Jungfraujoch/docs/JFJOCH_VIEWER.md

jfjoch_viewer

jfjoch_viewer is the interactive desktop application of Jungfraujoch. It opens diffraction datasets, displays each image together with the analysis overlay (spots, predictions, azimuthal integration, per-image statistics), and can follow a live data collection by syncing with a running jfjoch_broker over its HTTP interface.

It is a standalone Qt 6 application, distributed pre-built for Linux, Windows and macOS on the Gitea release page, and for Linux also in the Jungfraujoch RPM/APT repositories — see Release contents for what each package contains and what it requires, and Deployment for how to install it. The macOS build needs macOS 13 (Ventura) or newer on an Apple Silicon Mac (M1 and newer; Intel Macs are not supported) and is CPU-only — see Release contents ▸ macOS, which also covers opening it for the first time, as the release is not yet notarized by Apple.

Where it fits among the three analysis tools

Tool	Mode	Driven by	Output
jfjoch_broker	Online, real-time streaming analysis on FPGA + GPU	HTTP/REST + ZeroMQ	Live results and statistics, images streamed to jfjoch_writer
jfjoch_viewer	Interactive, on-screen exploration	Qt desktop application	On screen; a processing job can write the same files as rugnux
rugnux	Offline batch processing of a stored dataset	Command-line interface	_process.h5, and .mtz/.cif/.hkl when merging

Functionality

• Opens HDF5 files written by jfjoch_writer (*_master.h5) and the *_process.h5 files produced by rugnux. It also opens NXmx files written by DECTRIS detectors, though that path has had only limited testing.
• Opens PILATUS miniCBF rotation sweeps. These store one frame per file, so naming any frame opens the whole sweep it belongs to. A raw CBF carries the images and the geometry but no analysis results, so the spot, reflection and per-image plot panels stay empty until something is computed.
• Runs an embedded data-processing pipeline — the same analysis code as the rest of Jungfraujoch — performing spot finding, indexing and integration on the displayed image, with the result drawn over it. This interactive analysis is not written anywhere.
• Runs full processing jobs on the open dataset with Analyze dataset, on the same rugnux engine and off the GUI thread. The settings panel's MX / AzInt / Calib toggle decides what a run does — full analysis, azimuthal integration only, or a detector calibration — over a chosen image range, optionally writing _process.h5 and the merged .mtz/.cif. A finished run becomes a selectable view of the dataset, so several processing runs can be compared against each other, and its merging statistics (or, for a calibration, its fitted geometry) open in their own window; the Processing panel lists the runs and reopens those results. The equivalent rugnux command line can also be copied out to run the same job on a cluster instead.
• Detector calibration against a powder standard, on the Calib page: pick the calibrant (LaB6, AgBh, CeO2, Si, ice, or the open dataset's own unit cell) and fit either the image on screen (Guess / Refine detector calibration) or the whole dataset (Analyze dataset, which writes a pyFAI <output prefix>.poni). The whole-dataset fit measures the rings either from the azimuthally-binned profile summed over the run (Rings, the default) or from the pooled spot lists (Spots), and reports PONI x/y, the two tilts and the distance against the header values. Judge it by the radial rms, not the beam-centre sigma: the sigma shrinks with the number of ring points, so a fit that sits a couple of pixels off every ring can still report a small one. Rings needs the run to be integrated in azimuthal sectors — with the AzInt page's Azimuthal bins below 4 the calibration run raises it to 32, as rugnux --mode calibration does, and says so. Refine detector tilt is ticked by default and fits the two tilts along with the centre and the distance; unticking it holds them where they are, for a calibration meant for a program that cannot express a tilted detector (rugnux --no-refine-tilt). It applies to both buttons and to Analyze dataset.
• Settings panel for the geometry, unit cell, spot finding, indexing, azimuthal integration, Bragg integration, scaling, powder calibration and a reference dataset — the same settings the CLI takes.
• Auxiliary windows: image list, dataset metadata, spot list, reflection list, 2D azimuthal-integration image and calibration-image viewer; plus the Inspector (per-image statistics, image features, resolution rings, ROI statistics), the Magnifier below it (three zoom levels: ×64 and ×32 with the pixel values written on the pixels, ×10 without; Pop out moves it to a window of its own) and dataset-info charts.
• The Inspector's Image features section decides what the overlay draws — spots, predictions, saturated and highest pixels, the beam stop — including whether the non-indexed spots and the spots that fall on an ice ring are drawn at all.
• User-mask editing: build a user mask interactively, load one from TIFF (replacing or adding to the current one), save it as TIFF, clear it, or upload it to a connected server.
• Mouse-driven navigation of the image, the grid scan and the plots — see Mouse shortcuts below, which the viewer also shows under Help ▸ Mouse Shortcuts.
• Help shows the mouse shortcuts, the acknowledgements and the third-party licenses.
• Layout presets (View ▸ Image layout / Processing layout / Reset layout) rearrange the docks for looking at images or at processing results.
• View ▸ Font size (or Ctrl++ / Ctrl+-) enlarges the text to 125 % or 150 %, on top of whatever scaling the desktop already applies, and the choice is remembered across restarts. The viewer also follows the desktop's own text scaling or display scaling on its own; over ssh -X, where no settings daemon delivers it, launch as QT_SCALE_FACTOR=1.5 jfjoch_viewer instead.

