leonarski_fandjungfrau
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**Files written by Jungfraujoch now import correctly in DIALS, XDS and pyFAI.** A tilted detector, a grid scan, a still recorded at a goniometer position, and saturated or unreadable pixels were each described in a way that a third-party program acted on wrongly. If you process Jungfraujoch data outside Jungfraujoch, prefer this release to any earlier one. * HDF5: the detector tilt (`rot1`/`rot2`/`rot3`) is exported correctly in the NXmx transformation chain; untilted geometries are unaffected. * HDF5: a still recorded at a goniometer position is no longer read back as a single image, and a grid scan records a stationary spindle so a program that requires a rotation axis can open it. * HDF5: the sample transformation chain is written in mounting order, with a Smargon head position told apart from the spindle, one entry per image, `module_offset` as a float unit vector, and `offset_units` on every offset. * HDF5: saturated, underloaded and unreadable pixels are described so a downstream program masks them - `saturation_value`, `underload_value`, `error_value` and `bit_depth_readout` are written correctly, and a data file missing next to a VDS master reads as the error marker rather than as zero counts. * HDF5: the rotation axis is read back under whatever name it carries, and `mirror_y` records whether the assembled image is mirrored in Y relative to the detector's raw readout. * A grid scan and a goniometer axis can both be set; they are no longer alternatives. * `images_per_file` is chosen from the acquisition when it is not given: a rotation sweep of at most 20000 images goes into a single data file, a grid scan splits on whole fast-axis rows, and stills and serial keep 1000. * The writer refuses a stream whose start message declares a different pixel format than its images carry, and a DECTRIS detector sending signed images is no longer declared unsigned. * The image stream can carry the sample transformation chain (`transformations`, in the END message); a producer that does not send it gets the same chain built by the writer. * rugnux: fixing the space group with `-S` no longer prevents the lattice from being found - a lattice indexed in a different setting is reindexed into that group's own setting, and a run whose crystal does not have that group's lattice stops and names the cell it indexed as, rather than reporting statistics that cannot describe it. * rugnux: the per-image resolution estimate now predicts the resolution the merged data reach rather than the highest-resolution spot found, and is reported as `SPOT_RESOLUTION_ESTIMATE`. * rugnux: two runs of the same command on the same images produce the same merged intensities; the azimuthal profile written alongside them is not yet reproducible in the same way. * rugnux: the offline lattice refinement is bounded by iterations rather than by a wall clock, so a loaded machine can no longer refine to a different lattice; a live acquisition keeps its real-time bound. * rugnux: the detector-frame modulation correction is fitted on a grid spanning the detector, so whether it is applied no longer depends on how far integration reached. * rugnux: the geometry pre-pass no longer writes `<prefix>_01.mtz`, `_01.cif`, `_01.hkl` and `_01_image.dat`; the refined second pass writes those files under `<prefix>`, and that is the result to use. * rugnux: `_process.h5` describes the pixel format of the images it links to, and is written on a thread of its own. * rugnux: the detector geometry is also logged in XDS's convention (`ORGX`/`ORGY`, detector axis vectors, rotation axis), so it can be compared with an XDS refinement. * rugnux: an image integrated in pyFAI through the `.poni` file written by `--mode calibration` comes out with the correct azimuth, and the file declares pyFAI's `orientation`, which needs pyFAI 2024.01 or newer. Radial integration is unchanged. * rugnux: a rotation run is substantially faster throughout - beam-stop detection, first-pass indexing, geometry refinement, integration, scaling and merging - and observations outside the scaling resolution range are dropped as they are ingested. The refined geometry, the space group chosen and the merged statistics are unchanged. * Faster spot finding and indexing, on the broker as well as in rugnux; the spots found and the lattices indexed are unchanged. * A run reserves substantially less GPU memory: nothing is allocated for buffers that are never read, and a worker builds only the engines it uses. * rugnux: with `-N` left at its default the per-image loop of `--mode mx` uses at most 16 workers per GPU, rather than one per hardware thread; an explicit `-N` is obeyed as given. * CUDA 12 builds now contain device code for Volta, so the RHEL 8 packages and the portable Linux `.tgz` run on a V100; the CUDA 13 artefacts (RHEL 9, Ubuntu, Windows) remain Turing and newer. * The build resolves a single Eigen for the whole project, and refuses to configure if Ceres picks up a different one; a build that mixed two Eigen versions was undefined behaviour and crashed at -O2. * Documentation: a security page, and the supported GPU generations and minimum NVIDIA driver version of every released artefact. **Breaking