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Reference Guide
df007_snapshot.py File Reference



Detailed Description

View in nbviewer Open in SWAN Write ROOT data with RDataFrame.

This tutorial shows how to write out datasets in ROOT format using the RDataFrame

import ROOT
# A simple helper function to fill a test tree: this makes the example stand-alone.
def fill_tree(treeName, fileName):
df = ROOT.RDataFrame(10000)
df.Define("b1", "(int) rdfentry_")\
.Define("b2", "(float) rdfentry_ * rdfentry_").Snapshot(treeName, fileName)
# We prepare an input tree to run on
fileName = "df007_snapshot_py.root"
outFileName = "df007_snapshot_output_py.root"
outFileNameAllColumns = "df007_snapshot_output_allColumns_py.root"
treeName = "myTree"
fill_tree(treeName, fileName)
# We read the tree from the file and create a RDataFrame.
d = ROOT.RDataFrame(treeName, fileName)
# ## Select entries
# We now select some entries in the dataset
d_cut = d.Filter("b1 % 2 == 0")
# ## Enrich the dataset
# Build some temporary columns: we'll write them out
getVector_code ='''
std::vector<float> getVector (float b2)
std::vector<float> v;
for (int i = 0; i < 3; i++) v.push_back(b2*i);
return v;
d2 = d_cut.Define("b1_square", "b1 * b1") \
.Define("b2_vector", "getVector( b2 )")
# ## Write it to disk in ROOT format
# We now write to disk a new dataset with one of the variables originally
# present in the tree and the new variables.
# The user can explicitly specify the types of the columns as template
# arguments of the Snapshot method, otherwise they will be automatically
# inferred.
branchList = ROOT.vector('string')()
for branchName in ["b1", "b1_square", "b2_vector"]:
d2.Snapshot(treeName, outFileName, branchList)
# Open the new file and list the columns of the tree
f1 = ROOT.TFile(outFileName)
t = f1.myTree
print("These are the columns b1, b1_square and b2_vector:")
for branch in t.GetListOfBranches():
print("Branch: %s" %branch.GetName())
# We are not forced to write the full set of column names. We can also
# specify a regular expression for that. In case nothing is specified, all
# columns are persistified.
d2.Snapshot(treeName, outFileNameAllColumns)
# Open the new file and list the columns of the tree
f2 = ROOT.TFile(outFileNameAllColumns)
t = f2.myTree
print("These are all the columns available to this dataframe:")
for branch in t.GetListOfBranches():
print("Branch: %s" %branch.GetName())
# We can also get a fresh RDataFrame out of the snapshot and restart the
# analysis chain from it.
snapshot_df = d2.Snapshot(treeName, outFileName, branchList);
h = snapshot_df.Histo1D("b1_square")
c = ROOT.TCanvas()
print("Saved figure to df007_snapshot.png")
April 2017
Danilo Piparo (CERN)

Definition in file df007_snapshot.py.

ROOT's RDataFrame offers a high level interface for analyses of data stored in TTree,...
Definition: RDataFrame.hxx:42