leonarski_fandClaude Opus 5
f6bfa54624
Symmetry operators were scored by Pearson correlation on raw merged intensities. Both members of a symmetry pair sit at the same |s|, so the resolution fall-off is variance the two arms share exactly, and it inflates the correlation of true and false operators alike. The clearest demonstration is the test this commit adds: a synthetic data set with no symmetry at all - a radial fall-off times an independent per- reflection factor - is assigned point group 432 by the shipped code, with all 23 rotations confirmed at 0.632 to 0.656. The existing suite passes identically before and after, because nothing covered this. The new case fails 64 of its 100 assertions on the old scoring and passes on the new. On real data the same effect had the gate leaking: on one cubic crystal three pseudo-symmetric operators scored 0.506 to 0.517, above the 0.5 bound, so they were confirmed and 432 had to be refused further downstream by the twin-law and systematic-b guards. Scored on E-squared they read 0.283 to 0.298 and exactly the eleven genuine rotations of 23 are confirmed. The normalisation has to be over the reflections the correlation actually pairs. Reusing the existing normalised array is worse than doing nothing: it is normalised over the set the absence tests use, whose surviving fraction is itself resolution-dependent, and the coupling to the resolution cut rises from 0.086 to 0.262 against a raw baseline of 0.086. Normalised over the paired set it falls to 0.023. The twin-law H statistic keeps its own vectors on raw intensities. It shares the pair arrays with the correlation, and normalising in place moves it by up to 12% against a bound whose window is 5.5% wide. Verified rather than assumed: two instrumented binaries print the same H to twelve significant figures while the correlation differs. min_operator_cc goes 0.5 to 0.30. Normalised correlations run lower, and the observed window on rugnux's own search merges is 0.298 to 0.351; 0.35 is too high, because one crystal's weakest genuine operator reads 0.351. The headroom between a crystal's weakest true operator and its own measured false -operator floor widens on 14 of 14 crystals, median 0.430 to 0.619, and the worst operational margin goes from 0.031 to 0.051. Battery, twice, against a baseline reproducible to zero: the space group is identical on all 38 crystals, per-shell merging is 0 better and 0 worse across all 380 shells, and the merged mmCIF is byte-identical on 38 of 38 - on the 12 crystals whose integration radius now adapts as well as the 25 that do not. The arm is live rather than inert: all 313 operator correlations move while every pair count and every H value stays bit-identical, and on one cubic crystal three operators that raw intensities confirmed are rejected, with the space group unchanged. Following Padilla and Yeates (2003) Acta Cryst. D59, 1124-1130 for why a resolution-normalised statistic is the right one for a symmetry test. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01CHMmeM1d489zvNFT7ZMN2P
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Acknowledgements
Citation: F. Leonarski, M. Bruckner, C. Lopez-Cuenca, A. Mozzanica, H.-C. Stadler, Z. Matej, A. Castellane, B. Mesnet, J. Wojdyla, B. Schmitt and M. Wang "Jungfraujoch: hardware-accelerated data-acquisition system for kilohertz pixel-array X-ray detectors" (2023), J. Synchrotron Rad., 30, 227-234 doi:10.1107/S1600577522010268.
The project is supported by :
- Innosuisse via Innovation Project "NextGenDCU high data rate acquisition system for X-ray detectors in structural biology applications" (101.535.1 IP-ENG; Apr 2023 - Sep 2025).
- ETH Domain via Open Research Data Contribute project (Jan - Dec 2023)
- AMD University Program with donation of licenses of Ethernet IP cores and Vivado software
Decoding bitshuffle+LZ4 images on the GPU, rather than decompressing them on the host and uploading the result, follows Jon Wright (ESRF): "Experiences with GPU decompression for bitshuffle + LZ4 data", HDF5 User Group meeting (2021), and bslz4decoders. The CUDA kernels in Jungfraujoch are its own, but the approach is his.
Spot extraction groups strong pixels into spots with the sparse connected-component labelling of the ACTS traccc project: P. Gessinger, H. M. Gray, A. Krasznahorkay, C. Leggett, J. Niermann, A. Salzburger, S. N. Swatman and B. Yeo, "traccc: GPU track reconstruction library for HEP experiments" (2025), arXiv:2505.22822; traccc. The CPU spot extractor adapts its SparseCCL source, and the CUDA spot extractor follows the design of its GPU counterpart - a backward-neighbour graph over a sorted hit list, resolved by a parallel union-find. traccc is MPL-2.0; see THIRD_PARTY_NOTICES.md.
