leonarski_fandClaude Opus 5
7dea9eaf9c
FFTW's planner (every fftwf_plan_* and fftwf_destroy_plan) shares global state and is not thread-safe; executing a plan is. FFTIndexerCPU and BeamCenterFFTCPU each guarded their planning with a lock of their own, TranslationalNCS and the viewer's spectrum with none, and ModelFFT with a third - which does not stop two of them planning at once. common/FFTWPlannerLock.h holds the one mutex they all now take. No numerical change. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_013nW6FNRP1bBJJ8pfHiByAT
653 lines
33 KiB
C++
653 lines
33 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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#include "TranslationalNCS.h"
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#include <algorithm>
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#include <cmath>
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#include <cstring>
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#include <iomanip>
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#include <limits>
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#include <numeric>
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#include <random>
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#include <sstream>
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#include <unordered_set>
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#include <fftw3.h>
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#include "../../common/FFTWPlannerLock.h"
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namespace {
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// The Patterson is measured in a band, not over the whole dataset: below ~20 A the bulk solvent
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// and the beam stop dominate, above ~5 A the map answers a question about atoms rather than about
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// the arrangement of molecules, which is what a pseudo-translation is.
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constexpr double kPattersonDMin = 5.0;
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constexpr double kPattersonDMax = 20.0;
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// A pseudo-translation shorter than this is a vector inside one molecule, not a translation
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// between two of them; the origin peak's own shoulder also reaches out this far.
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constexpr double kMinPeakDistanceA = 15.0;
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// A peak this high is not a pseudo-symmetry: the origin peak is the total, so a peak at this
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// fraction of it means essentially every reflection is invariant under the translation, which
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// makes it a lattice vector. Measured over 137 datasets the two populations do not overlap - the
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// strongest genuine pseudo-translation reaches 62% of the origin, while an undeclared centring
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// (a C-, I- or F-centred crystal merged in P1) reads 83-102%.
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constexpr double kLatticeTranslationPct = 75.0;
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constexpr double kGridSampling = 2.5; // grid spacing = d_min / this
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constexpr int kPattersonShells = 20; // equal-count shells for the E^2 normalisation
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constexpr int kMinPattersonReflections = 50;
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constexpr double kModulationDMin = 4.0; // coarse enough that a small error in u does not
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// scramble the phase h.u
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constexpr int kModulationShells = 10;
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constexpr int kPhaseBins = 12;
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constexpr int kMinModulationReflections = 500;
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constexpr double kWilsonOutlierE2 = 8.0;
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constexpr double kMinShellISig = 1.0;
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// 20 rather than a handful: the null is the median of that many greedy refinements from random
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// starts, and with 6 draws its scatter was enough to move datasets sitting near the gate across
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// it from one RNG seed to the next (measured: three of them). The refinement is a dot product
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// per trial, so the extra draws cost a few ms.
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constexpr int kModulationNullDraws = 20;
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// Gate constants. Read off a corpus of 128 merged datasets, on which the pair fires on 7.0% of
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// them - a pathology rate, and close to the ~6% of PDB entries Read, Adams & McCoy report with a
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// large off-origin Patterson peak. Either criterion ALONE fires on roughly twice as many: a 40%
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// peak can carry no modulation at all, and on a 70,000-reflection dataset a 4% peak reaches
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// z = 18 while modulating nothing.
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constexpr double kPeakZThreshold = 5.0;
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constexpr double kModulationRatioThreshold = 2.5;
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// A modulation this deep means the suppressed class carries under 5% of the other, i.e. it is
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// very nearly systematically absent - which is what a LATTICE translation of a smaller cell looks
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// like measured in the larger one. Over 137 datasets exactly one crystal reaches it (at 89x, the
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// next-deepest genuine pseudo-symmetry being 14x), and its deposited entry is indeed a cell of
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// half the volume, whose axis length this detector reports to within 0.2 A.
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constexpr double kNearExtinctModulation = 20.0;
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// Above this many grid points the null is cut from 30 draws to 10. A deterministic function of
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// the cell, so the answer does not depend on the machine; no dataset in the corpus reaches it
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// (the largest grid measured was 2.5 M points on a 545 A cell).
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constexpr size_t kLargeGridPoints = 4u << 20;
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struct Vec3 { double x, y, z; };
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// Round up to the next even number whose only prime factors are 2, 3 and 5 - the sizes FFTW has
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// codelets for.
