peak a learned cell is background noise centred on zero, and clamping it at zero turns that
    // noise into a positive pedestal that the rad^2 / tan^2 weights read as extra width.
    auto widths = [](const Moments &m) {
        Sigma2 s;
        if (m.w > 0.0) {
            s.rad = std::max(0.25, m.rad / m.w);
            s.tan = std::max(0.25, m.tan / m.w);
        }
        return s;
    };
    // Normalised empirical profile (sum = 1), the average grid over the strong spots of a shell.
    // ProfileGaussian does not use it - it rebuilds a per-reflection Gaussian in Pass B.
    auto build_profile = [&](const std::vector<double> &grid) {
        std::vector<double> P(GG, 0.0);
        double sum = 0.0;
        for (int k = 0; k < GG; ++k) {
            P[k] = std::max(0.0, grid[k]);
            sum += P[k];
        }
        if (sum > 0.0)
            for (double &p : P) p /= sum;
        return P;
    };

    const std::vector<double> global_P = empirical && global_n > 0 ? build_profile(global_grid)
                                                                   : std::vector<double>(GG, 0.0);
    const Sigma2 global_sigma2 = widths(global_mom);
    std::vector<std::vector<double>> shell_P(N_SHELL, global_P);
    std::vector<Sigma2> shell_sigma2(N_SHELL, global_sigma2);
    for (int s = 0; s < N_SHELL; ++s) {
        if (shell_n[s] < MIN_STRONG_PER_SHELL) continue;
        if (empirical) shell_P[s] = build_profile(shell_grid[s]);
        shell_sigma2[s] = widths(shell_mom[s]);
    }

    // --- Pass B: profile-fit each reflection (Kabsch, de-biased variance v = B + I*P; iterate). The
    //     reweighting is the Kabsch/Otwinowski iteration: Kabsch, Acta Cryst D66, 133-144 (2010);
    //     Otwinowski & Minor, Methods Enzymol 276, 307-326 (1997). ---
    std::vector<double> Pbuf;
    for (size_t i = 0; i < npredicted; ++i) {
        const auto &rh = rough[i];
        if (!rh.ok) continue;
        const int sh = rh.shell < 0 ? 0 : rh.shell;

        int Rf = R;
        const std::vector<double> *Pvec = &shell_P[sh];
        if (!empirical) {
            const double rx = predicted[i].predicted_x - beam_x, ry = predicted[i].predicted_y - beam_y;
            const double Rpx = std::hypot(rx, ry);
            const double tan2t = Rpx / F_px;
            const double s2t = shell_sigma2[sh].tan;
            double s2r = s2t, ux = 1.0, uy = 0.0;
            bool elong = false;
            if (use_ellipse) {
                // Radial excess over the tangential width: measured where the peak is resolved inside
                // the r1 disk, with the analytic bandwidth + parallax/capture term as the floor. The
                // analytic term is what carries a streak the disk is too small to measure.
                const double sbw = bw_sigma * Rpx;
                const double radial_extra = std::max(shell_sigma2[sh].rad - s2t,
                                                     sbw * sbw + c_radial * tan2t * tan2t);
                if (Rpx > 1e-6 && radial_extra > 0.25) {
                    ux = rx / Rpx; uy = ry / Rpx;
                    s2r = s2t + radial_extra;
                    elong = true;
                }
            }
            // Build the Gaussian per reflection, centred on the sub-pixel predicted position and (when
            // needed) radially elongated, on a grid grown to hold the streak.
            const double fx = predicted[i].predicted_x - rh.cx, fy = predicted[i].predicted_y - rh.cy;
            Rf = elong ? std::min(3 * R, static_cast<int>(std::ceil(r2 + 2.0 * std::sqrt(s2r)))) : R;
            const int Gf = 2 * Rf + 1;
            Pbuf.assign(static_cast<size_t>(Gf) * Gf, 0.0);
            double gs = 0.0;
            for (int dy = -Rf; dy <= Rf; ++dy)
                for (int dx = -Rf; dx <= Rf; ++dx) {
                    const double ex = dx - fx, ey = dy - fy;
                    const double rad = ex * ux + ey * uy, tn = -ex * uy + ey * ux;
                    const double g = std::exp(-rad * rad / (2.0 * s2r) - tn * tn / (2.0 * s2t));
                    Pbuf[(dy + Rf) * Gf + (dx + Rf)] = g;
                    gs += g;
                }
            for (double &p : Pbuf) p /= gs;
            Pvec = &Pbuf;
        }

        const int Gf = 2 * Rf + 1;

