def outputRedirect():
""" Redirect standard output and error to String IO and return """
try:
_stdout, _stderr = sys.stdout, sys.stderr
sys.stdout = sStdout = StringIO()
sys.stderr = sStderr = StringIO()
yield (sStdout, sStderr)
finally:
sys.stdout, sys.stderr = _stdout, _stderr
def _testStreamRoundtrip(self):
inD = open(self.fName).read()
supp = Chem.SDMolSupplier(self.fName)
outName = tempfile.mktemp('.sdf')
writer = Chem.SDWriter(outName)
m1 = next(supp)
for m in supp:
writer.write(m)
writer.flush()
writer = None
outD = open(outName,'r').read()
try:
os.unlink(outName)
except:
import time
time.sleep(1)
try:
os.unlink(outName)
except:
pass
assert inD.count('$$$$')==outD.count('$$$$'),'bad nMols in output'
io = StringIO(outD)
supp = Chem.SDMolSupplier(stream=io)
outD2 = supp.Dump()
assert outD2.count('$$$$')==len(supp),'bad nMols in output'
assert outD2.count('$$$$')==outD.count('$$$$'),'bad nMols in output'
assert outD2==outD,'bad outd'
def _initPatterns(self):
"""
>>> remover = SaltRemover()
>>> len(remover.salts)>0
True
>>> remover = SaltRemover(defnData="[Cl,Br]")
>>> len(remover.salts)
1
"""
whitespace = re.compile(r'[\t ]+')
if self.defnData:
from rdkit.six.moves import cStringIO as StringIO
inF = StringIO(self.defnData)
else:
inF = open(self.defnFilename, 'r')
self.salts = []
for line in inF:
line = line.strip().split('//')[0]
if line:
splitL = whitespace.split(line)
try:
salt = Chem.MolFromSmarts(splitL[0])
except:
import traceback
traceback.print_exc()
raise ValueError(line)
self.salts.append(salt)
def test_load_from_sio(self):
sio = StringIO(methane + peroxide)
df = PandasTools.LoadSDF(sio)
self.assertEqual(len(df), 2)
self.assertEqual(list(df["ID"]), ["Methane", "Peroxide"])
atom_counts = [mol.GetNumAtoms() for mol in df["ROMol"]]
self.assertEqual(atom_counts, [1, 2])
def test_load_specify_column_names(self):
sio = StringIO(methane + peroxide)
df = PandasTools.LoadSDF(sio, idName="CorpID", molColName="_rdmol")
self.assertEqual(len(df), 2)
self.assertEqual(list(df["CorpID"]), ["Methane", "Peroxide"])
atom_counts = [mol.GetNumAtoms() for mol in df["_rdmol"]]
self.assertEqual(atom_counts, [1, 2])
def test_all_numeric_with_no_numeric_columns(self):
sio = StringIO()
PandasTools.WriteSDF(self.df, sio, allNumeric=True)
s = sio.getvalue()
self.assertFalse(">" in s, s)
self.assertNotIn("7\n\n", s) # double-check that the numeric tests don't pass by accident
self.assertNotIn("8\n\n", s)
def _initPatterns(self):
"""
>>> remover = SaltRemover()
>>> len(remover.salts)>0
True
>>> remover = SaltRemover(defnData="[Cl,Br]")
>>> len(remover.salts)
1
>>> from rdkit import RDLogger
>>> RDLogger.DisableLog('rdApp.error')
>>> remover = SaltRemover(defnData="[Cl,fail]")
Traceback (most recent call last):
...