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int SmoothSize(int v) {
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v = std::max(2, v);
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for (;; ++v) {
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if (v % 2 != 0)
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continue;
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int x = v;
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for (int p : {2, 3, 5})
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while (x % p == 0)
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x /= p;
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if (x == 1)
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return v;
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}
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}
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// Origin-equivalent points of the lattice: the origin plus the centring translations, which
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// produce a large Patterson peak by construction and are not pseudo-translations.
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std::vector<Vec3> OriginEquivalents(const gemmi::SpaceGroup *sg) {
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std::vector<Vec3> out{{0.0, 0.0, 0.0}};
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if (!sg)
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return out;
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for (const auto &t : sg->operations().cen_ops) {
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const Vec3 v{t[0] / 24.0, t[1] / 24.0, t[2] / 24.0};
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if (v.x != 0.0 || v.y != 0.0 || v.z != 0.0)
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out.push_back(v);
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}
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return out;
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}
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// Shortest distance in A from a fractional position to any origin-equivalent lattice point.
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double DistanceToOrigin(const gemmi::UnitCell &cell, const Vec3 &frac,
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const std::vector<Vec3> &equivalents) {
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double best = std::numeric_limits<double>::infinity();
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for (const auto &c : equivalents)
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for (int dx = -1; dx <= 1; ++dx)
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for (int dy = -1; dy <= 1; ++dy)
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for (int dz = -1; dz <= 1; ++dz) {
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const gemmi::Position p = cell.orthogonalize(
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gemmi::Fractional{frac.x - c.x + dx, frac.y - c.y + dy, frac.z - c.z + dz});
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best = std::min(best, p.length());
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}
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return best;
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}
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}
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TranslationalNCSResult AnalyzeTranslationalNCS(const std::vector<MergedReflection> &merged,
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const gemmi::UnitCell &cell,
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const gemmi::SpaceGroup *space_group,
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int null_draws, uint32_t seed) {
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TranslationalNCSResult result;
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if (cell.a <= 0.0 || cell.b <= 0.0 || cell.c <= 0.0) {
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result.refusal = "no unit cell";
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return result;
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}
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const gemmi::GroupOps gops = space_group ? space_group->operations() : gemmi::GroupOps{};
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auto acentric = [&](const MergedReflection &r) {
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return !space_group || !gops.is_reflection_centric(gemmi::Op::Miller{{r.h, r.k, r.l}});
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};
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// ------------------------------------------------------------------ (a) the Patterson peak
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// Following the native-Patterson construction of Read, Adams & McCoy (2013) Acta Cryst. D69,
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// 176-183, with the fixed peak-height table replaced by a per-dataset permutation null: the
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// noise floor of this statistic runs from ~1% of the origin on a 74,000-reflection merge to ~18%
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// on a 138-reflection one, so no fixed percentage can serve both.
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std::vector<int> idx;
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for (size_t i = 0; i < merged.size(); ++i) {
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const auto &r = merged[i];
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if (std::isfinite(r.I) && std::isfinite(r.d) && r.d >= kPattersonDMin && r.d <= kPattersonDMax)
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idx.push_back(static_cast<int>(i));
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}
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result.n_reflections = static_cast<int>(idx.size());
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if (result.n_reflections < kMinPattersonReflections) {
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std::ostringstream os;
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os << "only " << result.n_reflections << " reflections between " << kPattersonDMax << " and "
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<< kPattersonDMin << " A - too few for a Patterson";
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result.refusal = os.str();
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return result;
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}
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// E^2: divide each intensity by the mean of an equal-count resolution shell, so the map is not
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// dominated by the Wilson fall-off and the peak percentage is comparable between datasets.
