def _propagate_replicas(self):
# Reset statistics for MC trial times.
self.displacement_trial_time = 0.0
self.rotation_trial_time = 0.0
self.displacement_trials_accepted = 0
self.rotation_trials_accepted = 0
# Propagate replicas.
ReplicaExchange._propagate_replicas(self)
# Print summary statistics.
# TODO: Streamline this idiom.
if self.mpicomm:
# MPI
from mpi4py import MPI
if self.mc_displacement and (self.mc_atoms is not None):
self.displacement_trials_accepted = self.mpicomm.reduce(self.displacement_trials_accepted, op=MPI.SUM)
self.displacement_trial_time = self.mpicomm.reduce(self.displacement_trial_time, op=MPI.SUM)
if self.mpicomm.rank == 0:
logger.debug("Displacement MC trial times consumed %.3f s aggregate (%d accepted)" % (self.displacement_trial_time, self.displacement_trials_accepted))
if self.mc_rotation and (self.mc_atoms is not None):
self.rotation_trials_accepted = self.mpicomm.reduce(self.rotation_trials_accepted, op=MPI.SUM)
self.rotation_trial_time = self.mpicomm.reduce(self.rotation_trial_time, op=MPI.SUM)
if self.mpicomm.rank == 0:
logger.debug("Rotation MC trial times consumed %.3f s aggregate (%d accepted)" % (self.rotation_trial_time, self.rotation_trials_accepted))
else:
# SERIAL
if self.mc_displacement and (self.mc_atoms is not None):
logger.debug("Displacement MC trial times consumed %.3f s aggregate (%d accepted)" % (self.displacement_trial_time, self.displacement_trials_accepted))
if self.mc_rotation and (self.mc_atoms is not None):
logger.debug("Rotation MC trial times consumed %.3f s aggregate (%d accepted)" % (self.rotation_trial_time, self.rotation_trials_accepted))
return
def resume(self, options=None):
"""
"""
ReplicaExchange.resume(self, options=options)
#
# Cache Context and integrator.
#
# Use first state as reference state.
state = self.states[0]
# If temperature and pressure are specified, make sure MonteCarloBarostat is attached.
if state.temperature and state.pressure:
forces = { state.system.getForce(index).__class__.__name__ : state.system.getForce(index) for index in range(state.system.getNumForces()) }
if 'MonteCarloAnisotropicBarostat' in forces:
raise Exception('MonteCarloAnisotropicBarostat is unsupported.')
if 'MonteCarloBarostat' in forces:
barostat = forces['MonteCarloBarostat']
# Set temperature and pressure.
barostat.setTemperature(state.temperature)
barostat.setDefaultPressure(state.pressure)
barostat.setRandomNumberSeed(int(np.random.randint(0, MAX_SEED)))
else:
# Create barostat and add it to the system if it doesn't have one already.
barostat = openmm.MonteCarloBarostat(state.pressure, state.temperature)
barostat.setRandomNumberSeed(int(np.random.randint(0, MAX_SEED)))
state.system.addForce(barostat)
def _display_citations(self):
ReplicaExchange._display_citations(self)
yank_citations = """\
Chodera JD, Shirts MR, Wang K, Friedrichs MS, Eastman P, Pande VS, and Branson K. YANK: An extensible platform for GPU-accelerated free energy calculations. In preparation."""
print yank_citations
print ""
return
def _display_citations(self):
ReplicaExchange._display_citations(self)
yank_citations = """\
Chodera JD, Shirts MR, Wang K, Friedrichs MS, Eastman P, Pande VS, and Branson K. YANK: An extensible platform for GPU-accelerated free energy calculations. In preparation."""
print yank_citations
print ""
return
def _finalize(self):
"""
Do anything necessary to finish run except close files.
"""
ReplicaExchange._finalize(self)
# Clean up cached context and integrator.
if hasattr(self, 'context'):
del self._context, self._integrator
return
def _finalize(self):
"""
Do anything necessary to finish run except close files.
"""
ReplicaExchange._finalize(self)
# Clean up cached context and integrator.
if hasattr(self, 'context'):
del self._context, self._integrator
return
def create(self,
reference_state,
alchemical_states,
positions,
displacement_sigma=None,
mc_atoms=None,
options=None,
metadata=None):
"""
Initialize a modified Hamiltonian exchange simulation object.
