How to fit multiple gaussians on one plot?

Question

I'm interested in fitting multiple Gaussian curves to the plot below in python. I need to be able to determine the mean of each gaussian to be able to estimate what 1 photoelectron corresponds to for a signal reading device that took this data. I need to know how to do this for an undetermined amount of peaks as each dataset might contain fewer / more photoelectron peaks. Any help would be appreciated!

Looked into gaussian mixtures, but couldn't find how to extract the individual Gaussians that fit the overall curve.

Answer 1

I suppose you're using Gaussian Mixture Model from sklearn.

In that case from the docs

import numpy as np
from sklearn.mixture import GaussianMixture
X = np.array([[1, 2], [1, 4], [1, 0], [10, 2], [10, 4], [10, 0]])
gm = GaussianMixture(n_components=2, random_state=0).fit(X)

The attributes gm.means_ are the means of each mixture component.

And gm.covariances_ are the covariance (or variance for 1D) of each mixture component.

With those (using a for) you can easily plot each component using, for example, something like bellow for the first component:

from scipy.stats import norm
from matplotlib import pyplot as plt 
import numpy as np

x = np.linspace(...) # your x space sampled
p = norm.pdf(x, gm.means_[0], gm.covariances_[0])
plt.plot(x, p)

And you can even sum them up, as you wish, to make a combined plot of their pdf's.