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std::sort(idx.begin(), idx.end(),
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[&](int a, int b) { return merged[a].d != merged[b].d ? merged[a].d > merged[b].d : a < b; });
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const int n = result.n_reflections;
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std::vector<double> e2(n, 0.0);
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std::vector<int> shell_of(n, 0);
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for (int s = 0; s < kPattersonShells; ++s) {
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const int lo = static_cast<int>(static_cast<int64_t>(s) * n / kPattersonShells);
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const int hi = static_cast<int>(static_cast<int64_t>(s + 1) * n / kPattersonShells);
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if (hi <= lo)
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continue;
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double sum = 0.0;
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for (int i = lo; i < hi; ++i)
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sum += merged[idx[i]].I;
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const double mean = sum / (hi - lo);
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for (int i = lo; i < hi; ++i) {
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e2[i] = mean != 0.0 ? merged[idx[i]].I / mean : 0.0;
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shell_of[i] = s;
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}
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}
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// Grid. Sampled at d_min / 2.5, rounded up to an FFT-friendly size. A very large cell is measured
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// on a coarser map rather than refused: the map only has to resolve a peak 15 A from the origin.
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double patterson_dmin = kPattersonDMin;
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std::array<int, 3> g{};
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const double axis[3] = {cell.a, cell.b, cell.c};
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for (;;) {
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for (int i = 0; i < 3; ++i)
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g[i] = SmoothSize(static_cast<int>(std::ceil(axis[i] * kGridSampling / patterson_dmin)));
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const size_t points = static_cast<size_t>(g[0]) * g[1] * g[2];
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if (points <= (64u << 20) || patterson_dmin >= 12.0)
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break;
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patterson_dmin *= 1.25;
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}
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result.grid = g;
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const int nx = g[0], ny = g[1], nz = g[2];
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const int nzh = nz / 2 + 1;
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const size_t n_points = static_cast<size_t>(nx) * ny * nz;
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const size_t n_complex = static_cast<size_t>(nx) * ny * nzh;
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if (n_points > kLargeGridPoints)
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null_draws = std::min(null_draws, 10);
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// Expand the asymmetric unit onto the full sphere: every rotation of the space group and both
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// Friedel mates, deduplicated. Only the half-space l' <= nz/2 is stored - the Patterson
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// coefficients are real and centrosymmetric, so the other half is what the c2r transform infers.
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std::vector<std::pair<size_t, int>> fill; // (index into the half-space grid, source reflection)
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fill.reserve(idx.size() * 8);
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{
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std::unordered_set<int64_t> seen;
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seen.reserve(idx.size() * 16);
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std::vector<gemmi::Op> ops;
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if (space_group)
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ops = gops.sym_ops;
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else
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ops.push_back(gemmi::Op::identity());
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for (const auto &op : ops)
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for (int sign : {1, -1})
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for (int i = 0; i < n; ++i) {
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const auto &r = merged[idx[i]];
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const gemmi::Op::Miller m = op.apply_to_hkl({{r.h, r.k, r.l}});
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const int64_t h = sign * m[0], k = sign * m[1], l = sign * m[2];
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const int64_t key = ((h + 1048576) << 42) | ((k + 1048576) << 21) | (l + 1048576);
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if (!seen.insert(key).second)
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continue;
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const int gl = static_cast<int>(((l % nz) + nz) % nz);
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if (gl > nz / 2)
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continue; // its Friedel mate is stored instead
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const int gh = static_cast<int>(((h % nx) + nx) % nx);
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const int gk = static_cast<int>(((k % ny) + ny) % ny);
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fill.emplace_back((static_cast<size_t>(gh) * ny + gk) * nzh + gl, i);
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}
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}
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float *coeff = fftwf_alloc_real(2 * n_complex);
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float *map = fftwf_alloc_real(n_points);
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if (coeff == nullptr || map == nullptr) {
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if (coeff) fftwf_free(coeff);
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if (map) fftwf_free(map);
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result.refusal = "could not allocate the Patterson grid";
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return result;
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}
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fftwf_plan plan;
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{
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std::lock_guard lock(FFTWPlannerMutex());
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plan = fftwf_plan_dft_c2r_3d(nx, ny, nz, reinterpret_cast<fftwf_complex *>(coeff), map,
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FFTW_ESTIMATE);
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}
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// The near-origin exclusion, as a set of grid points rather than a per-point distance test: only
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// the points inside a 15 A ball around each origin-equivalent point can be excluded, and there
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// are a few thousand of those however large the grid is.
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std::vector<uint8_t> near(n_points, 0);
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std::vector<Vec3> equivalents = OriginEquivalents(space_group);
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auto build_near_mask = [&]() {
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std::fill(near.begin(), near.end(), 0);
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const gemmi::UnitCell &c = cell;
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// Fractional half-width of a sphere of radius R along axis i is R * |a*_i|, so the ball is
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// enumerated directly instead of testing every grid point: the cost is a few thousand points
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// however large the grid is.
