coreglib.py 文件源码

def bin_stats(x, y, stat='median', nbins=360):
    import scipy.stats
    #bin_range = (x.min(), x.max())
    bin_range = (0., 360.)
    bin_width = (bin_range[1] - bin_range[0]) / nbins
    print("Computing 1-degree bin statistics: %s" % stat)
    bin_stat, bin_edges, bin_number = scipy.stats.binned_statistic(x, y, \
            statistic=stat, bins=nbins, range=bin_range)
    bin_centers = bin_edges[:-1] + bin_width/2.
    bin_stat = np.ma.masked_invalid(bin_stat)

    """
    #Mask bins in grid directions, can potentially contain biased stats
    badbins = [0, 45, 90, 180, 225, 270, 315]
    bin_stat = np.ma.masked_where(np.around(bin_edges[:-1]) % 45 == 0, bin_stat)
    bin_edges = np.ma.masked_where(np.around(bin_edges[:-1]) % 45 == 0, bin_edges)
    """

    #Generate plots
    if False:
        plt.figure()
        #Need to pull out original values for each bin
        #Loop through unique bin numbers, pull out original values into list of lists
        #plt.boxplot(bin_stat, sym='')
        plt.xlim(*bin_range)
        plt.xticks(np.arange(bin_range[0],bin_range[1],30))
        plt.ylabel('dh/tan(slope) (m)')
        plt.xlabel('Aspect (1-deg bins)')

        plt.figure()
        plt.bar(bin_centers, bin_count)
        plt.xlim(*bin_range)
        plt.xticks(np.arange(bin_range[0],bin_range[1],30))
        plt.ylabel('Count')
        plt.xlabel('Aspect (1-deg bins)')
        plt.show()

    return bin_stat, bin_edges, bin_centers

#Function for fitting Nuth and Kaab (2011)