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()
