看一下将任意曲线拟合到数据的答案。基本上，您可以使用它
scipy.optimize.curve_fit来使您想要的任何功能适合您的数据。下面的代码显示了如何使高斯拟合某些随机数据（此SciPy-User邮件列表帖子的贷方）。
import numpyfrom scipy.optimize import curve_fitimport matplotlib.pyplot as plt# Define some test data which is close to Gaussiandata = numpy.random.normal(size=10000)hist, bin_edges = numpy.histogram(data, density=True)bin_centres = (bin_edges[:-1] + bin_edges[1:])/2# Define model function to be used to fit to the data above:def gauss(x, *p): A, mu, sigma = p return A*numpy.exp(-(x-mu)**2/(2.*sigma**2))# p0 is the initial guess for the fitting coefficients (A, mu and sigma above)p0 = [1., 0., 1.]coeff, var_matrix = curve_fit(gauss, bin_centres, hist, p0=p0)# Get the fitted curvehist_fit = gauss(bin_centres, *coeff)plt.plot(bin_centres, hist, label='Test data')plt.plot(bin_centres, hist_fit, label='Fitted data')# Finally, lets get the fitting parameters, i.e. the mean and standard deviation:print 'Fitted mean = ', coeff[1]print 'Fitted standard deviation = ', coeff[2]plt.show()