change to OpenAPI** - regenerate the client (`jfjoch-client` 1.0.0-rc.162, `frontend/src/client`): * `dataset_settings.images_per_file` is no longer `default: 1000` and no longer accepts `0`; it is optional, and its minimum is 1. A client sending `0` (previously "one file for the whole run") is now rejected - omit the field instead, which for a rotation sweep gives the same single file. * `file_writer_format` now defaults to `NXmxVDS`, matching the server's own default and the layout recommended for DIALS, XDS and CrystFEL. A generated client that fills in schema defaults and does not set the format explicitly will write VDS masters where it previously wrote legacy ones; set `NXmxLegacy` explicitly to keep them. --------- Co-authored-by: jungfrau <jungfrau@mx-aare-test.psi.ch> Reviewed-on: #72 Co-authored-by: Filip Leonarski <filip.leonarski@psi.ch>
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# Acknowledgements
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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](https://doi.org/10.1107/S1600577522010268).
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The project is supported by :
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* 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).
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* ETH Domain via Open Research Data Contribute project (Jan - Dec 2023)
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* AMD University Program with donation of licenses of Ethernet IP cores and Vivado software
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Decoding bitshuffle+LZ4 images on the GPU, rather than decompressing them on the host and uploading
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the result, follows Jon Wright (ESRF): "Experiences with GPU decompression for bitshuffle + LZ4
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data", HDF5 User Group meeting (2021), and [bslz4decoders](https://github.com/jonwright/bslz4decoders).
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The CUDA kernels in Jungfraujoch are its own, but the approach is his.
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Spot extraction groups strong pixels into spots with the sparse connected-component labelling of the
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ACTS traccc project: P. Gessinger, H. M. Gray, A. Krasznahorkay, C. Leggett, J. Niermann,
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A. Salzburger, S. N. Swatman and B. Yeo, "traccc: GPU track reconstruction library for HEP
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experiments" (2025), [arXiv:2505.22822](https://arxiv.org/abs/2505.22822);
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[traccc](https://github.com/acts-project/traccc). The CPU spot extractor adapts its SparseCCL source,
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and the CUDA spot extractor follows the design of its GPU counterpart - a backward-neighbour graph
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over a sorted hit list, resolved by a parallel union-find. traccc is MPL-2.0; see
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[THIRD_PARTY_NOTICES.md](THIRD_PARTY_NOTICES.md).
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This software uses Viridis, Magma and Inferno colormaps from Matplotlib under its BSD-compatible license
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## Crystallographic methods adopted from other packages
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The analysis pipeline reimplements methods first published, and in most cases first implemented, by
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other crystallographic software. The code below is Jungfraujoch's own; the methods are theirs, and
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are acknowledged here. Where a package's source was consulted this is said explicitly. None of these
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packages is linked or vendored, with the single exception of GEMMI (see
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[THIRD_PARTY_NOTICES.md](THIRD_PARTY_NOTICES.md)).
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**[XDS](https://xds.mr.mpg.de/)** — rotation geometry and notation, the reciprocal Lorentz and
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partiality treatment, the maximum-likelihood mosaicity estimate, the `MINPK` criterion for rejecting
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a reflection whose predicted profile is not cleanly its own, the intensity-based test for a
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centred lattice, and the scaling correction surfaces indexed by image number and detector region. W. Kabsch, "XDS" (2010), Acta Cryst. D66, 125-132
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[doi:10.1107/S0907444909047337](https://doi.org/10.1107/S0907444909047337); W. Kabsch, "Integration,
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scaling, space-group assignment and post-refinement" (2010), Acta Cryst. D66, 133-144
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[doi:10.1107/S0907444909047374](https://doi.org/10.1107/S0907444909047374).
