Files
Jungfraujoch/image_analysis/indexing/FFTIndexer.cpp
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leonarski_fandClaude Opus 5 4e8db41785 indexing: refuse a coplanar candidate, and search the plane normal when the shortlist is flat
Three related changes to the FFT candidate path, batteried together because they touch the same
function.

A COPLANAR CANDIDATE REACHED REFINEMENT. ReduceResults filtered triples on lengths and angles only -
the 30-150 degree bound admits any flat combination - and there was no volume test. On one dataset 41
of 5535 candidates had |V|/abc below 0.05, with a clean decade gap to the next, and three of them
reached the optimizer. UnitCell is float, and for a cell that flat the metric determinant is around
1.5e-7, so float32 gets its sign wrong 19% of the time where float64 never does. The guard against a
negative argument to sqrt then CREATES the singularity it was meant to prevent: it puts c in the a-b
plane, the reciprocal volume is 1/0, and the residual is 0 times infinity. Ceres reported a
not-a-number Jacobian and wrote several hundred lines of solver output per failed solve.

VolumeFraction() is |V|/(|a||b||c|), rejected below 0.02 - about 1.1 degrees off flat, ten times below
the flattest real candidate observed and a thousand times above where float loses the sign. It is
enforced at the producer and at the two optimizer entry points. Note the existing sanity checks use
ABSOLUTE volume, which a 320 cubic-angstrom flat cell passes. The same reciprocal-volume division is
now guarded at the two remaining sites that share the pattern.

A SHORTLIST CONFINED TO ONE PLANE cannot close a cell, and the row it is missing is the plane normal.
That is detected from the scatter-matrix eigenvalue ratio - measured, degenerate clouds score 2e-5 to
3.3e-4 against 0.026 or more for every non-degenerate one, a factor of eighty - and one further
transform is spent with the same direction count inside a three-degree cap about the normal, so the
plan and buffers are untouched. More directions cannot substitute: at the exact true direction the
long axis ranks 1422 of 16384 by prominence while the shortlist cut is four times higher. Ranking, not
sampling, is the obstacle. A four-fold denser grid was measured and rejected - it reaches the same
answer to three decimal places and takes a run from 2.5 to 8 GB of device memory.

fft_min_unit_cell_A is reachable as --fft-min-unit-cell and is lowered automatically by -C, mirroring
how the maximum is already raised. The default of 10 is unchanged: a lower floor admits spurious
sub-cells on protein data, and over 73 protein runs the floor was never lowered while the sibling
maximum did fire twice, so the path is live and correctly inert.

Corpus of 93 datasets, both arms, one build: 72 bit-identical on report content and p.hkl checksum, 13
failing identically, and the count of working datasets rises by one. The volume guard fires on 58 of
93 and 47 of those stay bit-identical - it fires constantly and almost never changes an answer, which
is what it should do. Solver chatter falls from 919 lines across three datasets to none. The cap
fires on 4 of 93, none of them in the in-house or private arms.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Lc5JG6kJqZoCWaoZ43JGTW
2026-08-29 20:31:20 +02:00

484 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"
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;
}