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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 mtFeatureExtraction(signal, fs, mt_win, mt_step, st_win, st_step):
 """ Mid-term feature extraction """


    mt_win_ratio = int(round(mt_win / st_step))
    mt_step_ratio = int(round(mt_step / st_step))

    mt_features = []

    st_features, f_names = stFeatureExtraction(signal, fs, st_win, st_step)
    n_feats = len(st_features)
    n_stats = 2

    mt_features, mid_feature_names = [], []
    #for i in range(n_stats * n_feats + 1):
    for i in range(n_stats * n_feats):
        mt_features.append([])
        mid_feature_names.append("")

    for i in range(n_feats):        # for each of the short-term features:
        cur_p = 0
        N = len(st_features[i])
        mid_feature_names[i] = f_names[i] + "_" + "mean"
        mid_feature_names[i + n_feats] = f_names[i] + "_" + "std"

        while (cur_p < N):
            N1 = cur_p
            N2 = cur_p + mt_win_ratio
            if N2 > N:
                N2 = N
            cur_st_feats = st_features[i][N1:N2]

            mt_features[i].append(numpy.mean(cur_st_feats))
            mt_features[i + n_feats].append(numpy.std(cur_st_feats))
            #mt_features[i+2*n_feats].append(numpy.std(cur_st_feats) / (numpy.mean(cur_st_feats)+0.00000010))
            cur_p += mt_step_ratio
    return numpy.array(mt_features), st_features, mid_feature_names