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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def process_full_data(fname, rhomin, mass1, mass2, lo_mchirp, hi_mchirp):
 """Read the zero-lag and time-lag triggers identified by templates in a specified range of chirp mass. Parameters hdfile: File that stores all the triggers rhomin: float Minimum value of SNR threhold (will need including ifar) mass1: array First mass of the waveform in the template bank mass2: array Second mass of the waveform in the template bank lo_mchirp: float Minimum chirp mass for the template hi_mchirp: float Maximum chirp mass for the template Returns ------- dictionary containing foreground triggers and background information """

    with h5py.File(fname, 'r') as bulk:

        id_bkg = bulk['background_exc/template_id'][:]
        id_fg = bulk['foreground/template_id'][:]

        mchirp_bkg = mchirp_from_mass1_mass2(mass1[id_bkg], mass2[id_bkg])
        bound = np.sign((mchirp_bkg - lo_mchirp) * (hi_mchirp - mchirp_bkg))
        idx_bkg = np.where(bound == 1)
        mchirp_fg = mchirp_from_mass1_mass2(mass1[id_fg], mass2[id_fg])
        bound = np.sign((mchirp_fg - lo_mchirp) * (hi_mchirp - mchirp_fg))
        idx_fg = np.where(bound == 1)

        zerolagstat = bulk['foreground/stat'][:][idx_fg]
        cstat_back_exc = bulk['background_exc/stat'][:][idx_bkg]
        dec_factors = bulk['background_exc/decimation_factor'][:][idx_bkg]

    return {'zerolagstat': zerolagstat[zerolagstat > rhomin],
           'dec_factors': dec_factors[cstat_back_exc > rhomin],
           'cstat_back_exc': cstat_back_exc[cstat_back_exc > rhomin]}