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Source code for galaxy.datatypes.molecules
import logging
import os
import re
from typing import (
Callable,
Dict,
List,
)
from galaxy.datatypes import metadata
from galaxy.datatypes.binary import Binary
from galaxy.datatypes.data import (
get_file_peek,
Text,
)
from galaxy.datatypes.metadata import MetadataElement
from galaxy.datatypes.protocols import DatasetProtocol
from galaxy.datatypes.sniff import (
build_sniff_from_prefix,
FilePrefix,
get_headers,
iter_headers,
)
from galaxy.datatypes.tabular import Tabular
from galaxy.datatypes.util.generic_util import count_special_lines
from galaxy.datatypes.xml import GenericXml
from galaxy.util import (
commands,
unicodify,
)
# optional import to enhance metadata
try:
from ase import io as ase_io
except ImportError:
ase_io = None
log = logging.getLogger(__name__)
[docs]def count_lines(filename, non_empty=False):
"""
counting the number of lines from the 'filename' file
"""
if non_empty:
cmd = ["grep", "-cve", r"^\s*$", filename]
else:
cmd = ["wc", "-l", filename]
try:
out = commands.execute(cmd)
except commands.CommandLineException as e:
log.error(unicodify(e))
return 0
return int(out.split()[0])
[docs]class GenericMolFile(Text):
"""
Abstract class for most of the molecule files.
"""
MetadataElement(
name="number_of_molecules",
default=0,
desc="Number of molecules",
readonly=True,
visible=True,
optional=True,
no_value=0,
)
[docs] def set_peek(self, dataset: DatasetProtocol, **kwd) -> None:
if not dataset.dataset.purged:
if dataset.metadata.number_of_molecules == 1:
dataset.blurb = "1 molecule"
else:
dataset.blurb = f"{dataset.metadata.number_of_molecules} molecules"
dataset.peek = get_file_peek(dataset.file_name)
else:
dataset.peek = "file does not exist"
dataset.blurb = "file purged from disk"
element_symbols = [
"Ac",
"Ag",
"Al",
"Am",
"Ar",
"As",
"At",
"Au",
"B ",
"Ba",
"Be",
"Bh",
"Bi",
"Bk",
"Br",
"C ",
"Ca",
"Cd",
"Ce",
"Cf",
"Cl",
"Cm",
"Co",
"Cr",
"Cs",
"Cu",
"Ds",
"Db",
"Dy",
"Er",
"Es",
"Eu",
"F ",
"Fe",
"Fm",
"Fr",
"Ga",
"Gd",
"Ge",
"H ",
"He",
"Hf",
"Hg",
"Ho",
"Hs",
"I ",
"In",
"Ir",
"K ",
"Kr",
"La",
"Li",
"Lr",
"Lu",
"Md",
"Mg",
"Mn",
"Mo",
"Mt",
"N ",
"Na",
"Nb",
"Nd",
"Ne",
"Ni",
"No",
"Np",
"O ",
"Os",
"P ",
"Pa",
"Pb",
"Pd",
"Pm",
"Po",
"Pr",
"Pt",
"Pu",
"Ra",
"Rb",
"Re",
"Rf",
"Rg",
"Rh",
"Rn",
"Ru",
"S ",
"Sb",
"Sc",
"Se",
"Sg",
"Si",
"Sm",
"Sn",
"Sr",
"Ta",
"Tb",
"Tc",
"Te",
"Th",
"Ti",
"Tl",
"Tm",
"U ",
"V ",
"W ",
"Xe",
"Y ",
"Yb",
"Zn",
"Zr",
]
[docs]class MOL(GenericMolFile):
file_ext = "mol"
[docs] def set_meta(self, dataset: DatasetProtocol, overwrite: bool = True, **kwd) -> None:
"""
Set the number molecules, in the case of MOL its always one.
"""
dataset.metadata.number_of_molecules = 1
[docs]@build_sniff_from_prefix
class SDF(GenericMolFile):
file_ext = "sdf"
[docs] def sniff_prefix(self, file_prefix: FilePrefix) -> bool:
"""
Try to guess if the file is a SDF2 file.
An SDfile (structure-data file) can contain multiple compounds.
Each compound starts with a block in V2000 or V3000 molfile format,
which ends with a line equal to 'M END'.
This is followed by a non-structural data block, which ends with a line
equal to '$$$$'.
>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('drugbank_drugs.sdf')
>>> SDF().sniff(fname)
True
>>> fname = get_test_fname('github88.v3k.sdf')
>>> SDF().sniff(fname)
True
>>> fname = get_test_fname('chebi_57262.v3k.mol')
>>> SDF().sniff(fname)
False
"""
m_end_found = False
limit = 10000
idx = 0
for line in file_prefix.line_iterator():
idx += 1
line = line.rstrip("\n\r")
if idx < 4:
continue
elif idx == 4:
if len(line) != 39 or not (line.endswith(" V2000") or line.endswith(" V3000")):
return False
elif not m_end_found:
if line == "M END":
m_end_found = True
elif line == "$$$$":
return True
if idx == limit:
break
return False
[docs] def set_meta(self, dataset: DatasetProtocol, overwrite: bool = True, **kwd) -> None:
"""
Set the number of molecules in dataset.
"""
dataset.metadata.number_of_molecules = count_special_lines(r"^\$\$\$\$$", dataset.file_name)
[docs] @classmethod
def split(cls, input_datasets: List, subdir_generator_function: Callable, split_params: Dict) -> None:
"""
Split the input files by molecule records.
"""
if split_params is None:
return None
if len(input_datasets) > 1:
raise Exception("SD-file splitting does not support multiple files")
input_files = [ds.file_name for ds in input_datasets]
chunk_size = None
if split_params["split_mode"] == "number_of_parts":
raise Exception(f"Split mode \"{split_params['split_mode']}\" is currently not implemented for SD-files.")
elif split_params["split_mode"] == "to_size":
chunk_size = int(split_params["split_size"])
else:
raise Exception(f"Unsupported split mode {split_params['split_mode']}")
def _read_sdf_records(filename):
lines = []
with open(filename) as handle:
for line in handle:
lines.append(line)
if line.startswith("$$$$"):
yield lines
lines = []
def _write_part_sdf_file(accumulated_lines):
part_dir = subdir_generator_function()
part_path = os.path.join(part_dir, os.path.basename(input_files[0]))
with open(part_path, "w") as part_file:
part_file.writelines(accumulated_lines)
try:
sdf_records = _read_sdf_records(input_files[0])
sdf_lines_accumulated = []
for counter, sdf_record in enumerate(sdf_records, start=1):
sdf_lines_accumulated.extend(sdf_record)
if counter % chunk_size == 0:
_write_part_sdf_file(sdf_lines_accumulated)
sdf_lines_accumulated = []
if sdf_lines_accumulated:
_write_part_sdf_file(sdf_lines_accumulated)
except Exception as e:
log.error("Unable to split files: %s", unicodify(e))
raise
[docs]@build_sniff_from_prefix
class MOL2(GenericMolFile):
file_ext = "mol2"
[docs] def sniff_prefix(self, file_prefix: FilePrefix) -> bool:
"""
Try to guess if the file is a MOL2 file.