This software uses Viridis, Magma and Inferno colormaps from Matplotlib under its BSD-compatible license
Crystallographic methods adopted from other packages
The analysis pipeline reimplements methods first published, and in most cases first implemented, by other crystallographic software. The code below is Jungfraujoch's own; the methods are theirs, and are acknowledged here. Where a package's source was consulted this is said explicitly. None of these packages is linked or vendored, with the single exception of GEMMI (see THIRD_PARTY_NOTICES.md).
XDS — rotation geometry and notation, the reciprocal Lorentz and
partiality treatment, the maximum-likelihood mosaicity estimate, the MINPK criterion for rejecting
a reflection whose predicted profile is not cleanly its own, the intensity-based test for a
centred lattice, and the scaling correction surfaces indexed by image number and detector region. W. Kabsch, "XDS" (2010), Acta Cryst. D66, 125-132
doi:10.1107/S0907444909047337; W. Kabsch, "Integration,
scaling, space-group assignment and post-refinement" (2010), Acta Cryst. D66, 133-144
doi:10.1107/S0907444909047374.
Profile fitting with reweighted, de-biased variances is the Kabsch/Otwinowski iteration, from the second XDS paper above and from Z. Otwinowski and W. Minor, "Processing of X-ray diffraction data collected in oscillation mode" (1997), Methods Enzymol. 276, 307-326 doi:10.1016/S0076-6879(97)76066-X.
DIALS — the resolution cutoff from the CC1/2 fall-off, per-observation outlier rejection at merge, the scaling error model, and the treatment of a reflection whose background is contaminated. Its published behaviour, and in places its source, settled several choices here. G. Winter, D. G. Waterman, J. M. Parkhurst et al., "DIALS: implementation and evaluation of a new integration package" (2018), Acta Cryst. D74, 85-97 doi:10.1107/S2059798317017235; D. G. Waterman, G. Winter, R. J. Gildea et al., "Diffraction-geometry refinement in the DIALS framework" (2016), Acta Cryst. D72, 558-575 doi:10.1107/S2059798316002187; J. Beilsten-Edmands, G. Winter, R. Gildea et al., "Scaling diffraction data in the DIALS software package: algorithms and new approaches for multi-crystal scaling" (2020), Acta Cryst. D76, 385-399 doi:10.1107/S2059798320003198; J. M. Parkhurst, G. Winter, D. G. Waterman et al., "Robust background modelling in DIALS" (2016), J. Appl. Cryst. 49, 1912-1921 doi:10.1107/S1600576716013595.
POINTLESS (CCP4) — the space-group search. Stage A scores each candidate rotation operator by the correlation of I(h) with I(Rh) on resolution-normalised intensities (E²), as POINTLESS does — both arms of a symmetry pair sit at the same |s|, so on raw intensities the resolution fall-off is variance shared between them and lifts a false operator's correlation as much as a true one's; the screw-axis test scores a predicted-absent class against the rest of its own axial row rather than against a global mean or a fixed cut, and lets confidence fall away with the number of axial reflections instead of refusing below a count. P. Evans, "Scaling and assessment of data quality" (2006), Acta Cryst. D62, 72-82 doi:10.1107/S0907444905036693; P. R. Evans, "An introduction to data reduction: space-group determination, scaling and intensity statistics" (2011), Acta Cryst. D67, 282-292 doi:10.1107/S090744491003982X; P. R. Evans and G. N. Murshudov, "How good are my data and what is the resolution?" (2013), Acta Cryst. D69, 1204-1214 doi:10.1107/S0907444913000061; J. Agirre, M. Atanasova, H. Bagdonas et al., "The CCP4 suite: integrative software for macromolecular crystallography" (2023), Acta Cryst. D79, 449-461 doi:10.1107/S2059798323003595.
