Multi-gaussian function

[Laplace12]

Hey! I need help developing a code for a multi-gaussian function. The point would be to create a function that uses the number of gaussian requested by the user to make the final fitting function. All parameters are passed as *params and number of gaussians is deduced from the number of items in *params (1 + n*3). However, I am very new to coding and was left with a base to work with, only I am confused of what to do next.

This is the code so far:

def multi_gaussian(x, *params):
    y0 = params[0]
    n_gauss = ...
    y = np.ones_like()
    for i in range(n_gauss):
        parameters = ...
        y += gaussian(x, parameters)
    return y

I understand that n_gauss should be extracted from the length of *params, but I am unsure of how the parameters should be defined: in the file that I use the function in, or in this same code, such as here

 def two_gaussian(x, *param):
    y0, A1, w1, xc1, A2, w2, xc2 = param
    y = y0 + (A1/(w1*np.sqrt(np.pi/2)))*np.exp(-2*np.power((x-xc1)/w1, 2.))
    + (A2/(w2*np.sqrt(np.pi/2)))*np.exp(-2*np.power((x-xc2)/w2, 2.))
    return y

y should be a constant background function, but I am unsure of how extracting parameters works. Any kind of help is appreciated!

[scidam]

Within multi_gaussian the params variable will be interpreted as a tuple of size 3 * n + 1. So, the answer to your question would be something like the following:

def multi_gaussian(x, *params):
    y0 = params[0]
    n_gauss = (len(params) - 1) // 3    
    y = y0 * np.ones_like(x)  # ??? 
    for i in range(n_gauss):
        parameters = params[1+3*i:1+3*i + 3]
        y += gaussian(x, *parameters)
    return y

However, you probably need to check if len(params) is correct (e.g. len(params) - 1 is divisible by 3).

[Laplace12]

Hey, thank you for the help! Now that I'm trying to run the code, there seems to be an error in the function I've defined as gaussian

def gaussian(x, *param):
    y0, A1, w1, xc1 = param
    y = y0 + (A1/(w1*np.sqrt(np.pi/2)))*np.exp(-2*np.power((x-xc1)/w1, 2.))
    return y

It returns an error at the y0, A1, w1, xc1 = param line:

ValueError: not enough values to unpack (expected 4, got 3)

But to my understanding each value has been used in a function, and in higher gaussian fittings (two etc.) this seems to work perfectly fine.

[scidam]

The "parameters" variable is expected to have len = 4 (i.e. the first element is always y0);
So, you need to change the line of code where the "parameters" variable is defined:

parameters = [y0] + params[1 + 3 * i:1 + 3 * i + 3]

[Laplace12]

Changed the code, now there's another error:

parameters = [y0] +  params[1 + 3 * i:1 + 3 * i + 3]

TypeError: can only concatenate list (not "tuple") to list

[scidam]

Changed the code, now there's another error:

Not an error actually, I just forgot that params is a tuple, try instead parameters = (y0, ) + params[1 + 3 * i:1 + 3 * i + 3]