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Troubleshoot

Pipeline crash

If the coppafish pipeline is crashing, first read the error message. If there is a suggestion about how to fix the issue in the config, try changing the config variable and run the pipeline again. If the suggestion does not make sense to you, feel free to reach out to the developers for help or create an issue on GitHub!

Notebook will not open

A notebook file can be corrupted if a process is killed while the notebook is being re-saved. When this happens, an error like:

TypeError: byte indices must be integers or slices, not tuple

will occur when trying to load the notebook. To fix this issue, delete the corrupted notebook, rename the backup notebook called notebook_backup.npz to the original notebook name and continue from there.

Cannot open napari issues

If napari fails to open and you see an error such as

WARNING: composeAndFlush: makeCurrent() failed

when trying to open the Viewer or RegistrationViewer, here are a few suggestions that might fix the issue:

  • In the conda environment, run conda install -c conda-forge libstdcxx-ng
  • In the conda environment, run conda install -c conda-forge libffi.

Filter image clip error

An error can occur when a filtered image clips off too many pixels when trying to save. This happens because the filter step will scale up every non-DAPI image by a common factor to improve precision. There are two options to deal with this issue:

  • Reduce image clipping by lowering scale_multiplier below the default value found in the filter config (the default is found here). After this, delete the filter directory found in the tiles directory and the scale.txt. Then, restart the pipeline.
  • Follow a "I don't care" strategy by increasing percent_clip_error above the default to allow for more clipped pixels. You can then restart the pipeline without deleting any files. If you wish to ignore warnings too, increase percent_clip_warn.

Memory crash at OMP

Try lowering subset_size_xy in the OMP config. This will cause OMP to compute on fewer pixels at time. It has a minimal effect on compute times, but can lower the RAM/VRAM usage. The default is found here.