Jungfraujoch/common/BraggIntegrationSettings.h

// SPDX-FileCopyrightText: 2025 Filip Leonarski, Paul Scherrer Institute <filip.leonarski@psi.ch>
// SPDX-License-Identifier: GPL-3.0-only

#pragma once

#include <optional>

// Spot-intensity extraction method used by the Bragg integration engine. ProfileGaussian (default)
// profile-fits with a measured-width Gaussian (Kabsch-style) - more accurate intensities than the
// classical uniform BoxSum; validated on anomalous data (stronger S/Cl peaks vs box-sum). BoxSum is
// the simpler, faster fallback. ProfileEmpirical learns the profile per resolution shell from strong
// spots - see docs/CPU_DATA_ANALYSIS.md (Bragg integration).
enum class IntegratorMode { BoxSum, ProfileGaussian, ProfileEmpirical };

// The hkl half-width the broker bootstraps when a config carries no bragg_integration block. Matches
// the max_hkl default in broker/jfjoch_api.yaml, so an omitting client and an omitting config agree.
constexpr int BRAGG_ONLINE_DEFAULT_MAX_HKL = 100;

class BraggIntegrationSettings {
    IntegratorMode integrator_mode = IntegratorMode::ProfileGaussian;
    float r_1 = 4;
    float r_2 = 6;
    float r_3 = 10;
    // Integration/prediction resolution limit. Unset means "as far as the detector reaches", resolved
    // from the geometry where it is used. The predictor independently rejects any reflection that misses
    // the detector, so this is a bound on how far the lattice walk goes rather than a second opinion on
    // what is measurable - a fixed default simply truncated every experiment whose detector reached
    // past it.
    std::optional<float> d_min_limit_A;
    std::optional<float> fixed_profile_radius;
    float minimum_sigma_in_regards_to_i = 0.02;
    // The r2..r3 background ring is estimated with ONE of two robust means, never both: a high-side
    // sigma-clip (bkg_clip_nsigma, the default) or a symmetric trimmed mean (bkg_trim_fraction). Setting
    // either through its setter clears the other, so whichever was asked for last is the one in force;
    // with both at 0 the ring is a plain mean.
    //
    // Symmetric trimmed-mean fraction: drop the lowest and highest this fraction of ring pixels before
    // averaging. Robust to the high-side contamination (neighbour-spot wings, tails, zingers) that
    // biases a plain ring mean up, but a symmetric trim is NOT a consistent estimator of the mean of a
    // right-skewed (Poisson) sample - it sits ~0.1 ct/px low at every level, which with ~50 ring pixels
    // adds ~5 counts to every partial. Kept reachable (rugnux --background-trim) for back compatibility;
    // 0.10 was the shipped value.
    float bkg_trim_fraction = 0.0f;
    // High-side-only sigma clip: reject ring pixels above mean + this many sqrt(mean). Rejects the same
    // contamination as the trim - measurably better, in fact - without cutting the low side, so it does
    // not carry the trim's skew bias. Measured empty-aperture pedestal, counts: plain mean -0.03..-0.20,
    // 10% symmetric trim +5.05..+6.34, 4 sigma clip +0.02..+0.54. Applied to monochromatic data
    // (rugnux --background-clip); broadband (non-zero bandwidth) data always clip, at their tuned 3 sigma.
    float bkg_clip_nsigma = 4.0f;
    // Half-width of the hkl cube the predictor walks: every reflection with |h|,|k|,|l| <= this is
    // tested against the Ewald sphere, and nothing outside it can ever be predicted. An axis is
    // truncated once a/d_min exceeds this, and the GPU cost is the cube (2n+1)^3 of candidates, so
    // neither a small nor a large fixed value is right for every crystal.
    //
    // Unset (the default) means "take it from the refined cell", which is exact: the predictor keeps
    // only |q| <= 1/d_min and h = a.q, so no reflection can have |h| > a/d_min. See MaxHKLForCell.
    // Offline that is what is wanted. ONLINE it is not: the broker bootstraps a concrete value
    // (BRAGG_ONLINE_DEFAULT_MAX_HKL) so per-image cost stays predictable across samples.
    std::optional<int> max_hkl;

public:
    BraggIntegrationSettings& R1(float input);
    BraggIntegrationSettings& R2(float input);
    BraggIntegrationSettings& R3(float input);
    BraggIntegrationSettings& DMinLimit_A(std::optional<float> input);
    BraggIntegrationSettings& FixedProfileRadius_recipA(std::optional<float> input);
    BraggIntegrationSettings& Integrator(IntegratorMode input);
    BraggIntegrationSettings& BackgroundTrimFraction(float input);
    BraggIntegrationSettings& BackgroundClipNSigma(float input);
    BraggIntegrationSettings& MaxHKL(std::optional<int> input);


    [[nodiscard]] IntegratorMode GetIntegrator() const;
    [[nodiscard]] float GetR1() const;
    [[nodiscard]] float GetR2() const;
    [[nodiscard]] float GetR3() const;
    [[nodiscard]] std::optional<float> GetFixedProfileRadius_recipA() const;
    [[nodiscard]] std::optional<float> GetDMinLimit_A() const;

    [[nodiscard]] float GetMinimumSigmaInRegardsToI() const;
    [[nodiscard]] float GetBackgroundTrimFraction() const;
    [[nodiscard]] float GetBackgroundClipNSigma() const;
    [[nodiscard]] std::optional<int> GetMaxHKL() const;
};