import ROOT def fill_tree(treeName, fileName): tdf = ROOT.ROOT.RDataFrame(50) tdf.Define("b1", "(double) tdfentry_")\ .Define("b2", "(int) tdfentry_ * tdfentry_").Snapshot(treeName, fileName)
Welcome to JupyROOT 6.19/01
We prepare an input tree to run on
fileName = 'df004_cutFlowReport_py.root' treeName = 'myTree' fill_tree(treeName, fileName)
We read the tree from the file and create a RDataFrame, a class that allows us to interact with the data contained in the tree.
RDF = ROOT.ROOT.RDataFrame d = RDF(treeName, fileName)
An optional string parameter name can be passed to the Filter method to create a named filter. Named filters work as usual, but also keep track of how many entries they accept and reject.
filtered1 = d.Filter('b1 > 25', 'Cut1') filtered2 = d.Filter('0 == b2 % 2', 'Cut2') augmented1 = filtered2.Define('b3', 'b1 / b2') filtered3 = augmented1.Filter('b3 < .5','Cut3')
Statistics are retrieved through a call to the Report method: when Report is called on the main RDataFrame object, it retrieves stats for all named filters declared up to that point. When called on a stored chain state (i.e. a chain/graph node), it retrieves stats for all named filters in the section of the chain between the main RDataFrame and that node (included). Stats are printed in the same order as named filters have been added to the graph, and refer to the latest event-loop that has been run using the relevant RDataFrame.
print('Cut3 stats:') filtered3.Report() print('All stats:') allCutsReport = d.Report() allCutsReport.Print()
Cut3 stats: All stats: Cut1 : pass=24 all=50 -- eff=48.00 % cumulative eff=48.00 % Cut2 : pass=25 all=50 -- eff=50.00 % cumulative eff=50.00 % Cut3 : pass=23 all=25 -- eff=92.00 % cumulative eff=46.00 %
Draw all canvases
from ROOT import gROOT gROOT.GetListOfCanvases().Draw()