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Jungfraujoch/image_analysis/bragg_prediction/BraggPrediction.cpp
T
leonarski_fandClaude Opus 4.8 5d9e1be814 portability: replace M_PI / std::numbers::pi with a host+device-safe PI
M_PI is a POSIX <math.h> extension that MSVC does not define without
_USE_MATH_DEFINES. std::numbers::pi (introduced in the viewer guard commit) is
C++20, but CUDA here is compiled as C++17 (CMAKE_CUDA_STANDARD 17) and several
common/ headers are pulled into .cu device translation units, so std::numbers is
not available there.

Add common/JFJochMath.h with a dependency-free `constexpr double PI` that works
in host code (including MSVC), in CUDA device code, and under C++17/20, and use
it everywhere:
- common/ and image_analysis/ (incl. CUDA .cu): 78 M_PI occurrences, 22 files
- broker/OpenAPIConvert.cpp
- viewer/: the 5 files that used std::numbers::pi now use PI, for one consistent
  convention across the codebase

Verified to build: JFJochImageAnalysis (incl. CUDA), jfjoch_viewer, JFJochBroker.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
2026-06-18 14:54:01 +02:00

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// SPDX-FileCopyrightText: 2025 Filip Leonarski, Paul Scherrer Institute <filip.leonarski@psi.ch>
// SPDX-License-Identifier: GPL-3.0-only
#include "../../common/JFJochMath.h"
#include "BraggPrediction.h"
#include "../bragg_integration/SystematicAbsence.h"
namespace {
// Number of bandwidth sigmas included in the (radially thickened) Ewald-shell
// acceptance window. 3σ captures essentially the whole pink-beam smear; matches
// the conservative end of the mosaicity cutoff used by callers.
constexpr float kBandwidthCutoffSigmas = 3.0f;
}
BraggPrediction::BraggPrediction(int max_reflections)
: max_reflections(max_reflections), reflections(max_reflections) {}
const std::vector<Reflection> &BraggPrediction::GetReflections() const {
return reflections;
}
int BraggPrediction::Calc(const DiffractionExperiment &experiment, const CrystalLattice &lattice,
const BraggPredictionSettings &settings) {
const auto geom = experiment.GetDiffractionGeometry();
const auto det_width_pxl = static_cast<float>(experiment.GetXPixelsNum());
const auto det_height_pxl = static_cast<float>(experiment.GetYPixelsNum());
const float one_over_dmax = 1.0f / settings.high_res_A;
const float one_over_dmax_sq = one_over_dmax * one_over_dmax;
float one_over_wavelength = 1.0f / geom.GetWavelength_A();
const Coord Astar = lattice.Astar();
const Coord Bstar = lattice.Bstar();
const Coord Cstar = lattice.Cstar();
const Coord S0 = geom.GetScatteringVector();
std::vector<float> rot = geom.GetPoniRotMatrix().transpose().arr();
// Precompute detector geometry constants
float beam_x = geom.GetBeamX_pxl();
float beam_y = geom.GetBeamY_pxl();
float det_distance = geom.GetDetectorDistance_mm();
float pixel_size = geom.GetPixelSize_mm();
float F = det_distance / pixel_size;
const float epsilon = 1e-5f;
const float s0_sq = S0 * S0;
const float rad_to_deg = 180.0f / static_cast<float>(PI);
int i = 0;
for (int h = -settings.max_hkl; h <= settings.max_hkl; h++) {
// Precompute A* h contribution
const float Ah_x = Astar.x * h;
const float Ah_y = Astar.y * h;
