'''
Importing pandasTools enables several features that allow for using RDKit molecules as columns of a
Pandas dataframe.
If the dataframe is containing a molecule format in a column (e.g. smiles), like in this example:
>>> from rdkit.Chem import PandasTools
>>> import pandas as pd
>>> import os
>>> from rdkit import RDConfig
>>> antibiotics = pd.DataFrame(columns=['Name','Smiles'])
>>> antibiotics = antibiotics.append({'Smiles':'CC1(C(N2C(S1)C(C2=O)NC(=O)CC3=CC=CC=C3)C(=O)O)C',
...   'Name':'Penicilline G'}, ignore_index=True)#Penicilline G
>>> antibiotics = antibiotics.append({
...   'Smiles':'CC1(C2CC3C(C(=O)C(=C(C3(C(=O)C2=C(C4=C1C=CC=C4O)O)O)O)C(=O)N)N(C)C)O',
...   'Name':'Tetracycline'}, ignore_index=True)#Tetracycline
>>> antibiotics = antibiotics.append({
...   'Smiles':'CC1(C(N2C(S1)C(C2=O)NC(=O)C(C3=CC=CC=C3)N)C(=O)O)C',
...   'Name':'Ampicilline'}, ignore_index=True)#Ampicilline
>>> print([str(x) for x in  antibiotics.columns])
['Name', 'Smiles']
>>> print(antibiotics) # doctest: +ELLIPSIS
            Name                                             Smiles
0  Penicilline G    CC1(C(N2C(S1)C(C2=O)NC(=O)CC3=CC=CC=C3)C(=O)O)C
1   Tetracycline  CC1(C2CC3C(C(=O)C(=C(C3(C(=O)C2=C(C4=C1C=CC=C4*...*
2  Ampicilline  CC1(C(N2C(S1)C(C2=O)NC(=O)C(C3=CC=CC=C3)N)C(=O*...*

a new column can be created holding the respective RDKit molecule objects. The fingerprint can be
included to accelerate substructure searches on the dataframe.

>>> PandasTools.AddMoleculeColumnToFrame(antibiotics,'Smiles','Molecule',includeFingerprints=True)
>>> print([str(x) for x in  antibiotics.columns])
['Name', 'Smiles', 'Molecule']

A substructure filter can be applied on the dataframe using the RDKit molecule column,
because the ">=" operator has been modified to work as a substructure check.
Such the antibiotics containing the beta-lactam ring "C1C(=O)NC1" can be obtained by

>>> beta_lactam = Chem.MolFromSmiles('C1C(=O)NC1')
>>> beta_lactam_antibiotics = antibiotics[antibiotics['Molecule'] >= beta_lactam]
>>> print(beta_lactam_antibiotics[['Name','Smiles']]) # doctest: +ELLIPSIS
            Name                                             Smiles
0  Penicilline G    CC1(C(N2C(S1)C(C2=O)NC(=O)CC3=CC=CC=C3)C(=O)O)C
2  Ampicilline  CC1(C(N2C(S1)C(C2=O)NC(=O)C(C3=CC=CC=C3)N)C(=O*...*


It is also possible to load an SDF file can be load into a dataframe.

>>> sdfFile = os.path.join(RDConfig.RDDataDir,'NCI/first_200.props.sdf')
>>> frame = PandasTools.LoadSDF(sdfFile,smilesName='SMILES',molColName='Molecule',
...            includeFingerprints=True)
>>> frame.info # doctest: +SKIP
<bound method DataFrame.info of <class 'pandas.core.frame.DataFrame'>
Int64Index: 200 entries, 0 to 199
Data columns:
AMW                       200  non-null values
CLOGP                     200  non-null values
CP                        200  non-null values
CR                        200  non-null values
DAYLIGHT.FPG              200  non-null values
DAYLIGHT_CLOGP            200  non-null values
FP                        200  non-null values
ID                        200  non-null values
ISM                       200  non-null values
LIPINSKI_VIOLATIONS       200  non-null values
NUM_HACCEPTORS            200  non-null values
NUM_HDONORS               200  non-null values
NUM_HETEROATOMS           200  non-null values
NUM_LIPINSKIHACCEPTORS    200  non-null values
NUM_LIPINSKIHDONORS       200  non-null values
NUM_RINGS                 200  non-null values
NUM_ROTATABLEBONDS        200  non-null values
P1                        30  non-null values
SMILES                    200  non-null values
Molecule                  200  non-null values
dtypes: object(20)>

