__init__.py 文件源码

def binHisto(data, verbose=False):
    """
    Calculates bin width and number of bins for histogram using Freedman-Diaconis rule, if rule fails, defaults to square-root method

    The Freedman-Diaconis method is detailed in:
        Freedman, D., and P. Diaconis (1981), On the histogram as a density estimator: L2 theory, Z. Wahrscheinlichkeitstheor. Verw. Geb., 57, 453–476

    and is also described by:
        Wilks, D. S. (2006), Statistical Methods in the Atmospheric Sciences, 2nd ed.

    Parameters
    ==========
    data : array_like
        list/array of data values
    verbose : boolean (optional)
        print out some more information

    Returns
    =======
    out : tuple
        calculated width of bins using F-D rule, number of bins (nearest integer) to use for histogram

    Examples
    ========
    >>> import numpy, spacepy
    >>> import matplotlib.pyplot as plt
    >>> numpy.random.seed(8675301)
    >>> data = numpy.random.randn(1000)
    >>> binw, nbins = spacepy.toolbox.binHisto(data)
    >>> print(nbins)
    19.0
    >>> p = plt.hist(data, bins=nbins, histtype='step', normed=True)

    See Also
    ========
    matplotlib.pyplot.hist
    """
    from matplotlib.mlab import prctile
    pul = prctile(data, p=(25,75)) #get confidence interval
    ql, qu = pul[0], pul[1]
    iqr = qu-ql
    binw = 2.*iqr/(len(data)**(1./3.))
    if binw != 0:
        nbins = round((max(data)-min(data))/binw)
    # if nbins is 0, NaN or inf don't use the F-D rule just use sqrt(num) rule
    if binw == 0 or nbins == 0 or not np.isfinite(nbins):
        nbins = round(np.sqrt(len(data)))
        binw = len(data)/nbins
        if verbose:
            print("Used sqrt rule")
    else:
        if verbose:
            print("Used F-D rule")
    return (binw, nbins)