def critical_z(alpha=0.05, tail="two"):
    """
    Given significance level, compute critical value.
    """
    if tail == "two":
        p = 1 - alpha / 2
    else:
        p = 1 - alpha

    return norm.ppf(p)

def compute_power(h_0, h_1, se, alpha=0.05, tail="two"):
    """
    Compute power given the centers of sampling distributions
    under the null and alternative hypotheses, and shared standard error.
    """
    z = critical_z(alpha=alpha, tail=tail)

    lower = h_0 - z * se
    upper = h_0 + z * se

    lower_a = norm.cdf(lower, h_1, se)
    upper_a = 1 - norm.cdf(upper, h_1, se)

    if tail == "two":
        print("acceptance region [%.3f, %.3f]"%(lower, upper))
        return lower_a + upper_a

    elif tail == "left":
        print("acceptance region > %.3f"%(lower))
        return lower_a

    elif tail == "right":
        print("acceptance region < %.3f"%(upper))
        return upper_a