tdf007_snapshot.py File Reference

Namespaces
	tdf007_snapshot

Detailed Description

This tutorial shows how to write out datasets in ROOT formatusing the TDataFrame.

import ROOT

fill_tree_code ='''
void fill_tree(const char *filename, const char *treeName)
{
   TFile f(filename, "RECREATE");
   TTree t(treeName, treeName);
   int b1;
   float b2;
   t.Branch("b1", &b1);
   t.Branch("b2", &b2);
   for (int i = 0; i < 10000; ++i) {
      b1 = i;
      b2 = i * i;
      t.Fill();
   }
   t.Write();
   f.Close();
   return;
}
'''

# We prepare an input tree to run on
fileName = "tdf007_snapshot_py.root"
outFileName = "tdf007_snapshot_output_py.root"
outFileNameAllColumns = "tdf007_snapshot_output_allColumns_py.root"
treeName = "myTree"
ROOT.gInterpreter.Declare(fill_tree_code)
ROOT.fill_tree(fileName, treeName)

# We read the tree from the file and create a TDataFrame.
TDF = ROOT.ROOT.Experimental.TDataFrame
d = TDF(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;
}
'''
ROOT.gInterpreter.Declare(getVector_code)

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"]:
    branchList.push_back(branchName)
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())

f1.Close()

# 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 tdf:")
for branch in t.GetListOfBranches():
    print("Branch: %s" %branch.GetName())

f2.Close()

# We can also get a fresh TDataFrame out of the snapshot and restart the
# analysis chain from it.

branchList.clear()
branchList.push_back("b1_square")
snapshot_tdf = d2.Snapshot(treeName, outFileName, branchList);
h = snapshot_tdf.Histo1D("b1_square")
c = ROOT.TCanvas()
h.Draw()

Date

April 2017

Author

Danilo Piparo

Definition in file tdf007_snapshot.py.