pyprophet/stats.py
def pnorm(stat, stat0):
    """ [P(X>pi, mu, sigma) for pi in pvalues] for normal distributed stat with
    expectation value mu and std deviation sigma """

    mu, sigma = mean_and_std_dev(stat0)

    stat = to_one_dim_array(stat, np.float64)
    args = (stat - mu) / sigma
    return 1-(0.5 * (1.0 + scipy.special.erf(args / np.sqrt(2.0))))
def pnorm(stat, stat0):
    """ [P(X>pi, mu, sigma) for pi in pvalues] for normal distributed stat with
    expectation value mu and std deviation sigma """

    mu, sigma = mean_and_std_dev(stat0)

    stat = to_one_dim_array(stat, np.float64)
    args = (stat - mu) / sigma
    return 1-(0.5 * (1.0 + scipy.special.erf(args / np.sqrt(2.0))))
def pnorm ( stat , stat0 ) : mu , sigma = mean_and_std_dev ( stat0 ) stat = to_one_dim_array ( stat , np . float64 ) args = ( stat - mu ) / sigma return 1 - ( 0.5 * ( 1.0 + scipy . special . erf ( args / np . sqrt ( 2.0 ) ) ) )
[P(X>pi, mu, sigma) for pi in pvalues] for normal distributed stat with
    expectation value mu and std deviation sigma
[ P ( X > pi mu sigma ) for pi in pvalues ] for normal distributed stat with expectation value mu and std deviation sigma