>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('drugbank_drugs.mol2')
>>> MOL2().sniff(fname)
True
>>> fname = get_test_fname('drugbank_drugs.cml')
>>> MOL2().sniff(fname)
False
"""
limit = 60
idx = 0
for line in file_prefix.line_iterator():
line = line.rstrip("\n\r")
if line == "@<TRIPOS>MOLECULE":
return True
idx += 1
if idx == limit:
break
return False
[docs] def set_meta(self, dataset: DatasetProtocol, overwrite: bool = True, **kwd) -> None:
"""
Set the number of lines of data in dataset.
"""
dataset.metadata.number_of_molecules = count_special_lines("@<TRIPOS>MOLECULE", dataset.file_name)
[docs] @classmethod
def split(cls, input_datasets: List, subdir_generator_function: Callable, split_params: Dict) -> None:
"""
Split the input files by molecule records.
"""
if split_params is None:
return None
if len(input_datasets) > 1:
raise Exception("MOL2-file splitting does not support multiple files")
input_files = [ds.file_name for ds in input_datasets]
chunk_size = None
if split_params["split_mode"] == "number_of_parts":
raise Exception(f"Split mode \"{split_params['split_mode']}\" is currently not implemented for MOL2-files.")
elif split_params["split_mode"] == "to_size":
chunk_size = int(split_params["split_size"])
else:
raise Exception(f"Unsupported split mode {split_params['split_mode']}")
def _read_mol2_records(filename):
lines = []
start = True
with open(filename) as handle:
for line in handle:
if line.startswith("@<TRIPOS>MOLECULE"):
if start:
start = False
else:
yield lines
lines = []
lines.append(line)
def _write_part_mol2_file(accumulated_lines):
part_dir = subdir_generator_function()
part_path = os.path.join(part_dir, os.path.basename(input_files[0]))
with open(part_path, "w") as part_file:
part_file.writelines(accumulated_lines)
try:
mol2_records = _read_mol2_records(input_files[0])
mol2_lines_accumulated = []
for counter, mol2_record in enumerate(mol2_records, start=1):
mol2_lines_accumulated.extend(mol2_record)
if counter % chunk_size == 0:
_write_part_mol2_file(mol2_lines_accumulated)
mol2_lines_accumulated = []
if mol2_lines_accumulated:
_write_part_mol2_file(mol2_lines_accumulated)
except Exception as e:
log.error("Unable to split files: %s", unicodify(e))
raise
[docs]@build_sniff_from_prefix
class FPS(GenericMolFile):
"""
chemfp fingerprint file: http://code.google.com/p/chem-fingerprints/wiki/FPS
"""
file_ext = "fps"
[docs] def sniff_prefix(self, file_prefix: FilePrefix) -> bool:
"""
Try to guess if the file is a FPS file.
>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('q.fps')
>>> FPS().sniff(fname)
True
>>> fname = get_test_fname('drugbank_drugs.cml')
>>> FPS().sniff(fname)
False
"""
header = get_headers(file_prefix, sep="\t", count=1)
if header[0][0].strip() == "#FPS1":
return True
else:
return False
[docs] def set_meta(self, dataset: DatasetProtocol, overwrite: bool = True, **kwd) -> None:
"""
Set the number of lines of data in dataset.
"""
dataset.metadata.number_of_molecules = count_special_lines("^#", dataset.file_name, invert=True)
[docs] @classmethod
def split(cls, input_datasets: List, subdir_generator_function: Callable, split_params: Dict) -> None:
"""
Split the input files by fingerprint records.
"""
if split_params is None:
return None
if len(input_datasets) > 1:
raise Exception("FPS-file splitting does not support multiple files")
input_files = [ds.file_name for ds in input_datasets]
chunk_size = None
if split_params["split_mode"] == "number_of_parts":
raise Exception(f"Split mode \"{split_params['split_mode']}\" is currently not implemented for MOL2-files.")
elif split_params["split_mode"] == "to_size":
chunk_size = int(split_params["split_size"])
else:
raise Exception(f"Unsupported split mode {split_params['split_mode']}")
def _write_part_fingerprint_file(accumulated_lines):
part_dir = subdir_generator_function()
part_path = os.path.join(part_dir, os.path.basename(input_files[0]))
with open(part_path, "w") as part_file:
part_file.writelines(accumulated_lines)
try:
header_lines = []
lines_accumulated = []
fingerprint_counter = 0
for line in open(input_files[0]):
if not line.strip():
continue
if line.startswith("#"):
header_lines.append(line)
else:
fingerprint_counter += 1
lines_accumulated.append(line)
if fingerprint_counter != 0 and fingerprint_counter % chunk_size == 0:
_write_part_fingerprint_file(header_lines + lines_accumulated)
lines_accumulated = []
if lines_accumulated:
_write_part_fingerprint_file(header_lines + lines_accumulated)
except Exception as e:
log.error("Unable to split files: %s", unicodify(e))
raise
[docs] @staticmethod
def merge(split_files: List[str], output_file: str) -> None:
"""
Merging fps files requires merging the header manually.
We take the header from the first file.
"""
if len(split_files) == 1:
# For one file only, use base class method (move/copy)
return Text.merge(split_files, output_file)
if not split_files:
raise ValueError(f"No fps files given, {split_files!r}, to merge into {output_file}")
with open(output_file, "w") as out:
first = True
for filename in split_files:
with open(filename) as handle:
for line in handle:
if line.startswith("#"):
if first:
out.write(line)
else:
# line is no header and not a comment, we assume the first header is written to out and we set 'first' to False
first = False
out.write(line)
[docs]class OBFS(Binary):
"""OpenBabel Fastsearch format (fs)."""
file_ext = "obfs"
composite_type = "basic"
MetadataElement(
name="base_name",
default="OpenBabel Fastsearch Index",
readonly=True,
visible=True,
optional=True,
)
[docs] def __init__(self, **kwd):
"""
A Fastsearch Index consists of a binary file with the fingerprints
and a pointer the actual molecule file.
