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# HOWTO: Using caching without using Workflow

Using nipype in an imperative way: caching without workflow

Note that in the following example, we are calling command-lines with disk I/O that persists across runs, but we never have to worry about the file names or the directories.

The disk location of the persistence is encoded by hashes. To find out where an operation has been persisted, simply look in it’s output variable:

    out.runtime.cwd

    from nipype.interfaces import fsl
    fsl.FSLCommand.set_default_output_type('NIFTI')

    from nipype.caching import Memory

    import glob

    # First retrieve the list of files that we want to work upon
    in_files = glob.glob('data/*/f3.nii')

    # Create a memory context
    mem = Memory('.')

    # Apply an arbitrary (and pointless, here) threshold to the files)
    threshold = [
        mem.cache(fsl.Threshold)(in_file=f, thresh=i)
        for i, f in enumerate(in_files)
    ]

    # Merge all these files along the time dimension
    out_merge = mem.cache(fsl.Merge)(
        dimension="t",
        in_files=[t.outputs.out_file for t in threshold],
    )
    # And finally compute the mean
    out_mean = mem.cache(fsl.MeanImage)(in_file=out_merge.outputs.merged_file)

    # To avoid having increasing disk size we can keep only what was touched
    # in this run
    # mem.clear_previous_runs()

    # or what wasn't used since the start of 2011
    # mem.clear_runs_since(year=2011)

Example source code

You can download `the full source code of this example`. This same script is also included in the Nipype source distribution under the `examples` directory.