Conversion to html is quite easy:
>>> htm = frame.to_html() # doctest: +ELLIPSIS
*...*
>>> str(htm[:36])
'<table border="1" class="dataframe">'

In order to support rendering the molecules as images in the HTML export of the dataframe,
the __str__ method is monkey-patched to return a base64 encoded PNG:
>>> molX = Chem.MolFromSmiles('Fc1cNc2ccccc12')
>>> print(molX) # doctest: +SKIP
<img src="data:image/png;base64,..." alt="Mol"/>
This can be reverted using the ChangeMoleculeRendering method
>>> ChangeMoleculeRendering(renderer='String')
>>> print(molX) # doctest: +SKIP
<rdkit.Chem.rdchem.Mol object at 0x10d179440>
>>> ChangeMoleculeRendering(renderer='PNG')
>>> print(molX) # doctest: +SKIP
<img src="data:image/png;base64,..." alt="Mol"/>

'''
from __future__ import print_function

from base64 import b64encode
import sys
import types

import numpy as np
from rdkit import Chem
from rdkit import DataStructs
from rdkit.Chem import AllChem
from rdkit.Chem import Draw
from rdkit.Chem.Draw import rdMolDraw2D
from rdkit.Chem import SDWriter
from rdkit.Chem import rdchem
from rdkit.Chem.Scaffolds import MurckoScaffold
from rdkit.six import BytesIO, string_types, PY3

try:
  import pandas as pd

  def _getPandasVersion():
    """ Get the pandas version as a tuple """
    import re
    try:
      v = pd.__version__
    except AttributeError:
      v = pd.version.version
    v = re.split(r'[^0-9,.]', v)[0].split('.')
    return tuple(int(vi) for vi in v)

  if _getPandasVersion() < (0, 10):
    print("Pandas version {0} not compatible with tests".format(_getPandasVersion()),
          file=sys.stderr)
    pd = None
  else:
    if 'display.width' in pd.core.config._registered_options:
      pd.set_option('display.width', 1000000000)
    if 'display.max_rows' in pd.core.config._registered_options:
      pd.set_option('display.max_rows', 1000000000)
    elif 'display.height' in pd.core.config._registered_options:
      pd.set_option('display.height', 1000000000)
    if 'display.max_colwidth' in pd.core.config._registered_options:
      pd.set_option('display.max_colwidth', 1000000000)
    if 'display.max_columns' in pd.core.config._registered_options:
      pd.set_option('display.max_columns', 20)
    # saves the default pandas rendering to allow restoration
    defPandasRendering = pd.core.frame.DataFrame.to_html
except ImportError:
  import traceback
  traceback.print_exc()
  pd = None

except Exception as e:
  import traceback
  traceback.print_exc()
  pd = None

if pd:
  try:
    from pandas.io.formats import format as fmt
  except:
    try:
      from pandas.formats import format as fmt
    except ImportError:
      from pandas.core import format as fmt  # older versions
else:
  fmt = 'Pandas not available'

highlightSubstructures = True
molRepresentation = 'png'  # supports also SVG
molSize = (200, 200)


def patchPandasHTMLrepr(self, **kwargs):
  '''
  Patched default escaping of HTML control characters to allow molecule image rendering dataframes
  '''
  formatter = fmt.DataFrameFormatter(
    self, buf=None, columns=None, col_space=None, colSpace=None, header=True, index=True,
    na_rep='NaN', formatters=None, float_format=None, sparsify=None, index_names=True, justify=None,
    force_unicode=None, bold_rows=True, classes=None, escape=False)
  formatter.to_html()
  html = formatter.buf.getvalue()
  return html


def patchPandasHeadMethod(self, n=5):
  '''Ensure inheritance of patched to_html in "head" subframe
  '''
  df = self[:n]
  df.to_html = types.MethodType(patchPandasHTMLrepr, df)
  df.head = types.MethodType(patchPandasHeadMethod, df)
  return df


