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Jungfraujoch/image_analysis/indexing/FFTIndexer.cpp
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leonarski_fandClaude Opus 5 6081b6bc43 docs: credit the L test, FFT indexing, TORO, Niggli, peakfinder8 and SparseCCL
Six methods the pages name or describe carried no citation: Padilla & Yeates
(the L test), Steller, Bolotovsky & Rossmann (the projection/FFT autoindexing
MOSFLM implements), TORO (what ffbidx implements), Krivy & Gruber and the
ITA lattice-character table (the reduction and Bravais assignment), Cheetah's
peakfinder8 (the per-ring background statistics of the adaptive finder) and
Hennequin et al.'s SparseCCL (already credited to traccc, now also to its
authors). Each gets its ACKNOWLEDGEMENT.md paragraph, a References entry in
CPU_DATA_ANALYSIS.md, and a one-line credit at the algorithm. The
Sheriff & Hendrickson / Popov & Bourenkov entry is re-scoped so each claim
sits on the paper that supports it - P&B 2003 is titled, and credited for the
sigma-aware anisotropy estimation its statistic modelling contains, not for
the tensor and its constraints. All DOIs verified against the publishers;
the SparseCCL DOI resolves to IEEE document 9049184 (IEEE blocks content
scraping, so verified by the resolved document id plus two independent
sources).

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01EFEJG6WBQv8th4UJFNe53N
2026-09-02 09:18:36 +02:00

485 lines
22 KiB
C++

// SPDX-FileCopyrightText: 2025 Filip Leonarski, Paul Scherrer Institute <filip.leonarski@psi.ch>
// SPDX-License-Identifier: GPL-3.0-only
#include "../../common/JFJochMath.h"
#include "FFTIndexer.h"
#include <Eigen/Eigen>
#include "PostIndexingRefinement.h"
// Following Steller, Bolotovsky & Rossmann (1997) J. Appl. Cryst. 30, 1036-1040
FFTIndexer::FFTIndexer(const IndexingSettings &settings)
: max_length_A(settings.GetFFT_MaxUnitCell_A()),
min_length_A(settings.GetFFT_MinUnitCell_A()),
min_angle_deg(settings.GetFFT_MinAngle_deg()),
max_angle_deg(settings.GetFFT_MaxAngle_deg()),
nDirections(settings.GetFFT_NumVectors()),
refine_threads(static_cast<unsigned>(settings.GetRefineThreads())),
result_fft(nDirections) {
// Reciprocal-magnitude histogram in one_over_d = 1/d units (the internal convention -
// the spot coordinates and histogram_spacing are 1/d too; the resolution limit converts
// as 1/HighRes). NB in this code Q always means the powder 2*pi/d, so 1/d is named
// one_over_d, never q. The histogram covers the data range [0, one_over_d_max] and is
// zero-padded by OVERSAMPLING for sub-bin peak localisation (finer cell lengths than the
// raw bin width gives). len_coeff (= 2*max_length/histogram_size) cancels the factor, so
// recovered lengths are independent of it. The padding factor is 2*pi: a historical value
// from when the extent was mistakenly written as 2*pi/d (the Q convention); it is kept
// because the exact amount sets which marginal frames index - rounding it to a nearby
// integer shifts the indexing rate ~0.5-1% (measured on the test datasets).
