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How do i get a smooth fit for my data points, using “scipy.optimize.curve_fit”?

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3

I want to fit some data points using scipy.optimize.curve_fit. Unfortunately I get an unsteady fit and I do not know why.

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

M = np.array([730,910,1066,1088,1150], dtype=float)
V = np.array([95.71581923, 146.18564513, 164.46723727, 288.49796413, 370.98703941], dtype=float)

def func(x, a, b, c):
 return a * np.exp(b * x) + c

popt, pcov = curve_fit(func, M, V, [0,0,1], maxfev=100000000)
print(*popt)

fig, ax = plt.subplots()
fig.dpi = 80

ax.plot(M, V, 'go', label='data')
ax.plot(M, func(M, *popt), '-', label='fit')

plt.xlabel("M")
plt.ylabel("V")
plt.grid()
plt.legend()
plt.show()

I would acutally expect some kind of a smooth curve. Can someone explain what I am doing wrong here?

python scipy curve-fitting

share|improve this question

asked Mar 25 at 12:29

Jack.O.Jack.O.

7199 bronze badges

• 
  
  
  

  
  2
  

  
  
  
  
  
  

  
  
  Because you are only plotting your function at M, func(M, *popt) try using something like np.arange(700,1200), func(np.arange(700,1200), *popt)
  
  – jeremycg
  Mar 25 at 12:34
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Ouh, that's embarassing..Thank you very much !
  
  – Jack.O.
  Mar 25 at 12:37
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  If it might be of some use, I got an OK fit to a two-parameter hyperbolic type equation "V = (a + M) / (b + M)" with parameters a = -4.8322540715601128E+04 and b = -1.2775297675354102E+03
  
  – James Phillips
  Mar 25 at 14:42
  

  
  
  
  

add a comment | 

3

I want to fit some data points using scipy.optimize.curve_fit. Unfortunately I get an unsteady fit and I do not know why.

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

M = np.array([730,910,1066,1088,1150], dtype=float)
V = np.array([95.71581923, 146.18564513, 164.46723727, 288.49796413, 370.98703941], dtype=float)

def func(x, a, b, c):
 return a * np.exp(b * x) + c

popt, pcov = curve_fit(func, M, V, [0,0,1], maxfev=100000000)
print(*popt)

fig, ax = plt.subplots()
fig.dpi = 80

ax.plot(M, V, 'go', label='data')
ax.plot(M, func(M, *popt), '-', label='fit')

plt.xlabel("M")
plt.ylabel("V")
plt.grid()
plt.legend()
plt.show()

I would acutally expect some kind of a smooth curve. Can someone explain what I am doing wrong here?

python scipy curve-fitting

share|improve this question

asked Mar 25 at 12:29

Jack.O.Jack.O.

7199 bronze badges

• 
  
  
  

  
  2
  

  
  
  
  
  
  

  
  
  Because you are only plotting your function at M, func(M, *popt) try using something like np.arange(700,1200), func(np.arange(700,1200), *popt)
  
  – jeremycg
  Mar 25 at 12:34
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Ouh, that's embarassing..Thank you very much !
  
  – Jack.O.
  Mar 25 at 12:37
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  If it might be of some use, I got an OK fit to a two-parameter hyperbolic type equation "V = (a + M) / (b + M)" with parameters a = -4.8322540715601128E+04 and b = -1.2775297675354102E+03
  
  – James Phillips
  Mar 25 at 14:42
  

  
  
  
  

add a comment | 

I want to fit some data points using scipy.optimize.curve_fit. Unfortunately I get an unsteady fit and I do not know why.

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

M = np.array([730,910,1066,1088,1150], dtype=float)
V = np.array([95.71581923, 146.18564513, 164.46723727, 288.49796413, 370.98703941], dtype=float)

def func(x, a, b, c):
 return a * np.exp(b * x) + c

popt, pcov = curve_fit(func, M, V, [0,0,1], maxfev=100000000)
print(*popt)

fig, ax = plt.subplots()
fig.dpi = 80

ax.plot(M, V, 'go', label='data')
ax.plot(M, func(M, *popt), '-', label='fit')

plt.xlabel("M")
plt.ylabel("V")
plt.grid()
plt.legend()
plt.show()

I would acutally expect some kind of a smooth curve. Can someone explain what I am doing wrong here?

python scipy curve-fitting

share|improve this question

asked Mar 25 at 12:29

Jack.O.Jack.O.

