<commit_before># Author: <NAME>  06/2018

################################################################################
# Copyright (C) 1995-2018, <NAME> and <NAME>.                      #
# All rights reserved.                                                         #
#                                                                              #
# For the licensing terms see $ROOTSYS/LICENSE.                                #
# For the list of contributors see $ROOTSYS/README/CREDITS.                    #
################################################################################

r'''
/**
\class TTree
\brief \parblock \endparblock
\htmlonly
<div class="pyrootbox">
\endhtmlonly
## 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
\htmlonly
</div>
\endhtmlonly
*/
'''

from libROOTPythonizations import AddBranchAttrSyntax, SetBranchAddressPyz, BranchPyz

import cppyy
from ROOT import pythonization

# TTree iterator
def _TTree__iter__(self):
    i = 0
    bytes_read = self.GetEntry(i)
    while 0 < bytes_read:
        yield self
        i += 1
        bytes_read = self.GetEntry(i)

    if bytes_read == -1:
        raise RuntimeError("TTree I/O error")

def _SetBranchAddress(self, *args):
    # Modify the behaviour if args is (const char*, void*)
    res = SetBranchAddressPyz(self, *args)

    if res is None:
        # Fall back to the original implementation for the rest of overloads
        res = self._OriginalSetBranchAddress(*args)

    return res

def _Branch(self, *args):
    # Modify the behaviour if args is one of:
    # ( const char*, void*, const char*, Int_t = 32000 )
    # ( const char*, const char*, T**, Int_t = 32000, Int_t = 99 )
    # ( const char*, T**, Int_t = 32000, Int_t = 99 )
    res = BranchPyz(self, *args)

    if res is None:
        # Fall back to the original implementation for the rest of overloads
        res = self._OriginalBranch(*args)

    return res

# TTree.AsMatrix functionality
def _TTreeAsMatrix(self, columns=None, exclude=None, dtype="double", return_labels=False):
    """Read-out the TTree as a numpy array.

    Note that the reading is performed in multiple threads if the implicit
    multi-threading of ROOT is enabled.

    Parameters:
        columns: If None return all branches as columns, otherwise specify names in iterable.
        exclude: Exclude branches from selection.
        dtype: Set return data-type of numpy array.
        return_labels: Return additionally to the numpy array the names of the columns.

    Returns:
        array(, labels): Numpy array(, labels of columns)
    """

    # Import numpy lazily
    try:
        import numpy as np
    except:
        raise ImportError("Failed to import numpy during call of TTree.AsMatrix.")

    # Check that tree has entries
    if self.GetEntries() == 0:
        raise Exception("Tree {} has no entries.".format(self.GetName()))

    # Get all columns of the tree if no columns are specified<commit_msg>[PyROOT] Add deprecation warning for TTree.AsMatrix<commit_after># Author: <NAME>  06/2018

################################################################################
# Copyright (C) 1995-2018, <NAME> and <NAME>.                      #
# All rights reserved.                                                         #
#                                                                              #
# For the licensing terms see $ROOTSYS/LICENSE.                                #
# For the list of contributors see $ROOTSYS/README/CREDITS.                    #
################################################################################

r'''
/**
\class TTree
\brief \parblock \endparblock
\htmlonly
<div class="pyrootbox">
\endhtmlonly
## 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
\htmlonly
</div>
\endhtmlonly
*/
'''

from libROOTPythonizations import AddBranchAttrSyntax, SetBranchAddressPyz, BranchPyz

import cppyy
from ROOT import pythonization

# TTree iterator
def _TTree__iter__(self):
    i = 0
    bytes_read = self.GetEntry(i)
    while 0 < bytes_read:
        yield self
        i += 1
        bytes_read = self.GetEntry(i)

    if bytes_read == -1:
        raise RuntimeError("TTree I/O error")

def _SetBranchAddress(self, *args):
    # Modify the behaviour if args is (const char*, void*)
    res = SetBranchAddressPyz(self, *args)

    if res is None:
        # Fall back to the original implementation for the rest of overloads
        res = self._OriginalSetBranchAddress(*args)

    return res

def _Branch(self, *args):
    # Modify the behaviour if args is one of:
    # ( const char*, void*, const char*, Int_t = 32000 )
    # ( const char*, const char*, T**, Int_t = 32000, Int_t = 99 )
    # ( const char*, T**, Int_t = 32000, Int_t = 99 )
    res = BranchPyz(self, *args)

    if res is None:
        # Fall back to the original implementation for the rest of overloads
        res = self._OriginalBranch(*args)

    return res

# TTree.AsMatrix functionality
def _TTreeAsMatrix(self, columns=None, exclude=None, dtype="double", return_labels=False):
    """Read-out the TTree as a numpy array.

    Note that the reading is performed in multiple threads if the implicit
    multi-threading of ROOT is enabled.

    Parameters:
        columns: If None return all branches as columns, otherwise specify names in iterable.
        exclude: Exclude branches from selection.
        dtype: Set return data-type of numpy array.
        return_labels: Return additionally to the numpy array the names of the columns.

    Returns:
        array(, labels): Numpy array(, labels of columns)
    """

    import warnings
    warnings.warn("TTree.AsMatrix is deprecated since v6.24 and will be removed in v6.26. Please use instead RDataFrame.AsNumpy.", FutureWarning)

    # Import numpy lazily
    try:
        import numpy as np
    except:
        raise ImportError("Failed to import numpy during call of TTree.AsMatrix.")

    # Check that tree has entries
    if self.GetEntries() == 0:
        raise Exception("Tree {} has no entries.".format(self.GetName()))

    # Get all columns of the tree if no columns are specified
{'fraction_non_alphanumeric': 0.12404263718910383, 'fraction_numerical': 0.009001184366363995, 'mean_word_length': 3.2546187437017133, 'pattern_counts': {'":': 0, '<': 19, '<?xml version=': 0, '>': 17, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 10, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '1890257', 'n_tokens_mistral': 3863, 'n_tokens_neox': 3759, 'n_words': 1516}