PyrootCK¶
Improving PyROOT for better productivity.
Collection of utilities are organized into subpackages:
mathutils:asymvar: class for variable with asymmetric-error, inspired fromuncertainties.ufloat.Eff,EffU,EffU_unguard: functions to compute efficiencies with Clopper-Pearson uncertainty.weighted_average,weighted_harmonic_average: when a simple average is not enough.combine_fully_correlated,combine_uncorrelated,combine_BLUE: for combining multiple observables with uncertainty into one, given choices of correlation. For BLUE (Best Linear Unbiased Estimator), see Valassi, 2013.
iouilsimport_treeto quickly loadTTreefrom (multiple)TFileoverloaded for different source types (local, ganga, eos, xrootd, …).
tmvautilsTMVA_Adapterto help setupTMVA.Readervariables, and returnTTreeof mva-response weights.
As well as miscellaneous monkey-patching on ROOT and uncertainties
for more methods:
ROOT:- Misc conversion to/from
ROOT(TH,TGraph,RooWorkspace,RooFitResult,…) andpandas(Series,DataFrame). TFile.slice_treeto extractTTreeinto smaller one.TTree.dropto make index-unique TTree.TH1.vlookup,TH2.vlookup: like in Microsoft Excel, to retrive value in a bin given point(s) on the axis.TMultiGraph.brazillian: for the upper limits plot.
- Misc conversion to/from
uncertainties:- class
var, based on ufloat but ready-made for statistical (Poisson) error. - Additional methods on
ufloat:rerr,upperlim,low,high,interval,rounding_PDG - More methods involving error tag:
tags,get_error,get_rerr
- class
See the docstring from module index for more details.
Installation¶
It’s available on pip: pip install pyrootck
Dependency: uncertainties, pandas, root_numpy, pyroot_zen, PythonCK
Disclaimer¶
This packacge was written and used during my PhD in 2013-2017 at EPFL (Lausanne) and LHCb collaboration (CERN), for the work in Z->tau tau cross-section measurement and H->mu tau searches at LHCb (8TeV).
I hope it can be of a good use for future analysis…