fit

nbi_stat.fit(f, *args, **kwargs)

Unified interface for curve fitting

This function provides a unified interface for fitting functions to data. Exactly which kind of fit is used depends on the data passed.

• If the function we provide is of the form

   f(x,p) = sum_i^M p_i f_i(x)

  given as the sequence (f_1,…,f_M), we perform a linear curve fitting,

where the following arguments are

  - The independent variable x   - The dependent variable y   - Optionally, the uncertainties delta

• Otherwise, if the number of the following arguments is less than 3, then we perform an MLE curve fitting, where the arguments are

  - The observations x, or bin boundaries and counts (b,x)   - The initial values p_0 of the parameters

For binnned MLE, the bin boundaries must be one larger than the counts. Counts are either raw counts (density=False), number density (dN/dx, density=1), or normalized number density (1/N dN/dx, density=N - possibly a float)

• If none of the above conditions are met, we perform a least-squares curve fit with the

  subsequent arguments

  - The independent variable x   - The dependent variable y   - The initial values p_0 of the parameters   - Optionally, the uncertainties delta_y   - Optionally, the uncertainties delta_x

Other arguments or keyword arguments are passed to the underlying functions.

Parameters:

• f (callable or sequence of callables) – Function to fit to data

• args (tuple) – Further arguments

• kwargs (dict) – Keyword arguments

See also

mle_fit, lin_fit, lsq_fit, plot_fit, plot_fit_func, plot_fit_table, chi2nu, residuals