The background is estimated from the r2..r3 ring and then subtracted from every pixel of the r1 disk, so the ring mean's own error enters the intensity n_inner times over: var(I) carries n_inner^2 * bkg / n_B. That term is first-order in sigma, and it is set by how many pixels the ring holds - not by anything about the reflection. At r3 = 10 the ring holds about 200 px against the disk's 50. Widening it to 13 roughly doubles that. The signal disk is untouched, and the pixels gained lie further from the reflection rather than nearer, so nothing is traded for them. The effect is not subtle once looked for. Matched observation by observation on one crystal, halving the ring's pixel count leaves the intensity alone and inflates sigma by 4.7%, and the inflation rank-orders with the ring collapse across the battery. Over the whole rotation battery, against the same binary at r3 = 10: ISa better on 14 crystals and worse on 4, the summed shortfall against the reference 164.7 -> 155.9, the summed low-resolution R_meas excess 69.7 -> 59.1 percentage points, and one more crystal reaching the reference space group (33/37 -> 34/37, a trigonal case that was over-promoting). Largest gains where the ring was starved worst; the four losses are 0.25 to 2.16 in ISa and none of them changes a space group. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
199 lines
12 KiB
C++
199 lines
12 KiB
C++
// SPDX-FileCopyrightText: 2026 Filip Leonarski, Paul Scherrer Institute <filip.leonarski@psi.ch>
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// SPDX-License-Identifier: GPL-3.0-only
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#pragma once
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// =============================================================================
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// BraggIntegrationEngine — box-sum + profile-fitting 2D integrator, GPU-ready
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// =============================================================================
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//
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// A reimplementation of BraggIntegrate2D (box sum) and ProfileIntegrate2D (Kabsch profile
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// fit) under one roof, following the AzIntEngine / ROIIntegration pattern: a base class that
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// extracts the fixed per-experiment configuration, a plain-C++ CPU engine (the fallback and the
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// numeric oracle), and a CUDA engine (BraggIntegrationEngineGPU) that reaches the same result up
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// to floating-point precision.
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//
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// Unlike BraggIntegrate2D/ProfileIntegrate2D, which read the raw CompressedImage per pixel type
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// and reject the special/saturation +/-1 band, this engine reads the already-preprocessed int32
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// image held in an ImagePreprocessorBuffer (the same buffer AzIntEngineGPU/ROIIntegrationGPU
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// consume): masked/bad pixels are INT32_MIN and saturated pixels INT32_MAX, so bad-pixel identity
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// is owned by the preprocessor and a pixel is valid iff v != INT32_MIN && v != INT32_MAX.
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//
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// The integrator is selected by BraggIntegrationSettings::Integrator:
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// BoxSum -> BraggIntegrate2D equivalent (rough disk sum minus ring-mean background)
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// ProfileGaussian -> per-reflection measured-width Gaussian profile fit (the default)
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// ProfileEmpirical-> per-shell learned empirical profile fit
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// The box sum is also the seed pass (Pass A) of the two profile modes, so it always runs.
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//
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// This is the Bragg integrator used by the pipeline (bound in MXAnalysisWithoutFPGA: the GPU
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// engine when a device is present, otherwise the CPU engine). It takes a preprocessed image +
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// the predicted reflections and returns the vector<Reflection> (I, sigma, bkg, partiality, ...)
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// that the downstream scaling/merge consumes unchanged.
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// =============================================================================
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#include <cmath>
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#include <cstddef>
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#include <cstdint>
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#include <optional>
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#include <vector>
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#include "../../common/BraggIntegrationSettings.h"
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#include "../../common/DiffractionExperiment.h"
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#include "../../common/DiffractionGeometry.h"
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#include "../../common/Reflection.h"
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#include "../image_preprocessing/ImagePreprocessorBuffer.h"
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#include "BraggStencil.h"
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namespace bragg_engine {
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// Shared with both engines so the CPU and GPU paths stay numerically aligned.