Parameters
----------
reference_state : ThermodynamicState
reference state containing all thermodynamic parameters and reference System object'
alchemical_states : list of AlchemicalState
list of alchemical states (one per replica)
positions : simtk.unit.Quantity of numpy natoms x 3 with units length
positions (or a list of positions objects) for initial assignment of replicas (will be used in round-robin assignment)
displacement_sigma : simtk.unit.Quantity with units distance
size of displacement trial for Monte Carlo displacements, if specified (default: 1 nm)
ligand_atoms : list of int, optional, default=None
atoms to use for trial displacements for translational and orientational Monte Carlo trials, if specified (all atoms if None)
options : dict, optional, default=None
Optional dict to use for specifying simulation options. Provided keywords will be matched to object variables to replace defaults.
metadata : dict, optional, default=None
metadata to store in a 'metadata' group in store file
"""
# If an empty set is specified for mc_atoms, set this to None.
if mc_atoms is not None:
if len(mc_atoms) == 0:
mc_atoms = None
# Store trial displacement magnitude and atoms to rotate in MC move.
self.displacement_sigma = 1.0 * unit.nanometer
if mc_atoms is not None:
self.mc_atoms = np.array(mc_atoms)
self.mc_displacement = True
self.mc_rotation = True
else:
self.mc_atoms = None
self.mc_displacement = False
self.mc_rotation = False
self.displacement_trials_accepted = 0 # number of MC displacement trials accepted
self.rotation_trials_accepted = 0 # number of MC displacement trials accepted
# Store reference system.
self.reference_system = copy.deepcopy(reference_state.system)
# TODO: Form metadata dict.
# Initialize replica-exchange simlulation.
states = list()
for alchemical_state in alchemical_states:
state = ThermodynamicState(system=self.reference_system,
temperature=reference_state.temperature,
pressure=reference_state.pressure)
setattr(state, 'alchemical_state',
copy.deepcopy(alchemical_state)) # attach alchemical state
states.append(state)
# Initialize replica-exchange simlulation.
ReplicaExchange.create(self,
states,
positions,
options=options,
metadata=metadata)
# Override title.
self.title = 'Alchemical Hamiltonian exchange simulation created using HamiltonianExchange class of repex.py on %s' % time.asctime(
time.localtime())
return
def create(self, reference_state, alchemical_states, positions, displacement_sigma=None, mc_atoms=None, options=None, metadata=None):
"""
Initialize a modified Hamiltonian exchange simulation object.
Parameters
----------
reference_state : ThermodynamicState
reference state containing all thermodynamic parameters and reference System object'
alchemical_states : list of AlchemicalState
list of alchemical states (one per replica)
positions : simtk.unit.Quantity of numpy natoms x 3 with units length
positions (or a list of positions objects) for initial assignment of replicas (will be used in round-robin assignment)
displacement_sigma : simtk.unit.Quantity with units distance
size of displacement trial for Monte Carlo displacements, if specified (default: 1 nm)
ligand_atoms : list of int, optional, default=None
atoms to use for trial displacements for translational and orientational Monte Carlo trials, if specified (all atoms if None)
options : dict, optional, default=None
Optional dict to use for specifying simulation options. Provided keywords will be matched to object variables to replace defaults.
metadata : dict, optional, default=None
metadata to store in a 'metadata' group in store file
"""
# If an empty set is specified for mc_atoms, set this to None.
if mc_atoms is not None:
if len(mc_atoms) == 0:
mc_atoms = None
# Store trial displacement magnitude and atoms to rotate in MC move.
self.displacement_sigma = 1.0 * unit.nanometer
if mc_atoms is not None:
self.mc_atoms = np.array(mc_atoms)
self.mc_displacement = True
self.mc_rotation = True
else:
self.mc_atoms = None
self.mc_displacement = False
self.mc_rotation = False
self.displacement_trials_accepted = 0 # number of MC displacement trials accepted
self.rotation_trials_accepted = 0 # number of MC displacement trials accepted
# Store reference system.
self.reference_system = copy.deepcopy(reference_state.system)
# TODO: Form metadata dict.
# Initialize replica-exchange simlulation.
states = list()
for alchemical_state in alchemical_states:
state = ThermodynamicState(system=self.reference_system, temperature=reference_state.temperature, pressure=reference_state.pressure)
setattr(state, 'alchemical_state', copy.deepcopy(alchemical_state)) # attach alchemical state
states.append(state)
# Initialize replica-exchange simlulation.
ReplicaExchange.create(self, states, positions, options=options, metadata=metadata)
# Override title.
self.title = 'Alchemical Hamiltonian exchange simulation created using HamiltonianExchange class of repex.py on %s' % time.asctime(time.localtime())
return
def __init__(self,
temperature,
nbins,
store_filename,
protocol=None,
mm=None):
"""
Initialize a Hamiltonian exchange simulation object.
