The profile is the MEAN of each bin, so a few strong reflections landing in a bin lift it exactly as a smooth powder ring does. That is the wrong quantity whenever the profile is wanted as a background rather than as a measurement of what is in the bin - the ice score being the case in point, where reading a plain profile INVERTED the metric: over 37 rotation crystals the two highest-scoring crystals had no ice at all. The adaptive spot finder already computes the right thing, a sigma-clipped per-resolution-ring background, as a byproduct of its own threshold. Where it runs, the ice score uses that. Where it does not - --no-adaptive-spots, --azint-only, and anything reading the profile the broker wrote - there was no way to get it. This adds one: azim_int_settings.sigma_clip (rugnux --azim-sigma-clip), 0 = off, minimum 2 because a tighter clip rejects a large part of a clean Gaussian bin and biases the estimate low rather than removing outliers. Two clip passes follow the plain one, matching the finder's recipe - the first pass's standard deviation is itself inflated by the peaks being removed, so one pass leaves a threshold that is still too generous. A bin with fewer than eight pixels is left alone: at the detector edge and behind the beam stop there is no spread to clip on. Both engines do it. On the GPU the accept range is computed by a small kernel and stays resident, so a clip pass is one more read of the same pixels and no round trip; the two accumulation kernels take the range as a pointer that is null on the plain pass. Measured on a JUNGFRAU rotation dataset, non-adaptive path: azimuthal integration 0.02 -> 0.06 ms per image, exactly the 3x the extra passes predict, against a 0.34 ms per-image total. Note what the result IS: the smooth background under the peaks, not the bin mean. It should not be switched on where a ring's integrated intensity is wanted - the powder-ring geometry fit reads ring peaks, and those are what a clip is designed to remove. Off by default, so nothing changes unless it is asked for. Not exposed over the REST API - that needs the generated model regenerated, which is a separate step. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
190 lines
9.5 KiB
C++
190 lines
9.5 KiB
C++
// SPDX-FileCopyrightText: 2025 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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#include <algorithm>
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#include <cmath>
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#include <map>
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#include <unordered_map>
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#include <vector>
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#include "../../common/Logger.h"
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#include "../../common/DiffractionExperiment.h"
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#include "../../common/Reflection.h"
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#include "../IntegrationOutcome.h"
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#include "HKLKey.h"
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// The error model splits a reflection's variance into a statistical part (a*sigma^2, which averages
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// down with multiplicity) and a systematic part ((b*I)^2 - absorption, beam flicker, partiality,
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// detector non-uniformity - correlated across a reflection's repeats). Inverse-variance merging
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// (sigma = 1/sqrt(sum_w)) wrongly divides BOTH by the multiplicity, so high-multiplicity reflections
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// get an unphysically small merged sigma (merged I/sigma far above ISa). Floor the merged sigma at
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// b*|I| so the systematic term survives the merge and ISa = 1/b stays the asymptotic I/sigma ceiling.
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// error_model_b <= 0 (no active error model) leaves the sigma unchanged.
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inline float SigmaWithSystematicFloor(double inv_variance_sigma, float merged_I, double error_model_b) {
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const auto floor = static_cast<float>(error_model_b * std::abs(static_cast<double>(merged_I)));
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return std::max(static_cast<float>(inv_variance_sigma), floor);
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}
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struct MergeStatisticsShell {
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float d_min = 0.0f;
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float d_max = 0.0f;
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float mean_one_over_d2 = 0;
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int total_observations = 0;
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int unique_reflections = 0;
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int possible_unique_reflections = 0;
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double mean_i_over_sigma = 0.0;
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double cc_half = 0.0f;
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double cc_ref = NAN;
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// Redundancy-independent merging R-factor (Diederichs & Karplus 1997), computed over the
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// observations that enter the merge: R_meas = sum_hkl sqrt(n/(n-1)) sum_i|I_i-<I>| / sum I_i.
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double r_meas = NAN;
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// Anomalous signal-to-noise (XDS "SigAno" / mmCIF pdbx_absDiff_over_sigma_anomalous):
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// <|I(+)-I(-)|> / <sigma(I(+)-I(-))> over acentric reflections measured in both hands. NaN when
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// there is no anomalous split (e.g. Friedel-merged with no mates, or the stills path).
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double abs_diff_over_sigma_anomalous = NAN;
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};
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struct MergeStatistics {
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std::vector<MergeStatisticsShell> shells;
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MergeStatisticsShell overall;
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// Dataset-wide isotropic Wilson B-factor estimate (A^2) from the log-linear fit of the shell-mean
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// merged intensity against 1/d^2 (CalcGlobalWilsonB) - the analogue of XDS's "WILSON LINE ... B=".
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// Diagnostic only; not used in scaling. NaN when not determined.
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double wilson_b = NAN;
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double wilson_b_correlation = NAN;
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// Radiation-damage monitor (rotation only): the relative Debye-Waller B change measured from the first
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// to the last frame of the run (A^2; positive = high-resolution intensity fades with dose = damage) and
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// the per-batch relative-B curve it was derived from. Measured before any decay/relative-B correction is
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// applied, so it reports how much radiation damage was present. Diagnostic; NaN / empty for stills or
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// when not determined. batch_deg is the rotation width per batch of the curve.
