def write_ctf_comp(fid, comps):
    if len(comps) <= 0:
        return

    #  This is very simple in fact
    start_block(fid, FIFF.FIFFB_MNE_CTF_COMP)
    for comp in comps:
        start_block(fid, FIFF.FIFFB_MNE_CTF_COMP_DATA)
        #    Write the compensation kind
        write_int(fid, FIFF.FIFF_MNE_CTF_COMP_KIND, comp['ctfkind'])
        if comp.get('save_calibrated', False):
            write_int(fid, FIFF.FIFF_MNE_CTF_COMP_CALIBRATED,
                      comp['save_calibrated'])

        if not comp.get('save_calibrated', True):
            # Undo calibration
            comp = deepcopy(comp)
            data = ((1. / comp['rowcals'][:, None]) * comp['data']['data'] *
                    (1. / comp['colcals'][None, :]))
            comp['data']['data'] = data
        write_named_matrix(fid, FIFF.FIFF_MNE_CTF_COMP_DATA, comp['data'])
        end_block(fid, FIFF.FIFFB_MNE_CTF_COMP_DATA)

    end_block(fid, FIFF.FIFFB_MNE_CTF_COMP)
Low-level logging routine which creates a LogRecord and then calls
dataset/ETHPy150Open mne-tools/mne-python/mne/io/ctf_comp.py/write_ctf_comp--crossover with--dataset/ETHPy150Open babble/babble/include/jython/Lib/logging/__init__.py/Logger._log