Source code for MDAnalysis.topology.DMSParser

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DESRES Molecular Structure file format topology parser

Classes to read a topology from a DESRES_ Molecular Structure file
format (DMS_) coordinate files (as used by the Desmond_ MD package).

.. _Desmond:
.. _DMS:


.. autoclass:: DMSParser

from __future__ import absolute_import
from six import raise_from

import numpy as np
import sqlite3
import os

from . import guessers
from .base import TopologyReaderBase, squash_by
from ..core.topology import Topology
from ..core.topologyattrs import (
    AtomAttr,  # for custom Attributes

class Atomnums(AtomAttr):
    """The number for each Atom"""
    attrname = 'atomnums'
    singular = 'atomnum'

[docs]class DMSParser(TopologyReaderBase): """Read a topology from a DESRES_ Molecular Structure file. Format (DMS_) coordinate files (as used by the Desmond_ MD package). Reads the following attributes: Atom: - Atomids - Atomnums - Atomnames - Masses - Charges - Chainids Residue: - Resnames - Resids Segment: - Segids Guesses the following attributes - Atomtypes .. _DESRES: .. _Desmond: .. _DMS: """ format = 'DMS'
[docs] def parse(self, **kwargs): """Parse DMS file *filename* and return the Topology object""" # Fix by SB: Needed because sqlite3.connect does not raise anything # if file is not there if not os.path.isfile(self.filename): raise IOError("No such file: {0}".format(self.filename)) def dict_factory(cursor, row): """ Fetch SQL records as dictionaries, rather than the default tuples. """ d = {} for idx, col in enumerate(cursor.description): d[col[0]] = row[idx] return d attrs = {} # Row factories for different data types facs = {np.int32: lambda c, r: r[0], np.float32: lambda c, r: r[0], object: lambda c, r: str(r[0].strip())} with sqlite3.connect(self.filename) as con: # Selecting single column, so just strip tuple for attrname, dt in [ ('id', np.int32), ('anum', np.int32), ('mass', np.float32), ('charge', np.float32), ('name', object), ('resname', object), ('resid', np.int32), ('chain', object), ('segid', object), ]: try: cur = con.cursor() cur.row_factory = facs[dt] cur.execute('SELECT {} FROM particle' ''.format(attrname)) vals = cur.fetchall() except sqlite3.DatabaseError: raise_from( IOError("Failed reading the atoms from DMS Database"), None) else: attrs[attrname] = np.array(vals, dtype=dt) try: cur.row_factory = dict_factory cur.execute('SELECT * FROM bond') bonds = cur.fetchall() except sqlite3.DatabaseError: raise_from( IOError("Failed reading the bonds from DMS Database"), None) else: bondlist = [] bondorder = {} for b in bonds: desc = tuple(sorted([b['p0'], b['p1']])) bondlist.append(desc) bondorder[desc] = b['order'] attrs['bond'] = bondlist attrs['bondorder'] = bondorder atomtypes = guessers.guess_types(attrs['name']) topattrs = [] # Bundle in Atom level objects for attr, cls in [ ('id', Atomids), ('anum', Atomnums), ('mass', Masses), ('charge', Charges), ('name', Atomnames), ('chain', ChainIDs), ]: topattrs.append(cls(attrs[attr])) topattrs.append(Atomtypes(atomtypes, guessed=True)) # Residues atom_residx, res_resids, (res_resnames, res_segids) = squash_by( attrs['resid'], attrs['resname'], attrs['segid']) topattrs.append(Resids(res_resids)) topattrs.append(Resnums(res_resids.copy())) topattrs.append(Resnames(res_resnames)) # Segments res_segidx, seg_segids = squash_by( res_segids)[:2] topattrs.append(Segids(seg_segids)) # Bonds topattrs.append(Bonds(attrs['bond'])) top = Topology(len(attrs['id']), len(res_resids), len(seg_segids), attrs=topattrs, atom_resindex=atom_residx, residue_segindex=res_segidx) return top