data_values = initialize_data_values()
# do the curve fitting
for key in sorted(data):
imin = 9
imax = len(T)
# fit the curve
A, _ = opt.curve_fit(fitting_curve,
T[imin:imax],
data[key][imin:imax],
maxfev=1000000,
p0=(0., 1., 100., 1., 0.01, 0.01, 0.),
jac=jacobian_fitting_curve)
A = round_parameters(*A)
# compute the xi2 difference between the data values (in the fitting
# interval) and the curve
xi2 = sum((data[key][imin:imax] - fitting_curve(T[imin:imax], *A))**2)
# output some info
print "Transition:", key
print_fit_variables(*A)
print "convergence:", xi2
print "validity: [", T[imin], ",", T[imax - 1], "]"
# write the fitting code for this transition
code += get_code("OII", transitions[key], *A)
# add the values to the list strings
append_data_values(data_values, *A)
# plot the data and fit for visual comparison
Trange = np.logspace(3., 5., 100)
pl.plot(T, data[key], "k.")
pl.plot(Trange, fitting_curve(Trange, *A), "r-")
pl.xlim(0., 1.e5)
imin = 0
imax = len(T)
# fit the curve
A, _ = opt.curve_fit(
fitting_curve,
T[imin:imax],
data[imin:imax],
maxfev=1000000,
method="trf",
p0=(0.0, 1.0, 1.0, 1.0, 0.01, 0.01, 0.0),
jac=jacobian_fitting_curve,
)
A = round_parameters(*A)
# compute the xi2 difference between the data values (in the fitting
# interval) and the curve
xi2 = sum((data[imin:imax] - fitting_curve(T[imin:imax], *A)) ** 2)
# output some info
print("Transition: 0 to 1")
print_fit_variables(*A)
print("convergence:", xi2)
print("validity: [", T[imin], ",", T[imax - 1], "]")
# write the fitting code for this transition
code += get_code("SIV", "REMOVE_THIS_BLOCK", *A)
# add the values to the list strings
append_data_values(data_values, *A)
# plot the data and fit for visual comparison
Trange = np.logspace(3.0, 5.0, 100)
pl.plot(T, data, "k.")
pl.plot(Trange, fitting_curve(Trange, *A), "r-")
pl.xlim(0.0, 1.0e5)