leonarski_fandClaude Opus 4.8
7b464e4b3c
The profile radius (intrinsic excitation-error width = mosaicity + divergence) was the plain RMS of dist_ewald over indexed spots. With a finite energy bandwidth that spread is broadened by the bandwidth's radial smear sigma_bw = bandwidth_sigma*lambda/(2 d^2), which prediction then re-applies per reflection - so bandwidth was counted twice and the radius was inflated (most at high resolution, sigma_bw ~ 1/d^2). Subtract the bandwidth variance from the measured spread so the radius is the intrinsic width. bandwidth = 0 (monochromatic / rotation) is unchanged. Small for narrow bandwidths (~6% of the variance, ~4% radius on the 1% jet); matters for wide-bandwidth / pink beam. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
62 lines
2.3 KiB
C++
62 lines
2.3 KiB
C++
// SPDX-FileCopyrightText: 2025 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 "FitProfileRadius.h"
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#include <algorithm> // std::nth_element
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#include <cmath> // std::fabs
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std::optional<float> FitProfileRadius_MAD(const std::vector<SpotToSave>& xs) {
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std::vector<float> absx;
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absx.reserve(xs.size());
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for (const auto &s: xs) {
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if (s.indexed)
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absx.push_back(std::fabs(s.dist_ewald_sphere));
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}
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if (absx.empty())
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return std::nullopt;
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std::nth_element(absx.begin(), absx.begin() + absx.size() / 2, absx.end());
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float med;
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if (absx.size() % 2 == 1) {
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med = absx[absx.size() / 2];
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} else {
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auto it1 = absx.begin() + (absx.size() / 2 - 1);
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auto it2 = absx.begin() + (absx.size() / 2);
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float a = *it1;
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float b = *it2;
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med = 0.5f * (a + b);
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}
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// Normal consistency factor for MAD
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return 1.4826f * med;
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}
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std::optional<float> FitProfileRadius(const std::vector<SpotToSave>& spots,
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float bandwidth_sigma, float wavelength_A) {
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double sum_squares = 0.0; // measured excitation-error variance (sum dist_ewald^2)
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double sum_bw_var = 0.0; // energy-bandwidth contribution to subtract out
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int count = 0;
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for (const auto &s: spots) {
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if (!s.indexed)
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continue;
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sum_squares += static_cast<double>(s.dist_ewald_sphere) * s.dist_ewald_sphere;
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// The energy bandwidth smears each reflection radially by sigma_bw = bandwidth_sigma*|recip_z|
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// = bandwidth_sigma*lambda/(2 d^2) (the same term prediction re-adds per reflection, ~1/d^2 so
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// largest at high resolution). Deconvolve it from the measured spread so the profile radius is
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// the *intrinsic* mosaicity+divergence width and bandwidth is not double-counted at prediction.
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if (bandwidth_sigma > 0.0f && s.d_A > 0.0f) {
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const double sigma_bw = bandwidth_sigma * wavelength_A / (2.0 * static_cast<double>(s.d_A) * s.d_A);
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sum_bw_var += sigma_bw * sigma_bw;
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}
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count++;
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}
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if (count == 0)
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return std::nullopt;
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const double variance = std::max(0.0, (sum_squares - sum_bw_var) / count);
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return static_cast<float>(std::sqrt(variance));
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}
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