demo_corr.py 文件源码

def plot_correlation_demo():
    np.random.seed(0)  # to reproduce the data later on
    pylab.clf()
    pylab.figure(num=None, figsize=(8, 8))

    x = np.arange(0, 10, 0.2)

    pylab.subplot(221)
    y = 0.5 * x + norm.rvs(1, scale=.01, size=len(x))
    _plot_correlation_func(x, y)

    pylab.subplot(222)
    y = 0.5 * x + norm.rvs(1, scale=.1, size=len(x))
    _plot_correlation_func(x, y)

    pylab.subplot(223)
    y = 0.5 * x + norm.rvs(1, scale=1, size=len(x))
    _plot_correlation_func(x, y)

    pylab.subplot(224)
    y = norm.rvs(1, scale=10, size=len(x))
    _plot_correlation_func(x, y)

    pylab.autoscale(tight=True)
    pylab.grid(True)

    filename = "corr_demo_1.png"
    pylab.savefig(os.path.join(CHART_DIR, filename), bbox_inches="tight")

    pylab.clf()
    pylab.figure(num=None, figsize=(8, 8))

    x = np.arange(-5, 5, 0.2)

    pylab.subplot(221)
    y = 0.5 * x ** 2 + norm.rvs(1, scale=.01, size=len(x))
    _plot_correlation_func(x, y)

    pylab.subplot(222)
    y = 0.5 * x ** 2 + norm.rvs(1, scale=.1, size=len(x))
    _plot_correlation_func(x, y)

    pylab.subplot(223)
    y = 0.5 * x ** 2 + norm.rvs(1, scale=1, size=len(x))
    _plot_correlation_func(x, y)

    pylab.subplot(224)
    y = 0.5 * x ** 2 + norm.rvs(1, scale=10, size=len(x))
    _plot_correlation_func(x, y)

    pylab.autoscale(tight=True)
    pylab.grid(True)

    filename = "corr_demo_2.png"
    pylab.savefig(os.path.join(CHART_DIR, filename), bbox_inches="tight")