})
fitpars = [
fitter.const_pars[j] for j in range(len(fitter.parnames))
if fitter.fitting[j]
]
full_model = fitter.GenerateModel(fitpars,
separate_source=False,
return_resolution=False,
broaden=False,
nofit=True)
for i, order in enumerate(orders):
left = np.searchsorted(full_model.x, order.x[0] - 5)
right = np.searchsorted(full_model.x, order.x[-1] + 5)
if min(full_model.y[left:right]) > 0.95:
model = FittingUtilities.ReduceResolution(
full_model[left:right].copy(), resolution)
model = FittingUtilities.RebinData(model, order.x)
data = order.copy()
data.cont = np.ones(data.size())
else:
print "\n\nGenerating model for order %i of %i\n" % (
i, len(orders))
order.cont = FittingUtilities.Continuum(order.x,
order.y,
fitorder=3,
lowreject=1.5,
highreject=10)
fitter.ImportData(order)
fitter.resolution_fit_mode = "gauss"
model = fitter.GenerateModel(