ARGUMENTS
store_filename (string) - name of NetCDF file to bind to for simulation output and checkpointing
OPTIONAL ARGUMENTS
protocol (dict) - Optional protocol to use for specifying simulation protocol as a dict. Provided keywords will be matched to object variables to replace defaults.
NOTES
von Mises distribution used for restraints
http://en.wikipedia.org/wiki/Von_Mises_distribution
"""
import simtk.pyopenmm.extras.testsystems as testsystems
# Create reference system and state.
[system, coordinates] = testsystems.AlanineDipeptideImplicit()
self.reference_system = system
self.reference_state = repex.ThermodynamicState(
system=system, temperature=temperature)
self.nbins = nbins
self.kT = (repex.kB * temperature)
self.beta = 1.0 / self.kT
self.delta = 360.0 / float(
nbins) * units.degrees # bin spacing (angular)
self.sigma = self.delta / 3.0 # standard deviation (angular)
self.kappa = (self.sigma / units.radians)**(
-2) # kappa parameter (unitless)
# Create list of thermodynamic states with different bias potentials.
states = list()
# Create a state without a biasing potential.
[system, coordinates] = testsystems.AlanineDipeptideImplicit()
state = repex.ThermodynamicState(system=system,
temperature=temperature)
states.append(state)
# Create states with biasing potentials.
for phi_index in range(nbins):
for psi_index in range(nbins):
print "bin (%d,%d)" % (phi_index, psi_index)
# Create system.
[system, coordinates] = testsystems.AlanineDipeptideImplicit()
# Add biasing potentials.
phi0 = (float(phi_index) +
0.5) * self.delta - 180.0 * units.degrees
psi0 = (float(psi_index) +
0.5) * self.delta - 180.0 * units.degrees
force = openmm.CustomTorsionForce(
'-kT * kappa * cos(theta - theta0)')
force.addGlobalParameter('kT',
self.kT / units.kilojoules_per_mole)
force.addPerTorsionParameter('kappa')
force.addPerTorsionParameter('theta0')
force.addTorsion(4, 6, 8, 14,
[self.kappa, phi0 / units.radians])
force.addTorsion(6, 8, 14, 16,
[self.kappa, psi0 / units.radians])
system.addForce(force)
# Add state.
state = repex.ThermodynamicState(system=system,
temperature=temperature)
states.append(state)
# Initialize replica-exchange simlulation.
ReplicaExchange.__init__(self,
states,
coordinates,
store_filename,
protocol=protocol,
mm=mm)
# Override title.
self.title = '2D umbrella sampling replica-exchange simulation created on %s' % time.asctime(
time.localtime())
return
def test_velocity_assignment(mpicomm=None, verbose=True):
"""
Test Maxwell-Boltzmann velocity assignment subtroutine produces correct distribution, raising an exception if this test fails.
"""
# Stop here if not root node.
if mpicomm and (mpicomm.rank != 0): return
if verbose: print "Testing Maxwell-Boltzmann velocity assignment: ",
# Make a list of all test system constructors.
import testsystems
# Test parameters
temperature = 298.0 * units.kelvin # test temperature
kT = kB * temperature # thermal energy
ntrials = 1000 # number of test trials
systems_to_test = ['HarmonicOscillator', 'HarmonicOscillatorArray', 'AlanineDipeptideImplicit'] # systems to test
for system_name in systems_to_test:
#print '*' * 80
#print system_name
# Create system.
constructor = getattr(testsystems, system_name)
[system, coordinates] = constructor()
# Create temporary filename.
import tempfile # use a temporary file for testing
file = tempfile.NamedTemporaryFile()
store_filename = file.name
# Create repex instance.
from repex import ReplicaExchange
from thermodynamics import ThermodynamicState
states = [ ThermodynamicState(system, temperature=temperature) ]
simulation = ReplicaExchange(states=states, coordinates=coordinates, store_filename=store_filename)
# Create integrator and context.
natoms = system.getNumParticles()
velocity_trials = numpy.zeros([ntrials, natoms, 3])
kinetic_energy_trials = numpy.zeros([ntrials])
for trial in range(ntrials):
velocities = simulation._assign_Maxwell_Boltzmann_velocities(system, temperature)
kinetic_energy = 0.5 * units.sum(units.sum(system.masses * velocities**2))
velocity_trials[trial,:,:] = velocities / (units.nanometers / units.picosecond)
kinetic_energy_trials[trial] = kinetic_energy / units.kilocalories_per_mole
velocity_mean = velocity_trials.mean(0)
velocity_stderr = velocity_trials.std(0) / numpy.sqrt(ntrials)
kinetic_analytical = (3.0/2.0) * natoms * kT / units.kilocalories_per_mole
kinetic_mean = kinetic_energy_trials.mean()
kinetic_error = kinetic_mean - kinetic_analytical
kinetic_stderr = kinetic_energy_trials.std() / numpy.sqrt(ntrials)