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const double rstar[3] = {c.ar, c.br, c.cr};
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for (const auto &e : equivalents) {
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const int span[3] = {static_cast<int>(std::ceil(kMinPeakDistanceA * rstar[0] * nx)) + 1,
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static_cast<int>(std::ceil(kMinPeakDistanceA * rstar[1] * ny)) + 1,
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static_cast<int>(std::ceil(kMinPeakDistanceA * rstar[2] * nz)) + 1};
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const int c0[3] = {static_cast<int>(std::lround(e.x * nx)),
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static_cast<int>(std::lround(e.y * ny)),
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static_cast<int>(std::lround(e.z * nz))};
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for (int i = c0[0] - span[0]; i <= c0[0] + span[0]; ++i)
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for (int j = c0[1] - span[1]; j <= c0[1] + span[1]; ++j)
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for (int k = c0[2] - span[2]; k <= c0[2] + span[2]; ++k) {
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const gemmi::Position p = cell.orthogonalize(gemmi::Fractional{
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static_cast<double>(i) / nx - e.x, static_cast<double>(j) / ny - e.y,
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static_cast<double>(k) / nz - e.z});
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if (p.length() >= kMinPeakDistanceA)
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continue;
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const size_t gi = static_cast<size_t>(((i % nx) + nx) % nx);
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const size_t gj = static_cast<size_t>(((j % ny) + ny) % ny);
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const size_t gk = static_cast<size_t>(((k % nz) + nz) % nz);
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near[(gi * ny + gj) * nz + gk] = 1;
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}
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}
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};
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build_near_mask();
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auto transform = [&](const std::vector<double> &values) {
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std::memset(coeff, 0, 2 * n_complex * sizeof(float));
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for (const auto &[at, src] : fill)
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coeff[2 * at] += static_cast<float>(values[src]);
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fftwf_execute(plan);
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};
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auto max_far = [&]() {
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double best = -std::numeric_limits<double>::infinity();
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for (size_t i = 0; i < n_points; ++i)
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if (!near[i] && map[i] > best)
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best = map[i];
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return best;
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};
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transform(e2);
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const double origin = map[0];
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// The vector to report is the largest off-origin LOCAL maximum, which is not always the largest
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// off-origin grid value: the tail of the origin peak can be higher right at the 15 A boundary,
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// and that is a shoulder, not a peak.
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auto top_local_max = [&]() {
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double best = -std::numeric_limits<double>::infinity();
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std::array<double, 3> at_frac{{0.0, 0.0, 0.0}};
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for (int i = 0; i < nx; ++i)
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for (int j = 0; j < ny; ++j)
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for (int k = 0; k < nz; ++k) {
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const size_t at = (static_cast<size_t>(i) * ny + j) * nz + k;
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if (near[at] || map[at] <= best)
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continue;
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bool is_max = true;
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for (int dx = -1; dx <= 1 && is_max; ++dx)
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for (int dy = -1; dy <= 1 && is_max; ++dy)
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for (int dz = -1; dz <= 1; ++dz) {
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if (dx == 0 && dy == 0 && dz == 0)
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continue;
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const size_t nb = (static_cast<size_t>((i + dx + nx) % nx) * ny
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+ (j + dy + ny) % ny) * nz + (k + dz + nz) % nz;
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if (map[nb] > map[at]) { is_max = false; break; }
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}
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if (is_max) {
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best = map[at];
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at_frac = {static_cast<double>(i) / nx, static_cast<double>(j) / ny,
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static_cast<double>(k) / nz};
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}
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}
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return std::pair<std::array<double, 3>, double>{at_frac, best};
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};
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// Absorb any translation the data are invariant under - a centring the space group in use does
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// not declare - into the exclusion, and look again underneath it. Without this, a C-centred
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// crystal merged in P1 reports its own centring vector as a pseudo-symmetry (measured: 11 of the
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// 95 P1 merges in the corpus), and a genuine pseudo-translation hiding below a centring vector is
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// never reached.
