def run(self, niterations=None, mpicomm=None, options=None): """ Run a free energy calculation. Parameters ---------- niterations : int, optional, default=None If specified, only this many iterations will be run for each phase. This is useful for running simulation incrementally, but may incur a good deal of overhead. mpicomm : MPI communicator, optional, default=None If an MPI communicator is passed, an MPI simulation will be attempted. options : dict of str, optional, default=None If specified, these options will override any other options. """ # Make sure we've been properly initialized first. if not self._initialized: raise Exception( "Yank must first be initialized by either resume() or create()." ) # Handle some logistics necessary for MPI. if mpicomm: # Turn off output from non-root nodes: if not (mpicomm.rank == 0): self.verbose = False # Make sure each thread's random number generators have unique seeds. # TODO: Do we need to store seed in repex object? seed = np.random.randint(sys.maxint - mpicomm.size) + mpicomm.rank np.random.seed(seed) # Run all phases sequentially. # TODO: Divide up MPI resources among the phases so they can run simultaneously? for phase in self._phases: store_filename = self._store_filenames[phase] # Resume simulation from store file. simulation = ModifiedHamiltonianExchange( store_filename=store_filename, mpicomm=mpicomm) simulation.resume(options=options) # TODO: We may need to manually update run options here if options=options above does not behave as expected. simulation.run(niterations_to_run=niterations) # Clean up to ensure we close files, contexts, etc. del simulation return
def run(self, niterations_to_run=None, mpicomm=None, options=None): """ Run a free energy calculation. Parameters ---------- niterations_to_run : int, optional, default=None If specified, only this many iterations will be run for each phase. This is useful for running simulation incrementally, but may incur a good deal of overhead. mpicomm : MPI communicator, optional, default=None If an MPI communicator is passed, an MPI simulation will be attempted. options : dict of str, optional, default=None If specified, these options will override any other options. """ # Make sure we've been properly initialized first. if not self._initialized: raise Exception("Yank must first be initialized by either resume() or create().") # Handle some logistics necessary for MPI. if mpicomm: # Turn off output from non-root nodes: if not (mpicomm.rank==0): self.verbose = False # Make sure each thread's random number generators have unique seeds. # TODO: Do we need to store seed in repex object? seed = np.random.randint(sys.maxint - mpicomm.size) + mpicomm.rank np.random.seed(seed) # Run all phases sequentially. # TODO: Divide up MPI resources among the phases so they can run simultaneously? for phase in self._phases: store_filename = self._store_filenames[phase] # Resume simulation from store file. simulation = ModifiedHamiltonianExchange(store_filename=store_filename, mpicomm=mpicomm) simulation.resume(options=options) # TODO: We may need to manually update run options here if options=options above does not behave as expected. simulation.run(niterations_to_run=niterations_to_run) # Clean up to ensure we close files, contexts, etc. del simulation return
def run(self, niterations_to_run=None): """ Run a free energy calculation. Parameters ---------- niterations_to_run : int, optional, default=None If specified, only this many iterations will be run for each phase. This is useful for running simulation incrementally, but may incur a good deal of overhead. """ # Make sure we've been properly initialized first. if not self._initialized: raise Exception( "Yank must first be initialized by either resume() or create()." ) # Handle some logistics necessary for MPI. if self._mpicomm is not None: logger.debug("yank.run starting for MPI...") # Make sure each thread's random number generators have unique seeds. # TODO: Do we need to store seed in repex object? seed = np.random.randint(4294967295 - self._mpicomm.size) + self._mpicomm.rank np.random.seed(seed) # Run all phases sequentially. # TODO: Divide up MPI resources among the phases so they can run simultaneously? for phase in self._phases: store_filename = self._store_filenames[phase] # Resume simulation from store file. simulation = ModifiedHamiltonianExchange( store_filename=store_filename, mpicomm=self._mpicomm) simulation.resume(options=self._repex_parameters) # TODO: We may need to manually update run options here if options=options above does not behave as expected. simulation.run(niterations_to_run=niterations_to_run) # Clean up to ensure we close files, contexts, etc. del simulation return
def run(self, niterations_to_run=None): """ Run a free energy calculation. Parameters ---------- niterations_to_run : int, optional, default=None If specified, only this many iterations will be run for each phase. This is useful for running simulation incrementally, but may incur a good deal of overhead. """ # Make sure we've been properly initialized first. if not self._initialized: raise Exception("Yank must first be initialized by either resume() or create().") # Handle some logistics necessary for MPI. if self._mpicomm is not None: logger.debug("yank.run starting for MPI...") # Make sure each thread's random number generators have unique seeds. # TODO: Do we need to store seed in repex object? seed = np.random.randint(4294967295 - self._mpicomm.size) + self._mpicomm.rank np.random.seed(seed) # Run all phases sequentially. # TODO: Divide up MPI resources among the phases so they can run simultaneously? for phase in self._phases: store_filename = self._store_filenames[phase] # Resume simulation from store file. simulation = ModifiedHamiltonianExchange(store_filename=store_filename, mpicomm=self._mpicomm) simulation.resume(options=self._repex_parameters) # TODO: We may need to manually update run options here if options=options above does not behave as expected. simulation.run(niterations_to_run=niterations_to_run) # Clean up to ensure we close files, contexts, etc. del simulation return
