Say I want to fit a sine function using `scipy.optimize.curve_fit`

. I don't know any parameters of the function. To get the frequency, I do Fourier transform and guess all the other parameters - amplitude, phase, and offset. When running my program, I do get a fit but it does not make sense. What is the problem? Any help will be appreciated.

```
import numpy as np
import matplotlib.pyplot as plt
import scipy as sp
ampl = 1
freq = 24.5
phase = np.pi/2
offset = 0.05
t = np.arange(0,10,0.001)
func = np.sin(2*np.pi*t*freq + phase) + offset
fastfft = np.fft.fft(func)
freq_array = np.fft.fftfreq(len(t),t[0]-t[1])
max_value_index = np.argmax(abs(fastfft))
frequency = abs(freq_array[max_value_index])
def fit(a, f, p, o, t):
return a * np.sin(2*np.pi*t*f + p) + o
guess = (0.9, frequency, np.pi/4, 0.1)
params, fit = sp.optimize.curve_fit(fit, t, func, p0=guess)
a, f, p, o = params
fitfunc = lambda t: a * np.sin(2*np.pi*t*f + p) + o
plt.plot(t, func, 'r-', t, fitfunc(t), 'b-')
```

The main problem in your program was a misunderstanding, how

`scipy.optimize.curve_fit`

is designed and its assumption of the fit function:This means that the fit function has to have the array for the x values as the first parameter followed by the function parameters in no particular order and must return an array for the y values. Here is an example, how to do this:

As you can see, I have also changed the way the fit function for the plot is calculated. You don't need another function - just utilise the fit function with the parameter list, the fit procedure gives you back.

The other problem was that you called the covariance array

`fit`

- overwriting the previously defined function`fit`

. I fixed that as well.P.S.: Of course now you only see one curve, because the perfect fit covers your data points.