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constexpr int N_SHELL = 6; // resolution shells for per-shell profile learning
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constexpr double STRONG_I_OVER_SIGMA = 5.0; // strong-spot threshold that seeds the profile
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constexpr int MIN_STRONG_PER_SHELL = 30; // below this a shell falls back to the global profile
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constexpr double C_CAPTURE = 2.5; // weak-spot radial capture term (coefficient of tan^2(2theta))
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// Lower bound on the background term of the Kabsch fit weights (v = max(bkg, floor) + signal). It
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// guards the background ESTIMATE, not the detector: the r2..r3 ring mean of a high-angle reflection
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// can come out exactly zero, and v = 0 makes the weights P^2/v diverge. A ring of n pixels cannot
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// resolve a background below ~1/n (0.003..0.02 for the default r2=6/r3=13 stencil), so that is the
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// scale the floor has to work at. Anything larger over-regularizes: the floor multiplies the reported
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// variance by floor/bkg for every pixel below it, so the previous 1/12 inflated sigma by 1.3x at
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// 0.05 ct/px and 1.7x at 0.03 - exactly where the weakest high-resolution data live. Digitisation
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// noise, where a detector has it, is additive on top of the background and does not belong here.
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constexpr double PIXEL_VARIANCE_FLOOR = 0.01;
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// The plug-in signal term of the fit weights may lower the per-pixel variance as well as raise it,
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// but not below this fraction of the background. Half-wave rectifying it (max(0, I)) instead makes
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// the weights - and so the reported 1/den - respond only to upward fluctuations of a noisy intensity
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// estimate, which adds ~0.4*sigma*sum(P^3)/sum(P^2)^2 to every sigma whatever the count rate.
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constexpr double WEIGHT_VARIANCE_MIN_FRACTION = 0.5;
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// Most background-ring pixels a block can hold for the GPU trimmed-mean sort. Shared with the CPU so
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// that a ring which overflows it falls back to the plain mean in BOTH engines: the CPU sorts an
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// unbounded vector and would otherwise keep trimming where the GPU had silently stopped. Note that
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// an elongated ring makes this a function of resolution rather than a property of the dataset - the
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// ring area grows with the elongation, so on a wide enough stencil the estimator changes at a fixed
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// detector radius. The growth cap below keeps the default r2=6/r3=13 ring under the bound at any
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// bandwidth; the wider stills radii can cross it, and only ever with --background-trim, which is
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// off by default and kept for back compatibility.
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constexpr int BKG_TRIM_MAX = 512;
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// Ceiling on how far the background ring may be pushed out radially, as a multiple of r3 - so the
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// outer ellipse never exceeds (1 + this) * r3. Bounds what a mis-declared bandwidth can do to the
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// per-reflection bounding box, and with it the shared memory the GPU sizes from the widest ring.
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constexpr float MAX_STENCIL_GROW_OVER_R3 = 2.0f;
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// Guard against profile-fit runaways: on a weak / near-zero reflection the reweighted Kabsch iteration
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// has no real peak to lock onto and can manufacture intensity the box sum never sees. Fall back to the
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// summation (box-sum) intensity when the profile result disagrees with the summation seed by more than
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// this many box-sum sigmas (a real fit agrees within counting noise, so the margin is generous).
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constexpr double PROFILE_SUMMATION_MAX_NSIGMA = 10.0;
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} // namespace bragg_engine
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// One reflection's extracted intensity, produced by the derived engine and turned into a
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// Reflection by Finalize() (which owns the polarization correction and scale bookkeeping).
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struct BraggFitResult {
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float I = 0.0f;
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float sigma = NAN;
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float bkg = 0.0f;
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float observed_x = 0.0f; // intensity-weighted centroid (BoxSum mode only)
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float observed_y = 0.0f;
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// Variance of everything in `sigma` that is NOT the reflection's own Poisson signal (the
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// background and the error of its estimate). The merge rebuilds each partial's variance at the
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// pooled intensity and needs this term; back-deriving it as sigma^2 - I only works while that
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// identity holds exactly, which it does not for a profile fit.
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float var_bkg = 0.0f;
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bool ok = false;
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bool has_observed = false;
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};
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class BraggIntegrationEngine {
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protected:
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// --- fixed configuration extracted from the experiment (see ProfileIntegrate2D) ---
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IntegratorMode mode;
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bool empirical; // ProfileEmpirical (vs ProfileGaussian)
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size_t xpixel, ypixel, npixel;
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float r1_sq;
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float r2, r2_sq;
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float r3, r3_sq;
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int R, G, GG; // profile-grid half-size, edge (2R+1) and area (G*G)
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double bw_sigma; // bandwidth sigma [dimensionless, * Rpx -> px]
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float bkg_clip_nsigma; // high-outlier background sigma-clip multiplier (0 = no clip)
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bool use_ellipse; // radially elongate the per-reflection Gaussian
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double c_radial; // radial variance coefficient of tan^2(2theta): parallax + capture
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double F_px; // detector distance expressed in pixels
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float beam_x, beam_y;
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double r_max; // distance from the beam centre to the far corner [px]
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// Per-reflection signal/background geometry (BraggStencil.h). With k_sigma = 0 this is the fixed
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// r1 disk + r2..r3 ring the integrator has always used, bit for bit; above 0 the RING is
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// elongated radially, per reflection, by the analytic radial smear. Both engines build every
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// stencil through MakeBraggStencil, so the geometry has one definition.