def _get_image(x):
  """displayhook function for PNG data"""
  s = b64encode(x).decode('ascii')
  pd.set_option('display.max_columns', len(s) + 1000)
  pd.set_option('display.max_rows', len(s) + 1000)
  if len(s) + 100 > pd.get_option("display.max_colwidth"):
    pd.set_option("display.max_colwidth", len(s) + 1000)
  return s


def _get_svg_image(mol, size=(200, 200), highlightAtoms=[]):
  """ mol rendered as SVG """
  from IPython.display import SVG
  from rdkit.Chem import rdDepictor
  from rdkit.Chem.Draw import rdMolDraw2D
  try:
    # If no coordinates, calculate 2D
    mol.GetConformer(-1)
  except ValueError:
    rdDepictor.Compute2DCoords(mol)
  drawer = rdMolDraw2D.MolDraw2DSVG(*size)
  drawer.DrawMolecule(mol, highlightAtoms=highlightAtoms)
  drawer.FinishDrawing()
  svg = drawer.GetDrawingText()
  return SVG(svg).data  # IPython's SVG clears the svg text


try:
  from rdkit.Avalon import pyAvalonTools as pyAvalonTools
  # Calculate the Avalon fingerprint
  _fingerprinter = lambda x, y: pyAvalonTools.GetAvalonFP(x, isQuery=y, bitFlags=pyAvalonTools.avalonSSSBits)
except ImportError:
  # Calculate fingerprint using SMARTS patterns
  _fingerprinter = lambda x, y: Chem.PatternFingerprint(x, fpSize=2048)


def _molge(x, y):
  """Allows for substructure check using the >= operator (X has substructure Y -> X >= Y) by
  monkey-patching the __ge__ function
  This has the effect that the pandas/numpy rowfilter can be used for substructure filtering
  (filtered = dframe[dframe['RDKitColumn'] >= SubstructureMolecule])
  """
  if x is None or y is None:
    return False
  if hasattr(x, '_substructfp'):
    if not hasattr(y, '_substructfp'):
      y._substructfp = _fingerprinter(y, True)
    if not DataStructs.AllProbeBitsMatch(y._substructfp, x._substructfp):
      return False
  match = x.GetSubstructMatch(y)
  x.__sssAtoms = []
  if match:
    if highlightSubstructures:
      x.__sssAtoms = list(match)
    return True
  else:
    return False


def PrintAsBase64PNGString(x, renderer=None):
  '''returns the molecules as base64 encoded PNG image
  '''
  if highlightSubstructures and hasattr(x, '__sssAtoms'):
    highlightAtoms = x.__sssAtoms
  else:
    highlightAtoms = []
  if molRepresentation.lower() == 'svg':
    from IPython.display import SVG
    svg = Draw._moltoSVG(x, molSize, highlightAtoms, "", True)
    return SVG(svg).data
  else:
    data = Draw._moltoimg(x, molSize, highlightAtoms, "", returnPNG=True, kekulize=True)
    return '<img src="data:image/png;base64,%s" alt="Mol"/>' % _get_image(data)


def PrintDefaultMolRep(x):
  return str(x.__repr__())


def _MolPlusFingerprint(m):
  '''Precomputes fingerprints and stores results in molecule objects to accelerate
     substructure matching
  '''
  if m is not None:
    m._substructfp = _fingerprinter(m, False)
  return m


def RenderImagesInAllDataFrames(images=True):
  '''Changes the default dataframe rendering to not escape HTML characters, thus allowing
  rendered images in all dataframes.
  IMPORTANT: THIS IS A GLOBAL CHANGE THAT WILL AFFECT TO COMPLETE PYTHON SESSION. If you want
  to change the rendering only for a single dataframe use the "ChangeMoleculeRendering" method
  instead.
  '''
  if images:
    pd.core.frame.DataFrame.to_html = patchPandasHTMLrepr
  else:
    pd.core.frame.DataFrame.to_html = defPandasRendering


