Observed upper limits stabilization

It was found in the resonant combination that when the rate to converge change a by orders of magnitude combine will be unstable. That can also be a thing to the shape BM results (@tolange)

The solution there was doing one fit with inference expected prior to do the " --unblinded True", and when the observed pick up starting point and boundaries from the expected run

Example bellow (there I have a json file with the limits that I pass to general plots area after and for each run take this one as "expected_limts" as the unblinded output of that same task removing the "__unblinded" of the path), but here this can be done smarter, eg taking from the root file in the same area that inference saves

        try:
            ff = open(expected_limits)
            data_exp = json.load(ff)
            #print("data_exp in mass point", data_exp[str(float(self.branch_map[self.branch]))])
            rstart=(data_exp[str(float(self.branch_map[self.branch]))]["limit"])/float(scale)
            #print("rstart = ", rstart, scale)
            rstart_srt=",r=%s" % str(float(rstart))
            rboundaries = " --rMin %f --rMax %f " % (rstart/10, rstart*10)
        except:
            rstart_srt="" 
            rboundaries = "" 

And then in the limits command do

" --setParameters MX={mass_X}{rstart_srt} "
" {rboundaries} "