如何通过指数曲线拟合数据(How to fit data by exponential curve)

 我的项目有点问题，因为我有一组数据，我绘制它以获得2条曲线，我想用指数曲线拟合这些图。 
 
 我看过这篇文章： 在没有初步猜测的情况下拟合指数衰减 。 但我的例子有点不同。 
 
 这就是我得到的数据： 
 
  
 
 我的脚本如下： 
 
mask_G = np.bitwise_and( tbdata['G'] < 99.99, tbdata['GERR'] < 0.2)
mask_R = np.bitwise_and( tbdata['R'] < 99.99, tbdata['RERR'] < 0.2)

G_corrected = tbdata[mask_G]
R_corrected = tbdata[mask_R]


fig13 = plt.gcf()
fig13.set_size_inches(16, 9)


fig13, (ax1,ax2) = plt.subplots(1,2)

fig_error_g = ax1.plot(G_corrected['G'], G_corrected['GERR'], '.')
ax1.set_xlabel('G')
ax1.set_ylabel('GERR')
ax1.set_title('Evolution de GERR en fonction de G')

fig_error_r = ax2.plot(R_corrected['R'], R_corrected['RERR'], '.')
ax2.set_xlabel('R')
ax2.set_ylabel('RERR')
ax2.set_title('Evolution de RERR en fonction de R')

fig13.tight_layout() 

plt.savefig('graphique.png')

plt.show()
 
 我试着写这个，基于scipy doc： 
 
def exponential(x,a,b,c) :
    return a * np.exp(-b * x) + c

xdata = G_corrected['G']
y = G_corrected['GERR']
ydata = y + 0.2 * np.random.normal(size=len(xdata))

popt, pcov = curve_fit(exponential, xdata, ydata)
 
 但我得到： 
 
 
 
 /home/user/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/scipy/optimize/minpack.py:601：OptimizeWarning：无法估计参数的协方差 
 类别= OptimizeWarning） 
 
 
 你对我如何处理有任何想法吗？ 
 
 非常感谢 ;） 
 
 编辑： 
 
 我试着像这样适合我的情节： 
 
mask_G = np.bitwise_and( tbdata['G'] < 99.99, tbdata['GERR'] < 0.2)
mask_R = np.bitwise_and( tbdata['R'] < 99.99, tbdata['RERR'] < 0.2)

G_corrected = tbdata[mask_G]
R_corrected = tbdata[mask_R]


params = np.polyfit(G_corrected['G'], np.log(G_corrected['GERR']),1)
a = params[0]
A = np.exp(params[1])


fig13 = plt.gcf()
fig13.set_size_inches(16, 9)


fig13, (ax1,ax2) = plt.subplots(1,2)

fig_error_g = ax1.plot(G_corrected['G'], (G_corrected['GERR']), '.')
fig_error_g = ax1.plot(G_corrected['G'], (A*np.exp(a*G_corrected['G'])),'.')

ax1.set_xlabel('G')
ax1.set_ylabel('GERR')
ax1.set_title('Evolution de GERR en fonction de G')

fig_error_r = ax2.plot(R_corrected['R'], np.log(R_corrected['RERR']), '.')
ax2.set_xlabel('R')
ax2.set_ylabel('RERR')
ax2.set_title('Evolution de RERR en fonction de R')

fig13.tight_layout() 

plt.savefig('graphique.png')

plt.show()
 
 我得到： 
 
  
 
 您如何看待结果？ 

I get a little problem with my project because I have a set of data, I plot it in order to get 2 curves and I would like to fit this plots by an exponential curve.
 
I watched this post : fitting exponential decay with no initial guessing. But my example is kind different.
 
This is what I'm getting with data :
 
 
My script is as following :
 
mask_G = np.bitwise_and( tbdata['G'] < 99.99, tbdata['GERR'] < 0.2)
mask_R = np.bitwise_and( tbdata['R'] < 99.99, tbdata['RERR'] < 0.2)

G_corrected = tbdata[mask_G]
R_corrected = tbdata[mask_R]


fig13 = plt.gcf()
fig13.set_size_inches(16, 9)


fig13, (ax1,ax2) = plt.subplots(1,2)

fig_error_g = ax1.plot(G_corrected['G'], G_corrected['GERR'], '.')
ax1.set_xlabel('G')
ax1.set_ylabel('GERR')
ax1.set_title('Evolution de GERR en fonction de G')

fig_error_r = ax2.plot(R_corrected['R'], R_corrected['RERR'], '.')
ax2.set_xlabel('R')
ax2.set_ylabel('RERR')
ax2.set_title('Evolution de RERR en fonction de R')

fig13.tight_layout() 

plt.savefig('graphique.png')

plt.show()
 
I tried to write that, which based on scipy doc :
 
def exponential(x,a,b,c) :
    return a * np.exp(-b * x) + c

xdata = G_corrected['G']
y = G_corrected['GERR']
ydata = y + 0.2 * np.random.normal(size=len(xdata))

popt, pcov = curve_fit(exponential, xdata, ydata)
 
but I get :
 
 
 
/home/user/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/scipy/optimize/minpack.py:601: OptimizeWarning: Covariance of the parameters could not be estimated
 category=OptimizeWarning)
 
 
Do you have any idea on how I can process ?
 
Thank you so much ;)
 
EDIT : 
 
I tried to fit my plot like that :
 
mask_G = np.bitwise_and( tbdata['G'] < 99.99, tbdata['GERR'] < 0.2)
mask_R = np.bitwise_and( tbdata['R'] < 99.99, tbdata['RERR'] < 0.2)

G_corrected = tbdata[mask_G]
R_corrected = tbdata[mask_R]


params = np.polyfit(G_corrected['G'], np.log(G_corrected['GERR']),1)
a = params[0]
A = np.exp(params[1])


fig13 = plt.gcf()
fig13.set_size_inches(16, 9)


fig13, (ax1,ax2) = plt.subplots(1,2)

fig_error_g = ax1.plot(G_corrected['G'], (G_corrected['GERR']), '.')
fig_error_g = ax1.plot(G_corrected['G'], (A*np.exp(a*G_corrected['G'])),'.')

ax1.set_xlabel('G')
ax1.set_ylabel('GERR')
ax1.set_title('Evolution de GERR en fonction de G')

fig_error_r = ax2.plot(R_corrected['R'], np.log(R_corrected['RERR']), '.')
ax2.set_xlabel('R')
ax2.set_ylabel('RERR')
ax2.set_title('Evolution de RERR en fonction de R')

fig13.tight_layout() 

plt.savefig('graphique.png')

plt.show()
 
and I get : 
 
 
What do you think about the result ?

原文：https://stackoverflow.com/questions/36834637