def AddMoleculeColumnToFrame(frame, smilesCol='Smiles', molCol='ROMol', includeFingerprints=False):
  '''Converts the molecules contains in "smilesCol" to RDKit molecules and appends them to the
  dataframe "frame" using the specified column name.
  If desired, a fingerprint can be computed and stored with the molecule objects to accelerate
  substructure matching
  '''
  if not includeFingerprints:
    frame[molCol] = frame[smilesCol].map(Chem.MolFromSmiles)
  else:
    frame[molCol] = frame[smilesCol].map(
      lambda smiles: _MolPlusFingerprint(Chem.MolFromSmiles(smiles)))
  RenderImagesInAllDataFrames(images=True)


def ChangeMoleculeRendering(frame=None, renderer='PNG'):
  '''Allows to change the rendering of the molecules between base64 PNG images and string
  representations.
  This serves two purposes: First it allows to avoid the generation of images if this is
  not desired and, secondly, it allows to enable image rendering for newly created dataframe
  that already contains molecules, without having to rerun the time-consuming
  AddMoleculeColumnToFrame. Note: this behaviour is, because some pandas methods, e.g. head()
  returns a new dataframe instance that uses the default pandas rendering (thus not drawing
  images for molecules) instead of the monkey-patched one.
  '''
  if renderer == 'String':
    Chem.Mol.__str__ = PrintDefaultMolRep
  else:
    Chem.Mol.__str__ = PrintAsBase64PNGString
  if frame is not None:
    frame.to_html = types.MethodType(patchPandasHTMLrepr, frame)


def LoadSDF(filename, idName='ID', molColName='ROMol', includeFingerprints=False,
            isomericSmiles=False, smilesName=None, embedProps=False):
  '''Read file in SDF format and return as Pandas data frame.
  If embedProps=True all properties also get embedded in Mol objects in the molecule column.
  If molColName=None molecules would not be present in resulting DataFrame (only properties
  would be read).
  '''
  if isinstance(filename, string_types):
    if filename.lower()[-3:] == ".gz":
      import gzip
      f = gzip.open(filename, "rb")
    else:
      f = open(filename, 'rb')
    close = f.close
  else:
    f = filename
    close = None  # don't close an open file that was passed in
  records = []
  indices = []
  for i, mol in enumerate(Chem.ForwardSDMolSupplier(f, sanitize=(molColName is not None))):
    if mol is None:
      continue
    row = dict((k, mol.GetProp(k)) for k in mol.GetPropNames())
    if molColName is not None and not embedProps:
      for prop in mol.GetPropNames():
        mol.ClearProp(prop)
    if mol.HasProp('_Name'):
      row[idName] = mol.GetProp('_Name')
    if smilesName is not None:
      row[smilesName] = Chem.MolToSmiles(mol, isomericSmiles=isomericSmiles)
    if molColName is not None and not includeFingerprints:
      row[molColName] = mol
    elif molColName is not None:
      row[molColName] = _MolPlusFingerprint(mol)
    records.append(row)
    indices.append(i)

  if close is not None:
    close()
  RenderImagesInAllDataFrames(images=True)
  return pd.DataFrame(records, index=indices)


def WriteSDF(df, out, molColName='ROMol', idName=None, properties=None, allNumeric=False):
  '''Write an SD file for the molecules in the dataframe. Dataframe columns can be exported as
  SDF tags if specified in the "properties" list. "properties=list(df.columns)" would export
  all columns.
  The "allNumeric" flag allows to automatically include all numeric columns in the output.
  User has to make sure that correct data type is assigned to column.
  "idName" can be used to select a column to serve as molecule title. It can be set to
  "RowID" to use the dataframe row key as title.
  '''
  close = None
  if isinstance(out, string_types):
    if out.lower()[-3:] == ".gz":
      import gzip
      if PY3:
        out = gzip.open(out, "wt")
      else:
        out = gzip.open(out, "wb")
      close = out.close

  writer = SDWriter(out)
  if properties is None:
    properties = []
  else:
    properties = list(properties)
  if allNumeric:
    properties.extend([
      dt for dt in df.dtypes.keys()
      if (np.issubdtype(df.dtypes[dt], float) or np.issubdtype(df.dtypes[dt], int))
    ])

  if molColName in properties:
    properties.remove(molColName)
  if idName in properties:
    properties.remove(idName)
  writer.SetProps(properties)
  for row in df.iterrows():
    # make a local copy I can modify
    mol = Chem.Mol(row[1][molColName])

