ax.annotate(f'FWHM = {FWHM:.2f}', xy=(u - FWHM/2, y_fwhm), xytext=(u - 2.5*sigma, A/1.2), arrowprops=dict(arrowstyle='-|>', color='red'),fontsize=14) # 画出左右两侧趋近于0的拐点 left_edge = u - 3 * sigma right_edge = u + 3 * sigma y_edge = gaussian(left_edge...
Whether to plot a gaussian kernel density estimate. rug : bool, optional #控制是否显示观测的小细条(边际毛毯) Whether to draw a rugplot on the support axis. fit : random variable object, optional #控制拟合的参数分布图形 An object with fit method, returning a tuple that can be passed to ...
Whether to plot a gaussian kernel density estimate. 是否绘制高斯核密度估计图。 rug: bool, optional 布尔值,可选参数。 Whether to draw a rugplot on the support axis. 是否在横轴上绘制观测值竖线。 fit: random variable object, optional 随机变量对象,可选参数。 An object withfitmethod, returning a...
plt.ylabel('current (nA)') plt.title('Gaussian colored noise') # this is an inset axes over the main axes a = plt.axes([.65, .6, .2, .2], axisbg='y') n, bins, patches = plt.hist(s, 400, normed=1) plt.title('Probability') plt.xticks([]) plt.yticks([]) # this is...
2. 运行程序：python plotene.py [ene.txt]需要Python3（查看版本：python --version），[ene.txt]...
当然也可以用更复杂的方式,代码如下: from scipy.stats.kde import gaussian_kde kde = gaussian_kde( line_bk_time ) plot_space = linspace( 0, 12, 100 ) plt.plot( plot_space , kde(plot_space ) ) 最后得到的图形如下: 至于cdf,下回补上,就酱紫...
gaussian_filter(attention, sigma=1.5) return attention sample_image = scipy.misc.face() attention_map = fake_attention() # display the data fig, axs = plt.subplots(1, 2, figsize=(10, 4)) axs[0].imshow(sample_image); axs[0].set_title('image') axs[1].imshow(attention_map, cmap=...
# Generate a Gaussian surface for(i in 1:length(x)) { for(j in 1:length(y)) { z[i, j] <- exp(-0.1 * x[i]^2 - 0.1 * y[j]^2) } } nrz <- nrow(z) ncz <- ncol(z) # Create a function interpolating colors in the range of specified colors ...
Seaborn是一个基于matplotlib的Python数据可视化库,提供了一些高级的绘图功能,使得数据可视化更加简单和美观。 kdeplot是Seaborn库中的一个函数,用于绘制核密度估计图。核密度估计是一种非参数统计方法,用于估计概率密度函数。kdeplot通过在每个数据点周围创建一个核函数,并将这些核函数叠加在一起,来估计数据的概率密度分布...
density_function=kde.gaussian_kde(data, bw_method=0.3) x=np.linspace(-2,10,300) plt.plot(x, density_function(x)) plt.show() The output for the above code: This may vary from dataset to dataset, but SciPy is generally faster than the other methods mentioned in this tutorial. ...