const float oversampling = 2.0f * static_cast<float>(PI);
const float one_over_d_max = 1.0f / settings.GetFFT_HighResolution_A();
histogram_spacing = 1.0f / (2.0f * max_length_A);
histogram_size = std::ceil(oversampling * one_over_d_max / histogram_spacing);
input_size = histogram_size * nDirections;
output_size = (histogram_size / 2 + 1) * nDirections;
if (max_length_A <= settings.GetFFT_HighResolution_A())
throw std::invalid_argument("Largest unit cell cannot be smaller than resolution");
if (nDirections <= 1)
throw std::invalid_argument("FFTWIndexer: number of directions must be > 1");
if (!(max_length_A > 0.f))
throw std::invalid_argument("FFTWIndexer: max_length_A must be > 0");
if (!(settings.GetFFT_HighResolution_A() > 0.f))
throw std::invalid_argument("FFTWIndexer: high resolution must be > 0");
if (histogram_size < 1)
throw std::invalid_argument("FFTWIndexer: histogram_size must be >= 1");
if (histogram_size > 1000000)
throw std::invalid_argument("FFTWIndexer: histogram_size too large");
SetupDirectionVectors();
}
void FFTIndexer::SetupDirectionVectors() {
direction_vectors.reserve(static_cast<size_t>(nDirections));
const double phi = (1.0 + std::sqrt(5.0)) / 2.0; // Golden ratio
const double golden_angle = 2.0 * PI / phi;
for (int i = 0; i < nDirections; i++) {
// Half-sphere distribution (z in [0,1])
double z = static_cast<double>(i) / static_cast<double>(nDirections - 1);
double theta = golden_angle * static_cast<double>(i);
double radius = std::sqrt(std::max(0.0, 1.0 - z * z));
double x = radius * std::cos(theta);
double y = radius * std::sin(theta);
// Add unit vector to the list
direction_vectors.emplace_back(static_cast<float>(x),
static_cast<float>(y),
static_cast<float>(z));
}
}
void FFTIndexer::SetupUnitCell(const std::optional<UnitCell> &cell) {
reference_unit_cell = cell;
}
void Sort(Coord &A, Coord &B, Coord &C) {
// A is the smallest, C is the largest
if (A.Length() > B.Length())
std::swap(A, B);
if (B.Length() > C.Length())
std::swap(B, C);
if (A.Length() > B.Length())
std::swap(A, B);
}
std::vector<CrystalLattice> FFTIndexer::ReduceResults(const std::vector<Coord> &results, bool widen) const {
if (results.size() < 3)
return {};
std::vector<CrystalLattice> candidates;
if (!widen) {
// Standard: combine only the shortest few filtered vectors (original behaviour, unchanged).
for (int i = 0; i < 3; i++) {
for (int j = 0; j < 3; j++) {
for (int k = 0; k < 3; k++) {
if (i + j + k + 2 >= results.size())
break;
Coord A = results[i];
Coord B = results[(i + j + 1)];
Coord C = results[(i + j + 1) + k + 1];
// sort vectors by length for reduction
Sort(A,B,C);
CrystalLattice raw(A, B, C);
CrystalLattice reduced = raw.NiggliReduce(); // Reduce cell
const auto uc = reduced.GetUnitCell();
if (uc.a < min_length_A || uc.b < min_length_A || uc.c < min_length_A)
continue;
float alpha = uc.alpha, beta = uc.beta, gamma = uc.gamma;
if (alpha < min_angle_deg || alpha > max_angle_deg ||
beta < min_angle_deg || beta > max_angle_deg ||
gamma < min_angle_deg || gamma > max_angle_deg)
continue;
// Three coplanar rows are invisible to the two tests above: they give perfectly
// ordinary lengths and angles - 30 to 150 deg admits any flat combination - and
// only the volume betrays them. Such a cell has no reciprocal basis at all
// (1/V -> infinity), so it cannot be refined, only crashed into.
if (reduced.VolumeFraction() < MIN_BASIS_VOLUME_FRACTION)
continue;
candidates.emplace_back(std::move(reduced));
}
}
}
return candidates;
}
// Fallback: anchor the two short axes (first 12) but let the third reach any longer axis, so a
// large/elongated cell whose long axis sits beyond the standard window is built. Dedup because
// most triples of one lattice Niggli-reduce to the same cell (keeps the refine set small).
const size_t n = std::min<size_t>(results.size(), 64);
const size_t n_short = std::min<size_t>(n, 12);
std::vector<UnitCell> candidate_cells; // parallel to `candidates`, see the dedup scan below
for (size_t i = 0; i < n_short; i++) {
for (size_t j = i + 1; j < n_short; j++) {
for (size_t k = j + 1; k < n; k++) {
Coord A = results[i], B = results[j], C = results[k];
Sort(A, B, C);
CrystalLattice reduced = CrystalLattice(A, B, C).NiggliReduce();
const auto uc = reduced.GetUnitCell();
if (uc.a < min_length_A || uc.b < min_length_A || uc.c < min_length_A)
continue;
if (uc.alpha < min_angle_deg || uc.alpha > max_angle_deg ||
uc.beta < min_angle_deg || uc.beta > max_angle_deg ||
uc.gamma < min_angle_deg || uc.gamma > max_angle_deg)
continue;
if (reduced.VolumeFraction() < MIN_BASIS_VOLUME_FRACTION)
continue;
bool duplicate = false;
for (const auto &c : candidate_cells)
if (c.is_close(uc, 0.02f, 1.0f)) { duplicate = true; break; }
if (!duplicate) {
// Keep each accepted candidate's cell rather than re-deriving it on the next
// triple: GetUnitCell costs three acos, and this scan runs over every candidate
// accepted so far, for every triple.
