def ks_2samp(data1, data2):
    """
    Computes the Kolmogorov-Smirnov statistic on 2 samples.
 
    This is a two-sided test for the null hypothesis that 2 independent samples
    are drawn from the same continuous distribution.
 
    Parameters
    ----------
    a, b : sequence of 1-D ndarrays
        two arrays of sample observations assumed to be drawn from a continuous
        distribution, sample sizes can be different
 
    Returns
    -------
    D : float
        KS statistic
    p-value : float
        two-tailed p-value
 
    Notes
    -----
    This tests whether 2 samples are drawn from the same distribution. Note
    that, like in the case of the one-sample K-S test, the distribution is
    assumed to be continuous.
 
    This is the two-sided test, one-sided tests are not implemented.
    The test uses the two-sided asymptotic Kolmogorov-Smirnov distribution.
 
    If the K-S statistic is small or the p-value is high, then we cannot
    reject the hypothesis that the distributions of the two samples
    are the same.
 
    Examples
    --------
    >>> from scipy import stats
    >>> np.random.seed(12345678)  #fix random seed to get the same result
    >>> n1 = 200  # size of first sample
    >>> n2 = 300  # size of second sample
 
    For a different distribution, we can reject the null hypothesis since the
    pvalue is below 1%:
 
    >>> rvs1 = stats.norm.rvs(size=n1, loc=0., scale=1)
    >>> rvs2 = stats.norm.rvs(size=n2, loc=0.5, scale=1.5)
    >>> stats.ks_2samp(rvs1, rvs2)
    (0.20833333333333337, 4.6674975515806989e-005)
 
    For a slightly different distribution, we cannot reject the null hypothesis
    at a 10% or lower alpha since the p-value at 0.144 is higher than 10%
 
    >>> rvs3 = stats.norm.rvs(size=n2, loc=0.01, scale=1.0)
    >>> stats.ks_2samp(rvs1, rvs3)
    (0.10333333333333333, 0.14498781825751686)
 
    For an identical distribution, we cannot reject the null hypothesis since
    the p-value is high, 41%:
 
    >>> rvs4 = stats.norm.rvs(size=n2, loc=0.0, scale=1.0)
    >>> stats.ks_2samp(rvs1, rvs4)
    (0.07999999999999996, 0.41126949729859719)
 
    """
    data1, data2 = map(asarray, (data1, data2))
    n1 = data1.shape[0]
    n2 = data2.shape[0]
    n1 = len(data1)
    n2 = len(data2)
    data1 = np.sort(data1)
    data2 = np.sort(data2)
    data_all = np.concatenate([data1,data2])
    cdf1 = np.searchsorted(data1,data_all,side='right')/(1.0*n1)
    cdf2 = (np.searchsorted(data2,data_all,side='right'))/(1.0*n2)
    d = np.max(np.absolute(cdf1-cdf2))
    # Note: d absolute not signed distance
    en = np.sqrt(n1*n2/float(n1+n2))
    try:
        prob = distributions.kstwobign.sf((en + 0.12 + 0.11 / en) * d)
    except:
        prob = 1.0
    return d, prob