'''
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)
            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']])
            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)>

The standard ForwardSDMolSupplier keywords are also available:

>>> sdfFile = os.path.join(RDConfig.RDDataDir,'NCI/first_200.props.sdf')
>>> frame = PandasTools.LoadSDF(sdfFile,smilesName='SMILES',molColName='Molecule',
...            includeFingerprints=True, removeHs=False, strictParsing=True)

Conversion to html is quite easy:

>>> htm = frame.to_html() # doctest:
...
>>> 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 base64 import b64encode
import sys
import types
import logging

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 import SDWriter
from rdkit.Chem import rdchem
from rdkit.Chem.Scaffolds import MurckoScaffold
from io import BytesIO
from xml.dom import minidom
from xml.parsers.expat import ExpatError

log = logging.getLogger(__name__)

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)

  pandasVersion = _getPandasVersion()
  if pandasVersion < (0, 10):
    print("Pandas version {0} not compatible with tests".format(_getPandasVersion()),
          file=sys.stderr)
    pd = None
  else:
    # saves the default pandas rendering to allow restoration
    defPandasRendering = pd.core.frame.DataFrame.to_html
    if pandasVersion > (0, 25, 0):
      # this was github #2673
      defPandasRepr = pd.core.frame.DataFrame._repr_html_
    else:
      defPandasRepr = None
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 _patched_HTMLFormatter_write_cell(self, s, *args, **kwargs):
  def_escape = self.escape
  try:
    if is_molecule_image(s):
      self.escape = False
    return defHTMLFormatter_write_cell(self, s, *args, **kwargs)
  finally:
    self.escape = def_escape


def _patched_get_adjustment():
  inner_adjustment = defPandasGetAdjustment()
  return RenderMoleculeAdjustment(inner_adjustment)


def patchPandasrepr(self, **kwargs):
  """  used to patch DataFrame._repr_html_ in pandas version > 0.25.0
  """
  global defHTMLFormatter_write_cell
  global defPandasGetAdjustment

  import pandas.io.formats.html  # necessary for loading HTMLFormatter
  defHTMLFormatter_write_cell = pandas.io.formats.html.HTMLFormatter._write_cell
  pandas.io.formats.html.HTMLFormatter._write_cell = _patched_HTMLFormatter_write_cell
  # Github #3701 was a problem with a private function being renamed (made public) in
  # pandas v1.2. Rather than relying on version numbers we just use getattr:
  if hasattr(pandas.io.formats.format, '_get_adjustment'):
    attr = '_get_adjustment'
  else:
    # if this one doesn't work at some point in the future it's another bug
    # and we'll add another patch for it. <sigh>
    attr = 'get_adjustment'

  defPandasGetAdjustment = getattr(pandas.io.formats.format, attr)
  setattr(pandas.io.formats.format, attr, _patched_get_adjustment)
  res = defPandasRepr(self, **kwargs)
  setattr(pandas.io.formats.format, attr, defPandasGetAdjustment)
  pandas.io.formats.html.HTMLFormatter._write_cell = defHTMLFormatter_write_cell
  return res


def patchPandasHTMLrepr(self, **kwargs):
  """A patched version of the DataFrame.to_html method that allows rendering
    molecule images in data frames.
  """
  global defHTMLFormatter_write_cell
  global defPandasGetAdjustment

  # Two things have to be done:
  # 1. Disable escaping of HTML in order to render img / svg tags
  # 2. Avoid truncation of data frame values that contain HTML content

  # The correct patch requires that two private methods in pandas exist. If
  # this is not the case, use a working but suboptimal patch:
  def patch_v1():
    with pd.option_context('display.max_colwidth', -1):  # do not truncate
      kwargs['escape'] = False  # disable escaping
      return defPandasRendering(self, **kwargs)

  try:
    import pandas.io.formats.html  # necessary for loading HTMLFormatter
  except:
    # this happens up until at least pandas v0.22
    return patch_v1()
  else:
    if not hasattr(pd.io.formats.html, 'HTMLFormatter') or \
      not hasattr(pd.io.formats.html.HTMLFormatter, '_write_cell') or \
      not hasattr(pd.io.formats.format, '_get_adjustment'):
      return patch_v1()