candidate_cells.push_back(uc);
candidates.emplace_back(std::move(reduced));
}
}
}
}
return candidates;
}
std::vector<Coord> FFTIndexer::FilterFFTResults(size_t max_vectors) const {
std::multimap<float, FFTResult> fft_result_map;
for (int i = 0; i < direction_vectors.size(); i++)
fft_result_map.insert(std::make_pair(result_fft[i].magnitude, result_fft[i]));
std::vector<FFTResult> fft_result_filtered;
int count = 0;
// `it` outlives the loop: the extra scan at the end of the function carries on from the
// first direction the budget did not reach.
auto it = fft_result_map.rbegin();
for (; it != fft_result_map.rend() && count < max_vectors; ++it, ++count) {
fft_result_filtered.emplace_back(it->second);
}
std::vector<Coord> ret;
// Remove vectors less than 5 deg apart, as most likely these are colinear
const float COS_5_DEG = std::cos(5.0f * PI / 180.0f);
// Minimum relative amplitude to accept a shorter vector (fundamental)
// over a longer one (harmonic).
// 0.25 means the fundamental frequency must have at least 25% of the
// intensity of the harmonic. If less, the odd-indexed spots are likely
// systematic absences or noise, and the longer vector is the true cell.
constexpr float MIN_FUNDAMENTAL_PEAK_RATIO = 0.25f;
std::vector<bool> ignore(fft_result_filtered.size(), false);
for (int i = 0; i < fft_result_filtered.size(); i++) {
if (ignore[i])
continue;
Coord dir_i = direction_vectors.at(fft_result_filtered[i].direction);
float len_i = fft_result_filtered[i].length;
int best_idx = i; // Index of the vector we currently plan to keep
for (int j = i + 1; j < fft_result_filtered.size(); j++) {
if (ignore[j]) continue;
Coord dir_j = direction_vectors.at(fft_result_filtered[j].direction);
// If vectors are colinear (angle < 5 deg)
if (std::fabs(dir_i * dir_j) > COS_5_DEG) {
ignore[j] = true;
// CHECK: Is the new candidate (j) shorter than current best (best_idx)?
// We prefer shorter vectors (fundamental periodicity) over longer ones (harmonics)
// BUT only if the shorter vector has "enough" amplitude to be real.
if (fft_result_filtered[j].length < len_i * 0.9f) {
// Compare against 'i' (the strongest in the cluster) to define the noise floor.
// Using 'best_idx' could allow stepping down into noise if we already swapped to a weak peak.
float magnitude_ratio = fft_result_filtered[j].magnitude / fft_result_filtered[i].magnitude;
// Heuristic: If the shorter vector has at least 25% of the amplitude of the
// stronger (longer) vector, assume the shorter one is the true unit cell.
// If it's less than 25%, the shorter peak is likely noise/aliasing,
// and the longer vector is the true primitive cell.
if (magnitude_ratio > MIN_FUNDAMENTAL_PEAK_RATIO) {
dir_i = dir_j;
len_i = fft_result_filtered[j].length;
best_idx = j;
}
}
}
}
Coord best_dir = direction_vectors.at(fft_result_filtered[best_idx].direction);
ret.push_back(best_dir * fft_result_filtered[best_idx].length);
}
// Sort filtered vectors by magnitude
std::sort(ret.begin(), ret.end(), [](const Coord &A, const Coord &B) {
return A.Length() < B.Length();
});
// max_vectors counts RAW search directions, but one lattice row is sampled by many neighbouring
// directions of the 16k half-sphere, so nearly all the strongest entries belong to the same two or
// three rows: 30 raw peaks prune down to only four or five distinct directions in practice, and
// those are the strongest - hence the shortest, densest - rows. When a crystal's densest rows share
// one plane, every direction that survives is coplanar, every triple ReduceResults forms from them
// is degenerate, and the indexer returns no cell at all. Keep walking the same magnitude order for
// a few more directions that are 5 deg clear of everything kept - the weak long axis that closes
// such a cell sits well past where the budget stops. They are appended AFTER the sort, so the
// entries above hold their positions and ReduceResults still forms every triple it formed before;
// the shortlist only gains candidates at its end. Four, because the standard reduction combines
// results[0..8] and a five-vector shortlist leaves most of that window unusable.