"""
super().__init__(**kwd)
self.add_composite_file("molecule.fs", is_binary=True, description="OpenBabel Fastsearch Index")
self.add_composite_file("molecule.sdf", optional=True, is_binary=False, description="Molecule File")
self.add_composite_file("molecule.smi", optional=True, is_binary=False, description="Molecule File")
self.add_composite_file("molecule.inchi", optional=True, is_binary=False, description="Molecule File")
self.add_composite_file("molecule.mol2", optional=True, is_binary=False, description="Molecule File")
self.add_composite_file("molecule.cml", optional=True, is_binary=False, description="Molecule File")
[docs] def set_peek(self, dataset: DatasetProtocol, **kwd) -> None:
"""Set the peek and blurb text."""
if not dataset.dataset.purged:
dataset.peek = "OpenBabel Fastsearch Index"
dataset.blurb = "OpenBabel Fastsearch Index"
else:
dataset.peek = "file does not exist"
dataset.blurb = "file purged from disk"
[docs] def display_peek(self, dataset: DatasetProtocol) -> str:
"""Create HTML content, used for displaying peek."""
try:
return dataset.peek
except Exception:
return "OpenBabel Fastsearch Index"
[docs] def get_mime(self) -> str:
"""Returns the mime type of the datatype (pretend it is text for peek)"""
return "text/plain"
[docs] @staticmethod
def merge(split_files: List[str], output_file: str) -> None:
"""Merging Fastsearch indices is not supported."""
raise NotImplementedError("Merging Fastsearch indices is not supported.")
[docs] @classmethod
def split(cls, input_datasets: List, subdir_generator_function: Callable, split_params: Dict) -> None:
"""Splitting Fastsearch indices is not supported."""
if split_params is None:
return None
raise NotImplementedError("Splitting Fastsearch indices is not possible.")
[docs]class DRF(GenericMolFile):
file_ext = "drf"
[docs] def set_meta(self, dataset: DatasetProtocol, overwrite: bool = True, **kwd) -> None:
"""
Set the number of lines of data in dataset.
"""
dataset.metadata.number_of_molecules = count_special_lines('"ligand id"', dataset.file_name, invert=True)
[docs]class PHAR(GenericMolFile):
"""
Pharmacophore database format from silicos-it.
"""
file_ext = "phar"
[docs] def set_peek(self, dataset: DatasetProtocol, **kwd) -> None:
if not dataset.dataset.purged:
dataset.peek = get_file_peek(dataset.file_name)
dataset.blurb = "pharmacophore"
else:
dataset.peek = "file does not exist"
dataset.blurb = "file purged from disk"
[docs]@build_sniff_from_prefix
class PDB(GenericMolFile):
"""
Protein Databank format.
http://www.wwpdb.org/documentation/format33/v3.3.html
"""
file_ext = "pdb"
MetadataElement(name="chain_ids", default=[], desc="Chain IDs", readonly=False, visible=True)
[docs] def sniff_prefix(self, file_prefix: FilePrefix) -> bool:
"""
Try to guess if the file is a PDB file.
>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('5e5z.pdb')
>>> PDB().sniff(fname)
True
>>> fname = get_test_fname('drugbank_drugs.cml')
>>> PDB().sniff(fname)
False
"""
headers = iter_headers(file_prefix, sep=" ", count=300)
h = t = c = s = k = e = False
for line in headers:
section_name = line[0].strip()
if section_name == "HEADER":
h = True
elif section_name == "TITLE":
t = True
elif section_name == "COMPND":
c = True
elif section_name == "SOURCE":
s = True
elif section_name == "KEYWDS":
k = True
elif section_name == "EXPDTA":
e = True
if h * t * c * s * k * e:
return True
else:
return False
[docs] def set_meta(self, dataset: DatasetProtocol, overwrite: bool = True, **kwd) -> None:
"""
Find Chain_IDs for metadata.
"""
try:
chain_ids = set()
with open(dataset.file_name) as fh:
for line in fh:
if line.startswith("ATOM ") or line.startswith("HETATM"):
if line[21] != " ":
chain_ids.add(line[21])
dataset.metadata.chain_ids = list(chain_ids)
except Exception as e:
log.error("Error finding chain_ids: %s", unicodify(e))
raise
[docs] def set_peek(self, dataset: DatasetProtocol, **kwd) -> None:
if not dataset.dataset.purged:
atom_numbers = count_special_lines("^ATOM", dataset.file_name)
hetatm_numbers = count_special_lines("^HETATM", dataset.file_name)
chain_ids = ",".join(dataset.metadata.chain_ids) if len(dataset.metadata.chain_ids) > 0 else "None"
dataset.peek = get_file_peek(dataset.file_name)
dataset.blurb = f"{atom_numbers} atoms and {hetatm_numbers} HET-atoms\nchain_ids: {chain_ids}"
else:
dataset.peek = "file does not exist"
dataset.blurb = "file purged from disk"
[docs]@build_sniff_from_prefix
class PDBQT(GenericMolFile):
"""
PDBQT Autodock and Autodock Vina format
http://autodock.scripps.edu/faqs-help/faq/what-is-the-format-of-a-pdbqt-file
"""
file_ext = "pdbqt"
[docs] def sniff_prefix(self, file_prefix: FilePrefix) -> bool:
"""
Try to guess if the file is a PDBQT file.
>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('NuBBE_1_obabel_3D.pdbqt')
>>> PDBQT().sniff(fname)
True
>>> fname = get_test_fname('drugbank_drugs.cml')
>>> PDBQT().sniff(fname)
False
"""
headers = iter_headers(file_prefix, sep=" ", count=300)
h = t = c = s = k = False
for line in headers:
section_name = line[0].strip()
if section_name == "REMARK":
h = True
elif section_name == "ROOT":
t = True
elif section_name == "ENDROOT":
c = True
elif section_name == "BRANCH":
s = True
elif section_name == "TORSDOF":
k = True
if h * t * c * s * k:
return True
else:
return False
[docs] def set_peek(self, dataset: DatasetProtocol, **kwd) -> None:
if not dataset.dataset.purged:
root_numbers = count_special_lines("^ROOT", dataset.file_name)
branch_numbers = count_special_lines("^BRANCH", dataset.file_name)
dataset.peek = get_file_peek(dataset.file_name)
dataset.blurb = f"{root_numbers} roots and {branch_numbers} branches"
else:
dataset.peek = "file does not exist"
dataset.blurb = "file purged from disk"
[docs]@build_sniff_from_prefix
class PQR(GenericMolFile):
"""
Protein Databank format.
https://apbs-pdb2pqr.readthedocs.io/en/latest/formats/pqr.html
"""
file_ext = "pqr"
MetadataElement(name="chain_ids", default=[], desc="Chain IDs", readonly=False, visible=True)
[docs] def get_matcher(self) -> re.Pattern:
"""
Atom and HETATM line fields are space separated, match group:
0: Field_name
A string which specifies the type of PQR entry: ATOM or HETATM.