  # The "clean" patch:
  # 1. Temporarily set escape=False in HTMLFormatter._write_cell
  defHTMLFormatter_write_cell = pd.io.formats.html.HTMLFormatter._write_cell

  # 2. Pandas uses TextAdjustment objects to measure the length of texts
  #    (e.g. for east asian languages). We take advantage of this mechanism
  #    and replace the original text adjustment object with a custom one.
  #    This "RenderMoleculeAdjustment" object assigns a length of 0 to a
  #    given text if it is valid HTML. And a value having length 0 will not
  #    be truncated.

  # store original _get_adjustment method
  defPandasGetAdjustment = pd.io.formats.format._get_adjustment

  try:
    # patch methods and call original to_html function
    pd.io.formats.format._get_adjustment = _patched_get_adjustment
    pd.io.formats.html.HTMLFormatter._write_cell = _patched_HTMLFormatter_write_cell
    return defPandasRendering(self, **kwargs)
  except:
    pass
  finally:
    # restore original methods
    pd.io.formats.format._get_adjustment = defPandasGetAdjustment
    pd.io.formats.html.HTMLFormatter._write_cell = defHTMLFormatter_write_cell

  # If this point is reached, an error occurred in the previous try block.
  # Use old patch:
  return patch_v1()


def is_molecule_image(s):
  result = False
  try:
    # is text valid XML / HTML?
    xml = minidom.parseString(s)
    root_node = xml.firstChild
    # check data-content attribute
    if root_node.nodeName in ['svg', 'img'] and \
       'data-content' in root_node.attributes.keys() and \
       root_node.attributes['data-content'].value == 'rdkit/molecule':
      result = True
  except ExpatError:
    pass  # parsing xml failed and text is not a molecule image

  return result


class RenderMoleculeAdjustment:

  def __init__(self, inner_adjustment):
    """Creates a new instance.

        @param inner_adjustment: The text adjustment that is used if the
            specified text is not valid XML / HTML.
        """
    self.inner_adjustment = inner_adjustment

  def len(self, text):
    if is_molecule_image(text):
      return 0
    else:
      return self.inner_adjustment.len(text)

  def justify(self, texts, max_len, mode='right'):
    return self.inner_adjustment.justify(texts, max_len, mode)

  def adjoin(self, space, *lists, **kwargs):
    return self.inner_adjustment.adjoin(space, *lists, **kwargs)


def _get_image(x):
  """displayhook function for PNG data"""
  return b64encode(x).decode('ascii')


try:
  from rdkit.Avalon import pyAvalonTools as pyAvalonTools

  # Calculate the Avalon fingerprint


  def _fingerprinter(x, y):
    return pyAvalonTools.GetAvalonFP(x, isQuery=y, bitFlags=pyAvalonTools.avalonSSSBits)
except ImportError:
  # Calculate fingerprint using SMARTS patterns
  def _fingerprinter(x, y):
    return 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 = []
  # TODO: should we generate coordinates if no coordinates available?
  # from rdkit.Chem import rdDepictor
  # try:
  #     # If no coordinates, calculate 2D
  #     x.GetConformer(-1)
  # except ValueError:
  #     rdDepictor.Compute2DCoords(x)
  if molRepresentation.lower() == 'svg':
    svg = Draw._moltoSVG(x, molSize, highlightAtoms, "", True)
    svg = minidom.parseString(svg)
    svg = svg.getElementsByTagName('svg')[0]
    svg.attributes['viewbox'] = f'0 0 {molSize[0]} {molSize[1]}'
    svg.attributes['style'] = f'max-width: {molSize[0]}px; height: {molSize[1]}px;'
    svg.attributes['data-content'] = 'rdkit/molecule'
    return svg.toxml()
  else:
    data = Draw._moltoimg(x, molSize, highlightAtoms, "", returnPNG=True, kekulize=True)
    return '<img data-content="rdkit/molecule" 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
    if defPandasRepr is not None:
      pd.core.frame.DataFrame._repr_html_ = patchPandasrepr
  else:
    pd.core.frame.DataFrame.to_html = defPandasRendering
    if defPandasRepr is not None:
      pd.core.frame.DataFrame._repr_html_ = defPandasRepr