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double observed = 0.0;
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for (int pass = 0; pass < 5; ++pass) {
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observed = max_far();
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result.peak_percent = origin > 0.0 ? 100.0 * observed / origin : 0.0;
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const auto [frac, height] = top_local_max();
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result.peak_frac = frac;
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if (result.peak_percent < kLatticeTranslationPct || !std::isfinite(height))
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break;
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result.undeclared_lattice_translations.push_back(frac);
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equivalents.push_back(Vec3{frac[0], frac[1], frac[2]});
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build_near_mask();
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}
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// Permutation null: the same reflections, the same map, with the intensities shuffled inside each
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// resolution shell. Everything that could make a Patterson peak except the correlation between
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// reflections is preserved, so this is the noise floor of THIS dataset.
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{
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std::mt19937 rng(seed);
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std::vector<double> permuted = e2;
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std::vector<double> nulls;
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nulls.reserve(null_draws);
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for (int draw = 0; draw < null_draws; ++draw) {
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permuted = e2;
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for (int s = 0; s < kPattersonShells; ++s) {
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const int lo = static_cast<int>(static_cast<int64_t>(s) * n / kPattersonShells);
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const int hi = static_cast<int>(static_cast<int64_t>(s + 1) * n / kPattersonShells);
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for (int i = hi - 1; i > lo; --i)
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std::swap(permuted[i], permuted[lo + static_cast<int>(rng() % (i - lo + 1))]);
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}
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transform(permuted);
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if (map[0] > 0.0)
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nulls.push_back(100.0 * max_far() / map[0]);
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}
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if (!nulls.empty()) {
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const double mean = std::accumulate(nulls.begin(), nulls.end(), 0.0) / nulls.size();
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double var = 0.0;
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for (double v : nulls)
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var += (v - mean) * (v - mean);
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var /= nulls.size();
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result.null_mean = mean;
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result.null_sd = std::sqrt(var);
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result.peak_z = result.null_sd > 0.0 ? (result.peak_percent - mean) / result.null_sd : 0.0;
|
|
}
|
|
}
|
|
|
|
{
|
|
std::lock_guard lock(FFTWPlannerMutex());
|
|
fftwf_destroy_plan(plan);
|
|
}
|
|
fftwf_free(coeff);
|
|
fftwf_free(map);
|
|
result.measurable = true;
|
|
|
|
// ------------------------------------------------------------------ (b) the modulation depth
|
|
// A pure translation u between two copies multiplies the intensity by |1 + exp(2 pi i h.u)|^2, so
|
|
// <I> depends on the phase h.u: strong where it is near an integer, extinct near a half-integer.
|
|
// The Patterson peak says a vector exists; this says whether it modulates the data.
|
|
std::vector<std::array<int, 3>> mod_hkl;
|
|
std::vector<double> mod_e2;
|
|
// Where each kept resolution shell ends in the two arrays above. The null below shuffles the
|
|
// intensities inside a shell, exactly as the Patterson null does, and the shells are written
|
|
// consecutively, so their boundaries are all it needs.
|
|
std::vector<size_t> mod_shell_bounds{0};
|
|
{
|
|
std::vector<int> mod_idx;
|
|
for (size_t i = 0; i < merged.size(); ++i) {
|
|
const auto &r = merged[i];
|
|
if (std::isfinite(r.I) && std::isfinite(r.d) && r.d >= kModulationDMin && acentric(r))
|
|
mod_idx.push_back(static_cast<int>(i));
|
|
}
|
|
if (!mod_idx.empty()) {
|
|
// Shells linear in 1/d^2, and the same defensive normalisation the twinning statistics
|
|
// use: noise-only shells skipped, Wilson outliers rejected, the shell mean re-fitted once
|
|
// so the outlier does not corrupt the mean it is measured against.
