Reporting issues

Bugs, documentation defects and feature requests are filed on the issue tracker, whose forms ask for the information below. Search the existing issues for the error message and the function name first.

A bug report contains:

• the observed and the expected behaviour, with the complete traceback;

• a minimal script on synthetic data — bartorch.tools.phantom(), or a random tensor with a fixed seed — rather than acquired data;

• the shapes, dtypes and devices of the inputs, the axis and trajectory conventions assumed, and the options used, including regularization terms and Fourier-transform normalization;

• the bartorch version (bartorch.__version__) or commit, the output of bartorch.build_info() and bartorch.bart_version(), the Python and PyTorch versions, the operating system and the installation command;

• for a source build, the repository and BART commits: git rev-parse HEAD and git -C external/bart rev-parse HEAD;

• on a CUDA device, the GPU model, the driver version, torch.version.cuda, and the values of torch.cuda.is_available() and bartorch.cuda_available();

• for non-Cartesian problems, the FINUFFT and cuFINUFFT versions.

If importing bartorch or loading its library fails, report that error; the diagnostic functions above need the library. A numerical discrepancy report states the reference it was compared with — an independent implementation, an explicit sum, a closed form — and the relative error observed. A performance report states the problem size, the thread count, whether the timing includes warm-up and device synchronization, and the peak memory.

Share only data you may publish. Remove patient identifiers, credentials and private paths from logs. Report a security vulnerability privately, as described in Security, not as an issue.