ValueError: [Cl,fail]
>>> RDLogger.EnableLog('rdApp.error')
"""
whitespace = re.compile(r'[\t ]+')
if self.defnData:
from rdkit.six.moves import cStringIO as StringIO
inF = StringIO(self.defnData)
else:
inF = open(self.defnFilename, 'r')
self.salts = []
for line in inF:
line = line.strip().split('//')[0]
if line:
splitL = whitespace.split(line)
salt = Chem.MolFromSmarts(splitL[0])
if salt is None:
raise ValueError(line)
self.salts.append(salt)
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 index, row in frame.iterrows():
image_data = StringIO()
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 test_specify_numeric_column(self):
sio = StringIO()
df = self.df
df["len2"] = df["ID"].map(len)
PandasTools.WriteSDF(df, sio, properties=["len2"])
s = sio.getvalue()
self.assertEqual(s.count("<len2>"), 2)
self.assertIn("7\n\n", s)
self.assertIn("8\n\n", s)
def test_all_numeric_with_numeric_columns(self):
sio = StringIO()
df = self.df
df["len"] = df["ID"].map(len)
PandasTools.WriteSDF(df, sio, allNumeric=True)
s = sio.getvalue()
self.assertEqual(s.count("<len>"), 2)
self.assertIn("7\n\n", s)
self.assertIn("8\n\n", s)
def _initPatterns(self):
"""
>>> remover = SaltRemover()
>>> len(remover.salts)>0
True
Default input format is SMARTS
>>> remover = SaltRemover(defnData="[Cl,Br]")
>>> len(remover.salts)
1
>>> remover = SaltRemover(defnData="[Na+]\\nCC(=O)O", defnFormat=InputFormat.SMILES)
>>> len(remover.salts)
2
>>> from rdkit import RDLogger
>>> RDLogger.DisableLog('rdApp.error')
>>> remover = SaltRemover(defnData="[Cl,fail]")
Traceback (most recent call last):
...
ValueError: [Cl,fail]
>>> RDLogger.EnableLog('rdApp.error')
"""
if self.defnData:
from rdkit.six.moves import cStringIO as StringIO
inF = StringIO(self.defnData)
with closing(inF):
self.salts = []
for line in inF:
if line:
if self.defnFormat == InputFormat.SMARTS:
salt = _smartsFromSmartsLine(line)
elif self.defnFormat == InputFormat.SMILES:
salt = Chem.MolFromSmiles(line)
else:
raise ValueError(
'Unsupported format for supplier.')
if salt is None:
raise ValueError(line)
self.salts.append(salt)
else:
if self.defnFormat == InputFormat.SMARTS:
self.salts = [
mol for mol in _getSmartsSaltsFromFile(self.defnFilename)
]
elif self.defnFormat == InputFormat.MOL:
self.salts = [mol for mol in SDMolSupplier(self.defnFilename)]
elif self.defnFormat == InputFormat.SMILES:
self.salts = [
mol for mol in SmilesMolSupplier(self.defnFilename)
]
else:
raise ValueError('Unsupported format for supplier.')
def test_properties(self):
sio = StringIO(peroxide + methane)
df = PandasTools.LoadSDF(sio)
self.assertEqual(set(df.columns), set("ROMol ID prop1 prop2 prop3".split()))
prop1 = list(df["prop1"])
self.assertTrue(numpy.isnan(prop1[0]), prop1[0])
self.assertEqual(prop1[1], "12.34")
self.assertEqual(list(df["prop2"]), ["rtz", "qwe"])
prop3 = list(df["prop3"])
self.assertEqual(prop3[0], "yxcv")
self.assertTrue(numpy.isnan(prop3[1]), prop3[1])
def test2(self):
fName = os.path.join(RDConfig.RDDataDir, 'NCI', 'first_200.props.sdf')
suppl = Chem.SDMolSupplier(fName)
io = StringIO()
try:
Convert(suppl, io, keyCol='AMW', stopAfter=5)
except Exception:
import traceback
traceback.print_exc()
self.fail('conversion failed')
txt = io.getvalue()
lines = [line for line in txt.split('\n') if line.strip() != '']
self.assertTrue(len(lines) == 6, 'bad num lines: %d' % len(lines))
line0 = lines[0].split(',')
self.assertEqual(len(line0), 20)
self.assertTrue(line0[0] == 'AMW')
self.assertTrue(line0[1] == 'SMILES')
def test1(self):
fName = os.path.join(RDConfig.RDDataDir, 'NCI', 'first_200.props.sdf')
suppl = Chem.SDMolSupplier(fName)
io = StringIO()
try:
Convert(suppl, io)
