I would like to fit a sinc function to a bunch of datalines. Using a gauss the fit itself does work but the data does not seem to be sufficiently gaussian, so I figured I could just switch to sinc..
I just tried to put together a short piece of self running code but realized, that I probably do not fully understand, how arrays are handled if handed over to a function, which could be part of the reason, why I get error messages calling my program
So my code currently looks as follows:
from numpy import exp
from scipy.optimize import curve_fit
from math import sin, pi
def gauss(x,*p):
print(p)
A, mu, sigma = p
return A*exp(-1*(x[:]-mu)*(x[:]-mu)/sigma/sigma)
def sincSquare_mod(x,*p):
A, mu, sigma = p
return A * (sin(pi*(x[:]-mu)*sigma) / (pi*(x[:]-mu)*sigma))**2
p0 = [1., 30., 5.]
xpos = range(100)
fitdata = gauss(xpos,p0)
p1, var_matrix = curve_fit(sincSquare_mod, xpos, fitdata, p0)
What I get is:
Traceback (most recent call last):
File "orthogonal_fit_test.py", line 18, in <module>
fitdata = gauss(xpos,p0)
File "orthogonal_fit_test.py", line 7, in gauss
A, mu, sigma = p
ValueError: need more than 1 value to unpack
From my understanding p is not handed over correctly, which is odd, because it is in my actual code. I then get a similar message from the sincSquare function, when fitted, which could probably be the same type of error. I am fairly new to the star operator, so there might be a glitch hidden...
Anybody some ideas? :)
Thanks!
You need to make three changes,
def gauss(x, A, mu, sigma):
return A*exp(-1*(x[:]-mu)*(x[:]-mu)/sigma/sigma)
def sincSquare_mod(x, A, mu, sigma):
x=np.array(x)
return A * (np.sin(pi*(x[:]-mu)*sigma) / (pi*(x[:]-mu)*sigma))**2
fitdata = gauss(xpos,*p0)
1, See Documentation
2, replace sin by the numpy version for array broadcasting
3, straight forward right? :P
Note, i think you are looking for p1, var_matrix = curve_fit(gauss,... rather than the one in the OP, which appears do not have a solution.