    if idName is not None:
      if idName == 'RowID':
        mol.SetProp('_Name', str(row[0]))
      else:
        mol.SetProp('_Name', str(row[1][idName]))
    for p in properties:
      cell_value = row[1][p]
      # Make sure float does not get formatted in E notation
      if np.issubdtype(type(cell_value), float):
        s = '{:f}'.format(cell_value).rstrip("0")  # "f" will show 7.0 as 7.00000
        if s[-1] == ".":
          s += "0"  # put the "0" back on if it's something like "7."
        mol.SetProp(p, s)
      else:
        mol.SetProp(p, str(cell_value))
    writer.write(mol)
  writer.close()
  if close is not None:
    close()


_saltRemover = None


def RemoveSaltsFromFrame(frame, molCol='ROMol'):
  '''
  Removes salts from mols in pandas DataFrame's ROMol column
  '''
  global _saltRemover
  if _saltRemover is None:
    from rdkit.Chem import SaltRemover
    _saltRemover = SaltRemover.SaltRemover()
  frame[molCol] = frame.apply(lambda x: _saltRemover.StripMol(x[molCol]), axis=1)


def SaveSMILESFromFrame(frame, outFile, molCol='ROMol', NamesCol='', isomericSmiles=False):
  '''
  Saves smi file. SMILES are generated from column with RDKit molecules. Column
  with names is optional.
  '''
  w = Chem.SmilesWriter(outFile, isomericSmiles=isomericSmiles)
  if NamesCol != '':
    for m, n in zip(frame[molCol], (str(c) for c in frame[NamesCol])):
      m.SetProp('_Name', n)
      w.write(m)
    w.close()
  else:
    for m in frame[molCol]:
      w.write(m)
    w.close()


def SaveXlsxFromFrame(frame, outFile, molCol='ROMol', size=(300, 300)):
  """
    Saves pandas DataFrame as a xlsx file with embedded images.
    It maps numpy data types to excel cell types:
    int, float -> number
    datetime -> datetime
    object -> string (limited to 32k character - xlsx limitations)

    Cells with compound images are a bit larger than images due to excel.
    Column width weirdness explained (from xlsxwriter docs):
    The width corresponds to the column width value that is specified in Excel.
    It is approximately equal to the length of a string in the default font of Calibri 11.
    Unfortunately, there is no way to specify "AutoFit" for a column in the Excel file format.
    This feature is only available at runtime from within Excel.
    """

  import xlsxwriter  # don't want to make this a RDKit dependency

  cols = list(frame.columns)
  cols.remove(molCol)
  dataTypes = dict(frame.dtypes)

  workbook = xlsxwriter.Workbook(outFile)  # New workbook
  worksheet = workbook.add_worksheet()  # New work sheet
  worksheet.set_column('A:A', size[0] / 6.)  # column width

  # Write first row with column names
  c2 = 1
  for x in cols:
    worksheet.write_string(0, c2, x)
    c2 += 1

  c = 1
  for _, row in frame.iterrows():
    image_data = BytesIO()
    img = Draw.MolToImage(row[molCol], size=size)
    img.save(image_data, format='PNG')

    worksheet.set_row(c, height=size[1])  # looks like height is not in px?
    worksheet.insert_image(c, 0, "f", {'image_data': image_data})

    c2 = 1
    for x in cols:
      if str(dataTypes[x]) == "object":
        worksheet.write_string(c, c2, str(row[x])[:32000])  # string length is limited in xlsx
      elif ('float' in str(dataTypes[x])) or ('int' in str(dataTypes[x])):
        if (row[x] != np.nan) or (row[x] != np.inf):
          worksheet.write_number(c, c2, row[x])
      elif 'datetime' in str(dataTypes[x]):
        worksheet.write_datetime(c, c2, row[x])
      c2 += 1
    c += 1

  workbook.close()
  image_data.close()


def FrameToGridImage(frame, column='ROMol', legendsCol=None, **kwargs):
  '''
  Draw grid image of mols in pandas DataFrame.
  '''
  if legendsCol:
    if legendsCol == frame.index.name:
      kwargs['legends'] = [str(c) for c in frame.index]
    else:
      kwargs['legends'] = [str(c) for c in frame[legendsCol]]
  return Draw.MolsToGridImage(frame[column], **kwargs)


def AddMurckoToFrame(frame, molCol='ROMol', MurckoCol='Murcko_SMILES', Generic=False):
  '''
  Adds column with SMILES of Murcko scaffolds to pandas DataFrame.

