latools/filtering/signal_optimiser.py
def bayes_scale(s):
    """
    Remove mean and divide by standard deviation, using bayes_kvm statistics.
    """
    if sum(~np.isnan(s)) > 1:
        bm, bv, bs = bayes_mvs(s[~np.isnan(s)])
        return (s - bm.statistic) / bs.statistic
    else:
        return np.full(s.shape, np.nan)
def bayes_scale(s):
    if sum(~np.isnan(s)) > 1:
        bm, bv, bs = bayes_mvs(s[~np.isnan(s)])
        return (s - bm.statistic) / bs.statistic
    else:
        return np.full(s.shape, np.nan)
def bayes_scale ( s ) : if sum ( ~ np . isnan ( s ) ) > 1 : bm , bv , bs = bayes_mvs ( s [ ~ np . isnan ( s ) ] ) return ( s - bm . statistic ) / bs . statistic else : return np . full ( s . shape , np . nan )
Remove mean and divide by standard deviation, using bayes_kvm statistics.
Remove mean and divide by standard deviation using bayes_kvm statistics .