Two things, both about telling one ice quantity from another. The ice score's spot channel had its own band half-width of 0.02 A^-1 while the spot finder marks ice rings at 0.03 (ice_ring_width_Q_recipA). The 0.02 was justified by a 5 pp specificity gain measured on the PYTHON PROTOTYPE, which used a fitted beam centre and a mask-derived coverage table; the shipped port, which takes the geometry's centre and the azimuthal profile's own live pixel count, does not reproduce it. Measured over the corpus by truth class rather than by directory label, at 0.02 vs 0.03 on the combined score: ice loops 62.13/62.19%, _icy protein 89.03/89.79%, _clean protein 16.51/16.31%, water 17.19/20.03%. The widths are indistinguishable except on water, where one of the four loops is independently known to carry a full hexagonal pattern. So the width is now a parameter and the pipeline's own value is passed in - one band width, not two. The 0.012 tolerance in the radial channel is NOT a second band width, and is renamed CENTRE_SMEAR_Q to say so: it is how far either side the channel looks for the bin a mis-set beam centre moved the ring to. The rest is naming. Three kinds of number were all called score, or built from things called count, and a reader could not tell from the name whether 1 meant "none" or "certain" - which are opposite. The convention, now stated in docs/CPU_DATA_ANALYSIS.md: *_score is bounded [0,1] and 1 is certainty, *_ratio is unbounded and 1 is nothing, *_count is a count. The C++ identifiers for the ice ring ratio follow it (ice_ring_score -> ice_ring_ratio, GetIceRingScore -> GetIceRingRatio, PlotType::IceRingScore -> IceRingRatio), and the local in the scaling gate that shadowed the new ice_score while meaning the ring ratio is renamed with them. Nothing outside the source moved: the CBOR keys ice_ring_score and ice_ring_score_mean, the datasets /entry/MX/iceRingScore and iceRingScoreMean, the ice_ring_score plot type and the --ice-min-score flag are all unchanged, and were checked to be after the rename. Renaming those changes stored files, the stream format, the REST API and a CLI flag, and is a separate decision. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01EFEJG6WBQv8th4UJFNe53N
299 lines
11 KiB
C++
299 lines
11 KiB
C++
// SPDX-FileCopyrightText: 2024 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 "AzimuthalIntegrationProfile.h"
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#include "JFJochException.h"
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#include "Definitions.h"
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#include <algorithm>
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inline float sum_to_count(float sum, uint64_t count) {
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if (count == 0)
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return NAN;
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return sum / (static_cast<float>(count));
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}
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inline float calc_std(float sum, float sum2, uint64_t count) {
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if (count == 0 || count == 1)
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return NAN;
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const auto fp_count = static_cast<float>(count);
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const float variance = (sum2 - sum * sum / fp_count) / (fp_count - 1);
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// The two sums are floats accumulated over millions of pixels, so on a near-constant ring the
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// difference of two nearly equal large numbers lands either side of zero; clamp before the root
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// rather than emit a NaN standard deviation. Both spot-finder ring accumulators do the same.
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return std::sqrt(std::max(0.0f, variance));
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}
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AzimuthalIntegrationProfile::AzimuthalIntegrationProfile(const AzimuthalIntegrationMapping &mapping)
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: sum(mapping.GetBinNumber(), 0),
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sum2(mapping.GetBinNumber(), 0),
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count(mapping.GetBinNumber(), 0),
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bin_to_q(mapping.GetBinToQ()),
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bin_to_d(mapping.GetBinToD()),
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bin_to_2theta(mapping.GetBinToTwoTheta()),
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bin_to_phi(mapping.GetBinToPhi()),
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q_bins(mapping.GetQBinCount()),
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azim_bins(mapping.GetAzimuthalBinCount()) {
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}
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void AzimuthalIntegrationProfile::Clear(const AzimuthalIntegrationMapping &mapping) {
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std::unique_lock ul(m);
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bin_to_d = mapping.GetBinToD();
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bin_to_q = mapping.GetBinToQ();
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bin_to_2theta = mapping.GetBinToTwoTheta();
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bin_to_phi = mapping.GetBinToPhi();
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q_bins = mapping.GetQBinCount();
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azim_bins = mapping.GetAzimuthalBinCount();
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sum = std::vector<float>(mapping.GetBinNumber(), 0);
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sum2 = std::vector<float>(mapping.GetBinNumber(), 0);
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count = std::vector<uint64_t>(mapping.GetBinNumber(), 0);
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}
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void AzimuthalIntegrationProfile::Add(int64_t bin, int64_t value) {
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if (bin < 0 || bin >= sum.size())
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return;
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std::unique_lock ul(m);
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sum[bin] += static_cast<float>(value);
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sum2[bin] += static_cast<float>(value * value);
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count[bin]++;
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}
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void AzimuthalIntegrationProfile::Add(const std::vector<float> &in_sum,
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const std::vector<float> &in_sum2,
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const std::vector<uint32_t> &in_count) {
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std::unique_lock ul(m);
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if ((in_sum.size() == sum.size()) && (in_count.size() == count.size())) {
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for (int i = 0; i < sum.size(); i++) {
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sum[i] += in_sum[i];
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count[i] += in_count[i];
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}
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if (in_sum2.size() == sum2.size()) {
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for (int i = 0; i < sum.size(); i++) {
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sum2[i] += in_sum2[i];
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}
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}
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} else if (!in_sum.empty() && !in_count.empty())
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throw JFJochException(JFJochExceptionCategory::InputParameterInvalid, "Mismatch in size of sum/count datasets");
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}