        // --- How much of the expected profile the fit can actually see. p_valid is the readable
        //     fraction of it - XDS's MINPK, dials' valid_foreground_threshold - measured against the
        //     mass that falls on the detector at all, so a reflection is judged on the pixels that
        //     exist and not on where the sensor ends. p_own is the same quantity over neighbour-owned
        //     pixels, what Reject cuts on. m_read / m_all is the fraction of the r1 disk the
        //     summation seed the runaway guard compares against actually summed; with nothing missing
        //     and nothing excluded it is 1 and the guard is untouched. ---
        double p_grid = 0.0, p_valid = 0.0, p_own = 0.0, m_all = 0.0, m_read = 0.0;
        double p_peak = 0.0, p_lost_peak = 0.0;
        for (int dy = -Rf; dy <= Rf; ++dy)
            for (int dx = -Rf; dx <= Rf; ++dx) {
                const double Pp = (*Pvec)[(dy + Rf) * Gf + (dx + Rf)];
                if (Pp <= 0.0) continue;
                const int x = rh.cx + dx, y = rh.cy + dy;
                if (x < 0 || y < 0 || x >= W || y >= H) continue;
                const bool in_disk = dx * dx + dy * dy < r1_sq;
                p_grid += Pp;
                p_peak = std::max(p_peak, Pp);
                if (in_disk) m_all += Pp;
                if (!valid(img[y * W + x])) {
                    p_lost_peak = std::max(p_lost_peak, Pp);
                    continue;
                }
                p_valid += Pp;
                const bool own = clean(x, y, i);
                if (own) p_own += Pp;
                if (in_disk && (own || !exclude)) m_read += Pp;
            }
        if (p_valid < overlap_min_peak * p_grid) continue;
        // A hole in the profile's PEAK is a different defect from a hole in its wings, and the mass
        // fraction above cannot tell them apart - the peak of a broad spot is a few percent of the
        // mass, so MINPK passes a reflection that has lost the one part of the profile its amplitude
        // is determined by. See the header.
        if (p_lost_peak > MINPK_MAX_MISSING_PEAK * p_peak) continue;
        if (overlap == OverlapMode::Reject && p_own < overlap_min_peak) continue;

        const double B = std::max(rh.bkg, PIXEL_VARIANCE_FLOOR);
        double I = rh.I, den = 0.0, wsum = 0.0;
        for (int iter = 0; iter < 4; ++iter) {
            double num = 0.0;
            den = 0.0;
            wsum = 0.0;
            for (int dy = -Rf; dy <= Rf; ++dy)
                for (int dx = -Rf; dx <= Rf; ++dx) {
                    const double Pp = (*Pvec)[(dy + Rf) * Gf + (dx + Rf)];
                    if (Pp <= 0.0) continue;
                    const int x = rh.cx + dx, y = rh.cy + dy;
                    if (x < 0 || y < 0 || x >= W || y >= H) continue;
                    const int32_t px = img[y * W + x];
                    if (!valid(px)) continue;
                    if (exclude && !clean(x, y, i)) continue;
                    const double v = std::max(B + I * Pp, WEIGHT_VARIANCE_MIN_FRACTION * B);
                    num += Pp * (static_cast<double>(px) - rh.bkg) / v;
                    den += Pp * Pp / v;
                    wsum += Pp / v;
                }
            if (den > 0.0) I = num / den; else break;
        }
        if (!(den > 0.0)) continue;