def _create_phase(self, phase, reference_system, positions, atom_indices, thermodynamic_state, protocols=None, options=None, mpicomm=None): """ Create a repex object for a specified phase. Parameters ---------- phase : str The phase being initialized (one of ['complex', 'solvent', 'vacuum']) reference_system : simtk.openmm.System The reference system object from which alchemical intermediates are to be construcfted. positions : list of simtk.unit.Qunatity objects containing (natoms x 3) positions (as numpy or lists) The list of positions to be used to seed replicas in a round-robin way. atom_indices : dict atom_indices[phase][component] is the set of atom indices associated with component, where component is ['ligand', 'receptor'] thermodynamic_state : ThermodynamicState Thermodynamic state from which reference temperature and pressure are to be taken. protocols : dict of list of AlchemicalState, optional, default=None If specified, the alchemical protocol protocols[phase] will be used for phase 'phase' instead of the default. options : dict of str, optional, default=None If specified, these options will override default repex simulation options. """ # Make sure positions argument is a list of coordinate snapshots. if hasattr(positions, 'unit'): # Wrap in list. positions = [positions] # Check the dimensions of positions. for index in range(len(positions)): # Make sure it is recast as a numpy array. positions[index] = unit.Quantity(numpy.array(positions[index] / positions[index].unit), positions[index].unit) [natoms, ndim] = (positions[index] / positions[index].unit).shape if natoms != reference_system.getNumParticles(): raise Exception("positions argument must be a list of simtk.unit.Quantity of (natoms,3) lists or numpy array with units compatible with nanometers.") # Create metadata storage. metadata = dict() # Make a deep copy of the reference system so we don't accidentally modify it. reference_system = copy.deepcopy(reference_system) # TODO: Use more general approach to determine whether system is periodic. is_periodic = self._is_periodic(reference_system) # Make sure pressure is None if not periodic. if not is_periodic: thermodynamic_state.pressure = None # Compute standard state corrections for complex phase. metadata['standard_state_correction'] = 0.0 # TODO: Do we need to include a standard state correction for other phases in periodic boxes? if phase == 'complex-implicit': # Impose restraints for complex system in implicit solvent to keep ligand from drifting too far away from receptor. if self.verbose: print "Creating receptor-ligand restraints..." reference_positions = positions[0] if self.restraint_type == 'harmonic': restraints = HarmonicReceptorLigandRestraint(thermodynamic_state, reference_system, reference_positions, atom_indices['receptor'], atom_indices['ligand']) elif self.restraint_type == 'flat-bottom': restraints = FlatBottomReceptorLigandRestraint(thermodynamic_state, reference_system, reference_positions, atom_indices['receptor'], atom_indices['ligand']) else: raise Exception("restraint_type of '%s' is not supported." % self.restraint_type) force = restraints.getRestraintForce() # Get Force object incorporating restraints reference_system.addForce(force) metadata['standard_state_correction'] = restraints.getStandardStateCorrection() # standard state correction in kT elif phase == 'complex-explicit': # For periodic systems, we do not use a restraint, but must add a standard state correction for the box volume. # TODO: What if the box volume fluctuates during the simulation? box_vectors = reference_system.getDefaultPeriodicBoxVectors() box_volume = thermodynamic_state._volume(box_vectors) STANDARD_STATE_VOLUME = 1660.53928 * unit.angstrom**3 metadata['standard_state_correction'] = numpy.log(STANDARD_STATE_VOLUME / box_volume) # TODO: Check sign. # Use default alchemical protocols if not specified. if not protocols: protocols = self.default_protocols # Create alchemically-modified states using alchemical factory. if self.verbose: print "Creating alchemically-modified states..." factory = AbsoluteAlchemicalFactory(reference_system, ligand_atoms=atom_indices['ligand']) systems = factory.createPerturbedSystems(protocols[phase]) # Randomize ligand position if requested, but only for implicit solvent systems. if self.randomize_ligand and (phase == 'complex-implicit'): if self.verbose: print "Randomizing ligand positions and excluding overlapping configurations..." randomized_positions = list() nstates = len(systems) for state_index in range(nstates): positions_index = numpy.random.randint(0, len(positions)) current_positions = positions[positions_index] new_positions = ModifiedHamiltonianExchange.randomize_ligand_position(current_positions, atom_indices['receptor'], atom_indices['ligand'], self.randomize_ligand_sigma_multiplier * restraints.getReceptorRadiusOfGyration(), self.randomize_ligand_close_cutoff) randomized_positions.append(new_positions) positions = randomized_positions # Identify whether any atoms will be displaced via MC. mc_atoms = list() if 'ligand' in atom_indices: mc_atoms = atom_indices['ligand'] # Combine simulation options with defaults. options = dict(self.default_options.items() + options.items()) # Set up simulation. # TODO: Support MPI initialization? if self.verbose: print "Creating replica exchange object..." store_filename = os.path.join(self._store_directory, phase + '.nc') self._store_filenames[phase] = store_filename simulation = ModifiedHamiltonianExchange(store_filename, mpicomm=mpicomm) simulation.create(thermodynamic_state, systems, positions, displacement_sigma=self.mc_displacement_sigma, mc_atoms=mc_atoms, protocol=options, metadata=metadata) simulation.verbose = self.verbose # Initialize simulation. # TODO: Use the right scheme for initializing the simulation without running. if self.verbose: print "Initializing simulation..." simulation.run(0) # Clean up simulation. del simulation return