Fit Steinhart-Hart Equation to Measurements

#!/usr/bin/python
import math
import scipy.optimize
import numpy

# Steinhart-Hart Equation
# from https://en.wikipedia.org/wiki/Steinhart%E2%80%93Hart_equation

def steinhart_hart(r,a,b,c) :
    ''' Steinhart-Hart Equation, as found on Wikipedia.
    r : resistance of a thermistor, measured in Ohms
    a,b,c : steinhart-hart coefficients [a] = 1/K

    returns : temperature in degrees celsius!
    '''

    one_over_kelvins = a + b*numpy.log(r) + c*numpy.log(r)**3
    return 1.0/one_over_kelvins - 273.15

def main():
    exp_data = numpy.array([
        [  1.9, 627.0e3 ], # Ice water
        [ 19.8, 255.1e3 ], # Room Temperature water
        [ 65.9,  37.0e3 ]  # Hot water
    ])

    temps = exp_data[:,0]
    resist = exp_data[:,1]

    # give a ballpark estimate to start from, minimization might not
    # converge if we start off too far away...
    p0 = [0.1e-3, 0.1e-4, 0.1e-7]

    print('Experimental Data:')
    print('Temperatures [degC]:', temps)
    print('Resistances  [Ohm] :', resist)

    par, cov = scipy.optimize.curve_fit(steinhart_hart, resist, temps, p0)

    print('A=',par[0])
    print('B=',par[1])
    print('C=',par[2])

    temps2 = steinhart_hart(resist, *par)

    print('Can we reproduce the measured temperatures?')
    print('Temperatures [degC]:', temps2)

if __name__ == '__main__' :
    main()