1: Atom_number
An integer which provides the atom index.
2: Atom_name
A string which provides the atom name.
3: Residue_name
A string which provides the residue name.
5: Chain_ID (Optional, group 4 is whole field)
An optional string which provides the chain ID of the atom.
Note that chain ID support is a new feature of APBS 0.5.0 and later versions.
6: Residue_number
An integer which provides the residue index.
7: X 8: Y 9: Z
3 floats which provide the atomic coordinates (in angstroms)
10: Charge
A float which provides the atomic charge (in electrons).
11: Radius
A float which provides the atomic radius (in angstroms).
"""
pat = (
r"(ATOM|HETATM)\s+"
+ r"(\d+)\s+"
+ r"([A-Z0-9]+)\s+"
+ r"([A-Z0-9]+)\s+"
+ r"(([A-Z]?)\s+)?"
+ r"([-+]?\d*\.\d+|\d+)\s+"
+ r"([-+]?\d*\.\d+|\d+)\s+"
+ r"([-+]?\d*\.\d+|\d+)\s+"
+ r"([-+]?\d*\.\d+|\d+)\s+"
+ r"([-+]?\d*\.\d+|\d+)\s+"
)
return re.compile(pat)
[docs] def sniff_prefix(self, file_prefix: FilePrefix) -> bool:
"""
Try to guess if the file is a PQR file.
>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('5e5z.pqr')
>>> PQR().sniff(fname)
True
>>> fname = get_test_fname('drugbank_drugs.cml')
>>> PQR().sniff(fname)
False
"""
prog = self.get_matcher()
headers = iter_headers(file_prefix, sep=None, comment_designator="REMARK 5", count=3000)
h = a = False
for line in headers:
section_name = line[0].strip()
if section_name == "REMARK":
h = True
elif section_name == "ATOM" or section_name == "HETATM":
if prog.match(" ".join(line)):
a = True
break
if h * a:
return True
else:
return False
[docs] def set_meta(self, dataset: DatasetProtocol, overwrite: bool = True, **kwd) -> None:
"""
Find Optional Chain_IDs for metadata.
"""
try:
prog = self.get_matcher()
chain_ids = set()
with open(dataset.file_name) as fh:
for line in fh:
if line.startswith("REMARK"):
continue
match = prog.match(line.rstrip())
if match and match.groups()[5]:
chain_ids.add(match.groups()[5])
dataset.metadata.chain_ids = list(chain_ids)
except Exception as e:
log.error("Error finding chain_ids: %s", unicodify(e))
raise
[docs] def set_peek(self, dataset: DatasetProtocol, **kwd) -> None:
if not dataset.dataset.purged:
atom_numbers = count_special_lines("^ATOM", dataset.file_name)
hetatm_numbers = count_special_lines("^HETATM", dataset.file_name)
chain_ids = ",".join(dataset.metadata.chain_ids) if len(dataset.metadata.chain_ids) > 0 else "None"
dataset.peek = get_file_peek(dataset.file_name)
dataset.blurb = f"{atom_numbers} atoms and {hetatm_numbers} HET-atoms\nchain_ids: {chain_ids}"
else:
dataset.peek = "file does not exist"
dataset.blurb = "file purged from disk"
[docs]@build_sniff_from_prefix
class Cell(GenericMolFile):
"""
CASTEP CELL format.
"""
file_ext = "cell"
meta_error = False
MetadataElement(
name="atom_data",
default=[],
desc="Atom symbols and positions",
readonly=True,
visible=False,
)
MetadataElement(
name="number_of_atoms",
desc="Number of atoms",
readonly=True,
visible=True,
)
MetadataElement(
name="chemical_formula",
desc="Chemical formula",
readonly=True,
visible=True,
)
MetadataElement(
name="is_periodic",
desc="Periodic boundary conditions",
readonly=True,
visible=True,
)
MetadataElement(
name="lattice_parameters",
desc="Lattice parameters",
readonly=True,
visible=True,
)
[docs] def sniff_prefix(self, file_prefix: FilePrefix) -> bool:
"""
Try to guess if the file is a CASTEP CELL file.
A fingerprint for CELL files is the use of %BLOCK and %ENDBLOCK to
denote data blocks (not case sensitive).
>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('Si_uppercase.cell')
>>> Cell().sniff(fname)
True
>>> fname = get_test_fname('Si_lowercase.cell')
>>> Cell().sniff(fname)
True
>>> fname = get_test_fname('Si.cif')
>>> Cell().sniff(fname)
False
"""
start_found = False
for line in file_prefix.line_iterator():
if not start_found and line.lower().startswith("%block"):
start_found = True
elif start_found and line.lower().startswith("%endblock"):
return True
return False
[docs] def set_meta(self, dataset: DatasetProtocol, overwrite: bool = True, **kwd) -> None:
"""
Find Atom IDs for metadata.
"""
self.meta_error = False
if ase_io is None:
# Don't have optional dependency, can't set advanced values
return
else:
# enhanced metadata
try:
ase_data = ase_io.read(dataset.file_name, format="castep-cell")
except Exception as e:
log.warning("%s, set_meta Exception during ASE read: %s", self, unicodify(e))
self.meta_error = True
return
try:
atom_data = [
str(sym) + str(pos) for sym, pos in zip(ase_data.get_chemical_symbols(), ase_data.get_positions())
]
chemical_formula = ase_data.get_chemical_formula()
pbc = ase_data.get_pbc()
lattice_parameters = ase_data.get_cell().cellpar()
except Exception as e:
log.warning("%s, set_meta Exception during ASE metadata collection: %s", self, unicodify(e))
self.meta_error = True
return
# CELL file can only have one molecule
dataset.metadata.number_of_molecules = 1
dataset.metadata.atom_data = atom_data
dataset.metadata.number_of_atoms = len(dataset.metadata.atom_data)
dataset.metadata.chemical_formula = chemical_formula
try:
dataset.metadata.is_periodic = bool(pbc)
except ValueError: # pbc is an array
dataset.metadata.is_periodic = bool(pbc.any())
dataset.metadata.lattice_parameters = list(lattice_parameters)
[docs] def set_peek(self, dataset: DatasetProtocol, **kwd) -> None:
if not dataset.dataset.purged:
dataset.peek = get_file_peek(dataset.file_name)
dataset.info = self.get_dataset_info(dataset.metadata)
structure_string = "structure" if dataset.metadata.number_of_molecules == 1 else "structures"
dataset.blurb = f"CASTEP CELL file containing {dataset.metadata.number_of_molecules} {structure_string}"
else:
dataset.peek = "file does not exist"
dataset.blurb = "file purged from disk"
[docs] def get_dataset_info(self, metadata):
if self.meta_error:
info = "Error finding metadata. The file may be formatted incorrectly."
