human
Complete this python program:
def anderson(x, dist='norm'):
assistant
#Here is the Python program you requested.

    """Anderson-Darling test for data coming from a particular distribution
    The Anderson-Darling test is a modification of the Kolmogorov-
    Smirnov test `kstest` for the null hypothesis that a sample is
    drawn from a population that follows a particular distribution.
    For the Anderson-Darling test, the critical values depend on
    which distribution is being tested against.  This function works
    for normal, exponential, logistic, or Gumbel (Extreme Value
    Type I) distributions.
    Parameters
    x : array_like
    array of sample data
    dist : {\norm\,\expon\,\logistic\,\gumbel\,\gumbel_l\, gumbel_r\,
    \extreme1\}, optional
    the type of distribution to test against.  The default is \norm\
    and \extreme1\, \gumbel_l\ and \gumbel\ are synonyms.
    Returns
    statistic : float
    The Anderson-Darling test statistic
    critical_values : list
    The critical values for this distribution
    significance_level : list
    The significance levels for the corresponding critical values
    in percents.  The function returns critical values for a
    differing set of significance levels depending on the
    distribution that is being tested against.
    Notes
    Critical values provided are for the following significance levels:
    normal/exponenential
    15%, 10%, 5%, 2.5%, 1%
    logistic
    25%, 10%, 5%, 2.5%, 1%, 0.5%
    Gumbel
    25%, 10%, 5%, 2.5%, 1%
    If A2 is larger than these critical values then for the corresponding
    significance level, the null hypothesis that the data come from the
    chosen distribution can be rejected.
    References
    .. [1] http://www.itl.nist.gov/div898/handbook/prc/section2/prc213.htm
    .. [2] Stephens, M. A. (1974). EDF Statistics for Goodness of Fit and
    Some Comparisons, Journal of the American Statistical Association,
    Vol. 69, pp. 730-737.
    .. [3] Stephens, M. A. (1976). Asymptotic Results for Goodness-of-Fit
    Statistics with Unknown Parameters, Annals of Statistics, Vol. 4,
    pp. 357-369.
    .. [4] Stephens, M. A...."""
    if (dist not in ['norm', 'expon', 'gumbel', 'gumbel_l', 'gumbel_r', 'extreme1', 'logistic']):
        raise ValueError("Invalid    distribution;    dist    must    be    'norm',    'expon',    'gumbel',    'extreme1'    or    'logistic'.")
    y = sort(x)
    xbar = np.mean(x, axis=0)
    N = len(y)
    if (dist == 'norm'):
        s = np.std(x, ddof=1, axis=0)
        w = ((y - xbar) / s)
        logcdf = distributions.norm.logcdf(w)
        logsf = distributions.norm.logsf(w)
        sig = array([15, 10, 5, 2.5, 1])
        critical = around((_Avals_norm / ((1.0 + (4.0 / N)) - ((25.0 / N) / N))), 3)
    elif (dist == 'expon'):
        w = (y / xbar)
        logcdf = distributions.expon.logcdf(w)
        logsf = distributions.expon.logsf(w)
        sig = array([15, 10, 5, 2.5, 1])
        critical = around((_Avals_expon / (1.0 + (0.6 / N))), 3)
    elif (dist == 'logistic'):
        def rootfunc(ab, xj, N):
            (a, b) = ab
            tmp = ((xj - a) / b)
            tmp2 = exp(tmp)
            val = [(np.sum((1.0 / (1 + tmp2)), axis=0) - (0.5 * N)), (np.sum(((tmp * (1.0 - tmp2)) / (1 + tmp2)), axis=0) + N)]
            return array(val)
        sol0 = array([xbar, np.std(x, ddof=1, axis=0)])
        sol = optimize.fsolve(rootfunc, sol0, args=(x, N), xtol=1e-05)
        w = ((y - sol[0]) / sol[1])
        logcdf = distributions.logistic.logcdf(w)
        logsf = distributions.logistic.logsf(w)
        sig = array([25, 10, 5, 2.5, 1, 0.5])
        critical = around((_Avals_logistic / (1.0 + (0.25 / N))), 3)
    elif (dist == 'gumbel_r'):
        (xbar, s) = distributions.gumbel_r.fit(x)
        w = ((y - xbar) / s)
        logcdf = distributions.gumbel_r.logcdf(w)
        logsf = distributions.gumbel_r.logsf(w)
        sig = array([25, 10, 5, 2.5, 1])
        critical = around((_Avals_gumbel / (1.0 + (0.2 / sqrt(N)))), 3)
    else:
        (xbar, s) = distributions.gumbel_l.fit(x)
        w = ((y - xbar) / s)
        logcdf = distributions.gumbel_l.logcdf(w)
        logsf =...