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std::vector<float> AzimuthalIntegrationProfile::GetResult() const {
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std::unique_lock ul(m);
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std::vector<float> rad_int_profile(sum.size(), 0);
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for (int i = 0; i < sum.size(); i++)
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rad_int_profile[i] = sum_to_count(sum[i], count[i]);
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return rad_int_profile;
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}
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std::vector<float> AzimuthalIntegrationProfile::GetStd() const {
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std::unique_lock ul(m);
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std::vector<float> rad_int_profile(sum.size(), 0);
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for (int i = 0; i < sum.size(); i++)
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rad_int_profile[i] = calc_std(sum[i], sum2[i], count[i]);
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return rad_int_profile;
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}
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std::vector<uint64_t> AzimuthalIntegrationProfile::GetPixelCount() const {
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std::unique_lock ul(m);
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return count;
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}
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std::vector<float> AzimuthalIntegrationProfile::GetResult1D() const {
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std::unique_lock ul(m);
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std::vector<float> sum_q(q_bins, 0.0f);
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std::vector<uint64_t> count_q(q_bins, 0);
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for (int i = 0; i < sum.size(); i++) {
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const int q_bin = i % q_bins;
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sum_q[q_bin] += sum[i];
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count_q[q_bin] += count[i];
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}
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std::vector<float> rad_int_profile(q_bins, 0.0f);
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for (int q = 0; q < q_bins; q++)
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rad_int_profile[q] = sum_to_count(sum_q[q], count_q[q]);
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return rad_int_profile;
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}
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void AzimuthalIntegrationProfile::SetTitle(const std::string &input) {
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title = input;
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}
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const std::vector<float> &AzimuthalIntegrationProfile::GetXAxis(PlotAzintUnit unit) const {
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switch (unit) {
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case PlotAzintUnit::TwoTheta_deg:
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return bin_to_2theta;
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case PlotAzintUnit::d_A:
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return bin_to_d;
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default:
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case PlotAzintUnit::Q_recipA:
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return bin_to_q;
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}
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}
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MultiLinePlot AzimuthalIntegrationProfile::GetPlot(bool force_1d, PlotAzintUnit plot_unit) const {
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MultiLinePlot ret;
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const std::vector<float> &x_coord = GetXAxis(plot_unit);
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if (azim_bins == 1)
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ret.AddPlot(MultiLinePlotStruct{.title = title, .x = x_coord, .y = GetResult()});
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else {
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if (force_1d) {
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std::vector<float> x_shortened(q_bins);
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for (int i = 0; i < q_bins; i++)
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x_shortened[i] = x_coord[i];
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ret.AddPlot(MultiLinePlotStruct{.title = title, .x = x_shortened, .y = GetResult1D()});
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} else {
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ret.AddPlot(MultiLinePlotStruct{.title = title, .x = x_coord, .y= bin_to_phi, .z = GetResult()});
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}
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}
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return ret;
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}
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float AzimuthalIntegrationProfile::GetMeanValueOfBins(uint16_t min_bin, uint16_t max_bin) const {
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std::unique_lock ul(m);
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float ret_sum = 0;
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uint64_t ret_count = 0;
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for (int i = 0; i < sum.size(); i++) {
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uint16_t q_bin = i % q_bins;
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if (q_bin >= min_bin && q_bin <= max_bin) {
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ret_sum += sum[i];
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ret_count += count[i];
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}
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}
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return sum_to_count(ret_sum, ret_count);
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}
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float AzimuthalIntegrationProfile::GetBkgEstimate(const AzimuthalIntegrationSettings &settings) const {
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auto min_bin = settings.QToBin(settings.GetBkgEstimateLowQ_recipA());
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auto max_bin = settings.QToBin(settings.GetBkgEstimateHighQ_recipA());
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return GetMeanValueOfBins(min_bin, max_bin);
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}
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float AzimuthalIntegrationProfile::GetIceRingRatio(const AzimuthalIntegrationSettings &settings,
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float half_width_q) const {
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return IceRingRatio(GetResult1D(), q_bins, settings, half_width_q);
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}
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float AzimuthalIntegrationProfile::IceRingRatio(const std::vector<float> &profile, int32_t q_bins,
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const AzimuthalIntegrationSettings &settings,
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float half_width_q) {
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// Strongest hexagonal-ice ring's intensity relative to the background *under* it (1 = no ice). The
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// background is a smooth whole-profile estimate: a running median of the NON-ice bins, interpolated to
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// each ring position - not a couple of adjacent shoulder bins (the azint binning is coarser than the
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// ring width, so a local shoulder is only ~1 bin and a narrow ratio is noisy and can double-count the
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// ring's own edge). Clean profiles then sit at ~1 at every ring; ice makes the ring bin stand out.
