from sherpa.plot import DataPlot, ModelPlot dplot = DataPlot() dplot.prepare(f.data) mplot = ModelPlot() mplot.prepare(f.data, f.model) dplot.plot() mplot.overplot() savefig("data_model_c0_c2.png") dump("f.method.name") original_method = f.method from sherpa.optmethods import NelderMead f.method = NelderMead() resn = f.fit() print("Change in statistic: {}".format(resn.dstatval)) fit2 = Fit(d, mdl, method=NelderMead()) fit2.fit() mdl.c1.thaw() f.method = original_method res2 = f.fit() report("res2.format()") from sherpa.plot import DelchiPlot, FitPlot, SplitPlot fplot = FitPlot() rplot = DelchiPlot()
report("f") print("Starting statistic: {}".format(f.calc_stat())) fitres = f.fit() report("fitres.format()") print("Reduced chi square = {:.2f}".format(fitres.rstat)) mplot.prepare(d, mdl) dplot.plot() mplot.overplot() savefig("model_data_fit1.png") from sherpa.optmethods import NelderMead f.method = NelderMead() fitres2 = f.fit() report("mdl") dump("fitres2.dstatval") mdl.reset() report("mdl") plateau.c0 = np.max(d.y) mplot.prepare(d, mdl) dplot.plot() mplot.overplot() savefig("model_data_reset.png") fitres3 = f.fit()