|
|
double min_s = std::numeric_limits<double>::infinity(), max_s = -min_s;
|
|
for (int i : mod_idx) {
|
|
const double s = 1.0 / (merged[i].d * merged[i].d);
|
|
min_s = std::min(min_s, s);
|
|
max_s = std::max(max_s, s);
|
|
}
|
|
if (max_s > min_s) {
|
|
std::vector<std::vector<int>> bins(kModulationShells);
|
|
for (int i : mod_idx) {
|
|
const double t = (1.0 / (merged[i].d * merged[i].d) - min_s) / (max_s - min_s);
|
|
bins[std::clamp(static_cast<int>(t * kModulationShells), 0, kModulationShells - 1)]
|
|
.push_back(i);
|
|
}
|
|
for (const auto &b : bins) {
|
|
if (b.empty())
|
|
continue;
|
|
double isig = 0.0;
|
|
int n_isig = 0;
|
|
for (int i : b)
|
|
if (std::isfinite(merged[i].sigma) && merged[i].sigma > 0.0) {
|
|
isig += merged[i].I / merged[i].sigma;
|
|
++n_isig;
|
|
}
|
|
if (n_isig == 0 || isig / n_isig < kMinShellISig)
|
|
continue;
|
|
double sum = 0.0;
|
|
for (int i : b)
|
|
sum += merged[i].I;
|
|
double mean = sum / b.size();
|
|
if (mean <= 0.0)
|
|
continue;
|
|
sum = 0.0;
|
|
int kept = 0;
|
|
for (int i : b)
|
|
if (merged[i].I / mean <= kWilsonOutlierE2) { sum += merged[i].I; ++kept; }
|
|
if (kept == 0)
|
|
continue;
|
|
mean = sum / kept;
|
|
if (mean <= 0.0)
|
|
continue;
|
|
for (int i : b) {
|
|
const double v = merged[i].I / mean;
|
|
if (v > kWilsonOutlierE2)
|
|
continue;
|
|
mod_hkl.push_back({merged[i].h, merged[i].k, merged[i].l});
|
|
mod_e2.push_back(v);
|
|
}
|
|
mod_shell_bounds.push_back(mod_e2.size());
|
|
}
|
|
}
|
|
}
|
|
}
|
|
result.n_modulation_reflections = static_cast<int>(mod_e2.size());
|
|
if (result.n_modulation_reflections < kMinModulationReflections)
|
|
return result;
|
|
|
|
const int floor_count = std::max(50, result.n_modulation_reflections / (4 * kPhaseBins));
|
|
auto contrast = [&](const std::vector<double> &e2v, const std::array<double, 3> &u,
|
|
std::vector<double> *profile) {
|
|
std::array<double, kPhaseBins> sum{}, cnt{};
|
|
sum.fill(0.0);
|
|
cnt.fill(0.0);
|
|
for (size_t i = 0; i < e2v.size(); ++i) {
|
|
const double t = mod_hkl[i][0] * u[0] + mod_hkl[i][1] * u[1] + mod_hkl[i][2] * u[2];
|
|
const double f = t - std::floor(t);
|
|
const int b = std::min(static_cast<int>(f * kPhaseBins), kPhaseBins - 1);
|
|
sum[b] += e2v[i];
|
|
cnt[b] += 1.0;
|
|
}
|
|
double lo = std::numeric_limits<double>::infinity(), hi = -lo;
|
|
int populated = 0;
|
|
if (profile)
|
|
profile->assign(kPhaseBins, std::numeric_limits<double>::quiet_NaN());
|
|
for (int b = 0; b < kPhaseBins; ++b) {
|
|
if (cnt[b] < floor_count)
|
|
continue;
|
|
const double m = sum[b] / cnt[b];
|
|
if (profile)
|
|
(*profile)[b] = m;
|
|
lo = std::min(lo, m);
|
|
hi = std::max(hi, m);
|
|
++populated;
|
|
}
|
|
if (populated < 2 || lo <= 0.0)
|
|
return std::numeric_limits<double>::quiet_NaN();
|
|
return hi / lo;
|
|
};
|
|
|
|
// Coarse-to-fine local search for the vector that modulates the data most strongly, started from
|
|
// the Patterson peak. The Patterson grid is coarse and a real pseudo-translation is rarely at an
|
|
// exactly rational position, so the peak position alone understates the modulation.
