Christian Kristukat wrote:

basvandijk@home.nl wrote:

...

Hello,

I would like to integrate some data. The data has a somewhat similar form as the picture of the peak in [1]. I think a good function describing the data is:

The peak in [1] actually is a gaussian, the formula is given below the graph:

amp * exp(-(x-center)**2/(2*width**2))

...

I don't know much about linear algebra but I've read I can use a non linear least square fitting method to fit the data. Maybe I can also use others? My question is can I do this with Scipy and if so how?

with scipy you can do a leastsq fit like this assuming x and y are arrays with your data:

from scipy import optimize

def residuals(amp,center,width): return y-(amp * exp(-(x-center)**2/(2*width**2)))

guess = [2.0, 4.5, 0.2] # the inital guess for the parameters

out = optimize.leastsq(residuals, guess)

solution = out[0]

print solution

You can also use a general purpose minimizer in optimize and calculate the residual error (least squares error) by yourself (actually that is what leastsq() does for you). Assuming you have x and y in the appropriate namespace): from scipy import optimize from numpy import dot, sqrt def err(params): amp = params[0] center = params[1] width = params[2] r = residuals(amp, center, width) return sqrt(dot(r,r)) You may also skip the sqrt(). out = optimize.fmin_powell(err, guess) -- Random number generation is the art of producing pure gibberish as quickly as possible.