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**Profile fitting** with reweighted, de-biased variances is the Kabsch/Otwinowski iteration, from the
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second XDS paper above and from Z. Otwinowski and W. Minor, "Processing of X-ray diffraction data
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collected in oscillation mode" (1997), Methods Enzymol. 276, 307-326
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[doi:10.1016/S0076-6879(97)76066-X](https://doi.org/10.1016/S0076-6879%2897%2976066-X).
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**[DIALS](https://dials.github.io/)** — the resolution cutoff from the CC1/2 fall-off, per-observation
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outlier rejection at merge, the scaling error model, and the treatment of a reflection whose
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background is contaminated. Its published behaviour, and in places its source, settled several
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choices here. G. Winter, D. G. Waterman, J. M. Parkhurst et al., "DIALS: implementation and
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evaluation of a new integration package" (2018), Acta Cryst. D74, 85-97
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[doi:10.1107/S2059798317017235](https://doi.org/10.1107/S2059798317017235); D. G. Waterman,
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G. Winter, R. J. Gildea et al., "Diffraction-geometry refinement in the DIALS framework" (2016),
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Acta Cryst. D72, 558-575 [doi:10.1107/S2059798316002187](https://doi.org/10.1107/S2059798316002187);
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J. Beilsten-Edmands, G. Winter, R. Gildea et al., "Scaling diffraction data in the DIALS software
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package: algorithms and new approaches for multi-crystal scaling" (2020), Acta Cryst. D76, 385-399
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[doi:10.1107/S2059798320003198](https://doi.org/10.1107/S2059798320003198); J. M. Parkhurst,
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G. Winter, D. G. Waterman et al., "Robust background modelling in DIALS" (2016), J. Appl. Cryst. 49,
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1912-1921 [doi:10.1107/S1600576716013595](https://doi.org/10.1107/S1600576716013595).
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**[POINTLESS](https://www.ccp4.ac.uk/)** (CCP4) — the space-group search. Stage A scores each
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candidate rotation operator by the correlation of I(h) with I(Rh); the screw-axis test scores a
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predicted-absent class against the rest of its own axial row rather than against a global mean or a
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fixed cut, and lets confidence fall away with the number of axial reflections instead of refusing
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below a count. P. Evans, "Scaling and assessment of data quality" (2006), Acta Cryst. D62, 72-82
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[doi:10.1107/S0907444905036693](https://doi.org/10.1107/S0907444905036693); P. R. Evans, "An
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introduction to data reduction: space-group determination, scaling and intensity statistics" (2011),
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Acta Cryst. D67, 282-292 [doi:10.1107/S090744491003982X](https://doi.org/10.1107/S090744491003982X);
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P. R. Evans and G. N. Murshudov, "How good are my data and what is the resolution?" (2013), Acta
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Cryst. D69, 1204-1214 [doi:10.1107/S0907444913000061](https://doi.org/10.1107/S0907444913000061);
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J. Agirre, M. Atanasova, H. Bagdonas et al., "The CCP4 suite: integrative software for macromolecular
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crystallography" (2023), Acta Cryst. D79, 449-461
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[doi:10.1107/S2059798323003595](https://doi.org/10.1107/S2059798323003595).