constexpr int EXTRA_DISTINCT_DIRECTIONS = 4;
for (int extra = 0; it != fft_result_map.rend() && extra < EXTRA_DISTINCT_DIRECTIONS; ++it) {
Coord dir = direction_vectors.at(it->second.direction);
bool distinct = true;
for (const auto &v: ret) {
if (std::fabs(dir * v.Normalize()) > COS_5_DEG) {
distinct = false;
break;
}
}
if (distinct) {
ret.push_back(dir * it->second.length);
extra++;
}
}
return ret;
}
float FFTIndexer::IndexedFraction(const CrystalLattice &latt,
const std::vector<Coord> &coord, size_t nspots) const {
if (nspots == 0)
return 0.0f;
const Coord a = latt.Vec0(), b = latt.Vec1(), c = latt.Vec2();
const float tol_sq = indexing_tolerance * indexing_tolerance;
size_t indexed = 0;
for (size_t i = 0; i < nspots; i++) {
const Coord &s = coord[i];
const float hf = a * s, kf = b * s, lf = c * s; // Coord operator* = dot product = Miller index
// std::rint, not std::round: rounding half away from zero has to be a libm call, half to even is
// inlined. Only the squared residual is used, and the rules can differ only at an exact .5, where
// either leaves |frac| = 0.5 - so the indexed count is the same either way.
const float dh = hf - std::rint(hf);
const float dk = kf - std::rint(kf);
const float dl = lf - std::rint(lf);
if (dh * dh + dk * dk + dl * dl < tol_sq)
++indexed;
}
return static_cast<float>(indexed) / static_cast<float>(nspots);
}
std::vector<CrystalLattice> FFTIndexer::ReduceAndRefine(const std::vector<Coord> &coord, size_t nspots,
const std::vector<Coord> &filtered, bool widen) {
const auto r = ReduceResults(filtered, widen);
Eigen::MatrixX3<float> oCell(r.size() * 3u, 3u);
Eigen::VectorX<float> scores(r.size());
for (int i = 0; i < r.size(); i++) {
oCell(i * 3u, 0u) = r[i].Vec0().x;
oCell(i * 3u, 1u) = r[i].Vec0().y;
oCell(i * 3u, 2u) = r[i].Vec0().z;
oCell(i * 3u + 1, 0u) = r[i].Vec1().x;
oCell(i * 3u + 1, 1u) = r[i].Vec1().y;
oCell(i * 3u + 1, 2u) = r[i].Vec1().z;
oCell(i * 3u + 2, 0u) = r[i].Vec2().x;
oCell(i * 3u + 2, 1u) = r[i].Vec2().y;
oCell(i * 3u + 2, 2u) = r[i].Vec2().z;
// Bootstrap score
scores(i) = 0.2;
}
RefineParameters parameters{
.viable_cell_min_spots = viable_cell_min_spots,
.dist_tolerance_vs_reference = dist_tolerance_vs_reference,
.reference_unit_cell = reference_unit_cell,
.min_length_A = min_length_A,
.max_length_A = max_length_A,
.min_angle_deg = min_angle_deg,
.max_angle_deg = max_angle_deg,
.indexing_tolerance = indexing_tolerance,
.refine_threads = refine_threads
};
return Refine(coord, nspots, oCell, scores, parameters);
}
// Vector perpendicular to a shortlist that lies in a single plane. Three vectors of one plane can
// never be reduced to a cell - every triple ReduceResults forms from them is degenerate - so a
// shortlist like that means the row that closes the cell was not among the search directions at all.
// The scatter matrix's smallest eigenvector is that plane's normal, and its eigenvalue ratio says how
// flat the set is: lambda_min/lambda_max = 2 <sin^2(out-of-plane angle)>. The threshold below is a
// 3.5 deg rms out-of-plane spread, which sits in a wide empty gap - measured over the accumulated
// clouds of ten rotation first passes, a degenerate shortlist scores 2e-5 to 3e-4 (0.2-0.8 deg) and
// every non-degenerate one 0.026 or more (7.7 deg and up).
std::optional<Coord> FFTIndexer::DegeneratePlaneNormal(const std::vector<Coord> &filtered) const {
// Below five rows the test says nothing: three directions drawn at random pass it 23% of the time
// and four 4.2%, against 0.7% at five and under 0.01% at eight (measured, 20000 draws each). A net
// that really is planar contributes far more rows than five - the crystal here gives sixteen.
if (filtered.size() < 5)
return {};
Eigen::Matrix3f scatter = Eigen::Matrix3f::Zero();
for (const auto &v : filtered) {
const Coord n = v.Normalize();
const Eigen::Vector3f e(n.x, n.y, n.z);
scatter += e * e.transpose();
}
Eigen::SelfAdjointEigenSolver<Eigen::Matrix3f> es(scatter);
if (es.info() != Eigen::Success)
return {};
constexpr float MAX_OUT_OF_PLANE = 0.0076f; // 2*<sin^2 eps> at eps_rms = 3.5 deg
if (es.eigenvalues()(0) > MAX_OUT_OF_PLANE * es.eigenvalues()(2))
return {};
const auto v = es.eigenvectors().col(0);
return Coord(v(0), v(1), v(2));
}
// One more transform, with every search direction inside a cap about `axis`. The peak of a length-L
// row stays coherent only within roughly 10.7/(L * sigma_perp) degrees of its own direction (sigma_perp
// = the spread of the accumulated cloud across that direction), which for a long axis is finer than the
// ~0.43 deg the half-sphere grid leaves between a direction and its nearest node. Spending the same
// number of directions on a 3 deg cap samples it to ~0.016 deg instead, so the row is found where the
// global pass could only miss it. The count is unchanged, so the transform and its plan are untouched
// and this costs exactly one extra pass.
