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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 correct_peaks(sig, peak_inds, search_radius, smooth_window_size, peak_dir='compare'):
 """ Adjust a set of detected peaks to coincide with local signal maxima, and Parameters sig : numpy array The 1d signal array peak_inds : np array Array of the original peak indices max_gap : int The radius within which the original peaks may be shifted. smooth_window_size : int The window size of the moving average filter applied on the signal. Peak distance is calculated on the difference between the original and smoothed signal. peak_dir : str, optional The expected peak direction: 'up' or 'down', 'both', or 'compare'. - If 'up', the peaks will be shifted to local maxima - If 'down', the peaks will be shifted to local minima - If 'both', the peaks will be shifted to local maxima of the rectified signal - If 'compare', the function will try both 'up' and 'down' options, and choose the direction that gives the largest mean distance from the smoothed signal. Returns ------- corrected_peak_inds : numpy array Array of the corrected peak indices Examples -------- """

    sig_len = sig.shape[0]
    n_peaks = len(peak_inds)

    # Subtract the smoothed signal from the original
    sig = sig - smooth(sig=sig, window_size=smooth_window_size)


    # Shift peaks to local maxima
    if peak_dir == 'up':
        shifted_peak_inds = shift_peaks(sig=sig,
                                        peak_inds=peak_inds,
                                        search_radius=search_radius,
                                        peak_up=True)
    elif peak_dir == 'down':
        shifted_peak_inds = shift_peaks(sig=sig,
                                        peak_inds=peak_inds,
                                        search_radius=search_radius,
                                        peak_up=False)
    elif peak_dir == 'both':
        shifted_peak_inds = shift_peaks(sig=np.abs(sig),
                                        peak_inds=peak_inds,
                                        search_radius=search_radius,
                                        peak_up=True)
    else:
        shifted_peak_inds_up = shift_peaks(sig=sig,
                                           peak_inds=peak_inds,
                                           search_radius=search_radius,
                                           peak_up=True)
        shifted_peak_inds_down = shift_peaks(sig=sig,
                                             peak_inds=peak_inds,
                                             search_radius=search_radius,
                                             peak_up=False)

        # Choose the direction with the biggest deviation
        up_dist = np.mean(np.abs(sig[shifted_peak_inds_up]))
        down_dist = np.mean(np.abs(sig[shifted_peak_inds_down]))

        if up_dist >= down_dist:
            shifted_peak_inds = shifted_peak_inds_up
        else:
            shifted_peak_inds = shifted_peak_inds_down

    return shifted_peak_inds