# >>> This is where the RADIS calculation is done!
s = sf.non_eq_spectrum((T12, T12, T3),
Trot,
Ttrans=Trot,
vib_distribution='treanor',
name='treanor. fit')
# <<<
# ... output should be a Spectrum object
return s
# Calculate initial Spectrum, by showing all steps.
sf.verbose = 3 # increase verbose level for more details.
theoretical_model(model_input).plot('transmittance_noslit',
nfig='Initial spectrum')
sf.verbose = 0 # reduce verbose during calculation.
# %% Leastsq version
# %%
# User Params
# -----------
#T0 = 1000
fit_params = ['T12', 'T3', 'Trot']
bounds = np.array([[300, 2000], [300, 5000], [300, 2000]])
fit_units = ['K', 'K', 'K']
fit_variable = 'transmittance_noslit'
#ax.set_ylim((bounds[0]))
fit_values_min, fit_values_max = bounds.T
res0 = log_cost_function(fit_values_min)
res1 = log_cost_function(fit_values_max, plot=figSpec.get_label())
lineRes, = axRes.plot((1, 2), (res0, res1), '-ko')
lineLast, = axRes.plot(2, res0, 'or') # last iteration in red
lineValues = {}
for i, k in enumerate(fit_params):
lineValues[k] = axValues.plot((1, 2), (fit_values_min[i], fit_values_max[i]), '-', label=k)[0]
axRes.set_xlim((0, maxiter))
axRes.set_ylim(ymin=0)
axRes.set_xlabel('Iteration')
axRes.set_ylabel('Residual')
figRes.legend()
sf.verbose = False
sf.warnings['NegativeEnergiesWarning'] = 'ignore'
ite = 2
plot_every = 1 # plot spectra every # iterations
def cost_and_plot_function(fit_values):
''' Return error on Spectrum s vs experimental spectrum '''
global ite
ite += 1
# Plot one spectrum every 10 ites
plot = None
if not ite % plot_every:
plot=figSpec.get_label()
res = log_cost_function(fit_values, plot=plot)