Hardware

As with the rest of Jungfraujoch, serious performance requires an NVIDIA GPU. On systems with a GPU, use the CUDA build (a separate package variant everywhere: RPM/APT repository, .tgz and Windows installer) for the embedded indexing and integration; the non-CUDA build runs the same pipeline on the CPU at much lower throughput. The CUDA build also runs on a machine without a GPU — see Release contents ▸ CUDA and non-CUDA builds.

The CUDA build needs an NVIDIA driver on the host but no CUDA toolkit — 525.60.13 or newer for the CUDA 12 artefacts (RHEL 8 packages, portable Linux .tgz), 580.65.06 or newer on Linux and an R580 driver on Windows for the CUDA 13 ones (RHEL 9, Ubuntu, Windows installer). The Windows installer and the .tgz are CUDA 13 and CUDA 12 respectively, which also decides the oldest GPU they run on — a V100 needs the CUDA 12 .tgz. See Release contents ▸ GPU generations and the NVIDIA driver.

On a Mac there is no CUDA: the viewer always runs the same pipeline on the CPU, with the FFTW indexer, like the non-CUDA build elsewhere.

Remote displays

The viewer detects a remote display session (ssh -X and the like) and limits how often panning, zooming and live playback repaint, since on such a link every repaint is shipped as pixels. The detection can be overridden in View ▸ Remote display mode or with JFJOCH_VIEWER_REMOTE=0/1. A VNC- or xpra-based remote desktop still transports the viewer far more efficiently than plain X11 forwarding.

Mouse shortcuts

The same list is available in the application under Help ▸ Mouse Shortcuts.

Diffraction image

Action	Effect
Wheel	Zoom in / out, centred on the cursor
Shift + wheel	Move the foreground (upper contrast limit) in linear steps
Ctrl + wheel	Move the foreground in multiplicative steps (×1.15 per notch)
F held + wheel	Same as Shift + wheel, for as long as F is held
A	Apply auto-contrast once; press it again to switch on continuous Auto
Home / End	Jump to the first / last image in the dataset
Page Up / Page Down	Step one image forward / back
Hover	Status bar shows the pixel position, its value and the resolution
Drag	Pan the image
Shift + move	Move the magnifier panel to the cursor; a frame shows the area it covers
Shift + drag	Draw a rectangular ROI
Shift + Ctrl + drag	Draw a circular ROI
Drag an ROI or its handle	Move or resize the selected ROI
Right click	Copy / save the image, fit to view, clear the ROI

Grid scan

Action	Effect
Hover	Status bar shows the image number, the grid position and its value
Shift + hover	Load the image under the cursor while moving over the grid
Double click	Load the image under the cursor

Other views

Action	Effect
2D azimuthal image: double click	Zoom the diffraction image on the corresponding detector position
Dataset-info plot: hover	Status bar shows the image number and the plotted value
Dataset-info plot: Shift + hover	Load the hovered image
Spot / reflection list: double click	Zoom the diffraction image on that spot or prediction
Image list: double click	Load that image

Opening data

• File ▸ Open (Ctrl+O) — open a local HDF5 file, or any frame of a miniCBF sweep.
• File ▸ Open HTTP (Ctrl+H) — connect to a jfjoch_broker HTTP endpoint to follow a live collection. The dialog defaults to host localhost and port 8080; these defaults can be overridden with the environment variables JUNGFRAUJOCH_HTTP_HOST and JUNGFRAUJOCH_HTTP_PORT.
• Command line — jfjoch_viewer <file> opens a file (or an http://host:port URL) on start-up. --dbus <true|false> (-d) enables or disables the D-Bus interface (default: enabled); --help and --version behave as usual.

D-Bus interface

When enabled, the viewer registers the D-Bus interface ch.psi.jfjoch_viewer, so other processes can drive it. D-Bus is Linux only: the Windows and macOS builds have no D-Bus interface, and --dbus has no effect there.