elif ase_io:
# enhanced info
info_list = []
info_list.append(f"Chemical formula:\n{metadata.chemical_formula}")
if metadata.is_periodic:
info_list.append("Periodic:\nYes")
info_list.append(
f"Lattice parameters in axis-angle format:\n{', '.join([str(round(x, 2)) for x in metadata.lattice_parameters])}"
)
else:
info_list.append("Periodic:\nNo")
info_list.append(f"Atoms in file:\n{metadata.number_of_atoms}")
info = "\n--\n".join(info_list)
else:
info = """
Metadata is limited as the Atomic Simulation Environment (ASE) is not installed.
You can still use this dataset in tools and workflows.
For full metadata, ask your admin to install the 'ase' Python package."""
return info
[docs]@build_sniff_from_prefix
class CIF(GenericMolFile):
"""
CIF format.
"""
file_ext = "cif"
meta_error = False
MetadataElement(
name="data_block_names",
default=[],
desc="Names of the data blocks",
readonly=True,
visible=True,
)
MetadataElement(
name="atom_data",
default=[],
desc="Atom symbols and positions",
readonly=True,
visible=False,
)
MetadataElement(
name="number_of_atoms",
desc="Number of atoms",
readonly=True,
visible=True,
)
MetadataElement(
name="chemical_formula",
desc="Chemical formula",
readonly=True,
visible=True,
)
MetadataElement(
name="is_periodic",
desc="Periodic boundary conditions",
readonly=True,
visible=True,
)
MetadataElement(
name="lattice_parameters",
desc="Lattice parameters",
readonly=True,
visible=True,
)
[docs] def sniff_prefix(self, file_prefix: FilePrefix) -> bool:
"""
Try to guess if the file is a CIF file.
The CIF format and the Relion STAR format have a shared origin.
Note therefore that STAR files and the STAR sniffer also use ``data_`` blocks.
STAR files will not pass the CIF sniffer, but CIF files can pass the STAR sniffer.
>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('Si.cif')
>>> CIF().sniff(fname)
True
>>> fname = get_test_fname('Si_lowercase.cell')
>>> CIF().sniff(fname)
False
>>> fname = get_test_fname('1.star')
>>> CIF().sniff(fname)
False
"""
# check for optional CIF version marker '#\#CIF_<version>' at start of file
if file_prefix.startswith("#\\#CIF_"):
return True
# no version marker, search for mandatory CIF keywords
# first non-comment line must begin with 'data_'
# and '_atom_site_fract_(x|y|z)' must be specified somewhere in the file
for line in file_prefix.line_iterator():
if not line:
continue
elif line[0] == "#": # comment so skip
continue
elif line.startswith("data_"):
return file_prefix.search_str("_atom_site_fract_")
else: # line has some other content
return False
return False
[docs] def set_meta(self, dataset: DatasetProtocol, overwrite: bool = True, **kwd) -> None:
"""
Find Atom IDs for metadata.
"""
self.meta_error = False
if ase_io is None:
# Don't have optional dependency, can't set advanced values
return
else:
# enhanced metadata
try:
ase_data = ase_io.read(dataset.file_name, index=":", format="cif")
except Exception as e:
log.warning("%s, set_meta Exception during ASE read: %s", self, unicodify(e))
self.meta_error = True
return
atom_data = []
chemical_formula = []
is_periodic = []
lattice_parameters = []
try:
for block in ase_data:
atom_data.append(
[str(sym) + str(pos) for sym, pos in zip(block.get_chemical_symbols(), block.get_positions())]
)
chemical_formula.append(block.get_chemical_formula())
pbc = block.get_pbc()
try:
p = bool(pbc)
except ValueError: # pbc is an array
p = bool(pbc.any())
is_periodic.append(p)
lattice_parameters.append(list(block.get_cell().cellpar()))
except Exception as e:
log.warning("%s, set_meta Exception during ASE metadata collection: %s", self, unicodify(e))
self.meta_error = True
return
dataset.metadata.number_of_molecules = len(ase_data)
dataset.metadata.atom_data = atom_data
dataset.metadata.number_of_atoms = [len(atoms) for atoms in dataset.metadata.atom_data]
dataset.metadata.chemical_formula = chemical_formula
dataset.metadata.is_periodic = is_periodic
dataset.metadata.lattice_parameters = list(lattice_parameters)
[docs] def set_peek(self, dataset: DatasetProtocol, **kwd) -> None:
if not dataset.dataset.purged:
dataset.peek = get_file_peek(dataset.file_name)
dataset.info = self.get_dataset_info(dataset.metadata)
structure_string = "structure" if dataset.metadata.number_of_molecules == 1 else "structures"
dataset.blurb = f"CIF file containing {dataset.metadata.number_of_molecules} {structure_string}"
else:
dataset.peek = "file does not exist"
dataset.blurb = "file purged from disk"
[docs] def get_dataset_info(self, metadata):
if self.meta_error:
info = "Error finding metadata. The file may be formatted incorrectly."
elif ase_io:
# enhanced info
info_list = []
if metadata.number_of_molecules == 1:
info_list.append(f"Chemical formula:\n{metadata.chemical_formula[0]}")
if metadata.is_periodic[0]:
info_list.append("Periodic:\nYes")
info_list.append(
f"Lattice parameters in axis-angle format:\n{', '.join([str(round(x, 2)) for x in metadata.lattice_parameters[0]])}"
)
else:
info_list.append("Periodic:\nNo")
info_list.append(f"Atoms in file:\n{metadata.number_of_atoms[0]}")
elif metadata.number_of_molecules > 1:
info_list.append("File contains multiple structures; full metadata will not be displayed.")
formulae = "\n".join(metadata.chemical_formula)
info_list.append(f"Chemical formula for each structure in this file:\n{formulae}")
info = "\n--\n".join(info_list)
else:
info = """
Metadata is limited as the Atomic Simulation Environment (ASE) is not installed.
You can still use this dataset in tools and workflows.
For full metadata, ask your admin to install the 'ase' Python package."""
return info
[docs]@build_sniff_from_prefix
class XYZ(GenericMolFile):
"""
XYZ format.