7199 bronze badges

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

M = np.array([730,910,1066,1088,1150], dtype=float)
V = np.array([95.71581923, 146.18564513, 164.46723727, 288.49796413, 370.98703941], dtype=float)

def func(x, a, b, c):
 return a * np.exp(b * x) + c

popt, pcov = curve_fit(func, M, V, [0,0,1], maxfev=100000000)
print(*popt)

fig, ax = plt.subplots()
fig.dpi = 80

ax.plot(M, V, 'go', label='data')
ax.plot(M, func(M, *popt), '-', label='fit')

plt.xlabel("M")
plt.ylabel("V")
plt.grid()
plt.legend()
plt.show()

share|improve this question

asked Mar 25 at 12:29

Jack.O.Jack.O.

7199 bronze badges

share|improve this question

asked Mar 25 at 12:29

Jack.O.Jack.O.

7199 bronze badges

asked Mar 25 at 12:29

Jack.O.Jack.O.

7199 bronze badges

asked Mar 25 at 12:29

Jack.O.Jack.O.

7199 bronze badges

1 Answer
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3

You are only plotting the same x points as the original data in your call:

ax.plot(M, V, 'go', label='data')
ax.plot(M, func(M, *popt), '-', label='fit')

To fix this, you can use a wider range - here we use all the values from 700 to 1200:

toplot = np.arange(700,1200)
ax.plot(toplot, func(toplot, *popt), '-', label='fit')

share|improve this answer

answered Mar 25 at 12:38

jeremycgjeremycg

19.3k44 gold badges4444 silver badges5858 bronze badges

add a comment | 

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3

You are only plotting the same x points as the original data in your call:

ax.plot(M, V, 'go', label='data')
ax.plot(M, func(M, *popt), '-', label='fit')

To fix this, you can use a wider range - here we use all the values from 700 to 1200:

toplot = np.arange(700,1200)
ax.plot(toplot, func(toplot, *popt), '-', label='fit')

share|improve this answer

answered Mar 25 at 12:38

jeremycgjeremycg

19.3k44 gold badges4444 silver badges5858 bronze badges

add a comment | 

3

You are only plotting the same x points as the original data in your call:

ax.plot(M, V, 'go', label='data')
ax.plot(M, func(M, *popt), '-', label='fit')

To fix this, you can use a wider range - here we use all the values from 700 to 1200:

toplot = np.arange(700,1200)
ax.plot(toplot, func(toplot, *popt), '-', label='fit')

share|improve this answer

answered Mar 25 at 12:38

jeremycgjeremycg

19.3k44 gold badges4444 silver badges5858 bronze badges

add a comment | 

You are only plotting the same x points as the original data in your call:

ax.plot(M, V, 'go', label='data')
ax.plot(M, func(M, *popt), '-', label='fit')

To fix this, you can use a wider range - here we use all the values from 700 to 1200:

toplot = np.arange(700,1200)
ax.plot(toplot, func(toplot, *popt), '-', label='fit')

share|improve this answer

answered Mar 25 at 12:38

jeremycgjeremycg

19.3k44 gold badges4444 silver badges5858 bronze badges

ax.plot(M, V, 'go', label='data')
ax.plot(M, func(M, *popt), '-', label='fit')

toplot = np.arange(700,1200)
ax.plot(toplot, func(toplot, *popt), '-', label='fit')

share|improve this answer

answered Mar 25 at 12:38

jeremycgjeremycg

19.3k44 gold badges4444 silver badges5858 bronze badges

answered Mar 25 at 12:38

jeremycgjeremycg

19.3k44 gold badges4444 silver badges5858 bronze badges

answered Mar 25 at 12:38

jeremycgjeremycg

19.3k44 gold badges4444 silver badges5858 bronze badges