Not getting a proper fit

Question:

Getting an error of "IndexError: index 1 is out of bounds for axis 0 with size 1". I am a newbie. Please help. Thanks in advance.

``````def logistic(x, l, k, x1):
return l / 1+np.exp(-k*(x-x1))
distance= [1.000*70, 2.000*70, 3.000*70, 4.000*70, 5.000*70, 6.000*70, 7.000*70, 8.000*70,
9.000*70, 11.00*70, 12.000*70, 13.000*70, 14.000*70, 15.000*70, 16.000*70,
17.000*70, 18.000*70, 19.000*70, 21.000*70, 22.000*70, 23.000*70, 24.000*70, 25.000*70, 26.000*70,
27.000*70, 28.000*70, 29.000*70, 30.000*70, 31.000*70, 32.000*70, 33.000*70,
34.000*70, 35.000*70, 36.000*70]
amplitude= [26, 31, 29, 26, 27, 24, 24, 28, 24, 24, 28, 31, 24, 26, 55, 30, 73, 101, 168, 219, 448, 833, 1280, 1397, 1181, 1311,
1715, 1975, 2003, 2034, 2178, 2180, 2182]
plt.plot(distance,amplitude, 'o')
popt, pcov = curve_fit(logistic, distance, amplitude,maxfev=100, bounds=((100, 10, 0), (200000, 200000, 200000)),p0=[2700, 3000, 1200])
print(popt)
plt.plot(distance, logistic(distance, *popt), 'r', label='logistic fit')
plt.show()
``````

there are few points on your code which had to be fixed.

1. distance and amplititle are not same size
2. both are list however inside you logistics definition you treat them as numpy array which you do vector operations
3. bounds value for k is wrong, it should be below 1 but you set minimum value to be 10, which make optimizing difficult
4. logistic definition you could add some safety measure for stability

from scipy.optimize import curve_fit

``````from scipy.optimize import curve_fit
def logistic(x, l, k, x1):
e =  -k * (x - x1)
e[ e > 30 ] = 30
return l / (1 + np.exp(e))

distance= [1.000*70, 2.000*70, 3.000*70, 4.000*70, 5.000*70, 6.000*70, 7.000*70, 8.000*70,
9.000*70, 11.00*70, 12.000*70, 13.000*70, 14.000*70, 15.000*70, 16.000*70,
17.000*70, 18.000*70, 19.000*70, 21.000*70, 22.000*70, 23.000*70, 24.000*70, 25.000*70, 26.000*70,
27.000*70, 28.000*70, 29.000*70, 30.000*70, 31.000*70, 32.000*70, 33.000*70,
34.000*70, 35.000*70, 36.000*70]
amplitude= [26, 31, 29, 26, 27, 24, 24, 28, 24, 24, 28, 31, 24, 26, 55, 30, 73, 101, 168, 219, 448, 833, 1280, 1397, 1181, 1311,
1715, 1975, 2003, 2034, 2178, 2180, 2182]
plt.plot(distance[1:], amplitude, 'o')
popt, pcov = curve_fit(logistic, np.array(distance[1:]), np.array(amplitude[:]),maxfev=100, bounds=((100, 0, 0), (3000, 1, 200000)),p0=[2700, 0.1, 1200])

print(popt)
plt.plot(distance, logistic(distance, *popt), 'r', label='logistic fit')

plt.show()
``````

output:

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