Issue reports

Issue reports#

Search the issue tracker for the error and function name before opening a report. Include:

  • expected and observed results, with the complete traceback;

  • a minimal script using a synthetic phantom or a random tensor with a fixed seed;

  • input shapes, dtype, device, axis and trajectory conventions, and reconstruction options, including regularization and transform normalization;

  • OS, Python, PyTorch and bartorch versions, the installation command, and bartorch.build_info(). For source builds, the repository and BART commits (git rev-parse HEAD and git -C external/bart rev-parse HEAD);

  • for CUDA: GPU model, driver, torch.version.cuda, and both availability checks. For non-Cartesian transforms: FINUFFT and cuFINUFFT versions.

If import or loading the library fails, report that error directly; diagnostics that need the library may fail too. For numerical discrepancies, include a reference calculation and the relative error. For performance reports, give warm-up, synchronization, thread count, problem size and peak memory alongside timing.

Share only data you are entitled to publish; a synthetic reproduction is usually easier to investigate. Remove patient identifiers, credentials and private paths from logs. Report documentation issues with the page URL, the unclear passage, and the command that failed if there is one.