CCF_Systematics.py 文件源码

def fit_sigma(df, i):
    """
    Find the largest allowable standard deviation, given the possible values Tactual can take.
    """
    Tmeasured, Tactual, _, _ = get_values(df)
    Tm = Tmeasured[i]

    # Get the possible values, and bin those with this measured value
    possible_values = sorted(pd.unique(df.Tactual))
    edges = [(possible_values[i] + possible_values[i+1])/2 for i in range(len(possible_values)-1)]
    bins = [0] + edges + [9e9]
    good = df.loc[df.Temperature == Tm]
    values, _= np.histogram(good.Tactual.values, bins=bins)

    mean = np.mean(good.Tactual.values)
    std = np.std(good.Tactual.values, ddof=1)
    if std > 0:
        return std

    sigma_test = np.arange(500, 10, -10) #Just test a bunch of values
    idx = np.searchsorted(bins, mean)
    idx = np.argmin(abs(np.array(bins) - mean))
    x1 = bins[idx-2] if idx > 2 else -1
    x2 = bins[idx-1]
    x3 = bins[idx]
    x4 = bins[idx+1] if idx < len(bins)-2 else np.inf
    N = len(good)
    probs = [get_probability(x1, x2, x3, x4, N, mean, s) for s in sigma_test]
    for s, p in zip(sigma_test, probs):
        if p > 0.5:
            return s

    # If we get here, just return a guess value
    return 200.0

    #raise ValueError('No probability > 0!')