python - How to fit datasets so that they share some (but not all) parameter values -


say want fit 2 arrays x_data_one , y_data_one exponential function. in order might use following code (in x_data_one , y_data_one given dummy definitions):

import numpy np scipy.optimize import curve_fit  def power_law(x, a, b, c):     return * (x + c) ** b  x_data_one = np.random.rand(10) y_data_one = np.random.rand(10)  (a_one, b_one, c_one), _ = curve_fit(power_law, x_data_one, y_data_one)

now suppose want fit second dataset:

x_data_two = np.random.rand(10) y_data_two = np.random.rand(10)  (a_two, b_two, c_two), _ = curve_fit(power_law, x_data_two, y_data_two)

how perform these 2 fits such constrained have a_one == a_two , b_one == b_two, not c_one == c_two? don't want constrain a_one or b_one particular value; want find values provide best fit both datasets.

you overwrite function second data set:

def power_law2(x, c):     return a_one * (x + c) ** b_one  x_data_two = np.random.rand(10) y_data_two = np.random.rand(10)  c_two = curve_fit(power_law2, x_data_two, y_data_two)[0][0]

or use (it finds optimal a,b data , optimal c1 data_one , c2 data_two):

def power_law(x, a, b, c1, c2):     l = len(x_data_one)     return * np.hstack([x[:l] + c1, x[l:] + c2]) ** b  x_data_one_two = np.hstack([x_data_one,x_data_two]) y_data_one_two = np.hstack([y_data_one,y_data_two])  (a_one, b_one, c_one, c_two), _ = curve_fit(power_law, x_data_one_two, y_data_one_two)

in opinion second code nicer , pythonic :)


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