xy_d2.axis_labels = ['x', 'y(2) & f(x)']
# 3. create the Fit objects
xyFit1 = Fit(xy_d1, model1)
xyFit2 = Fit(xy_d2, model2)
# set meaningful names for model
xyFit1.model_label = 'Lineares Modell'
xyFit2.model_label = 'Lineares Modell'
# add the parameter constraints
xyFit1.add_parameter_constraint(name='g1', value=c1, uncertainty=ec1)
xyFit2.add_parameter_constraint(name='g2', value=c2, uncertainty=ec2)
# combine the two fit objects to form a MultiFit
multiFit = MultiFit(fit_list=[xyFit1, xyFit2])
# 4. perform the fit
multiFit.do_fit()
# 5. report fit results
multiFit.report()
# 6. create and draw plots
multiPlot = Plot(multiFit)
##multiPlot = Plot(multiFit, separate_figures=True)
multiPlot.plot(figsize=(13., 7.))
# 7. show or save plots #
##for i, fig in enumerate(multiPlot.figures):
## fig.savefig("MultiFit-"+str(i)+".pdf")
plt.show()
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)
multi_fit.assign_model_function_latex_expression(
r'\frac{{{x}}}{{{1} \cdot (1 + ({3}{x}^2 + {4}{x} + {5}) \cdot {2})}}',
fit_index=1)
# Step 4: do the fit
multi_fit.do_fit()
# (Optional): print the results
multi_fit.report()
# (Optional): plot the results
plot = Plot(multi_fit, separate_figures=True)
plot.plot()
plt.show()