_ttree.pyzdoc

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/**
\class TTree
\brief \parblock \endparblock
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## PyROOT
 
The TTree class has several additions for its use from Python, which are also
available in its subclasses e.g. TChain and TNtuple.
 
First, TTree instances are iterable in Python. Therefore, assuming `t` is
a TTree instance, we can do:
\code{.py}
for entry in t:
    x = entry.branch_name
    ...
\endcode
 
At each iteration, a new entry of the tree will be read. In the code above,
`entry` allows to access the branch values for the current entry. This can be
done with the syntax `entry.branch_name` or, if the branch name is incompatible
with Python naming rules, with e.g. "getattr(entry, '1_branch_name')".
 
<em>Please note</em> that iterating in Python can be slow, so only iterate over
a tree as described above if performance is not an issue or when dealing with
a small dataset. To read and process the entries of a tree in a much faster
way, please use ROOT::RDataFrame.
 
Second, a couple of TTree methods have been modified to facilitate their use
from Python: TTree::Branch and TTree::SetBranchAddress.
 
Regarding TTree::Branch, the following example shows how we can create
different types of branches of a TTree. Note that `Branch` will just link
the new branch with a given Python object, so it is still necessary to fill
such object with the desired content before calling TTree::Fill.
\code{.py}
from array import array
import numpy as np
import ROOT
from ROOT import addressof
 
# Basic type branch (float) - use array of length 1
n = array('f', [ 1.5 ])
t.Branch('floatb', n, 'floatb/F')
 
# Array branch - use array of length N
N = 10
a = array('d', N*[ 0. ])
t.Branch('arrayb', a, 'arrayb[' + str(N) + ']/D')
 
# Array branch - use NumPy array of length N
npa = np.array(N*[ 0. ])
t.Branch('nparrayb', npa, 'nparrayb[' + str(N) + ']/D')
 
# std::vector branch
v = ROOT.std.vector('double')(N*[ 0. ])
t.Branch('vectorb0', v)
 
# Class branch / struct in single branch
cb = ROOT.MyClass()
t.Branch('classb', cb)
 
# Struct as leaflist
# Assuming:
# struct MyStruct {
#   int myint;
#   float myfloat;
# };
ms = ROOT.MyStruct()
t.Branch('structll', ms, 'myint/I:myfloat/F')
 
# Store struct members individually
ms = ROOT.MyStruct()
# Use `addressof` to get the address of the struct members
t.Branch('myintb', addressof(ms, 'myint'), 'myint/I')
t.Branch('myfloatb', addressof(ms, 'myfloat'), 'myfloat/F')
\endcode
 
Concerning TTree::SetBranchAddress, below is an example of prepare
the reading of different types of branches of a TTree. Note that
`SetBranchAddress` will just link a given branch with a certain
Python object; after that, in order to read the content of such
branch for a given TTree entry `x`, TTree::GetEntry(x) must be
invoked.
\code{.py}
from array import array
import numpy as np
import ROOT
 
# Basic type branch (float) - use array of length 1
n = array('f', [ 0. ])
t.SetBranchAddress('floatb', n)
 
# Array branch - use array of length N
N = 10
a = array('d', N*[ 0. ])
t.SetBranchAddress('arrayb', a)
 
# Array branch - use NumPy array of length N
npa = np.array(N*[ 0. ])
t.SetBranchAddress('nparrayb', a)
 
# std::vector branch
v = ROOT.std.vector('double')()
t.SetBranchAddress('vectorb', v)
 
# Class branch
cb = ROOT.MyClass()
t.SetBranchAddress('classb', cb)
 
# Struct branch (both single-branch and leaf list)
ms = ROOT.MyStruct()
ds.SetBranchAddress('structb', ms)
\endcode
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*/