问题描述
我正在尝试拟合二分量高斯拟合:
mu0 = sum(velo_peak * spec_peak) / sum(spec_peak)
sigma = np.sqrt(sum(spec_peak * (velo_peak - mu0)**2) / sum(spec_peak))
def Gauss(velo_peak,a,mu0,sigma):
res = a * np.exp(-(velo_peak - mu0)**2 / (2 * sigma**2))
return res
p0 = [max(spec_peak) - RMS,sigma] # a = max(spec_peak)
popt,pcov = curve_fit(Gauss,velo_peak,spec_peak,p0,maxfev=10000,bounds=((0,+np.inf,+np.inf),(0,+np.inf)))
#____________________two component gaussian fit_______________________#
def double_gaussian(velo_peak,a1,mu1,sigma1,a2,mu2,sigma2):
res_two = a1 * np.exp(-(velo_peak - mu1)**2/(2 * sigma1**2)) \
+ a2 * np.exp(-(velo_peak - mu2)**2/(2 * sigma2**2))
return res_two
##_____________________Initial guess values__________________________##
sigma1 = 0.7 * sigma
sigma2 = 0.7 * sigma
mu1 = mu0 + sigma
mu2 = mu0 - sigma
a1 = 3
a2 = 1
guess = [a1,sigma2]
popt_2,pcov_2 = curve_fit(double_gaussian,guess,+np.inf)))
但是我得到了一个我想避免的负面部分,但我不知道如何正确实现边界,因为我不太了解文档。
我收到以下错误:
ValueError: Inconsistent shapes between bounds and `x0`.
谁能指导我如何正确使用边界?
解决方法
它期待 "2-tuple of array_like,optional"
所以它看起来像:
((lower_bound0,lower_bound1,...,lower_boundn),(upper_bound0,upper_bound1,upper_boundn))
在我看来,如果您想避免负值,那么在双高斯中您需要将 a1
和 a2
限制为正值。
关注您的guess
:
[a1,mu1,sigma1,a2,mu2,sigma2]
那就是:
... bounds=[(0,-np.inf,-np.inf),(np.inf,np.inf,np.inf)],...
演示:
import matplotlib.pyplot as plt
def double_gaussian(velo_peak,a1,sigma2):
res_two = a1 * np.exp(-(velo_peak - mu1)**2/(2 * sigma1**2)) \
+ a2 * np.exp(-(velo_peak - mu2)**2/(2 * sigma2**2))
return res_two
x = np.linspace(0,10,1000)
y = double_gaussian(x,1,3,7,0.5) + 0.4*(np.random.random(x.shape) - 0.5)
popt,_ = curve_fit(double_gaussian,x,y,bounds=[(0,np.inf)])
plt.plot(x,y)
plt.plot(x,double_gaussian(x,*popt))