"""
file_ext = "xyz"
meta_error = False
MetadataElement(
name="atom_data",
default=[],
desc="Atom symbols and positions",
readonly=True,
visible=False,
)
MetadataElement(
name="number_of_atoms",
desc="Number of atoms",
readonly=True,
visible=True,
)
MetadataElement(
name="chemical_formula",
desc="Chemical formula",
readonly=True,
visible=True,
)
MetadataElement(
name="is_periodic",
desc="Periodic boundary conditions",
readonly=True,
visible=True,
)
MetadataElement(
name="lattice_parameters",
desc="Lattice parameters",
readonly=True,
visible=True,
)
[docs] def sniff_prefix(self, file_prefix: FilePrefix) -> bool:
"""
Try to guess if the file is a XYZ file.
XYZ has no fingerprint phrases, so the whole prefix must be checked
for the correct structure. If the prefix passes, assume the whole file
passes.
>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('Si.xyz')
>>> XYZ().sniff(fname)
True
>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('Si_multi.xyz')
>>> XYZ().sniff(fname)
True
>>> fname = get_test_fname('Si.cif')
>>> XYZ().sniff(fname)
False
>>> fname = get_test_fname('not_a_xyz_file.txt')
>>> XYZ().sniff(fname)
False
"""
try:
self.read_blocks(list(file_prefix.line_iterator()))
return True
except (TypeError, ValueError, IndexError):
return False
[docs] def read_blocks(self, lines: List) -> List:
"""
Parses and returns a list of dictionaries representing XYZ structure blocks (aka frames).
Raises IndexError, TypeError, ValueError
"""
# remove trailing blank lines
# blank lines not permitted elsewhere in file except for designated comment lines
while not lines[-1].strip():
lines = lines[:-1]
blocks = []
while lines:
n_atoms = None
comment = None
atoms = []
try:
n_atoms = int(lines.pop(0))
comment = lines.pop(0)
for _ in range(n_atoms):
atom = lines.pop(0)
atom = atom.split()
symbol = atom[0].lower().capitalize()
if symbol not in self.element_symbols:
raise ValueError(f"{atom[0]} is not a valid element symbol")
# raises ValueError if not valid XYZ format
position = [float(i) for i in atom[1:4]]
atoms.append(symbol + str(position))
blocks.append({"number_of_atoms": n_atoms, "comment": comment, "atom_data": atoms})
except IndexError as e:
if "pop from empty list" in str(e) and blocks:
# we'll require at least one valid block
pass
else:
raise
return blocks
[docs] def set_meta(self, dataset: DatasetProtocol, overwrite: bool = True, **kwd) -> None:
"""
Find Atom IDs for metadata.
"""
self.meta_error = False
if ase_io is None:
# Don't have optional dependency, can't set advanced values
return
else:
# enhanced metadata
try:
# ASE recommend always parsing as extended xyz
ase_data = ase_io.read(dataset.file_name, index=":", format="extxyz")
except Exception as e:
log.warning("%s, set_meta Exception during ASE read: %s", self, unicodify(e))
self.meta_error = True
return
atom_data = []
chemical_formula = []
is_periodic = []
lattice_parameters = []
try:
for block in ase_data:
atom_data.append(
[str(sym) + str(pos) for sym, pos in zip(block.get_chemical_symbols(), block.get_positions())]
)
chemical_formula.append(block.get_chemical_formula())
pbc = block.get_pbc()
try:
p = bool(pbc)
except ValueError: # pbc is an array
p = bool(pbc.any())
is_periodic.append(p)
lattice_parameters.append(list(block.get_cell().cellpar()))
except Exception as e:
log.warning("%s, set_meta Exception during ASE metadata collection: %s", self, unicodify(e))
self.meta_error = True
return
dataset.metadata.number_of_molecules = len(ase_data)
dataset.metadata.atom_data = atom_data
dataset.metadata.number_of_atoms = [len(atoms) for atoms in dataset.metadata.atom_data]
dataset.metadata.chemical_formula = chemical_formula
dataset.metadata.is_periodic = is_periodic
dataset.metadata.lattice_parameters = list(lattice_parameters)
[docs] def set_peek(self, dataset: DatasetProtocol, **kwd) -> None:
if not dataset.dataset.purged:
dataset.peek = get_file_peek(dataset.file_name)
dataset.info = self.get_dataset_info(dataset.metadata)
structure_string = "structure" if dataset.metadata.number_of_molecules == 1 else "structures"
dataset.blurb = f"XYZ file containing {dataset.metadata.number_of_molecules} {structure_string}"
else:
dataset.peek = "file does not exist"
dataset.blurb = "file purged from disk"
[docs] def get_dataset_info(self, metadata):
if self.meta_error:
info = "Error finding metadata. The file may be formatted incorrectly."
elif ase_io:
# enhanced info
info_list = []
if metadata.number_of_molecules == 1:
info_list.append(f"Chemical formula:\n{metadata.chemical_formula[0]}")
if metadata.is_periodic[0]:
info_list.append("Periodic:\nYes")
info_list.append(
f"Lattice parameters in axis-angle format:\n{', '.join([str(round(x, 2)) for x in metadata.lattice_parameters[0]])}"
)
else:
info_list.append("Periodic:\nNo")
info_list.append(f"Atoms in file:\n{metadata.number_of_atoms[0]}")
elif metadata.number_of_molecules > 1:
info_list.append("File contains multiple structures; full metadata will not be displayed.")
formulae = "\n".join(metadata.chemical_formula)
info_list.append(f"Chemical formula for each structure in this file:\n{formulae}")
info = "\n--\n".join(info_list)
else:
info = """
Metadata is limited as the Atomic Simulation Environment (ASE) is not installed.
You can still use this dataset in tools and workflows.
For full metadata, ask your admin to install the 'ase' Python package."""
return info
[docs]@build_sniff_from_prefix
class ExtendedXYZ(XYZ):
"""
Extended XYZ format.
Uses specification from https://github.com/libAtoms/extxyz.
"""
file_ext = "extxyz"
[docs] def sniff_prefix(self, file_prefix: FilePrefix) -> bool:
"""
Try to guess if the file is an Extended XYZ file.
XYZ files will not pass the ExtendedXYZ sniffer, but ExtendedXYZ files can pass the XYZ sniffer.
>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('Si.extxyz')
>>> ExtendedXYZ().sniff(fname)
True
>>> fname = get_test_fname('Si.xyz')
>>> ExtendedXYZ().sniff(fname)
False
"""
line_iterator = file_prefix.line_iterator()
try:
# fingerprint is 'Properties="<name>:<type>:<number>:..."'
# in the second line
# e.g. Properties="species:S:1:pos:R:3:vel:R:3:select:I:1"
next(line_iterator)
comment = next(line_iterator)
properties = re.search(r"Properties=\"?([a-zA-Z0-9:]+)\"?", comment) # returns None if no match
return bool(properties)
except StopIteration:
# insufficient lines
return False
[docs] def read_blocks(self, lines: List) -> List:
"""
Parses and returns a list of XYZ structure blocks (aka frames).
