def _map_topology(self):
"""
Create CG topology from given topology and mapping
"""
# Ensure that a trajectory has been loaded
if self._aa_traj is None:
raise OutOfOrderError("An atomistic trajectory has not "
"been loaded into this Mapper yet.")
self._atom_bead_mapping = dict()
self._cg_top = Topology()
self._solvent_counter = 0
# Loop over all residues
for residue in self._aa_top.residues:
if residue.name == self._solvent_name:
self._map_solvent_top(residue)
else:
self._map_nonsolvent_top(residue)
def _topology_from_arrays(AtomID, AtomNames, ChainID, ResidueID, ResidueNames):
"""Build topology object from the arrays stored in the lh5 file"""
# Delayed import due to wacky recursive imports in compatibilty
from mdtraj import Topology
topology = Topology()
# assert that the ChainID is just an array of empty strings, which appears
# to be the case in our test systems for this legacy format
if not np.all(chainid == '' for chainid in ChainID):
raise NotImplementedError('Im not prepared to parse multiple chains')
chain0 = topology.add_chain()
# register the residues
registered_residues = {}
for i in np.argsort(ResidueID):
residue_name = ResidueNames[i]
if not isinstance(residue_name, basestring):
residue_name = residue_name.decode()
if ResidueID[i] not in registered_residues:
res = topology.add_residue(residue_name, chain0)
registered_residues[ResidueID[i]] = res
# register the atoms
for i in np.argsort(AtomID):
atom_name = AtomNames[i]
if not isinstance(atom_name, basestring):
atom_name = atom_name.decode()
element_symbol = atom_name.lstrip('0123456789')[0]
try:
element = elem.get_by_symbol(element_symbol)
except KeyError:
element = None
topology.add_atom(atom_name, element,
registered_residues[ResidueID[i]])
topology.create_standard_bonds()
return topology
def to_mdtraj_Topology(item, atom_indices='all', check=True):
if check:
digest_item(item, 'molsysmt.Topology')
atom_indices = digest_atom_indices(atom_indices)
try:
from mdtraj import Topology
from mdtraj.core import element
except:
raise LibraryNotFound('mdtraj')
n_atoms = item.atoms_dataframe.shape[0]
atom_index_array = item.atoms_dataframe["atom_index"].to_numpy()
atom_name_array = item.atoms_dataframe["atom_name"].to_numpy()
atom_id_array = item.atoms_dataframe["atom_id"].to_numpy()
atom_type_array = item.atoms_dataframe["atom_type"].to_numpy()
group_index_array = item.atoms_dataframe["group_index"].to_numpy()
group_name_array = item.atoms_dataframe["group_name"].to_numpy()
group_id_array = item.atoms_dataframe["group_id"].to_numpy()
group_type_array = item.atoms_dataframe["group_type"].to_numpy()
chain_index_array = item.atoms_dataframe["chain_index"].to_numpy()
chain_name_array = item.atoms_dataframe["chain_name"].to_numpy()
chain_id_array = item.atoms_dataframe["chain_id"].to_numpy()
chain_type_array = item.atoms_dataframe["chain_type"].to_numpy()
bonds_atom1 = item.bonds_dataframe["atom1_index"].to_numpy()
bonds_atom2 = item.bonds_dataframe["atom2_index"].to_numpy()
tmp_item = Topology()
former_group_index = -1
former_chain_index = -1
list_new_atoms = []
for ii in range(n_atoms):
atom_index = atom_index_array[ii]
atom_name = atom_name_array[ii]
atom_id = atom_id_array[ii]
atom_type = atom_type_array[ii]
group_index = group_index_array[ii]
chain_index = chain_index_array[ii]
new_group = (former_group_index != group_index)
new_chain = (former_chain_index != chain_index)
if new_chain:
chain = tmp_item.add_chain()
former_chain_index = chain_index
if new_group:
residue_name = group_name_array[ii]
residue_id = group_id_array[ii]
residue = tmp_item.add_residue(residue_name,
chain,
resSeq=str(residue_id))
former_group_index = group_index
elem = element.get_by_symbol(atom_type)
atom = tmp_item.add_atom(atom_name, elem, residue)
list_new_atoms.append(atom)
for atom_1, atom_2 in zip(bonds_atom1, bonds_atom2):
tmp_item.add_bond(
list_new_atoms[atom_1],
list_new_atoms[atom_2]) # falta bond type and bond order
return tmp_item
