This is a simple script which tries to find the global minima using scipy.optimize.curve_fit as well as a parameter search over the parameter space. It first generates ntol random models, then selects ntol*returnnfactor best models and does scipy.optimize.curve_fit on all of them. It then returns the best model of them all. This script improves scipy.optimize.curve_fit in two ways - No need to give initial values and thus getting global minima instead of local minima. And second, it automatically normalize and standardizes the data. Has an option to run it parllelly

FUTURE:

1. Use Genetic algorithm instead of brute-force

USAGE EXAMPLE:

import brute_curvefit as bf

import numpy as np

import matplotlib.pyplot as plt

def yourfunc(x, A,B,C):

return (x-A)/B +C

params, error = bf.brute_scifit(func=yourfunc, x=np.arange(100), y=np.arange(100)**2, restrict=[[-1000,-1000,-1000],[1000,1000,1000]], ntol=100, returnnfacto=0.01, maxfev=1000, printerrors=True, parallel=False, savetofile='results.pkl')

plt.plot(np.arange(100), np.arange(100)**2) #plotting data

plt.plot(np.arange(100), yourfunc(np.arange(100), *params)) #plotting the fitted function

plt.show()

#As simple as that