qa_graphs.py 文件源码

def density(data, nbins=500, rng=None):
    """
    Computes density for metrics which return vectors

    Required parameters:
        data:
            - Dictionary of the vectors of data
    """
    density = OrderedDict()
    xs = OrderedDict()
    for subj in data:
        hist = np.histogram(data[subj], nbins)
        hist = np.max(hist[0])
        dens = gaussian_kde(data[subj])
        if rng is not None:
            xs[subj] = np.linspace(rng[0], rng[1], nbins)
        else:
            xs[subj] = np.linspace(0, np.max(data[subj]), nbins)
        density[subj] = dens.evaluate(xs[subj])*np.max(data[subj]*hist)
    return {"xs": xs, "pdfs": density}