Raises IndexError, TypeError, ValueError
"""
# remove trailing blank lines
# blank lines not permitted elsewhere in file except for designated comment lines
while not lines[-1].strip():
lines = lines[:-1]
blocks = []
while lines:
n_atoms = None
comment = None
atoms = []
n_atoms = int(lines.pop(0))
comment = lines.pop(0)
# extract column properties
# these have the format 'Properties="<name>:<type>:<number>:..."' in the comment line
# e.g. Properties="species:S:1:pos:R:3:vel:R:3:select:I:1"
properties_matches = re.search(r"Properties=\"?([a-zA-Z0-9:]+)\"?", comment)
if properties_matches is None: # re.search returned None
raise ValueError(f"Could not find column properties in line: {comment}")
properties = [s.split(":") for s in re.findall(r"[a-zA-Z]+:[SIRL]:[0-9]+", properties_matches.group(1))]
total_columns = sum(int(s[2]) for s in properties)
for _ in range(n_atoms):
atom_dict = {}
atom = lines.pop(0)
atom = atom.split()
if len(atom) != total_columns:
raise ValueError(f"Expected {total_columns} columns but found {len(atom)}: {atom}")
index = 0
# parse atom data according to column format specified by the properties
# this processing will raise errors if the format is incorrect
for property in properties:
to_process = atom[index : index + int(property[2])]
property_name = property[0]
property_type = property[1]
for i in to_process:
if property_type == "S": # string
# check that any element symbols are correct, otherwise ignore strings
if property_name.lower() == "species":
symbol = i.lower().capitalize()
if symbol not in self.element_symbols:
raise ValueError(f"{i} is not a valid element symbol")
else:
atom_dict[property_name] = symbol
else:
atom_dict[property_name] = i
elif property_type == "L": # logical
if re.match(r"(?:[tT]rue|TRUE|T)\b", i):
atom_dict[property_name] = True
elif re.match(r"(?:[fF]alse|FALSE|F)\b", i):
atom_dict[property_name] = False
else:
raise ValueError(f"{i} is not a valid logical element.")
elif property_type in ["I", "R"]: # integer, float
ii = int(i) if property_type == "I" else float(i)
if int(property[2]) > 1: # more than one column for this property - use array
if property_name in atom_dict:
atom_dict[property_name].append(ii)
else:
atom_dict[property_name] = [ii]
else:
atom_dict[property_name] = ii
else:
raise ValueError(f"Could not recognise property type {property_type}.")
index += int(property[2])
atoms.append(atom_dict)
blocks.append({"number_of_atoms": n_atoms, "comment": comment, "atom_data": atoms})
return blocks
[docs] def set_peek(self, dataset: DatasetProtocol, **kwd) -> None:
super().set_peek(dataset)
dataset.blurb = "Extended " + dataset.blurb
[docs]class grd(Text):
file_ext = "grd"
[docs] def set_peek(self, dataset: DatasetProtocol, **kwd) -> None:
if not dataset.dataset.purged:
dataset.peek = get_file_peek(dataset.file_name)
dataset.blurb = "grids for docking"
else:
dataset.peek = "file does not exist"
dataset.blurb = "file purged from disk"
[docs]class grdtgz(Binary):
file_ext = "grd.tgz"
[docs] def set_peek(self, dataset: DatasetProtocol, **kwd) -> None:
if not dataset.dataset.purged:
dataset.peek = "binary data"
dataset.blurb = "compressed grids for docking"
else:
dataset.peek = "file does not exist"
dataset.blurb = "file purged from disk"
[docs]@build_sniff_from_prefix
class InChI(Tabular):
file_ext = "inchi"
column_names = ["InChI"]
MetadataElement(name="columns", default=2, desc="Number of columns", readonly=True, visible=False)
MetadataElement(
name="column_types",
default=["str"],
param=metadata.ColumnTypesParameter,
desc="Column types",
readonly=True,
visible=False,
)
MetadataElement(
name="number_of_molecules",
default=0,
desc="Number of molecules",
readonly=True,
visible=True,
optional=True,
no_value=0,
)
[docs] def set_meta(self, dataset: DatasetProtocol, overwrite: bool = True, **kwd) -> None:
"""
Set the number of lines of data in dataset.
"""
dataset.metadata.number_of_molecules = self.count_data_lines(dataset)
[docs] def set_peek(self, dataset: DatasetProtocol, **kwd) -> None:
if not dataset.dataset.purged:
if dataset.metadata.number_of_molecules == 1:
dataset.blurb = "1 molecule"
else:
dataset.blurb = f"{dataset.metadata.number_of_molecules} molecules"
dataset.peek = get_file_peek(dataset.file_name)
else:
dataset.peek = "file does not exist"
dataset.blurb = "file purged from disk"
[docs] def sniff_prefix(self, file_prefix: FilePrefix) -> bool:
"""
Try to guess if the file is a InChI file.
>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('drugbank_drugs.inchi')
>>> InChI().sniff(fname)
True
>>> fname = get_test_fname('drugbank_drugs.cml')
>>> InChI().sniff(fname)
False
"""
inchi_lines = iter_headers(file_prefix, sep=" ", count=10)
found_lines = False
for inchi in inchi_lines:
if not inchi[0].startswith("InChI="):
return False
found_lines = True
return found_lines
[docs]class SMILES(Tabular):
# It is hard or impossible to sniff a SMILES File. We can try to import the
# first SMILES and check if it is a molecule, but currently it is not
# possible to use external libraries in datatype definition files.
# Moreover it seems impossible to include OpenBabel as Python library
# because OpenBabel is GPL licensed.
file_ext = "smi"
column_names = ["SMILES", "TITLE"]
MetadataElement(name="columns", default=2, desc="Number of columns", readonly=True, visible=False)
MetadataElement(
name="column_types",
default=["str", "str"],
param=metadata.ColumnTypesParameter,
desc="Column types",
readonly=True,
visible=False,
)
MetadataElement(
name="number_of_molecules",
default=0,
desc="Number of molecules",
readonly=True,
visible=True,
optional=True,
no_value=0,
)
[docs] def set_meta(self, dataset: DatasetProtocol, overwrite: bool = True, **kwd) -> None:
"""
Set the number of lines of data in dataset.