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)
    if defPandasRepr is not None:
      frame._repr_html_ = types.MethodType(defPandasRepr, frame)


def LoadSDF(filename, idName='ID', molColName='ROMol', includeFingerprints=False,
            isomericSmiles=True, smilesName=None, embedProps=False, removeHs=True,
            strictParsing=True):
  '''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, str):
    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 = []
  sanitize = bool(molColName is not None or smilesName is not None)
  for i, mol in enumerate(
      Chem.ForwardSDMolSupplier(f, sanitize=sanitize, removeHs=removeHs,
                                strictParsing=strictParsing)):
    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:
      try:
        row[smilesName] = Chem.MolToSmiles(mol, isomericSmiles=isomericSmiles)
      except:
        log.warning('No valid smiles could be generated for molecule %s', i)
        row[smilesName] = None
    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, str):
    if out.lower()[-3:] == ".gz":
      import gzip
      out = gzip.open(out, "wt")
      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], np.floating) or np.issubdtype(df.dtypes[dt], np.integer))
    ])

  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), np.floating):
        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":
        # string length is limited in xlsx
        worksheet.write_string(c, c2, str(row[x])[:32000])
      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(list(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:

    def func(x):
      return Chem.MolToSmiles(
        MurckoScaffold.MakeScaffoldGeneric(MurckoScaffold.GetScaffoldForMol(x[molCol])))
  else:

    def func(x):
      return 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)


def RGroupDecompositionToFrame(groups, mols, include_core=False, redraw_sidechains=False):
  """ returns a dataframe with the results of R-Group Decomposition

  >>> from rdkit import Chem
  >>> from rdkit.Chem import rdRGroupDecomposition
  >>> from rdkit.Chem import PandasTools
  >>> import pandas as pd
  >>> scaffold = Chem.MolFromSmiles('c1ccccn1')
  >>> mols = [Chem.MolFromSmiles(smi) for smi in 'c1c(F)cccn1 c1c(Cl)c(C)ccn1 c1c(O)cccn1 c1c(F)c(C)ccn1 c1cc(Cl)c(F)cn1'.split()]
  >>> groups,_ = rdRGroupDecomposition.RGroupDecompose([scaffold],mols,asSmiles=False,asRows=False) 
  >>> df = PandasTools.RGroupDecompositionToFrame(groups,mols,include_core=True)
  >>> list(df.columns)
  ['Mol', 'Core', 'R1', 'R2']
  >>> len(df)
  5
  >>> df.columns() # doctest: +SKIP
  <class 'pandas*...*DataFrame'>
  RangeIndex: 5 entries, 0 to 4
  Data columns (total 4 columns):
  Mol     5 non-null object
  Core    5 non-null object
  R1      5 non-null object
  R2      5 non-null object
  dtypes: object(4)
  memory usage: *...*

  """
  cols = ['Mol'] + list(groups.keys())
  if redraw_sidechains:
    from rdkit.Chem import rdDepictor
    for k, vl in groups.items():
      if k == 'Core':
        continue
      for i, v in enumerate(vl):
        vl[i] = Chem.RemoveHs(v)
        rdDepictor.Compute2DCoords(vl[i])

  if not include_core:
    cols.remove('Core')
    del groups['Core']
  groups['Mol'] = mols
  frame = pd.DataFrame(groups, columns=cols)
  return frame


# ==========================================================================================
# 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')

    def testGithub3701(self):
      ' problem with update to pandas v1.2.0 '
      df = pd.DataFrame({"name": ["ethanol", "furan"], "smiles": ["CCO", "c1ccoc1"]})
      AddMoleculeColumnToFrame(df, 'smiles', 'molecule')
      self.assertEqual(len(df.molecule), 2)

  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()

#
#  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.
#
