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Jungfraujoch/image_analysis/spot_finding/SpotUtils.cpp
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leonarski_fandClaude Opus 5 16bf3408f0 Address code-review findings; make detection limits detector-driven
One changeset, developed together in response to a review of this branch, so the
files carry several of the changes at once. Full test suite passes (733 cases).

Spot finding
- Split ImageSpotFinder into Detect() (flag strong pixels - the expensive
  per-pixel pass) and ExtractSpots() (CCL + min/max-pix + resolution mask), with
  Run() = both. The per-image min-pix escalation now detects ONCE and repeats
  only the cheap extraction, instead of re-running the whole finder four times
  per frame as it did on the default path. It also keeps the winning attempt's
  spot list rather than re-extracting it, so the frame that is integrated is
  exactly the frame that was scored - which a GPU re-extract could not guarantee
  (float atomic ordering).
- spot_finding_time_s no longer swallows indexing time, and indexing_time_s now
  sums every escalation call instead of reporting only the last.

Detection limits follow the detector
- The azimuthal-integration upper q and the spot-finding high-resolution limit
  are now std::optional, in the C++ structs AND in the OpenAPI schema, and
  resolve to the detector's own maximum (DiffractionExperiment::GetDetectorMaxQ_
  recipA). Adaptive detection reads a pixel's ring from the azimuthal bins, so a
  pixel outside that q range could never be strong - the integration range
  silently bounded what detection could see, regardless of the requested
  resolution limit. Regenerated the C++ and TypeScript clients; the viewer and
  the web frontend each gained a "to detector edge" switch.

Detection defaults are now per workflow (measured, not assumed)
- Stills: adaptive detection, min-pix chosen per image, no resolution clipping.
- Rotation: fixed-threshold finder, min-pix 2, 1.5 A limit.
  On a 33-crystal rotation battery, adaptive detection helped four hard crystals
  but deterministically broke three (a lost space group, a halved indexing rate,
  a collapsed merge), and the detector-edge limit cost indexing on a strong
  rotation set (100.0 -> 96.8%). Each is still overridable by its flag, and
  --no-adaptive-spots is new.

Indexer seed escalation
- Stop escalating once a seed's lattice explains >= 90% of the seed spots.
  Previously any frame with >= 80 spots always paid three indexer calls, online
  broker included.

Merge-consistency filter
- --min-image-cc gated on a per-image CC computed BEFORE the stills partiality
  post-refinement and never refreshed; the refiner now recomputes it, so the
  reported CC describes the data that are actually merged.
- Replaced the per-call cc_mask argument with one MergeOnTheFly flag, so the
  merge, the error model and MergeStats can no longer disagree about which
  images are in (the --scale path merged unfiltered while its statistics were
  filtered).

Per-image B-factor refinement (-B) removed
- Measured on four serial-stills datasets: it is a no-op where the per-image fit
  is well conditioned and actively harmful where it is not (CC1/2 -8.1, R_meas
  +23.2 on the weakest large-cell set, whose fits hit their [-50, 200] bounds on
  14-25% of images). It had also been silently DISCARDED since the partiality
  post-refinement landed - reported but not applied. Rather than fix and keep a
  knob with no demonstrated benefit, the flag and the whole image_scale_b_factor
  chain are gone: setting, scaling fit, message field, CBOR, HDF5 write and
  read-back, per-image plot, OpenAPI enum, viewer column and checkbox, docs.
  ScaleOnTheFly no longer needs Ceres at all - the fit is a linear IRLS.
  (The Wilson per-image b_factor is a different quantity and stays.)

Stills partiality width now fits both of its components
- sigma^2 = gamma0^2 + (gamma_e*d*)^2 instead of a purely angular gamma_e*d*
  with gamma0 pinned to 0. Fitted per crystal by least squares of dist_ewald^2
  on d*^2. The angular-only width is fitted over a d*^2-dense population, so it
  was pinned by the high-resolution edge and collapsed at low d*: median
  partiality 0.008 beyond 13 A for reflections that were plainly recorded, 55%
  of them under the merge's partiality floor, and the survivors divided by those
  values - which inflated the merged low-resolution intensity scale 3.6x
  (~ +9 A^2 of apparent B). Measured on 5000 stills: the ramp flattens to 0.89x,
  no observation is dropped any more (701750 -> 716811), shell-mean CC1/2 and
  R-free improve slightly. Note CC1/2, R_meas, completeness and a B-refining
  R-free are all blind to that ramp, which is why it survived earlier validation;
  the cost is high-resolution R_meas (98.5 -> 101.9 shell-averaged).

Removed dead code from add-then-remove churn
- Prediction-time "still partiality" (unreachable: no setter), the phantom
  IndexingSettings::min_indexed_spot_fraction knob (getter, no setter - now the
  constant it always was), StillsPartialityRefine's caller-less Settings
  constructor and its reference to a long-gone env var, ProcessImage's unread
  bool return, an unused include, and a dead viewer overlay hook.