• LoadFile(filename, image_number=0, summation=1) — open a file (or an http://host:port URL) and display the given image.
• LoadImage(image_number, summation=1) — navigate to an image in the already-open dataset.

summation sums that many consecutive images before display. A repeated LoadFile call naming the file that is already open is cheap (it just navigates, like LoadImage) rather than reopening it, but a client stepping through images of a dataset it opened itself should still prefer LoadImage — it needs no filename and avoids the file-identity comparison.

Building from source on Windows

jfjoch_viewer is cross-platform: it builds on Windows 11 with MSVC and the full CUDA GPU path, and on macOS (see below). (The rest of Jungfraujoch — broker, receiver, FPGA host — is Linux-only.) A pre-built installer is published with every release, so building from source is only needed to develop or to change the build options. On Windows the build is automatically restricted to the viewer and the libraries it needs (JFJOCH_VIEWER_ONLY is forced on), and the remaining dependencies are fetched and built automatically (the first configure needs network access).

Verified toolchain — the same one the released installer is built with:

• Windows 11
• Visual Studio 2026 with the C++ (MSVC) toolset — required; CUDA on Windows builds through MSVC
• CUDA Toolkit 13.3 (12.8 or newer is required) — for the GPU indexing/integration path
• Qt 6.11 for MSVC (msvc2022_64), including the Qt Charts module — e.g. C:\Qt\6.11.1\msvc2022_64
• CMake plus Ninja. The CMake that ships with Visual Studio is the simplest choice and works out of the box — it comes with the C++ workload, so there is nothing extra to install. Any recent standalone CMake (from cmake.org, or the one bundled with Qt in C:\Qt\Tools\CMake_64) works too.
• Optional: NSIS to build the .exe installer.

Configure and build from an x64 Native Tools Command Prompt for VS 2026 (so cl, nvcc and ninja are on PATH):

cmake -G Ninja -B build-win -DCMAKE_BUILD_TYPE=Release ^
  -DCMAKE_PREFIX_PATH="C:/Qt/6.11.1/msvc2022_64"
cmake --build build-win --target jfjoch_viewer

Notes:

• CMAKE_PREFIX_PATH (Qt) is the only required flag. Every other dependency, zlib and Eigen included, is downloaded and built by the configure itself, so nothing else has to be installed.
• The CUDA toolchain is located automatically from the CUDA_PATH environment variable that the CUDA installer sets (or from nvcc on PATH). Pass -DCMAKE_CUDA_COMPILER=".../bin/nvcc.exe" only if nvcc is installed in a nonstandard location and is not found.
• For a machine without an NVIDIA GPU, add -DJFJOCH_USE_CUDA=OFF: the viewer then runs the same pipeline on the CPU (FFTW indexer) at lower throughput.

To produce a self-contained installer (bundles the Qt runtime via windeployqt and — on the CUDA build — the cuFFT runtime DLL, so the target host needs neither Qt nor a CUDA toolkit), with NSIS installed:

cd build-win
cpack

The NSIS generator is selected automatically on Windows (no -G needed). What comes out, and how the CUDA and CPU variants are named and told apart, is described in Release contents ▸ Windows installer.

Building from source on macOS

The viewer also builds on macOS, Apple Silicon only and without CUDA; as on Windows, the build is automatically restricted to the viewer and the libraries it needs (JFJOCH_VIEWER_ONLY is forced on) and fetches every other dependency itself. A pre-built disk image is published with every release, so building from source is only needed to develop or to change the build options.

Verified toolchain — the same one the released .dmg is built with:

• An Apple Silicon Mac; the resulting app needs macOS 13 or newer, whatever the build machine runs
• Xcode (Apple Clang)
• Qt 6.11 for macOS, including the Qt Charts module — e.g. ~/Qt/6.11.2/macos from the Qt online installer
• CMake (e.g. CMake.app from cmake.org, with /Applications/CMake.app/Contents/bin on PATH)

cmake -S . -B build-mac -DCMAKE_BUILD_TYPE=Release -DCMAKE_PREFIX_PATH=$HOME/Qt/6.11.2/macos
cmake --build build-mac -j$(sysctl -n hw.ncpu) --target jfjoch_viewer
open build-mac/viewer/jfjoch_viewer.app

As on Windows, CMAKE_PREFIX_PATH (Qt) is the only required flag. To produce the disk image — the Qt frameworks and the license notices copied into the app with macdeployqt, packed into jfjoch-viewer-<version>-macos-arm64.dmg — run cpack in the build directory. What comes out is described in Release contents ▸ macOS.