|
|
const std::array<double, 3> step0{1.0 / nx, 1.0 / ny, 1.0 / nz};
|
|
auto refine = [&](const std::vector<double> &e2v, std::array<double, 3> u) {
|
|
std::array<double, 3> s = step0;
|
|
double best = contrast(e2v, u, nullptr);
|
|
for (int round = 0; round < 3; ++round) {
|
|
for (bool improved = true; improved;) {
|
|
improved = false;
|
|
for (int i = 0; i < 3; ++i)
|
|
for (double gstep : {-1.0, -0.5, 0.0, 0.5, 1.0}) {
|
|
std::array<double, 3> v = u;
|
|
v[i] += gstep * s[i];
|
|
const double c = contrast(e2v, v, nullptr);
|
|
if (std::isfinite(c) && c > best + 1e-6) {
|
|
best = c;
|
|
u = v;
|
|
improved = true;
|
|
}
|
|
}
|
|
}
|
|
for (double &x : s)
|
|
x /= 2.5;
|
|
}
|
|
return std::pair<std::array<double, 3>, double>{u, best};
|
|
};
|
|
|
|
const auto [u_refined, best_contrast] = refine(mod_e2, result.peak_frac);
|
|
if (!std::isfinite(best_contrast))
|
|
return result;
|
|
for (int i = 0; i < 3; ++i)
|
|
result.vector_frac[i] = u_refined[i] - std::floor(u_refined[i]);
|
|
result.modulation_measured = true;
|
|
result.modulation = best_contrast;
|
|
contrast(mod_e2, u_refined, &result.phase_bin_mean_e2);
|
|
result.vector_length_A = DistanceToOrigin(cell, Vec3{result.vector_frac[0], result.vector_frac[1],
|
|
result.vector_frac[2]},
|
|
std::vector<Vec3>{{0.0, 0.0, 0.0}});
|
|
|
|
// The modulation's own null: the SAME search, from the SAME start, on the same reflections with
|
|
// the intensities shuffled inside each resolution shell - the permutation the Patterson null
|
|
// above already uses, applied to the second half of the evidence. A greedy search over three
|
|
// free parameters can manufacture contrast out of nothing, and this measures how much rather
|
|
// than assuming it is negligible; the shuffle leaves the reflection count, the E^2 distribution
|
|
// and the phase-bin populations exactly as they were and removes only the one thing the
|
|
// modulation claims to see, the correlation between a reflection's intensity and h.u.
|
|
//
|
|
// Drawing the null from the unshuffled data instead - random starting vectors, the crystal's own
|
|
// intensities - measured the search AND whatever modulation the data carry, because a greedy
|
|
// search started anywhere walks into a real modulation's basin. Measured over 110 merged
|
|
// datasets: single draws reached 199x and 6162x where the crystal's own modulation read 4.0x and
|
|
// 67x, and 28 of the 110 had the control at or above the signal it was controlling for. The
|
|
// median was robust enough that this changes no verdict anywhere in that corpus - what it
|
|
// removes is a control that reads the same signal it is subtracted from, and with it the one
|
|
// way an upstream change can move the control while the modulation stands still.
|
|
{
|
|
std::mt19937 rng(seed + 7);
|
|
std::vector<double> permuted = mod_e2;
|
|
std::vector<double> nulls;
|
|
nulls.reserve(kModulationNullDraws);
|
|
for (int draw = 0; draw < kModulationNullDraws; ++draw) {
|
|
permuted = mod_e2;
|
|
for (size_t s = 1; s < mod_shell_bounds.size(); ++s) {
|
|
const size_t lo = mod_shell_bounds[s - 1], hi = mod_shell_bounds[s];
|
|
for (size_t i = hi; i > lo + 1; --i)
|
|
std::swap(permuted[i - 1], permuted[lo + rng() % (i - lo)]);
|
|
}
|
|
const auto [v, c] = refine(permuted, result.peak_frac);
|
|
(void) v;
|
|
nulls.push_back(std::isfinite(c) ? c : 1.0);
|
|
}
|
|
std::sort(nulls.begin(), nulls.end());
|
|
const size_t m = nulls.size();
|
|
result.modulation_null = m % 2 ? nulls[m / 2] : 0.5 * (nulls[m / 2 - 1] + nulls[m / 2]);
|
|
}
|
|
|
|
result.detected = result.peak_z >= kPeakZThreshold && result.modulation_null > 0.0 &&
|
|
result.modulation / result.modulation_null >= kModulationRatioThreshold;
|
|
|
|
result.near_extinct_class = result.detected && result.modulation >= kNearExtinctModulation;
|
|
|
|
// Is the vector a rational translation of the reported cell? If it is, the cell contains a
|
|
// sub-lattice: the pseudo-translation is a candidate for a REAL lattice translation the indexing
|
|
// missed, and the reported cell is a supercell candidate rather than a settled result.
