def construct_multi_fit(shared_x_error):
fit_1 = XYFit(xy_data=[U, T], model_function=empirical_T_U_model)
fit_1.add_error(axis='y', err_val=sigT) # declare errors on T
fit_2 = XYFit(xy_data=[U, I], model_function=I_U_model)
fit_2.add_error(axis='y', err_val=sigI) # declare errors on I
multi_fit = MultiFit(fit_list=[fit_1, fit_2], minimizer='iminuit')
if shared_x_error:
multi_fit.add_error(axis='x', err_val=sigU, fits='all')
else:
fit_1.add_error(axis='x', err_val=sigU)
fit_2.add_error(axis='x', err_val=sigU)
return multi_fit
T -= T0 # Measurements are in Kelvin, convert to °C
# -- Finally, go through the fitting procedure
# Step 1: construct the singular fit objects
fit_1 = XYFit(xy_data=[U, T], model_function=empirical_T_U_model)
fit_1.add_error(axis='y', err_val=sigT) # declare errors on T
fit_2 = XYFit(xy_data=[U, I], model_function=I_U_model)
fit_2.add_error(axis='y', err_val=sigI) # declare errors on I
# Step 2: construct a MultiFit object
multi_fit = MultiFit(fit_list=[fit_1, fit_2], minimizer='iminuit')
# Step 3: Add a shared error error for the x axis.
multi_fit.add_error(axis='x', err_val=sigU, fits='all')
# (Optional): assign names for models and parameters
multi_fit.assign_parameter_latex_names(x='U',
p2='p_2',
p1='p_1',
p0='p_0',
R0='R_0',
alph=r'\alpha_\mathrm{T}')
multi_fit.assign_model_function_expression('{1}*{x}^2 + {2}*{x} + {3}',
fit_index=0)
multi_fit.assign_model_function_latex_expression(
r'{1}\,{x}^2 + {2}\,{x} + {3}', fit_index=0)
multi_fit.assign_model_function_expression(
'{x} / ({1} * (1 + ({3}*{x}^2 + {4}*{x} + {5}) * {2}))', fit_index=1)