const float Ah_z = Astar.z * h;
for (int k = -settings.max_hkl; k <= settings.max_hkl; k++) {
// Accumulate B* k contribution
const float AhBk_x = Ah_x + Bstar.x * k;
const float AhBk_y = Ah_y + Bstar.y * k;
const float AhBk_z = Ah_z + Bstar.z * k;
for (int l = -settings.max_hkl; l <= settings.max_hkl; l++) {
if (systematic_absence(h, k, l, settings.centering))
continue;
if (i >= max_reflections)
continue;
float recip_x = AhBk_x + Cstar.x * l;
float recip_y = AhBk_y + Cstar.y * l;
float recip_z = AhBk_z + Cstar.z * l;
float recip_sq = recip_x * recip_x + recip_y * recip_y + recip_z * recip_z;
if (recip_sq > one_over_dmax_sq)
continue;
float S_x = recip_x + S0.x;
float S_y = recip_y + S0.y;
float S_z = recip_z + S0.z;
float S_len = sqrtf(S_x * S_x + S_y * S_y + S_z * S_z);
float dist_ewald_sphere = std::fabs(S_len - one_over_wavelength);
// Energy bandwidth thickens the Ewald shell radially: at the
// diffraction condition |S|-1/λ shifts by recip_z·(Δλ/λ), i.e.
// σ_bw = |recip_z|·bandwidth_sigma (= bλ/2d², identical to
// PixelRefine's R_bw). Broaden the acceptance window in quadrature so
// high-resolution shells (smeared most, ∝1/d²) are not clipped.
float radial_cutoff = settings.ewald_dist_cutoff;
if (settings.bandwidth_sigma > 0.0f) {
const float bw_tol = kBandwidthCutoffSigmas * settings.bandwidth_sigma * std::fabs(recip_z);
radial_cutoff = std::sqrt(radial_cutoff * radial_cutoff + bw_tol * bw_tol);
}
if (dist_ewald_sphere <= radial_cutoff ) {
const float s0_p0 = S0.x * recip_x + S0.y * recip_y + S0.z * recip_z;
const float val = s0_sq * recip_sq - s0_p0 * s0_p0;
float delta_phi_deg = NAN;
if (std::fabs(val) >= epsilon && s0_sq > epsilon) {
const float a_num = (s0_sq - 0.25f * recip_sq) * recip_sq;
if (a_num >= 0.0f) {
const float A = std::sqrt(a_num / val);
const float B = (A * s0_p0 + 0.5f * recip_sq) / s0_sq;
const float p_star_x = A * recip_x - B * S0.x;
const float p_star_y = A * recip_y - B * S0.y;
const float p_star_z = A * recip_z - B * S0.z;
const float p_star_sq = p_star_x * p_star_x + p_star_y * p_star_y + p_star_z * p_star_z;
const float denom = std::sqrt(p_star_sq * recip_sq);
if (denom >= epsilon) {
float c = (p_star_x * recip_x + p_star_y * recip_y + p_star_z * recip_z) / denom;
c = std::fmax(-1.0f, std::fmin(1.0f, c));
delta_phi_deg = std::acos(c) * rad_to_deg;
}
}
}
// Inlined RecipToDector with rot1 and rot2 (rot3 = 0)
// Apply rotation matrix transpose
float S_rot_x = rot[0] * S_x + rot[1] * S_y + rot[2] * S_z;
float S_rot_y = rot[3] * S_x + rot[4] * S_y + rot[5] * S_z;
float S_rot_z = rot[6] * S_x + rot[7] * S_y + rot[8] * S_z;
if (S_rot_z <= 0)
continue;
// Project to detector coordinates
// Assume detector is along x,y,z coordinates after rotation
float x = beam_x + F * S_rot_x / S_rot_z;
float y = beam_y + F * S_rot_y / S_rot_z;
if ((x < 0) || (x >= det_width_pxl) || (y < 0) || (y >= det_height_pxl))
continue;
float d = 1.0f / sqrtf(recip_sq);
reflections[i] = Reflection{
.h = h,
.k = k,
.l = l,
.delta_phi_deg = delta_phi_deg,
.predicted_x = x,
.predicted_y = y,
.observed_x = NAN,
.observed_y = NAN,
.d = d,
.dist_ewald = dist_ewald_sphere,
.rlp = 1.0,
.partiality = 1.0,
.zeta = 1.0,
.image_scale_corr = 1.0
};
++i;
}
}
}
}
return i;
}