except Exception:
import traceback
traceback.print_exc()
self.fail('conversion failed')
txt = io.getvalue()
lines = txt.split('\n')
if not lines[-1]:
del lines[-1]
self.assertTrue(len(lines) == 201, 'bad num lines: %d' % len(lines))
line0 = lines[0].split(',')
self.assertEqual(len(line0), 20)
self.assertTrue(line0[0] == 'SMILES')
def test2(self):
import os
from rdkit.six.moves import cStringIO as StringIO #@UnresolvedImport #pylint: disable=F0401
fName = os.path.join(RDConfig.RDDataDir, 'NCI', 'first_200.props.sdf')
suppl = Chem.SDMolSupplier(fName)
io = StringIO()
try:
Convert(suppl, io, keyCol='AMW', stopAfter=5)
except Exception:
import traceback
traceback.print_exc()
self.fail('conversion failed')
txt = io.getvalue()
lines = txt.split('\n')
if not lines[-1]:
del lines[-1]
self.assertTrue(len(lines) == 6, 'bad num lines: %d' % len(lines))
line0 = lines[0].split(',')
self.assertEqual(len(line0), 20)
self.assertTrue(line0[0] == 'AMW')
self.assertTrue(line0[1] == 'SMILES')
def test_passed_in_file_is_not_closed(self):
sio = StringIO(methane)
df = PandasTools.LoadSDF(sio)
self.assertEqual(len(df), 1)
self.assertFalse(sio.closed)
def test_empty_file(self):
# Should return an empty data frame with no rows or columns
sio = StringIO()
df = PandasTools.LoadSDF(sio)
self.assertEqual(len(df), 0)
self.assertEqual(len(df.index), 0)
def test_ignore_mol_column(self):
sio = StringIO(peroxide + methane)
df = PandasTools.LoadSDF(sio, molColName=None)
self.assertEqual(set(df.columns), set("ID prop1 prop2 prop3".split()))
def setUp(self):
sio = StringIO(methane + peroxide)
self.df = PandasTools.LoadSDF(sio)
def BuildFuncGroupHierarchy(fileNm=None,data=None,force=False):
global groupDefns,hierarchy,lastData,lastFilename
if not force and hierarchy and (not data or data==lastData) and \
(not fileNm or fileNm==lastFilename):
return hierarchy[:]
lastData=data
splitter = re.compile('\t+')
from rdkit import Chem
if not fileNm and not data:
fileNm = os.path.join(RDConfig.RDDataDir,'Functional_Group_Hierarchy.txt')
if fileNm:
inF = open(fileNm,'r')
lastFilename = fileNm
elif data:
inF = StringIO(data)
else:
raise ValueError("need data or filename")
groupDefns={}
res = []
lineNo=0
for line in inF.readlines():
lineNo+=1
line=line.strip()
line = line.split('//')[0]
if not line:
continue
splitL = splitter.split(line)
if len(splitL)<3:
raise FuncGroupFileParseError("Input line %d (%s) is not long enough."%(lineNo,repr(line)))
label = splitL[0].strip()
if label in groupDefns:
raise FuncGroupFileParseError("Duplicate label on line %d."%lineNo)
labelHierarchy = label.split('.')
if len(labelHierarchy)>1:
for i in range(len(labelHierarchy)-1):
tmp = '.'.join(labelHierarchy[:i+1])
if not tmp in groupDefns:
raise FuncGroupFileParseError("Hierarchy member %s (line %d) not found."%(tmp,lineNo))
parent = groupDefns['.'.join(labelHierarchy[:-1])]
else:
parent = None
smarts = splitL[1]
patt = Chem.MolFromSmarts(smarts)
if not patt:
raise FuncGroupFileParseError('Smarts "%s" (line %d) could not be parsed.'%(smarts,lineNo))
name = splitL[2].strip()
rxnSmarts=''
if len(splitL)>3:
rxnSmarts=splitL[3]
node = FGHierarchyNode(name,patt,smarts=smarts,label=label,parent=parent,rxnSmarts=rxnSmarts)
if parent:
parent.children.append(node)
else:
res.append(node)
groupDefns[label] = node
hierarchy=res[:]
return res
def test_default_write_does_not_include_tags(self):
sio = StringIO()
PandasTools.WriteSDF(self.df, sio)
s = sio.getvalue()
self.assertNotIn(s, "prop2")
def test_identifier_from_a_column(self):
sio = StringIO()
PandasTools.WriteSDF(self.df, sio, idName="prop2")
s = sio.getvalue()
first_line = s.split("\n", 1)[0]
self.assertEqual(first_line, "qwe")