def mol2_to_dataframes(filename):
"""Convert a GAFF (or sybyl) mol2 file to a pair of pandas dataframes.
Parameters
----------
filename : str
Name of mol2 filename
Returns
-------
atoms_frame : pd.DataFrame
DataFrame containing atom information
bonds_frame : pd.DataFrame
DataFrame containing bond information
Notes
-----
These dataframes may contain force field information as well as the
information necessary for constructing the coordinates and molecular
topology. This function has been tested for GAFF and sybyl-style
mol2 files but has been primarily tested on GAFF mol2 files.
This function does NOT accept multi-structure MOL2 files!!!
See Also
--------
If you just need the coordinates and bonds, use load_mol2(filename)
to get a Trajectory object.
"""
pd = import_('pandas')
with open(filename) as f:
data = dict((key, list(grp)) for key, grp in itertools.groupby(f, _parse_mol2_sections))
# Mol2 can have "status bits" at the end of the bond lines. We don't care
# about these, but they interfere with using pd_read_table because it looks
# like one line has too many columns. So we just regex out the offending
# text.
status_bit_regex = "BACKBONE|DICT|INTERRES|\|"
data["@<TRIPOS>BOND\n"] = [re.sub(status_bit_regex, lambda _: "", s)
for s in data["@<TRIPOS>BOND\n"]]
if len(data["@<TRIPOS>BOND\n"]) > 1:
csv = StringIO()
csv.writelines(data["@<TRIPOS>BOND\n"][1:])
csv.seek(0)
bonds_frame = pd.read_table(csv, names=["bond_id", "id0", "id1", "bond_type"],
index_col=0, header=None, sep="\s*", engine='python')
else:
bonds_frame = None
csv = StringIO()
csv.writelines(data["@<TRIPOS>ATOM\n"][1:])
csv.seek(0)
atoms_frame = pd.read_csv(csv, sep="\s*", engine='python', header=None)
ncols = atoms_frame.shape[1]
names=["serial", "name", "x", "y", "z", "atype", "code", "resName", "charge", "status"]
atoms_frame.columns = names[:ncols]
return atoms_frame, bonds_frame
def mol2_to_dataframes(filename):
"""Convert a GAFF (or sybyl) mol2 file to a pair of pandas dataframes.
Parameters
----------
filename : str
Name of mol2 filename
Returns
-------
atoms_frame : pd.DataFrame
DataFrame containing atom information
bonds_frame : pd.DataFrame
DataFrame containing bond information
Notes
-----
These dataframes may contain force field information as well as the
information necessary for constructing the coordinates and molecular
topology. This function has been tested for GAFF and sybyl-style
mol2 files but has been primarily tested on GAFF mol2 files.
This function does NOT accept multi-structure MOL2 files!!!
See Also
--------
If you just need the coordinates and bonds, use load_mol2(filename)
to get a Trajectory object.
"""
pd = import_('pandas')
with open(filename) as f:
data = dict((key, list(grp)) for key, grp in itertools.groupby(f, _parse_mol2_sections))
# Mol2 can have "status bits" at the end of the bond lines. We don't care
# about these, but they interfere with using pd_read_table because it looks
# like one line has too many columns. So we just regex out the offending
# text.
status_bit_regex = "BACKBONE|DICT|INTERRES|\|"
data["@<TRIPOS>BOND\n"] = [re.sub(status_bit_regex, lambda _: "", s)
for s in data["@<TRIPOS>BOND\n"]]
if len(data["@<TRIPOS>BOND\n"]) > 1:
csv = StringIO()
csv.writelines(data["@<TRIPOS>BOND\n"][1:])
csv.seek(0)
bonds_frame = pd.read_table(csv, names=["bond_id", "id0", "id1", "bond_type"],
index_col=0, header=None, sep="\s*", engine='python')
else:
bonds_frame = None
csv = StringIO()
csv.writelines(data["@<TRIPOS>ATOM\n"][1:])
csv.seek(0)
atoms_frame = pd.read_csv(csv, sep="\s*", engine='python', header=None)
ncols = atoms_frame.shape[1]
names=["serial", "name", "x", "y", "z", "atype", "code", "resName", "charge", "status"]
atoms_frame.columns = names[:ncols]
return atoms_frame, bonds_frame
def mol2_to_dataframes(filename):
"""Convert a GAFF (or sybyl) mol2 file to a pair of pandas dataframes.
Parameters
----------
filename : str
Name of mol2 filename
Returns
-------
atoms_frame : pd.DataFrame
DataFrame containing atom information
bonds_frame : pd.DataFrame
DataFrame containing bond information
Notes
-----
These dataframes may contain force field information as well as the
information necessary for constructing the coordinates and molecular
topology. This function has been tested for GAFF and sybyl-style
mol2 files but has been primarily tested on GAFF mol2 files.
This function does NOT accept multi-structure MOL2 files!!!
See Also
--------
If you just need the coordinates and bonds, use load_mol2(filename)
to get a Trajectory object.
"""
pd = import_('pandas')
with open(filename) as f:
data = dict((key, list(grp))
for key, grp in itertools.groupby(f, _parse_mol2_sections))
csv = StringIO()
csv.writelines(data["@<TRIPOS>BOND\n"][1:])
csv.seek(0)
bonds_frame = pd.read_table(csv,
names=["bond_id", "id0", "id1", "bond_type"],
index_col=0,
header=None,
sep="\s*",
engine='python')
csv = StringIO()
csv.writelines(data["@<TRIPOS>ATOM\n"][1:])
csv.seek(0)
atoms_frame = pd.read_csv(csv,
sep="\s*",
engine='python',
header=None,
names=[
"serial", "name", "x", "y", "z", "atype",
"code", "resName", "charge"
])
return atoms_frame, bonds_frame
def mol2_to_dataframes(filename):
"""Convert a GAFF (or sybyl) mol2 file to a pair of pandas dataframes.
Parameters
----------
filename : str
Name of mol2 filename
Returns
-------
atoms_frame : pd.DataFrame
DataFrame containing atom information
bonds_frame : pd.DataFrame
DataFrame containing bond information
Notes
-----
These dataframes may contain force field information as well as the
information necessary for constructing the coordinates and molecular
topology. This function has been tested for GAFF and sybyl-style
mol2 files but has been primarily tested on GAFF mol2 files.
This function does NOT accept multi-structure MOL2 files!!!
See Also
--------
If you just need the coordinates and bonds, use load_mol2(filename)
to get a Trajectory object.
"""
pd = import_("pandas")
with open(filename) as f:
data = dict((key, list(grp)) for key, grp in itertools.groupby(f, _parse_mol2_sections))
csv = StringIO()
csv.writelines(data["@<TRIPOS>BOND\n"][1:])
csv.seek(0)
bonds_frame = pd.read_table(csv, names=["bond_id", "id0", "id1", "bond_type"], index_col=0, header=None, sep="\s*")
csv = StringIO()
csv.writelines(data["@<TRIPOS>ATOM\n"][1:])
csv.seek(0)
atoms_frame = pd.read_csv(
csv, sep="\s*", names=["serial", "name", "x", "y", "z", "atype", "code", "resName", "charge"], header=None
) # , usecols=range(1, 10)) # usecols not available in pandas 0.11
return atoms_frame, bonds_frame