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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 eeg_gfp(raws, gflp_method="GFPL1", scale=True, normalize=True, smoothing=None):
 """ Run the GFP analysis. """


    # Load data if necessary
#    if isinstance(raws, str):
#        raws = load_object(filename=raws)

    # Initialize empty dict
    gfp = {}
    for participant in raws:

        gfp[participant] = {}
        for run in raws[participant]:

            # Generate empty dic
            gfp[participant][run] = {}

            # Assign raw object to raw
            raw = raws[participant][run].copy()

            # Check if MEG or EEG data
            if True in set(["MEG" in ch for ch in raw.info["ch_names"]]):
                meg = True
                eeg = False
            else:
                meg = False
                eeg = True

            # Save ECG channel
            try:
                gfp[participant][run]["ecg"] = np.array(raw.copy().pick_types(meg=False, eeg=False, ecg=True).to_data_frame())
            except ValueError:
                gfp[participant][run]["ecg"] = np.nan

            # Select appropriate channels
            data = raw.copy().pick_types(meg=meg, eeg=eeg)
            gfp[participant][run]["data_info"] = data.info
            gfp[participant][run]["data_freq"] = data.info["sfreq"]
            gfp[participant][run]["run_duration"] = len(data) / data.info["sfreq"]

            # Convert to numpy array
            data = np.array(data.to_data_frame())

            # find GFP peaks
            data, gfp_curve, gfp_peaks = eeg_gfp_peaks(data,
                                                 gflp_method=gflp_method,
                                                 smoothing=smoothing,
                                                 smoothing_window=100,
                                                 peak_method="wavelet",
                                                 normalize=normalize)
            # Store them
            gfp[participant][run]["microstates_times"] = gfp_peaks


            # Select brain state at peaks
            data_peaks = data[gfp_peaks]

            # Store the data and scale parameters
            if scale is True:
                gfp[participant][run]["data"] = z_score(data_peaks)
            else:
                gfp[participant][run]["data"] = data_peaks

            gfp[participant][run]["data_scale"] = scale
            gfp[participant][run]["data_normalize"] = normalize
            gfp[participant][run]["data_smoothing"] = smoothing



    return(gfp)