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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 standardize_by_allele_count(score, aac, bins=None, n_bins=None, diagnostics=True):
 """Standardize `score` within allele frequency bins. Parameters score : array_like, float The score to be standardized, e.g., IHS or NSL. aac : array_like, int An array of alternate allele counts. bins : array_like, int, optional Allele count bins, overrides `n_bins`. n_bins : int, optional Number of allele count bins to use. diagnostics : bool, optional If True, plot some diagnostic information about the standardization. Returns ------- score_standardized : ndarray, float Standardized scores. bins : ndarray, int Allele count bins used for standardization. """


    from scipy.stats import binned_statistic

    # check inputs
    score = asarray_ndim(score, 1)
    aac = asarray_ndim(aac, 1)
    check_dim0_aligned(score, aac)

    # remove nans
    nonan = ~np.isnan(score)
    score_nonan = score[nonan]
    aac_nonan = aac[nonan]

    if bins is None:
        # make our own similar sized bins

        # how many bins to make?
        if n_bins is None:
            # something vaguely reasonable
            n_bins = np.max(aac) // 2

        # make bins
        bins = make_similar_sized_bins(aac_nonan, n_bins)

    else:
        # user-provided bins
        bins = asarray_ndim(bins, 1)

    mean_score, _, _ = binned_statistic(aac_nonan, score_nonan,
                                        statistic=np.mean,
                                        bins=bins)
    std_score, _, _ = binned_statistic(aac_nonan, score_nonan,
                                       statistic=np.std,
                                       bins=bins)

    if diagnostics:
        import matplotlib.pyplot as plt
        x = (bins[:-1] + bins[1:]) / 2
        plt.figure()
        plt.fill_between(x,
                         mean_score - std_score,
                         mean_score + std_score,
                         alpha=.5,
                         label='std')
        plt.plot(x, mean_score, marker='o', label='mean')
        plt.grid(axis='y')
        plt.xlabel('Alternate allele count')
        plt.ylabel('Unstandardized score')
        plt.title('Standardization diagnostics')
        plt.legend()

    # apply standardization
    score_standardized = np.empty_like(score)
    for i in range(len(bins) - 1):
        x1 = bins[i]
        x2 = bins[i + 1]
        if i == 0:
            # first bin
            loc = (aac < x2)
        elif i == len(bins) - 2:
            # last bin
            loc = (aac >= x1)
        else:
            # middle bins
            loc = (aac >= x1) & (aac < x2)
        m = mean_score[i]
        s = std_score[i]
        score_standardized[loc] = (score[loc] - m) / s

    return score_standardized, bins