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df007_snapshot.py File Reference


namespace  df007_snapshot

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 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.
d2.Snapshot(treeName, outFileName, \
["b1", "b1_square", "b2_vector"])
# 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, ["b1_square"]);
h = snapshot_df.Histo1D("b1_square")
c = ROOT.TCanvas()
print("Saved figure to df007_snapshot.png")
ROOT's RDataFrame offers a modern, high-level interface for analysis of data stored in TTree ,...
Definition: RDataFrame.hxx:41
April 2017
Danilo Piparo (CERN)

Definition in file df007_snapshot.py.