plt.cla()
fit.run()
fit.result[0].plot_fit()
plt.savefig('debug_fit.png')
# TODO: implement properly
plt.cla()
fig = plt.figure()
ax = fig.add_subplot(111)
fit.result[0].fit.plot_butterfly(ax=ax, label='Fit result')
input_parameters = dict(index = 2.3 * u.Unit(''),
norm = 2.5 * 1e-12 * u.Unit('cm-2 s-1 TeV-1'),
reference = 1 * u.TeV)
input_parameter_errors = dict(index = 0 * u.TeV,
norm = 0 * u.Unit('cm-2 s-1 TeV-1'),
reference = 0 * u.TeV)
input_model = SpectrumFitResult(spectral_model = 'PowerLaw',
parameters = input_parameters,
parameter_errors = input_parameter_errors)
input_model.plot(ax=ax, label='Input model', energy_range = [0.1, 80] * u.TeV)
ax.legend(numpoints=1)
plt.savefig('model_fit.png')
