<filename>rdkit/Chem/Graphs.py<gh_stars>1-10
# $Id$
#
# Copyright (C) 2001-2008 <NAME> and rational discovery llc
#
#   @@ All Rights Reserved @@
#  This file is part of the RDKit.
#  The contents are covered by the terms of the BSD license
#  which is included in the file license.txt, found at the root
#  of the RDKit source tree.
#
""" Python functions for manipulating molecular graphs

In theory much of the functionality in here should be migrating into the
C/C++ codebase.

"""
import numpy
from rdkit import Chem
from rdkit import DataStructs
from rdkit.six.moves import xrange
import types


def CharacteristicPolynomial(mol, mat=None):
  """ calculates the characteristic polynomial for a molecular graph

    if mat is not passed in, the molecule's Weighted Adjacency Matrix will
    be used.

    The approach used is the Le Verrier-Faddeev-Frame method described
    in _Chemical Graph Theory, 2nd Edition_ by <NAME> (CRC Press,
    1992), pg 76.
    
  """
  nAtoms = mol.GetNumAtoms()
  if mat is None:
    # FIX: complete this:
    #A = mol.GetWeightedAdjacencyMatrix()
    pass
  else:
    A = mat
  I = 1. * numpy.identity(nAtoms)
  An = A
  res = numpy.zeros(nAtoms + 1, numpy.float)
  res[0] = 1.0
  for n in xrange(1, nAtoms + 1):
    res[n] = 1. / n * numpy.trace(An)
    Bn = An - res[n] * I
    An = numpy.dot(A, Bn)

  res[1:] *= -1
  return res

<filename>rdkit/Chem/Graphs.py<gh_stars>1-10
# $Id$
#
# Copyright (C) 2001-2008 <NAME> and rational discovery llc
#
#   @@ All Rights Reserved @@
#  This file is part of the RDKit.
#  The contents are covered by the terms of the BSD license
#  which is included in the file license.txt, found at the root
#  of the RDKit source tree.
#
""" Python functions for manipulating molecular graphs

In theory much of the functionality in here should be migrating into the
C/C++ codebase.

"""
import numpy
from rdkit import Chem
from rdkit import DataStructs
from rdkit.six.moves import xrange
import types


def CharacteristicPolynomial(mol, mat=None):
  """ calculates the characteristic polynomial for a molecular graph

    if mat is not passed in, the molecule's Weighted Adjacency Matrix will
    be used.

    The approach used is the Le Verrier-Faddeev-Frame method described
    in _Chemical Graph Theory, 2nd Edition_ by <NAME> (CRC Press,
    1992), pg 76.
    
  """
  nAtoms = mol.GetNumAtoms()
  if mat is None:
    # FIX: complete this:
    #A = mol.GetWeightedAdjacencyMatrix()
    pass
  else:
    A = mat
  I = 1. * numpy.identity(nAtoms)
  An = A
  res = numpy.zeros(nAtoms + 1, numpy.float)
  res[0] = 1.0
  for n in xrange(1, nAtoms + 1):
    res[n] = 1. / n * numpy.trace(An)
    Bn = An - res[n] * I
    An = numpy.dot(A, Bn)

  res[1:] *= -1
  return res

# $Id$ # # Copyright (C) 2001-2008 <NAME> and rational discovery llc # #   @@ All Rights Reserved @@ #  This file is part of the RDKit. #  The contents are covered by the terms of the BSD license #  which is included in the file license.txt, found at the root #  of the RDKit source tree. # Python functions for manipulating molecular graphs  In theory much of the functionality in here should be migrating into the C/C++ codebase. calculates the characteristic polynomial for a molecular graph      if mat is not passed in, the molecule's Weighted Adjacency Matrix will     be used.      The approach used is the Le Verrier-Faddeev-Frame method described     in _Chemical Graph Theory, 2nd Edition_ by <NAME> (CRC Press,     1992), pg 76. # FIX: complete this: #A = mol.GetWeightedAdjacencyMatrix()