print("rec.A =", rec.A)
print("rec.A.value =", rec.A.value)
# The names of the declared variables are stored in a rec.names
# and the corresponding values in rec.values.
print("rec.values =", rec.values)
print("rec.names =", rec.names)
# Finally the recipe objects provides a residual() function to calculate
# the difference between the observed and simulated values. The residual
# function can accept a list of new variable values in the same order as
# rec.names.
print("rec.residual() =", rec.residual())
print("rec.residual([2, 4]) =", rec.residual([2, 4]))
# <demo> --- stop ---
# The FitRecipe.residual function can be directly used with the scipy
# leastsq function for minimizing a sum of squares.
from scipy.optimize import leastsq
leastsq(rec.residual, rec.values)
# Recipe variables and the linked line-function parameters are set to the
# new optimized values.
print(rec.names, "-->", rec.values)
linefit.show()
# print("recipe.A =", recipe.A)
# print("recipe.A.value =", recipe.A.value)
# The names of the declared variables are stored in a rec.names
# and the corresponding values in rec.values.
# print("recipe.values =", recipe.values)
# print("recipe.names =", recipe.names)
# Finally the recipe objects provides a residual() function to calculate
# the difference between the observed and simulated values. The residual
# function can accept a list of new variable values in the same order as
# rec.names.
print("recipe.residual() =", recipe.residual())
print("recipe.residual([290000, 51, 29, 500, 71, 3]) =",
recipe.residual([290000, 51, 29, 500, 68, 3]))
# <demo> --- stop ---
# The FitRecipe.residual function can be directly used with the scipy
# leastsq function for minimizing a sum of squares.
from scipy.optimize import leastsq
leastsq(recipe.residual, recipe.values)
# Recipe variables and the linked line-function parameters are set to the
# new optimized values.
print(recipe.names, "-->", recipe.values)
print("rec.A =", rec.A)
print("rec.A.value =", rec.A.value)
# The names of the declared variables are stored in a rec.names
# and the corresponding values in rec.values.
print("rec.values =", rec.values)
print("rec.names =", rec.names)
# Finally the recipe objects provides a residual() function to calculate
# the difference between the observed and simulated values. The residual
# function can accept a list of new variable values in the same order as
# rec.names.
print("rec.residual() =", rec.residual())
print("rec.residual([2, 4]) =", rec.residual([2, 4]))
# <demo> --- stop ---
# The FitRecipe.residual function can be directly used with the scipy
# leastsq function for minimizing a sum of squares.
from scipy.optimize import leastsq
leastsq(rec.residual, rec.values)
# Recipe variables and the linked line-function parameters are set to the
# new optimized values.
print(rec.names, "-->", rec.values)
linefit.show()
print("recipe.A =", recipe.A)
print("recipe.A.value =", recipe.A.value)
# The names of the declared variables are stored in a rec.names
# and the corresponding values in rec.values.
print("recipe.values =", recipe.values)
print("recipe.names =", recipe.names)
# Finally the recipe objects provides a residual() function to calculate
# the difference between the observed and simulated values. The residual
# function can accept a list of new variable values in the same order as
# rec.names.
print("recipe.residual() =", recipe.residual())
print("recipe.residual([290000, 51, 29]) =", recipe.residual([290000, 51, 29]))
# <demo> --- stop ---
# The FitRecipe.residual function can be directly used with the scipy
# leastsq function for minimizing a sum of squares.
from scipy.optimize import leastsq
leastsq(recipe.residual, recipe.values)
# Recipe variables and the linked line-function parameters are set to the
# new optimized values.
print(recipe.names, "-->", recipe.values)
large_gaussian.show()
