scipy curve_fit fails on exponential fit

Question

When I try to do an exponential fit using curve_fit, scipy returns an error. Am I doing something wrong? Removing the negative sign from np.exp(-b * t) allows curve_fit to work, but the values it returns are way off. ``` #!/usr/bin/python import numpy as np import scipy as sp from scipy.optimize import curve_fit import scipy.optimize as opt import matplotlib.pyplot as plt x = [40,45,50,55,60] y = [0.99358851674641158, 0.79779904306220106, 0.60200956937799055, 0.49521531100478472, 0.38842105263157894] def model_func(t, a, b, c): return a * np.exp(-b * t) + c opt_parms, parm_cov = sp.optimize.curve_fit(model_func, x, y, maxfev=1000) a,b,c = opt_parms print a,b,c print x print y print model_func(x, a,b,c) ``` Fails with error: ``` Traceback (most recent call last): File "asdf.py", line 18, in <module> opt_parms, parm_cov = sp.optimize.curve_fit(model_func, x, y, maxfev=1000) File "/usr/lib/python2.7/dist-packages/scipy/optimize/minpack.py", line 426, in curve_fit res = leastsq(func, p0,…

Accepted Answer

change `x` and `y` to numpy arrays

```
x = np.array([40,45,50,55,60])
y = np.array([0.99358851674641158, 0.79779904306220106, 0.60200956937799055, 0.49521531100478472, 0.38842105263157894])
```

then I think you are good, because the function requires vectorized computation, whereas lists are not adequate.

scipy curve_fit fails on exponential fit

Solution

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