}) 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(