std::optional<Coord> FFTIndexer::SearchCap(const std::vector<Coord> &coord, size_t nspots,
const Coord &axis, float half_angle_deg) {
const Coord u = axis.Normalize();
Coord seed = std::fabs(u.x) < 0.9f ? Coord(1.0f, 0.0f, 0.0f) : Coord(0.0f, 1.0f, 0.0f);
const Coord e1 = (u % seed).Normalize();
const Coord e2 = u % e1;
const double cos_half = std::cos(half_angle_deg * PI / 180.0);
const double golden_angle = 2.0 * PI / ((1.0 + std::sqrt(5.0)) / 2.0);
std::vector<Coord> saved;
saved.swap(direction_vectors);
direction_vectors.reserve(saved.size());
for (size_t i = 0; i < saved.size(); i++) {
// Same spiral as SetupDirectionVectors, with z restricted to the cap instead of the half-sphere.
const double z = 1.0 - (1.0 - cos_half) * static_cast<double>(i) / static_cast<double>(saved.size() - 1);
const double r = std::sqrt(std::max(0.0, 1.0 - z * z));
const double theta = golden_angle * static_cast<double>(i);
direction_vectors.emplace_back(u * static_cast<float>(z)
+ e1 * static_cast<float>(r * std::cos(theta))
+ e2 * static_cast<float>(r * std::sin(theta)));
}
DirectionsChanged();
// The search borrows a member the object keeps using afterwards, and an indexer outlives a failed
// transform - the broker's pool logs the error and hands the same indexer the next image - so the
// grid has to go back even when ExecuteFFT throws.
try {
ExecuteFFT(coord, nspots);
} catch (...) {
direction_vectors.swap(saved);
DirectionsChanged();
throw;
}
int best = 0;
for (int i = 1; i < nDirections; i++)
if (result_fft[i].magnitude > result_fft[best].magnitude)
best = i;
const FFTResult peak = result_fft[best];
const Coord found = direction_vectors.at(peak.direction) * peak.length;
direction_vectors.swap(saved);
DirectionsChanged();
if (peak.length < min_length_A)
return {};
return found;
}
std::vector<CrystalLattice> FFTIndexer::RunInternal(const std::vector<Coord> &coord, size_t nspots) {
if (nspots > coord.size())
nspots = coord.size();
if (nspots < viable_cell_min_spots)
return {};
assert(nspots <= FFT_MAX_SPOTS);
assert(coord.size() <= FFT_MAX_SPOTS);
ExecuteFFT(coord, nspots);
// Standard reduction: 30 strongest peaks, shortest-vector triples. Unchanged for the common case.
auto lattices = ReduceAndRefine(coord, nspots, FilterFFTResults(30), false);
// If the best cell indexes few of the (un-refined) accumulated spots, the true cell may be large/
// elongated with a long axis beyond the standard triple window (a superstructure, or a satellite-
// bearing modulated crystal). OFFER widened alternatives too - the raw fraction here is not a
// reliable enough discriminator to replace, so the caller refines each candidate and picks the one
// that indexes best after geometry refinement. A well-indexing compact crystal keeps only its
// standard candidates (the widened pass never runs).
const float frac = lattices.empty() ? 0.0f : IndexedFraction(lattices.front(), coord, nspots);
if (frac < 0.5f) {
auto filtered = FilterFFTResults(60);
// A shortlist confined to one plane cannot close a cell whatever is done with it, and the row
// that would close it is the one perpendicular to that plane. Look for it there, at an angular
// resolution the global grid does not have.
if (const auto normal = DegeneratePlaneNormal(filtered))
if (const auto v = SearchCap(coord, nspots, *normal, 3.0f))
filtered.push_back(*v);
for (auto &w : ReduceAndRefine(coord, nspots, filtered, true)) {
bool duplicate = false;
for (const auto &l : lattices)
if (l.GetUnitCell().is_close(w.GetUnitCell(), 0.02f, 1.0f)) { duplicate = true; break; }
if (!duplicate)
lattices.push_back(std::move(w));
}
}
return lattices;
}