  Generic set to true results in SMILES of generic framework.
  '''
  if Generic:
    func = lambda x: Chem.MolToSmiles(MurckoScaffold.MakeScaffoldGeneric(
      MurckoScaffold.GetScaffoldForMol(x[molCol])))
  else:
    func = lambda x: Chem.MolToSmiles(MurckoScaffold.GetScaffoldForMol(x[molCol]))
  frame[MurckoCol] = frame.apply(func, axis=1)


def AlignMol(mol, scaffold):
  """
  Aligns mol (RDKit mol object) to scaffold (SMILES string)
  """
  scaffold = Chem.MolFromSmiles(scaffold)
  AllChem.Compute2DCoords(scaffold)
  AllChem.GenerateDepictionMatching2DStructure(mol, scaffold)
  return mol


def AlignToScaffold(frame, molCol='ROMol', scaffoldCol='Murcko_SMILES'):
  '''
  Aligns molecules in molCol to scaffolds in scaffoldCol
  '''
  frame[molCol] = frame.apply(lambda x: AlignMol(x[molCol], x[scaffoldCol]), axis=1)


# ==========================================================================================
# Monkey patching RDkit functionality
def InstallPandasTools():
  """ Monkey patch a few RDkit methods of Chem.Mol """
  global _originalSettings
  if len(_originalSettings) == 0:
    _originalSettings['Chem.Mol.__ge__'] = Chem.Mol.__ge__
    _originalSettings['Chem.Mol.__str__'] = Chem.Mol.__str__
  rdchem.Mol.__ge__ = _molge
  rdchem.Mol.__str__ = PrintAsBase64PNGString


def UninstallPandasTools():
  """ Monkey patch a few RDkit methods of Chem.Mol """
  global _originalSettings
  Chem.Mol.__ge__ = _originalSettings['Chem.Mol.__ge__']
  Chem.Mol.__str__ = _originalSettings['Chem.Mol.__str__']


_originalSettings = {}
InstallPandasTools()


# ------------------------------------
#
#  doctest boilerplate
#
def _runDoctests(verbose=None):  # pragma: nocover
  import doctest
  failed, _ = doctest.testmod(optionflags=doctest.ELLIPSIS + doctest.NORMALIZE_WHITESPACE,
                              verbose=verbose)
  if (failed):
    sys.exit(failed)


if __name__ == '__main__':  # pragma: nocover
  import unittest
  try:
    import xlsxwriter
  except ImportError:
    xlsxwriter = None

  class TestCase(unittest.TestCase):

    @unittest.skipIf(xlsxwriter is None, 'xlsxwriter not installed')
    def testGithub1507(self):
      import os
      from rdkit import RDConfig
      sdfFile = os.path.join(RDConfig.RDDataDir, 'NCI/first_200.props.sdf')
      frame = LoadSDF(sdfFile)
      SaveXlsxFromFrame(frame, 'foo.xlsx')

  if pd is None:
    print("pandas installation not found, skipping tests", file=sys.stderr)
  elif _getPandasVersion() < (0, 10):
    print("pandas installation >=0.10 not found, skipping tests", file=sys.stderr)
  else:
    _runDoctests()
    unittest.main()

# $Id$
#
#  Copyright (c) 2013, Novartis Institutes for BioMedical Research Inc.
#  All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met:
#
#     * Redistributions of source code must retain the above copyright
#       notice, this list of conditions and the following disclaimer.
#     * Redistributions in binary form must reproduce the above
#       copyright notice, this list of conditions and the following
#       disclaimer in the documentation and/or other materials provided
#       with the distribution.
#     * Neither the name of Novartis Institutes for BioMedical Research Inc.
#       nor the names of its contributors may be used to endorse or promote
#       products derived from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
# OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