"""
dataset.metadata.number_of_molecules = self.count_data_lines(dataset)
[docs] def set_peek(self, dataset: DatasetProtocol, **kwd) -> None:
if not dataset.dataset.purged:
if dataset.metadata.number_of_molecules == 1:
dataset.blurb = "1 molecule"
else:
dataset.blurb = f"{dataset.metadata.number_of_molecules} molecules"
dataset.peek = get_file_peek(dataset.file_name)
else:
dataset.peek = "file does not exist"
dataset.blurb = "file purged from disk"
[docs]@build_sniff_from_prefix
class CML(GenericXml):
"""
Chemical Markup Language
http://cml.sourceforge.net/
"""
file_ext = "cml"
MetadataElement(
name="number_of_molecules",
default=0,
desc="Number of molecules",
readonly=True,
visible=True,
optional=True,
no_value=0,
)
[docs] def set_meta(self, dataset: DatasetProtocol, overwrite: bool = True, **kwd) -> None:
"""
Set the number of lines of data in dataset.
"""
dataset.metadata.number_of_molecules = count_special_lines(r"^\s*<molecule", dataset.file_name)
[docs] def set_peek(self, dataset: DatasetProtocol, **kwd) -> None:
if not dataset.dataset.purged:
if dataset.metadata.number_of_molecules == 1:
dataset.blurb = "1 molecule"
else:
dataset.blurb = f"{dataset.metadata.number_of_molecules} molecules"
dataset.peek = get_file_peek(dataset.file_name)
else:
dataset.peek = "file does not exist"
dataset.blurb = "file purged from disk"
[docs] def sniff_prefix(self, file_prefix: FilePrefix) -> bool:
"""
Try to guess if the file is a CML file.
>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('interval.interval')
>>> CML().sniff(fname)
False
>>> fname = get_test_fname('drugbank_drugs.cml')
>>> CML().sniff(fname)
True
"""
for expected_string in ['<?xml version="1.0"?>', "http://www.xml-cml.org/schema"]:
if expected_string not in file_prefix.contents_header:
return False
return True
[docs] @classmethod
def split(cls, input_datasets: List, subdir_generator_function: Callable, split_params: Dict) -> None:
"""
Split the input files by molecule records.
"""
if split_params is None:
return None
if len(input_datasets) > 1:
raise Exception("CML-file splitting does not support multiple files")
input_files = [ds.file_name for ds in input_datasets]
chunk_size = None
if split_params["split_mode"] == "number_of_parts":
raise Exception(f"Split mode \"{split_params['split_mode']}\" is currently not implemented for CML-files.")
elif split_params["split_mode"] == "to_size":
chunk_size = int(split_params["split_size"])
else:
raise Exception(f"Unsupported split mode {split_params['split_mode']}")
def _read_cml_records(filename):
lines = []
with open(filename) as handle:
for line in handle:
if (
line.lstrip().startswith('<?xml version="1.0"?>')
or line.lstrip().startswith('<cml xmlns="http://www.xml-cml.org/schema')
or line.lstrip().startswith("</cml>")
):
continue
lines.append(line)
if line.lstrip().startswith("</molecule>"):
yield lines
lines = []
header_lines = ['<?xml version="1.0"?>\n', '<cml xmlns="http://www.xml-cml.org/schema">\n']
footer_line = ["</cml>\n"]
def _write_part_cml_file(accumulated_lines):
part_dir = subdir_generator_function()
part_path = os.path.join(part_dir, os.path.basename(input_files[0]))
with open(part_path, "w") as part_file:
part_file.writelines(header_lines)
part_file.writelines(accumulated_lines)
part_file.writelines(footer_line)
try:
cml_records = _read_cml_records(input_files[0])
cml_lines_accumulated = []
for counter, cml_record in enumerate(cml_records, start=1):
cml_lines_accumulated.extend(cml_record)
if counter % chunk_size == 0:
_write_part_cml_file(cml_lines_accumulated)
cml_lines_accumulated = []
if cml_lines_accumulated:
_write_part_cml_file(cml_lines_accumulated)
except Exception as e:
log.error("Unable to split files: %s", unicodify(e))
raise
[docs] @staticmethod
def merge(split_files: List[str], output_file: str) -> None:
"""
Merging CML files.
"""
if len(split_files) == 1:
# For one file only, use base class method (move/copy)
return Text.merge(split_files, output_file)
if not split_files:
raise ValueError(f"Given no CML files, {split_files!r}, to merge into {output_file}")
with open(output_file, "w") as out:
for filename in split_files:
with open(filename) as handle:
header = handle.readline()
if not header:
raise ValueError(f"CML file {filename} was empty")
if not header.lstrip().startswith('<?xml version="1.0"?>'):
out.write(header)
raise ValueError(f"{filename} is not a valid XML file!")
line = handle.readline()
header += line
if not line.lstrip().startswith('<cml xmlns="http://www.xml-cml.org/schema'):
out.write(header)
raise ValueError(f"{filename} is not a CML file!")
molecule_found = False
for line in handle.readlines():
# We found two required header lines, the next line should start with <molecule >
if line.lstrip().startswith("</cml>"):
continue
if line.lstrip().startswith("<molecule"):
molecule_found = True
if molecule_found:
out.write(line)
out.write("</cml>\n")
[docs]class GRO(GenericMolFile):
"""
GROMACS structure format.
https://manual.gromacs.org/current/reference-manual/file-formats.html#gro
"""
file_ext = "gro"
[docs] def sniff_prefix(self, file_prefix: FilePrefix) -> bool:
"""
Try to guess if the file is a GRO file.
>>> from galaxy.datatypes.sniff import get_test_fname
>>> fname = get_test_fname('5e5z.gro')
>>> GRO().sniff_prefix(fname)
True
>>> fname = get_test_fname('5e5z.pdb')
>>> GRO().sniff_prefix(fname)
False
"""
headers = get_headers(file_prefix, sep="\n", count=300)
try:
int(headers[1][0]) # the second line should just be the number of atoms
except ValueError:
return False
for line in headers[2:-1]: # skip the first, second and last lines
if not re.search(
r"^[0-9 ]{5}[a-zA-Z0-9 ]{10}[0-9 ]{5}[0-9 -]{4}\.[0-9]{3}[0-9 -]{4}\.[0-9]{3}[0-9 -]{4}\.[0-9]{3}",
line[0],
):
return False
return True
[docs] def set_peek(self, dataset: DatasetProtocol, **kwd) -> None:
if not dataset.dataset.purged:
dataset.peek = get_file_peek(dataset.file_name)
atom_number = int(dataset.peek.split("\n")[1])
dataset.blurb = f"{atom_number} atoms"
else:
dataset.peek = "file does not exist"
dataset.blurb = "file purged from disk"