I was wondering if there is a way I can use LMfit to fit datasets that have different sizes with the same model with a some shared and some independent parameters.
This link is the closest I found to a similar question but it assumes the same x for all y.
Thanks for all advice and input
Per your comment that a scipy example is OK:
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
y1 = np.array([ 16.00, 18.42, 20.84, 23.26])
y2 = np.array([-20.00, -25.50, -31.00, -36.50, -42.00])
comboY = np.append(y1, y2)
x1 = np.array([5.0, 6.1, 7.2, 8.3])
x2 = np.array([15.0, 16.1, 17.2, 18.3, 19.4])
comboX = np.append(x1, x2)
if len(y1) != len(x1):
raise(Exception('Unequal x1 and y1 data length'))
if len(y2) != len(x2):
raise(Exception('Unequal x2 and y2 data length'))
def function1(data, a, b, c): # not all parameters are used here, c is shared
return a * data + c
def function2(data, a, b, c): # not all parameters are used here, c is shared
return b * data + c
def combinedFunction(comboData, a, b, c):
# single data reference passed in, extract separate data
extract1 = comboData[:len(x1)] # first data
extract2 = comboData[len(x1):] # second data
result1 = function1(extract1, a, b, c)
result2 = function2(extract2, a, b, c)
return np.append(result1, result2)
# some initial parameter values
initialParameters = np.array([1.0, 1.0, 1.0])
# curve fit the combined data to the combined function
fittedParameters, pcov = curve_fit(combinedFunction, comboX, comboY, initialParameters)
# values for display of fitted function
a, b, c = fittedParameters
y_fit_1 = function1(x1, a, b, c) # first data set, first equation
y_fit_2 = function2(x2, a, b, c) # second data set, second equation
plt.plot(comboX, comboY, 'D') # plot the raw data
plt.plot(x1, y_fit_1) # plot the equation using the fitted parameters
plt.plot(x2, y_fit_2) # plot the equation using the fitted parameters
plt.show()
print('a, b, c:', fittedParameters)