Python fit and chi square comparison

I want to know which functions fits better to my data by calculating the chi square value and then compare it.

The data i have could like either like a gaussian or kind of like an horizontal line, so my goal would be to fit each time my data (using scipy.curve fit) to both functions and then calculate the chisquare value (using scipy.stats), but can i directly compare both values and take the 'lowest' one as both functions have the same number of points but different parameters (hence not the same df): 4 parameters for my gaussian and 1 for my horizontal line ?

def horiz(x,c):
    return np.full_like(x, c)

#Define the Gaussian function
def gauss(x, H, A, x0, sigma):
    return H + A * np.exp(-(x - x0) ** 2 / (2 * sigma ** 2))

example of code below:

p0_c = [moy]
popt_c, pcov = curve_fit(horiz, x, Smooth_data, p0 = p0_c)
fit_c = horiz(x, *popt_c)
chi_c, p_c = stats.chisquare(Counts_list[i], fit_c )

end.

You could use something like the Bayesian Information Criterion (BIC) for model selection, which accounts for the different numbers of parameters in each model with a penalty factor for more parameters.

Mocking up a version of your example, this could be done with the following steps.

1. Set up model functions and generate some simulated data containing a Gaussian "bump".

   import numpy as np
   from matplotlib import pyplot as plt
   from scipy.optimize import curve_fit
   
   def gauss(x, H, A, x0, sigma):
       return H + A * np.exp(-(x - x0) ** 2 / (2 * sigma ** 2))
   
   def horiz(x,c):
       return np.full_like(x, c)
   
   # generate some data containing a Gaussian bump
   n = 10_000  # number of data points
   x = np.linspace(0, 1_000_000, n, dtype=float)
   
   # random Gaussian noise
   y = np.random.randn(n) * 5e-8
   
   # Gaussian bump parameters
   sigma = 30_000
   x0 = 400_000
   H = 0
   A = 4.5e-7
   
   y += gauss(x, H, A, x0, sigma)
   

2. Use curve_fit to get a best fit, maximum likelihood, model (I've had to scale the parameters to get the fitting to work reasonably).

   # scale factors
   scaleamp = 1e8
   scalerange = 1e-5
   
   # initial parameter guesses
   iniguess = (0.1e-7 * scaleamp, 1e-7 * scaleamp, 350000.0 * scalerange, 10000.0 * scalerange)
   
   # fit Gaussian bump model
   gaussfit = curve_fit(gauss, x * scalerange, y * scaleamp, p0=iniguess)
   
   # fit horizontal line model
   linefit = curve_fit(horiz, x, y * scaleamp, p0=(0.0,))
   

3. Generate the best fit models and calculate the (log)likelihood for that model given the data (for the likelihood function, because we don't have an estimate of the uncertainty in the data, we can use the Student's t-like distribution defined in Eqn 3.38 in Sivia on pg. 53).

   Hf, Af, x0f, sigmaf = gaussfit[0]
   
   # rescale best fit parameters to generate the Gaussian model
   bestgaussfit = gauss(x, Hf / scaleamp, Af / scaleamp, x0f / scalerange, sigmaf / scalerange)
   
   # get log likelihood
   logLgauss = -((n - 1) / 2) * np.log(np.sum((y - bestgaussfit)**2))
   
   # generate the best fit horizontal line model
   bestlinefit = horiz(x, linefit[0][0] / scaleamp)
   
   # get log likelihood
   logLhoriz = -((n - 1) / 2) * np.log(np.sum((y - bestlinefit)**2))
   

4. Generate the BIC for both models.

   ngaussparams = 4  # number of Gaussian model parameters
   bicgauss = ngaussparams * np.log(n) - 2 * logLgauss
   
   nlineparams = 1  # number of parameters for the flat line
   bicline = nlineparams * np.log(n) - 2 * logLhoriz
   

With the BIC, the model with the lower number is the preferred model. In this case the BIC for the Gaussian model is a lot lower (by about 16000 for the data I generated).