MOSFLM — the Rossmann FFT autoindexing algorithm and post-refinement practice, including which parameters are safe to refine per image and which must be refined over a wedge. A. G. W. Leslie and H. R. Powell, "Processing diffraction data with MOSFLM" (2007), in Evolving Methods for Macromolecular Crystallography, NATO Science Series II, vol. 245, 41-51 doi:10.1007/978-1-4020-6316-9_4; T. G. G. Battye, L. Kontogiannis, O. Johnson, H. R. Powell and A. G. W. Leslie, "iMOSFLM: a new graphical interface for diffraction-image processing with MOSFLM" (2011), Acta Cryst. D67, 271-281 doi:10.1107/S0907444910048675; H. R. Powell, T. G. G. Battye, L. Kontogiannis, O. Johnson and A. G. W. Leslie, "Integrating macromolecular X-ray diffraction data with the graphical user interface iMosflm" (2017), Nat. Protoc. 12, 1310-1325 doi:10.1038/nprot.2017.037.
CrystFEL — spot finding, the three-ring integration
region, the serial/stills processing model, and the per-frame indexing acceptance test
(indexing_peak_check() in peaks.c). T. A. White, R. A. Kirian, A. V. Martin, A. Aquila, K. Nass,
A. Barty and H. N. Chapman, "CrystFEL: a software suite for snapshot serial crystallography" (2012),
J. Appl. Cryst. 45, 335-341 doi:10.1107/S0021889812002312.
GEMMI — symmetry operations, unit-cell and
structure-factor machinery, and MTZ / XDS_ASCII I/O. Vendored in gemmi_gph/, so it also carries a
licence obligation. M. Wojdyr, "GEMMI: A library for structural biology" (2022), J. Open Source
Softw. 7, 4200 doi:10.21105/joss.04200.
Hexagonal-ice ring positions — the eleven measured ring d spacings the ice-ring score, the
ice-ring flagging and the ice calibrant are all built on are taken from the measurements of, not
enumerated from a cell. D. W. Moreau, H. Atakisi and R. E. Thorne, "Ice in biomolecular
cryocrystallography" (2021), Acta Cryst. D77, 540-554
doi:10.1107/S2059798321001170.
Diffraction anisotropy — the description of the overall fall-off by a single anisotropic displacement tensor, its symmetry constraints, and the fact that only its deviatoric part is determined (the isotropic part being degenerate with the overall scale) are Sheriff and Hendrickson's. The estimator fits that tensor to the observed intensity distribution, taking sigma(I) into account, in the sense of Popov and Bourenkov. The directional diffraction limits - <I/sigma(I)> in a cone about each principal direction, and the reporting of the anisotropic deltaB as the range of the principal components - follow AIMLESS. rugnux reports these; it corrects no intensity and removes no reflection on a directional criterion. S. Sheriff and W. A. Hendrickson, "Description of overall anisotropy in diffraction from macromolecular crystals" (1987), Acta Cryst. A43, 118-121 doi:10.1107/S010876738709977X; A. N. Popov and G. P. Bourenkov, "Choice of data-collection parameters based on statistic modelling" (2003), Acta Cryst. D59, 1145-1153 doi:10.1107/S0907444903008163; P. R. Evans and G. N. Murshudov, "How good are my data and what is the resolution?" (2013), Acta Cryst. D69, 1204-1214 doi:10.1107/S0907444913000061.
Data-quality statistics follow the established conventions rather than any one program: R_meas and R_pim, CC1/2 and CC*, and the reporting of I/sigma(I). K. Diederichs and P. A. Karplus, "Improved R-factors for diffraction data analysis in macromolecular crystallography" (1997), Nat. Struct. Biol. 4, 269-275 doi:10.1038/nsb0497-269; P. A. Karplus and K. Diederichs, "Linking crystallographic model and data quality" (2012), Science 336, 1030-1033 doi:10.1126/science.1218231; K. Diederichs and P. A. Karplus, "Better models by discarding data?" (2013), Acta Cryst. D69, 1215-1222 doi:10.1107/S0907444913001121.
Uncertainty conventions follow the IUCr Commission on Crystallographic Nomenclature: D. Schwarzenbach, S. C. Abrahams, H. D. Flack et al., "Statistical descriptors in crystallography: Report of the IUCr Subcommittee on Statistical Descriptors" (1989), Acta Cryst. A45, 63-75 doi:10.1107/S0108767388009596; D. Schwarzenbach, S. C. Abrahams, H. D. Flack, E. Prince and A. J. C. Wilson, "Statistical descriptors in crystallography. II. Report of a Working Group on Expression of Uncertainty in Measurement" (1995), Acta Cryst. A51, 565-569 doi:10.1107/S0108767395002340.