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Note

Why is the --pipeline-name argument is so important...

The names of files and directories in a pipeline are eventually all based on the pipeline name. All files will end up in the {pipeline_name}_processed, {pipeline_name}_lsq6, ... etc directories. If you do not supply a pipeline name, the code will generate one based on the time you start your pipeline. For instance: pipeline-21-11-2016-at-16-11-33. This is fine if your pipeline finished without any issues, however... if for some reason you pipeline did not finish, and you need to restart your pipeline, you will most likely rerun the command you ran before. I.e., without the --pipeline-name argument. This means that once again, the pipeline will assign a pipeline name to your pipeline based on the current date/time, which will inevitably be different from the previously generated pipeline name, and as such you code thinks that nothing has been processed yet and will start your pipeline from scratch!

What to do when you forgot to supply a pipeline name and you want to restart your pipeline? Let's assume the example above and the following directories exist:
pipeline-21-11-2016-at-16-11-33_init_model
pipeline-21-11-2016-at-16-11-33_lsq12
pipeline-21-11-2016-at-16-11-33_lsq6
pipeline-21-11-2016-at-16-11-33_nlin
pipeline-21-11-2016-at-16-11-33_pipeline_stages.txt
pipeline-21-11-2016-at-16-11-33_processed
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You can use this to deduce the pipeline name that was given to that pipeline by the code (the common prefix to all these file/directory names) and when you rerun it, supply it as follows:

MBM.py --pipeline-name=pipeline-21-11-2016-at-16-11-33 [rest of your command]

Note

What does --maget-no-mask really mean? Am I not using masks in my pipeline???

No no... MAGeT can be used to generate custom masks of your input files. These masks should fit your input data better than the mask that comes with the initial model, and should improve for instance the estimation of the linear part of the transformation out of the entire transformation. If you specify --maget-no-mask those masks are not created, and instead the mask from the initial model is used. This is the same as what has happened in our pipelines so far (i.e. the last many years)

Disk Clean up

When you're finished with a registration, have the data analysed and are ready to archive the pipeline, you can remove more than just the tmp directories from the pipeline. See these notes on how to perform a thorough disk clean up after a MBM run

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