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* Rugnux: Performance improvements on GPU and CPU (more of the pre-scan and of scaling on the GPU, faster CPU spot finding and crystal refinement), with unchanged results. * Rugnux: More robust processing - patches of persistently hot pixels are masked, an inconsistent merge triggers a retry at the measured beam centre, and builds targeting different CPU levels give the same results. * Rugnux: Improved scaling and merging - reflections with an overloaded pixel are dropped, as in XDS, sparse rotation sweeps are scaled more reliably, and French-Wilson amplitudes use an anisotropic Wilson prior. * Rugnux: Improved space-group determination - glide planes in groups without a centre of symmetry, screw axes from short or weak axial rows kept when a higher group is adopted, and more reliable decisions on twinned and pseudo-symmetric crystals. * Rugnux: Improved small-molecule processing - spots that grow wider than the integration disk and split spots are integrated over their measured footprint, sparse lattices are integrated on every frame, and the `.hkl` file holds unmerged scaled reflections (SHELX HKLF 4). * Rugnux: Reads Rigaku d*TREK SMV images (Saturn CCD), including detector 2theta and encoded pixel overflows; home-source (rotating-anode) datasets were added to the validation battery. * jfjoch_viewer: Fixed processing failing at the end with "Wrong JPEG library version" on Linux; the merge window shows the space group with proper subscripts and a checklist of crystal pathologies. Reviewed-on: #84 Co-authored-by: Filip Leonarski <filip.leonarski@psi.ch>
40 lines
2.0 KiB
C++
40 lines
2.0 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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#pragma once
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// Included only under JFJOCH_USE_CUDA.
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#include <cstdint>
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#include <vector>
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#include <cuda_runtime.h>
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// What the mask is drawn about: the detector, the centre the rings are drawn about, and the geometry
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// the polarization factor is read off (ShadowFinder keeps it as a DiffractionGeometry; the device
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// takes it as numbers).
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struct ShadowMaskSetup {
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int width = 0, height = 0;
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float beam_x = 0.0f, beam_y = 0.0f;
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float det_matrix[9] = {}; // row major
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float pixel_size_mm = 0.0f;
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float distance_mm = 0.0f;
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bool has_polarization = false;
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float polarization = 0.0f;
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};
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// ShadowFinder::GetMask on the device, from the projection ShadowAccumulatorGPU holds there. Step for
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// step the host's algorithm - the same pooling, ring medians, components, morphology and arm search -
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// and the same answer wherever the arithmetic is exact: every integer, comparison, sort and component
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// is. What is not is the floating point the two compilers evaluate differently - the polarization
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// factor's trigonometry, the Poisson test's logarithm and the azimuth of the arm search - so a pixel
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// within a rounding of one of those thresholds can come out the other way.
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std::vector<uint32_t> ShadowMaskOnDevice(const ShadowMaskSetup &setup, const std::vector<uint32_t> &pixel_mask,
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const int64_t *max_value, const int64_t *sum_value,
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const uint32_t *valid_count, uint32_t frames, cudaStream_t stream);
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// The mean projection the host's GetMeanProjection makes, computed where the sums are: the same
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// division, so the same bits, and a quarter of the bytes to bring back.
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std::vector<float> MeanProjectionOnDevice(const std::vector<uint32_t> &pixel_mask, const int64_t *sum_value,
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const uint32_t *valid_count, size_t npixels, cudaStream_t stream);
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