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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 compute_accuracy(true_positives, n_ref, n_est):
 """Compute accuracy metrics. Parameters true_positives : np.ndarray Array containing the number of true positives at each time point. n_ref : np.ndarray Array containing the number of reference frequencies at each time point. n_est : np.ndarray Array containing the number of estimate frequencies at each time point. Returns ------- precision : float ``sum(true_positives)/sum(n_est)`` recall : float ``sum(true_positives)/sum(n_ref)`` acc : float ``sum(true_positives)/sum(n_est + n_ref - true_positives)`` """

    true_positive_sum = float(true_positives.sum())

    n_est_sum = n_est.sum()
    if n_est_sum > 0:
        precision = true_positive_sum/n_est.sum()
    else:
        warnings.warn("Estimate frequencies are all empty.")
        precision = 0.0

    n_ref_sum = n_ref.sum()
    if n_ref_sum > 0:
        recall = true_positive_sum/n_ref.sum()
    else:
        warnings.warn("Reference frequencies are all empty.")
        recall = 0.0

    acc_denom = (n_est + n_ref - true_positives).sum()
    if acc_denom > 0:
        acc = true_positive_sum/acc_denom
    else:
        acc = 0.0

    return precision, recall, acc