        // Guard against profile-fit runaways: on a weak / near-zero reflection the reweighted Kabsch
        // iteration has no real peak to lock onto and can manufacture intensity the box sum never sees.
        // Keep the profile intensity only if it agrees with the summation seed within the margin;
        // otherwise fall back to the summation, which is robust there.
        // 1/den is the profile-fit variance with the background taken as exact. The fit is
        // I = sum(P*(px-bkg)/v) / sum(P^2/v), so dI/dbkg = -wsum/den and the background estimate's
        // own error adds (wsum/den)^2 * var(bkg) - the same term the box sum was missing.
        double sigma = std::sqrt(1.0 / den + (wsum / den) * (wsum / den) * rh.bkg_var);
        // var_bkg is the NON-SIGNAL part of that variance, and 1/den is the fit's variance taken at the
        // fitted intensity, so the signal part to remove is I itself - not max(0, I). Clamping it leaves
        // a down-fluctuated reflection's own (deflated) variance standing as its background variance,
        // which is 2-3x too small; the merge then weights exactly the down-fluctuated observations up.
        // The whole point of a separate var_bkg is that it does not move with the observation's own
        // fluctuation, and 1/den - I is what does not (1/den grows with I one for one).
        double var_bkg = std::max(0.0, 1.0 / den - I
                                       + (wsum / den) * (wsum / den) * rh.bkg_var);
        // The seed is a sum over the disk the box sum actually read, so when pixels are missing from
        // both - excluded to a neighbour, or unreadable - the fit's full-profile intensity has to be
        // scaled down to that same disk before the two are comparable. Nothing dropped gives 1.
        const double guard_scale = m_all > 0.0 ? m_read / m_all : 1.0;
        if (std::abs(I * guard_scale - rh.I) > PROFILE_SUMMATION_MAX_NSIGMA * rh.sigma) {
            I = rh.I;
            sigma = rh.sigma;
            var_bkg = rh.var_bkg;
        }
        // Carry the Pass-A box-sum intensity-weighted centroid (observed spot position) through the
        // profile path too - post-refinement uses it as the observed position (beam-centre / distance).
        results[i] = {static_cast<float>(I), static_cast<float>(sigma),
                      static_cast<float>(rh.bkg),
                      static_cast<float>(rh.obs_x), static_cast<float>(rh.obs_y),
                      static_cast<float>(var_bkg), true, rh.has_obs};
    }

    return Finalize(predicted, npredicted, results, image_number);
}

std::vector<Reflection> BraggIntegrationEngineCPU::Run(const ImagePreprocessorBuffer &image,
                                                       const std::vector<Reflection> &predicted,
                                                       size_t npredicted, int64_t image_number) {
    if (image.size() != npixel)
        return Finalize(predicted, npredicted, std::vector<BraggFitResult>(npredicted), image_number);
    return RunImpl(BufferSampler{image.data()}, predicted, npredicted, image_number);
}

std::vector<Reflection> BraggIntegrationEngineCPU::Run(const CompressedImage &image,
                                                       const std::vector<Reflection> &predicted,
                                                       size_t npredicted, int64_t image_number) {
    if (image.GetWidth() * image.GetHeight() != npixel)
        return Finalize(predicted, npredicted, std::vector<BraggFitResult>(npredicted), image_number);

    std::vector<uint8_t> scratch;
    const auto *ptr = image.GetUncompressedPtr(scratch);
    switch (image.GetMode()) {
        case CompressedImageMode::Int8:
            return RunImpl(ImageSampler<int8_t>{reinterpret_cast<const int8_t *>(ptr), INT8_MIN, INT8_MAX},
                           predicted, npredicted, image_number);
        case CompressedImageMode::Int16:
            return RunImpl(ImageSampler<int16_t>{reinterpret_cast<const int16_t *>(ptr), INT16_MIN, INT16_MAX},
                           predicted, npredicted, image_number);
        case CompressedImageMode::Int32:
            return RunImpl(ImageSampler<int32_t>{reinterpret_cast<const int32_t *>(ptr), INT32_MIN, INT32_MAX},
                           predicted, npredicted, image_number);
        case CompressedImageMode::Uint8:
            return RunImpl(ImageSampler<uint8_t>{reinterpret_cast<const uint8_t *>(ptr), UINT8_MAX, UINT8_MAX},
                           predicted, npredicted, image_number);
        case CompressedImageMode::Uint16:
            return RunImpl(ImageSampler<uint16_t>{reinterpret_cast<const uint16_t *>(ptr), UINT16_MAX, UINT16_MAX},
                           predicted, npredicted, image_number);
        case CompressedImageMode::Uint32:
            return RunImpl(ImageSampler<uint32_t>{reinterpret_cast<const uint32_t *>(ptr), UINT32_MAX, UINT32_MAX},
                           predicted, npredicted, image_number);
        default:
            throw JFJochException(JFJochExceptionCategory::InputParameterInvalid, "Image mode not supported");
    }
}