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BraggStencilParams stencil;
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// Effective symmetric trimmed-mean background fraction (BraggIntegrationSettings), used only when
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// the high-side clip is switched off - the two are alternatives. 0 = plain ring mean. Read by both
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// the CPU and GPU engines.
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float bkg_trim = 0.0f;
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// --- radial background curvature correction (BraggIntegrationSettings) ---
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// The disk and the annulus are concentric, so any background LINEAR in position cancels between
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// them; what survives is the curvature of the radial background. mean_annulus(B) - mean_disk(B)
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// of a radial B is a kernel over radial offset:
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// bkg_error = sum_k k_diff[k] * B(r0 + k - k_off)
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// That is one short dot product per reflection and reads no pixels. Built in the constructor,
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// so bkg_radial can be flipped between images at no cost - which is what the auto mode does,
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// applying the correction only to the images whose background really is a smooth function of
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// radius (see BackgroundRadial below). It is built only when that mode, or an explicit setting,
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// could ever raise the correction; an engine that can never apply it keeps the single circular
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// kernel and never reads it.
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//
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// With a circular stencil ONE kernel serves every reflection. An elongated ring does not: its
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// radial-offset histogram depends on how far that reflection's ring was grown. So k_diff holds
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// n_kern kernels of k_len each, indexed by the growth quantized to whole pixels
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// (BraggStencilKernelIndex); n_kern is 1 when nothing is elongated, which is the old
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// single-kernel layout unchanged. The azimuthal average is kept, and still means what it did:
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// the stencil is rebuilt in the reflection's own frame at each azimuth, so it stays radially
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// aligned and what is averaged over is the sub-pixel phase of the detector grid against the
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// radius. What an elongated ring rules out is one kernel for ALL of them, not the average.
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bool bkg_radial = false;
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bool bkg_radial_auto = false; // settings left it unset: decide per image from the ice score
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bool bkg_radial_built = false; // the kernel table was sized for the rings this engine uses
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int k_off = 0; // index of offset 0 within one kernel
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int k_len = 0; // entries per kernel
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int n_kern = 1; // kernels in the table (1 = circular stencil)
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std::vector<float> k_diff; // n_kern * k_len, annulus-minus-disk weight per radial offset
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// One radial-offset kernel for a ring grown by `grow` px, appended to k_diff. Kept out of line
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// so the circular and elongated cases cannot drift apart. The signal disk does not change with
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// the growth, so its histogram is built on the first call and reused.
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void BuildRadialKernel(float grow);
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std::vector<double> hist_disk;
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double sum_disk = 0.0;
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DiffractionGeometry geom; // kept for the per-reflection polarization correction
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std::optional<float> polarization;
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// Assemble output reflections from the per-reflection fit results (polarization + scale corr).
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std::vector<Reflection> Finalize(const std::vector<Reflection> &predicted, size_t npredicted,
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const std::vector<BraggFitResult> &results,
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int64_t image_number) const;
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public:
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explicit BraggIntegrationEngine(const DiffractionExperiment &experiment);
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virtual ~BraggIntegrationEngine() = default;
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// predicted[0..npredicted) are the reflections to extract; image is the preprocessed int32
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// frame (image.size() == npixel). Returns only the observed reflections.
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virtual std::vector<Reflection> Run(const ImagePreprocessorBuffer &image,
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const std::vector<Reflection> &predicted, size_t npredicted,
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int64_t image_number) = 0;
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// Turn the radial background correction on or off for the images that follow. The caller owns
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// the decision; in the auto mode the analysis sets it per image from that image's ice score.
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//
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// It cannot be raised on an engine the constructor did not build the kernel table for: that
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// table would hold a single CIRCULAR kernel for rings that may be elongated, and on the GPU the
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// radial buffers were never allocated, so the CPU would correct and the GPU would not.
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void BackgroundRadial(bool on) { bkg_radial = on && bkg_radial_built; }
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[[nodiscard]] bool IsBackgroundRadialAuto() const { return bkg_radial_auto; }
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};
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