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double radiation_damage_delta_b = NAN;
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std::vector<float> radiation_damage_b_batch;
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double radiation_damage_batch_deg = 0.0;
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};
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std::ostream &operator<<(std::ostream &output, const MergeStatisticsShell &in);
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std::ostream &operator<<(std::ostream &output, const MergeStatistics &in);
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struct MergeAccum {
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int32_t h = 0;
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int32_t k = 0;
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int32_t l = 0;
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float d = NAN;
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double sum_wI = 0.0;
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double sum_w = 0.0;
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double sum_wI_half[2] = {0.0, 0.0};
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double sum_w_half[2] = {0.0, 0.0};
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size_t n_half[2] = {0, 0};
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};
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class MergeOnTheFly {
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mutable std::mutex merged_mutex;
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const int space_group_number = 1;
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ScalingSettings scaling_settings;
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IndexingSettings indexing_settings;
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std::optional<UnitCell> reference_cell;
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std::optional<double> high_resolution_limit;
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std::optional<double> image_cc_limit;
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// Apply image_cc_limit in Mask(). One flag for the whole engine, not a per-call argument, so the
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// merge, the error model and MergeStats can never disagree about which images are in.
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bool filter_by_image_cc = false;
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double min_partiality = 0.02;
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// When set, ice-ring-flagged reflections are left out of this merge. Used for the P1 pass whose
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// merged intensities feed the space-group search and the error model - those model fits must not
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// see the ice-contaminated intensities. The final in-symmetry merge keeps them (for completeness).
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bool exclude_ice_rings = false;
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HKLKeyGenerator generator;
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std::map<uint64_t, MergeAccum> accumulator;
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// Global error model (XDS form): sigma_corr^2 = a*sigma^2 + (b*<I>)^2. a rescales the
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// (under-estimated) counting variance; the (b*<I>)^2 term adds the intensity-
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// proportional systematic error that counting statistics miss, so strong reflections
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// are no longer over-weighted. ISa = 1/b is the asymptotic I/sigma. Refined from the
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// scatter of symmetry equivalents (RefineErrorModel); identity until then.
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bool error_model_active = false;
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double error_model_a = 1.0;
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double error_model_b = 0.0;
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double error_model_chi2 = 0.0; // achieved median reduced chi^2 (~1.0 = honestly calibrated sigmas)
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// The (b*I)^2 term uses the reflection's *mean* intensity (constant over its
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// observations), so it inflates sigma without biasing the inverse-variance weights -
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// using the per-observation I_i instead would over-weight down-fluctuated points.
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std::unordered_map<uint64_t, float> error_model_mean_I;
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[[nodiscard]] float CorrectedSigma(float I_corr, float sigma_corr, float image_scale_corr,
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uint64_t hkl_key) const;
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// Optional per-observation outlier rejection: drop observations whose corrected
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// intensity lies more than reject_nsigma error-model sigmas from the reflection's
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// *median* (a robust centre). The error-model sigma already captures the genuine
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// (e.g. partiality) scatter, so this removes only the tail beyond it - zingers,
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// overlaps, mis-indexed frames - not good partials. Populated by RefineErrorModel.
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bool reject_outliers = false;
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double reject_nsigma = 6.0;
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std::unordered_map<uint64_t, float> reject_median_I;
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size_t reject_count = 0;
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bool Mask(const IntegrationOutcome &outcome);
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public:
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MergeOnTheFly(const DiffractionExperiment &x);
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MergeOnTheFly& ReferenceCell(const std::optional<UnitCell> &cell);
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MergeOnTheFly& ExcludeIceRings(bool input) { exclude_ice_rings = input; return *this; }
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MergeOnTheFly& FilterByImageCC(bool input) { filter_by_image_cc = input; return *this; }
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// Fit the global error model from the spread of symmetry-equivalent observations.
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// Call once before merging; AddImage then applies it.
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void RefineErrorModel(const std::vector<IntegrationOutcome> &outcomes);
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[[nodiscard]] bool ErrorModelActive() const { return error_model_active; }
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[[nodiscard]] double ErrorModelA() const { return error_model_a; }
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[[nodiscard]] double ErrorModelB() const { return error_model_b; }
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[[nodiscard]] double ErrorModelChi2() const { return error_model_chi2; }
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// Outlier rejection (driven by ScalingSettings::GetOutlierRejectNsigma) reports its count.
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[[nodiscard]] size_t RejectedCount() const { return reject_count; }
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// image_id is the image's stable identity (its index in the outcomes vector). The CC1/2 half-set
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// is a deterministic hash of it, so the split is reproducible run-to-run and independent of the
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// order (or threading) of AddImage calls - not a draw from a shared RNG in call order.
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void AddImage(const IntegrationOutcome& outcome, int64_t image_id);
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// d_min_override, when set, is the effective high-resolution limit for the shell table (used for
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// the automatic resolution cutoff computed by the caller); otherwise the manual
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// ScalingSettings high-resolution limit stands. The number of shells is ScalingSettings::ReportShellCount.
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MergeStatistics MergeStats(const std::vector<MergedReflection> &merged,
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const std::vector<IntegrationOutcome> &reflections,
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const std::vector<MergedReflection> &reference = {},
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std::optional<double> d_min_override = std::nullopt);
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std::vector<MergedReflection> ExportReflections();
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};
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std::vector<MergedReflection> MergeAll(const DiffractionExperiment &x,
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const std::vector<IntegrationOutcome> &reflections);
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// Pearson CC between one image's corrected intensities (I * image_scale_corr) and a reference set of
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// full intensities, over the reflections that would enter the merge (non-ice, within the resolution
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// limit, partiality above the floor, finite). {NAN, n} when fewer than 20 reflections qualify.
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// This is the per-image image_scale_cc: ScaleOnTheFly sets it, and StillsPartialityRefine recomputes it
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// after refining the partiality model, so the reported CC always describes the corrections that will be
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// merged - which matters because --min-image-cc drops images by it.
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std::pair<double, size_t> ImageReferenceCC(const std::vector<Reflection> &reflections,
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const std::map<HKLKey, double> &reference,
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const HKLKeyGenerator &generator,
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std::optional<double> d_min_limit,
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double min_partiality);
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