拟合高斯函数

Question

我有一个直方图（请参见下文），我试图找到均值和标准差以及适合于我的直方图的曲线的代码。我认为SciPy或matplotlib中有一些可以帮助您的东西，但是我尝试过的每个示例都不起作用。

import matplotlib.pyplot as plt
import numpy as np

with open('gau_b_g_s.csv') as f:
    v = np.loadtxt(f, delimiter= ',', dtype="float", skiprows=1, usecols=None)

fig, ax = plt.subplots()

plt.hist(v, bins=500, color='#7F38EC', histtype='step')

plt.title("Gaussian")
plt.axis([-1, 2, 0, 20000])

plt.show()

Answer 1

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 numpy
from scipy.optimize import curve_fit
import matplotlib.pyplot as plt

# Define some test data which is close to Gaussian
data = 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 curve
hist_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()