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**[MOSFLM](https://www.mrc-lmb.cam.ac.uk/mosflm/)** — the Rossmann FFT autoindexing algorithm and
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post-refinement practice, including which parameters are safe to refine per image and which must be
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refined over a wedge. A. G. W. Leslie and H. R. Powell, "Processing diffraction data with MOSFLM"
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(2007), in *Evolving Methods for Macromolecular Crystallography*, NATO Science Series II, vol. 245,
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41-51 [doi:10.1007/978-1-4020-6316-9_4](https://doi.org/10.1007/978-1-4020-6316-9_4);
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T. G. G. Battye, L. Kontogiannis, O. Johnson, H. R. Powell and A. G. W. Leslie, "iMOSFLM: a new
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graphical interface for diffraction-image processing with MOSFLM" (2011), Acta Cryst. D67, 271-281
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[doi:10.1107/S0907444910048675](https://doi.org/10.1107/S0907444910048675); H. R. Powell,
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T. G. G. Battye, L. Kontogiannis, O. Johnson and A. G. W. Leslie, "Integrating macromolecular X-ray
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diffraction data with the graphical user interface iMosflm" (2017), Nat. Protoc. 12, 1310-1325
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[doi:10.1038/nprot.2017.037](https://doi.org/10.1038/nprot.2017.037).
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**[CrystFEL](https://www.desy.de/~twhite/crystfel/)** — spot finding, the three-ring integration
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region, the serial/stills processing model, and the per-frame indexing acceptance test
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(`indexing_peak_check()` in `peaks.c`). T. A. White, R. A. Kirian, A. V. Martin, A. Aquila, K. Nass,
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A. Barty and H. N. Chapman, "CrystFEL: a software suite for snapshot serial crystallography" (2012),
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J. Appl. Cryst. 45, 335-341 [doi:10.1107/S0021889812002312](https://doi.org/10.1107/S0021889812002312).
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**[GEMMI](https://github.com/project-gemmi/gemmi)** — symmetry operations, unit-cell and
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structure-factor machinery, and MTZ / XDS_ASCII I/O. Vendored in `gemmi_gph/`, so it also carries a
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licence obligation. M. Wojdyr, "GEMMI: A library for structural biology" (2022), J. Open Source
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Softw. 7, 4200 [doi:10.21105/joss.04200](https://doi.org/10.21105/joss.04200).
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**Hexagonal-ice ring positions** — the eleven measured ring $d$ spacings the ice-ring score, the
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ice-ring flagging and the ice calibrant are all built on are taken from the measurements of, not
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enumerated from a cell. D. W. Moreau, H. Atakisi and R. E. Thorne, "Ice in biomolecular
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cryocrystallography" (2021), Acta Cryst. D77, 540-554
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[doi:10.1107/S2059798321001170](https://doi.org/10.1107/S2059798321001170).
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**Data-quality statistics** follow the established conventions rather than any one program: R_meas
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and R_pim, CC1/2 and CC\*, and the reporting of I/sigma(I). K. Diederichs and P. A. Karplus, "Improved
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R-factors for diffraction data analysis in macromolecular crystallography" (1997), Nat. Struct. Biol.
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4, 269-275 [doi:10.1038/nsb0497-269](https://doi.org/10.1038/nsb0497-269); P. A. Karplus and
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K. Diederichs, "Linking crystallographic model and data quality" (2012), Science 336, 1030-1033
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[doi:10.1126/science.1218231](https://doi.org/10.1126/science.1218231); K. Diederichs and
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P. A. Karplus, "Better models by discarding data?" (2013), Acta Cryst. D69, 1215-1222
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[doi:10.1107/S0907444913001121](https://doi.org/10.1107/S0907444913001121).
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**Uncertainty conventions** follow the IUCr Commission on Crystallographic Nomenclature:
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D. Schwarzenbach, S. C. Abrahams, H. D. Flack et al., "Statistical descriptors in crystallography:
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Report of the IUCr Subcommittee on Statistical Descriptors" (1989), Acta Cryst. A45, 63-75
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[doi:10.1107/S0108767388009596](https://doi.org/10.1107/S0108767388009596); D. Schwarzenbach,
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S. C. Abrahams, H. D. Flack, E. Prince and A. J. C. Wilson, "Statistical descriptors in
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crystallography. II. Report of a Working Group on Expression of Uncertainty in Measurement" (1995),
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Acta Cryst. A51, 565-569 [doi:10.1107/S0108767395002340](https://doi.org/10.1107/S0108767395002340).
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