|
|
for (int q = 2; q <= 6 && !result.commensurate; ++q) {
|
|
std::array<int, 3> p{};
|
|
double err = 0.0;
|
|
bool fractional = false;
|
|
for (int i = 0; i < 3; ++i) {
|
|
p[i] = static_cast<int>(std::lround(result.vector_frac[i] * q));
|
|
err = std::max(err, std::fabs(result.vector_frac[i] * q - p[i]));
|
|
if (p[i] % q != 0)
|
|
fractional = true;
|
|
}
|
|
if (err / q <= 0.05 && fractional) {
|
|
result.commensurate = true;
|
|
result.commensurate_denominator = q;
|
|
result.commensurate_numerator = p;
|
|
}
|
|
}
|
|
return result;
|
|
}
|
|
|
|
std::string TranslationalNCSToText(const TranslationalNCSResult &result) {
|
|
std::ostringstream os;
|
|
os << "Translational pseudo-symmetry\n";
|
|
if (!result.measurable) {
|
|
os << " Not measured: " << result.refusal << ".\n"
|
|
<< " => This is not a statement that the crystal has none.\n";
|
|
return os.str();
|
|
}
|
|
os << std::fixed;
|
|
for (const auto &t : result.undeclared_lattice_translations)
|
|
os << " The data are invariant under (" << std::setprecision(3) << t[0] << ", " << t[1]
|
|
<< ", " << t[2] << ") - the Patterson is as high there as at the origin, so that is a\n"
|
|
<< " LATTICE translation, not a pseudo-symmetry, and the space group in use does not\n"
|
|
<< " declare it. Either the lattice is centred and the merge is not, or the cell is a\n"
|
|
<< " supercell. Excluded, and the search continued past it.\n";
|
|
os << " Native Patterson off-origin peak " << std::setprecision(1) << result.peak_percent
|
|
<< "% of the origin";
|
|
if (result.vector_length_A > 0.0)
|
|
os << " at (" << std::setprecision(3) << result.vector_frac[0] << ", " << result.vector_frac[1]
|
|
<< ", " << result.vector_frac[2] << "), " << std::setprecision(1) << result.vector_length_A
|
|
<< " A";
|
|
os << "\n [z = " << std::setprecision(1) << result.peak_z << " against a permutation null of "
|
|
<< result.null_mean << " +- " << std::setprecision(2) << result.null_sd << "%]\n";
|
|
if (result.modulation_measured)
|
|
os << " Intensity modulation along that vector: " << std::setprecision(1) << result.modulation
|
|
<< "x between the strongest and weakest phase class [random-vector control "
|
|
<< result.modulation_null << "x]\n";
|
|
if (!result.modulation_measured) {
|
|
os << " The modulation test could not be run - too few acentric reflections beyond 4 A - so\n"
|
|
<< " the peak above stands alone.\n"
|
|
<< " => Inconclusive. A Patterson peak on its own is not enough to call a pseudo-symmetry\n"
|
|
<< " (peaks of this size occur without one), and this dataset cannot supply the second\n"
|
|
<< " half of the evidence.\n";
|
|
return os.str();
|
|
}
|
|
if (!result.detected) {
|
|
os << " => No translational pseudo-symmetry indicated.\n";
|
|
return os.str();
|
|
}
|
|
os << " => Translational pseudo-symmetry. Molecular replacement will need it declared - Phaser\n"
|
|
<< " reads a tNCS vector and corrects the likelihood for it; without that the search can\n"
|
|
<< " fail on data that are otherwise good.\n";
|
|
if (result.near_extinct_class)
|
|
os << " => The class this vector suppresses is not weak but very nearly EXTINCT, which is\n"
|
|
<< " what a lattice translation of a smaller cell looks like measured in a larger one.\n"
|
|
<< " The reported cell is therefore a supercell candidate rather than a settled\n"
|
|
<< " result, and the rival hypothesis is a cell with this translation as a lattice\n"
|
|
<< " vector.\n";
|
|
else if (result.commensurate)
|
|
os << " => The vector is a 1/" << result.commensurate_denominator
|
|
<< " translation of the cell, so the crystal is pseudo-centred. The cell itself is not in\n"
|
|
<< " question - the suppressed class is weak, not absent, and a smaller cell would\n"
|
|
<< " contradict it.\n";
|
|
return os.str();
|
|
}
|