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constexpr float two_pi = 6.283185307f;
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// Average over the azimuthal bins, if there are any; a profile that is already 1-D passes through.
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std::vector<float> prof(std::max(q_bins, 0), 0.0f);
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std::vector<int> nbin(prof.size(), 0);
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for (size_t i = 0; i < profile.size() && !prof.empty(); i++) {
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const size_t q_bin = i % prof.size();
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if (std::isfinite(profile[i])) {
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prof[q_bin] += profile[i];
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nbin[q_bin]++;
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}
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}
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for (size_t i = 0; i < prof.size(); i++)
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prof[i] = nbin[i] ? prof[i] / static_cast<float>(nbin[i]) : NAN;
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const int nq = static_cast<int>(prof.size());
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const float low_q = settings.GetLowQ_recipA();
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const float dq = settings.GetQSpacing_recipA();
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if (nq < 12 || !(dq > 0.0f))
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return 1.0f;
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auto q_of = [&](int i) { return low_q + (static_cast<float>(i) + 0.5f) * dq; };
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auto on_ice = [&](float q) {
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for (const float d : ICE_RING_RES_A)
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if (std::fabs(q - two_pi / d) < 1.5f * half_width_q)
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return true;
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return false;
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};
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// Non-ice, finite, positive bins (ascending q) carry the background.
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std::vector<int> base;
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for (int i = 0; i < nq; ++i)
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if (std::isfinite(prof[i]) && prof[i] > 0.0f && !on_ice(q_of(i)))
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base.push_back(i);
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if (base.size() < 8)
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return 1.0f;
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// Running median over the base bins => a smooth background robust to the ice peaks.
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constexpr int K = 4;
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std::vector<float> base_bg(base.size());
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std::vector<float> window;
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for (int j = 0; j < static_cast<int>(base.size()); ++j) {
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const int lo = std::max(0, j - K);
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const int hi = std::min(static_cast<int>(base.size()), j + K + 1);
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window.clear();
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for (int m = lo; m < hi; ++m)
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window.push_back(prof[base[m]]);
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std::sort(window.begin(), window.end());
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base_bg[j] = window[window.size() / 2];
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}
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float score = 1.0f;
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for (const float d : ICE_RING_RES_A) {
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const float qr = two_pi / d;
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const int b = static_cast<int>(std::lround((qr - low_q) / dq - 0.5f));
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if (b < 0 || b >= nq || !std::isfinite(prof[b]) || prof[b] <= 0.0f)
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continue;
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// Linear-interpolate the smooth background to the ring position.
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float bg;
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if (qr <= q_of(base.front()))
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bg = base_bg.front();
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else if (qr >= q_of(base.back()))
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bg = base_bg.back();
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else {
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bg = NAN;
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for (int j = 0; j + 1 < static_cast<int>(base.size()); ++j) {
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const float qa = q_of(base[j]), qb = q_of(base[j + 1]);
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if (qa <= qr && qr <= qb) {
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const float t = (qb > qa) ? (qr - qa) / (qb - qa) : 0.0f;
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bg = base_bg[j] + t * (base_bg[j + 1] - base_bg[j]);
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break;
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}
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}
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}
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if (std::isfinite(bg) && bg > 0.0f)
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score = std::max(score, prof[b] / bg);
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}
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return score;
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}
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AzimuthalIntegrationProfile &AzimuthalIntegrationProfile::operator+=(const AzimuthalIntegrationProfile &other) {
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if ((other.bin_to_q != bin_to_q) || (sum.size() != other.sum.size())) {
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throw JFJochException(JFJochExceptionCategory::InputParameterInvalid,
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"Error combining two radial integration profiles");
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}
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for (int i = 0; i < sum.size(); i++) {
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sum[i] += other.sum[i];
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sum2[i] += other.sum2[i];
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count[i] += other.count[i];
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}
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return *this;
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}
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void AzimuthalIntegrationProfile::Add(const DeviceOutput &result) {
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std::unique_lock ul(m);
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if (sum.size() > FPGA_INTEGRATION_BIN_COUNT )
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throw JFJochException(JFJochExceptionCategory::InputParameterInvalid,
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"Error in getting result from FPGA");
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for (int i = 0; i < sum.size(); i++) {
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sum[i] += result.integration_result[i].sum;
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count[i] += result.integration_result[i].count;
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}
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}
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