高斯适合Python [英] Gaussian fit for Python

问题描述

我正在尝试将高斯拟合为我的数据(已经是粗略的高斯).我已经接受了这些建议，并尝试了curve_fit和leastsq，但是我认为我缺少一些更基本的东西(因为我不知道如何使用该命令). 这是到目前为止我拥有的脚本

I'm trying to fit a Gaussian for my data (which is already a rough gaussian). I've already taken the advice of those here and tried curve_fit and leastsq but I think that I'm missing something more fundamental (in that I have no idea how to use the command). Here's a look at the script I have so far

import pylab as plb
import matplotlib.pyplot as plt

# Read in data -- first 2 rows are header in this example. 
data = plb.loadtxt('part 2.csv', skiprows=2, delimiter=',')

x = data[:,2]
y = data[:,3]
mean = sum(x*y)
sigma = sum(y*(x - mean)**2)

def gauss_function(x, a, x0, sigma):
    return a*np.exp(-(x-x0)**2/(2*sigma**2))
popt, pcov = curve_fit(gauss_function, x, y, p0 = [1, mean, sigma])
plt.plot(x, gauss_function(x, *popt), label='fit')

# plot data

plt.plot(x, y,'b')

# Add some axis labels

plt.legend()
plt.title('Fig. 3 - Fit for Time Constant')
plt.xlabel('Time (s)')
plt.ylabel('Voltage (V)')
plt.show()

我从中得到的是一个高斯型形状，这是我的原始数据，还有一条水平直线.

What I get from this is a gaussian-ish shape which is my original data, and a straight horizontal line.

此外，我想使用点绘制图形，而不是将它们连接起来. 任何输入表示赞赏！

Also, I'd like to plot my graph using points, instead of having them connected. Any input is appreciated!

推荐答案

已更正代码:

import pylab as plb
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
from scipy import asarray as ar,exp

x = ar(range(10))
y = ar([0,1,2,3,4,5,4,3,2,1])

n = len(x)                          #the number of data
mean = sum(x*y)/n                   #note this correction
sigma = sum(y*(x-mean)**2)/n        #note this correction

def gaus(x,a,x0,sigma):
    return a*exp(-(x-x0)**2/(2*sigma**2))

popt,pcov = curve_fit(gaus,x,y,p0=[1,mean,sigma])

plt.plot(x,y,'b+:',label='data')
plt.plot(x,gaus(x,*popt),'ro:',label='fit')
plt.legend()
plt.title('Fig. 3 - Fit for Time Constant')
plt.xlabel('Time (s)')
plt.ylabel('Voltage (V)')
plt.show()

结果:

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