Functions.py 文件源码

def mk_test(x, alpha = 0.05):

    n = len(x)  

    # calculate S    
    listMa = np.matrix(x)               # convert input List to 1D matrix
    subMa = np.sign(listMa.T - listMa)  # calculate all possible differences in matrix
                                        # with itself and save only sign of difference (-1,0,1)
    s = np.sum( subMa[np.tril_indices(n,-1)] ) # sum lower left triangle of matrix

    # calculate the unique data
    # return_counts=True returns a second array that is equivalent to tp in old version
    unique_x = np.unique(x, return_counts=True)
    g = len(unique_x[0])

    # calculate the var(s)
    if n == g: # there is no tie
        var_s = (n*(n-1)*(2*n+5))/18
    else: # there are some ties in data       
        tp = unique_x[1]        
        var_s = (n*(n-1)*(2*n+5) + np.sum(tp*(tp-1)*(2*tp+5)))/18

    if s>0:
        z = (s - 1)/np.sqrt(var_s)
    elif s == 0:
            z = 0
    elif s<0:
        z = (s + 1)/np.sqrt(var_s)

    # calculate the p_value
    p = 2*(1-scipy.stats.norm.cdf(abs(z))) # two tail test
    h = abs(z) > scipy.stats.norm.ppf(1-alpha/2) 


    return h, p


#########################################################################################
#hdf to tiff
#########################################################################################