Also
- Viewer: the magnifier compared a QImage with itself, so its scene rect was set
  once ever and it could not pan into a larger dataset; the hover tail timer
  could fire after leaveEvent and resurrect the resolution readout outside the
  image.
- update_version.sh regenerated the frontend lock file BEFORE bumping the
  version (every release shipped an off-by-one lock), and did git rm/git add on
  a path that has not existed since the client moved to src/client - with no
  set -e, both failed silently.
- fpga/pcie_driver/postinstall.sh tested "[ ! occurrences > 0 ]", which is a
  redirect, not a test, so dkms add never ran.
- Unit tests for the adaptive-threshold host functions, which had none.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
2026-07-27 09:07:00 +02:00

159 lines
5.3 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 "SpotUtils.h"
#include "../../common/ResolutionShells.h"
void CountSpots(DataMessage &msg,
const std::vector<SpotToSave> &spots,
float d_min_A) {
int64_t low_res = 0;
int64_t ice_ring = 0;
for (auto &s: spots) {
if (s.ice_ring)
ice_ring++;
if (s.d_A > d_min_A)
low_res++;
}
msg.spot_count = spots.size();
msg.spot_count_low_res = low_res;
msg.spot_count_ice_rings = ice_ring;
}
void MarkIceRings(std::vector<SpotToSave> &spots, float tolerance_q_recipA) {
std::vector<float> ice_rings_q;
for (const auto &i: ICE_RING_RES_A)
ice_rings_q.push_back(2 * PI / i);
for (auto &s: spots) {
auto spot_q = 2 * PI / s.d_A;
bool tmp = false;
for (const auto &q: ice_rings_q)
tmp |= (fabs(spot_q - q) < tolerance_q_recipA);
s.ice_ring = tmp;
}
}
void FilterSpotsByCount(std::vector<SpotToSave> &input, int64_t count) {
size_t output_size = std::min<size_t>(input.size(), count);
std::ranges::partial_sort(input, input.begin() + output_size,
std::ranges::less{}, // comparator on the projected key
[](const SpotToSave &s) {
// projection: non-ice first (false < true), then strongest intensity first.
return std::tuple{s.ice_ring, -s.intensity};
});
input.resize(output_size);
}
void FilterSpuriousHighResolutionSpots(std::vector<SpotToSave> &spots, float threshold) {
std::ranges::sort(spots, [](SpotToSave &a, SpotToSave &b) {
return a.d_A > b.d_A;
});
// Apply 1/d gap threshold: find first gap in q = 1/d exceeding dist_threshold and ignore spots after it
if (spots.size() >= 2 && threshold > 0.0f) {
size_t cut_index = spots.size(); // default: keep all
// d_A sorted descending → q = 1/d_A sorted ascending
// We check consecutive q gaps: Δq_i = (1/d_i) - (1/d_{i+1})
for (size_t i = 0; i + 1 < spots.size(); ++i) {
float d1 = spots[i].d_A;
float d2 = spots[i + 1].d_A;
// Avoid division by zero; d_A should be > 0 in valid data
if (d1 <= 0.0f || d2 <= 0.0f)
continue;
float q1 = 2 * PI / d1;
float q2 = 2 * PI / d2;
float dq = q2 - q1; // should be >= 0 due to sorting
if (dq > threshold) {
cut_index = i + 1; // keep up to i inclusive
break;
}
}
if (cut_index < spots.size())
spots.resize(cut_index);
}
}
std::optional<float> GetResolution(const std::vector<SpotToSave> &spots) {
std::vector<float> resolutions;
resolutions.reserve(spots.size());
for (const auto &spot: spots) {
if (!spot.ice_ring)
resolutions.push_back(spot.d_A);
}
std::ranges::sort(resolutions);
if (resolutions.size() < 4)
return std::nullopt;
if (resolutions.size() < 20)
return resolutions[2];
return resolutions[static_cast<size_t>(resolutions.size() * 0.05)];
}
void GenerateSpotPlot(DataMessage &msg, const std::vector<SpotToSave> &spots, float d_min_A) {
const int nshells = 20;
ResolutionShells shells(d_min_A, 50.0, nshells);
std::vector<float> intensity(nshells);
std::vector<float> count(nshells);
for (const auto &s: spots) {
if (s.ice_ring)
continue;
if (auto shell = shells.GetShell(s.d_A)) {
intensity[*shell] += s.intensity;
count[*shell] += 1.0f;
}
}
std::vector<float> result(nshells);
for (int i = 0; i < nshells; ++i) {
if (count[i] > 0)
result[i] = intensity[i] / count[i];
else
result[i] = 0.0f;
}
msg.spot_plot_one_over_d_square = shells.GetShellMeanOneOverResSq();
msg.spot_plot_intensity = result;
msg.spot_plot_count = count;
}
void SpotAnalyze(const DiffractionExperiment &experiment,
const SpotFindingSettings &spot_finding_settings,
const std::vector<DiffractionSpot> &spots,
DataMessage &output) {
auto geom = experiment.GetDiffractionGeometry();
std::vector<SpotToSave> spots_out;
for (const auto &spot: spots) {
if (auto s = spot.Export(geom, output.number); s.has_value())
spots_out.push_back(s.value());
}
if (spot_finding_settings.high_res_gap_Q_recipA.has_value())
FilterSpuriousHighResolutionSpots(spots_out, spot_finding_settings.high_res_gap_Q_recipA.value());
if (experiment.GetDatasetSettings().IsDetectIceRings() && spot_finding_settings.ice_ring_width_Q_recipA > 0.0f)
MarkIceRings(spots_out, spot_finding_settings.ice_ring_width_Q_recipA);
CountSpots(output, spots_out, spot_finding_settings.cutoff_spot_count_low_res);
GenerateSpotPlot(output, spots_out,
spot_finding_settings.high_resolution_limit.value_or(experiment.GetDetectorMaxResolution_A()));
output.resolution_estimate = GetResolution(spots_out);
FilterSpotsByCount(spots_out, experiment.GetMaxSpotCount());
output.spots = spots_out;
}