couple of fixes to generation of shift up and down histograms. Also added some useful plots/cout statements (currently commented out)
Merge remote-tracking branch 'upstream/master'
Merge pull request #67 from ffrensch/master

changing names for new SM - not tested
adding fallback for failed fits, testing mode in which fits are run but datacards aren't altered, and adding a verbosity option to make extra plots and cout statements
Merge remote-tracking branch 'upstream/master'
removing --rebin from addFitNuisance.py commands, and commenting a few out which are not working well currently
removing normalization parameter from functions-this confuses the toy generation. Commenting out plot of parameter A so it doesn't seg fault for these functions
Merge remote-tracking branch 'upstream/master'
Adding fallback if the shift up/down functions are not integrable
quick hack to get script to pick up datacards with -fb postfix for tail fitting
pushing latest improvements, loosening range on one parameter and updating doMSSM.py having gotten more templates to run
cosmetic changes in print output
a few changes in --max-likelihood and in --goodness-of-fit
lxb-limit.py now also does its calculations in a dedicated tmp directory. This is transparent to before on normal use, but it allows lxb-limit.py to be called several times on the same inputs. The latter makes sense when running toys on expected (used for goodness-of-fit and in principle also for significance [bayesian type]) with varying random seeds.
small changes for the script to be more comfortable -- also added a bunch of more prinouts that have been used for tersting to really understand that the tool is doing what it is supposed to do. On unpleasant aspect for large scale submissions still remains, which is intrinsic to the method. If the toys fail for any of the mass points the toys is lost for all other mass points, as the global minimum as function of the mass cannot be determined...
adding cycles to goodness-of-fit -- this even more calls for a refarctorization of the options of the tools, as cycles are abused here, as an option, which is actually used for full CLs limits. For goodness-of-fit it allows to run e.g. 10toys for expected goodness-of-fit in 100 cycles, which at the end will add up to 1000 toys.
the determination of the global minimum as function of mass has to be brought back here for observed. For expected this has been done already by massDeltaNLL.py, when collecting the toys. For observed this is not the case.
Merge pull request #68 from roger-wolf/master

More tests (not 100% final yet) of massNLL and first fully operationg version for GoodneddOfFit
renaming bias study script to something more appropriate
some more improvements to the tail fits