plt.title('Line simulated at A=3, B=5')
# <demo> --- stop ---
# We want to find optimum model parameters that fit the simulated curve
# to the observations. This is done by associating FitContribution with
# a FitRecipe object. FitRecipe can manage multiple fit contributions and
# optimize all models to fit their respective profiles.
from diffpy.srfit.fitbase import FitRecipe
rec = FitRecipe()
# clearFitHooks suppresses printout of iteration number
rec.clearFitHooks()
rec.addContribution(linefit)
rec.show()
# <demo> --- stop ---
# FitContributions may have many parameters. We need to tell the recipe
# which of them should be tuned by the fit.
rec.addVar(rec.linefit.A)
rec.addVar(rec.linefit.B)
# The addVar function created two attributes A, B for the rec object
# which link ot the A and B parameters of the linefit contribution.
print("rec.A =", rec.A)
print("rec.A.value =", rec.A.value)
plt.title('Line simulated at A=3, B=5')
# <demo> --- stop ---
# We want to find optimum model parameters that fit the simulated curve
# to the observations. This is done by associating FitContribution with
# a FitRecipe object. FitRecipe can manage multiple fit contributions and
# optimize all models to fit their respective profiles.
from diffpy.srfit.fitbase import FitRecipe
rec = FitRecipe()
# clearFitHooks suppresses printout of iteration number
rec.clearFitHooks()
rec.addContribution(linefit)
rec.show()
# <demo> --- stop ---
# FitContributions may have many parameters. We need to tell the recipe
# which of them should be tuned by the fit.
rec.addVar(rec.linefit.A)
rec.addVar(rec.linefit.B)
# The addVar function created two attributes A, B for the rec object
# which link ot the A and B parameters of the linefit contribution.
print("rec.A =", rec.A)
print("rec.A.value =", rec.A.value)
# We want to find optimum model parameters that fit the simulated curve # to the observations. This is done by associating FitContribution with # a FitRecipe object. FitRecipe can manage multiple fit contributions and # optimize all models to fit their respective profiles. from diffpy.srfit.fitbase import FitRecipe recipe = FitRecipe() # clearFitHooks suppresses printout of iteration number recipe.clearFitHooks() recipe.addContribution(large_gaussian) recipe.addContribution(small_gaussian) recipe.show() # <demo> --- stop --- # FitContributions may have many parameters. We need to tell the recipe # which of them should be tuned by the fit. recipe.addVar(large_gaussian.lgA) recipe.addVar(large_gaussian.lgx0) recipe.addVar(large_gaussian.lgsig) recipe.addVar(small_gaussian.sgA) recipe.addVar(small_gaussian.sgx0) recipe.addVar(small_gaussian.sgsig) # The addVar function created two attributes A, B for the rec object