# Test if violations exceed tolerance.
MAX_SIGMA = 6.0 # maximum number of standard errors allowed
if numpy.any(numpy.abs(kinetic_error / kinetic_stderr) > MAX_SIGMA):
print "analytical kinetic energy"
print kinetic_analytical
print "mean kinetic energy (kcal/mol)"
print kinetic_mean
print "difference (kcal/mol)"
print kinetic_mean - kinetic_analytical
print "stderr (kcal/mol)"
print kinetic_stderr
print "nsigma"
print (kinetic_mean - kinetic_analytical) / kinetic_stderr
raise Exception("Mean kinetic energy exceeds error tolerance of %.1f standard errors." % MAX_SIGMA)
if numpy.any(numpy.abs(velocity_mean / velocity_stderr) > MAX_SIGMA):
print "mean velocity (nm/ps)"
print velocity_mean
print "stderr (nm/ps)"
print velocity_stderr
print "nsigma"
print velocity_mean / velocity_stderr
raise Exception("Mean velocity exceeds error tolerance of %.1f standard errors." % MAX_SIGMA)
if verbose: print "PASSED"
return
def test_replica_exchange(mpicomm=None, verbose=True):
"""
Test that free energies and avergae potential energies of a 3D harmonic oscillator are correctly computed.
TODO
* Test ParallelTempering and HamiltonianExchange subclasses as well.
* Test with different combinations of input parameters.
"""
if verbose and ((not mpicomm) or (mpicomm.rank==0)): print "Testing replica exchange facility with harmonic oscillators: ",
# Create test system of harmonic oscillators
import testsystems
[system, coordinates] = testsystems.HarmonicOscillatorArray()
# Define mass of carbon atom.
mass = 12.0 * units.amu
# Define thermodynamic states.
from thermodynamics import ThermodynamicState
states = list() # thermodynamic states
sigmas = [0.2, 0.3, 0.4] * units.angstroms # standard deviations: beta K = 1/sigma^2 so K = 1/(beta sigma^2)
temperatures = [300.0, 350.0, 400.0] * units.kelvin # temperatures
seed_positions = list()
analytical_results = list()
f_i_analytical = list() # dimensionless free energies
u_i_analytical = list() # reduced potential
for (sigma, temperature) in zip(sigmas, temperatures):
# Compute corresponding spring constant.
kB = units.BOLTZMANN_CONSTANT_kB * units.AVOGADRO_CONSTANT_NA
kT = kB * temperature # thermal energy
beta = 1.0 / kT # inverse temperature
K = 1.0 / (beta * sigma**2)
# Create harmonic oscillator system.
[system, positions] = testsystems.HarmonicOscillator(K=K, mass=mass, mm=openmm)
# Create thermodynamic state.
state = ThermodynamicState(system=system, temperature=temperature)
# Append thermodynamic state and positions.
states.append(state)
seed_positions.append(positions)
# Store analytical results.
results = computeHarmonicOscillatorExpectations(K, mass, temperature)
analytical_results.append(results)
f_i_analytical.append(results['f'])
reduced_potential = results['potential']['mean'] / kT
u_i_analytical.append(reduced_potential)
# Compute analytical Delta_f_ij
nstates = len(f_i_analytical)
f_i_analytical = numpy.array(f_i_analytical)
u_i_analytical = numpy.array(u_i_analytical)
s_i_analytical = u_i_analytical - f_i_analytical
Delta_f_ij_analytical = numpy.zeros([nstates,nstates], numpy.float64)
Delta_u_ij_analytical = numpy.zeros([nstates,nstates], numpy.float64)
Delta_s_ij_analytical = numpy.zeros([nstates,nstates], numpy.float64)
for i in range(nstates):
for j in range(nstates):
Delta_f_ij_analytical[i,j] = f_i_analytical[j] - f_i_analytical[i]
Delta_u_ij_analytical[i,j] = u_i_analytical[j] - u_i_analytical[i]
Delta_s_ij_analytical[i,j] = s_i_analytical[j] - s_i_analytical[i]
# Define file for temporary storage.
import tempfile # use a temporary file
file = tempfile.NamedTemporaryFile(delete=False)
store_filename = file.name
#print "node %d : Storing data in temporary file: %s" % (mpicomm.rank, str(store_filename)) # DEBUG
# Create and configure simulation object.
from repex import ReplicaExchange
simulation = ReplicaExchange(states, seed_positions, store_filename, mpicomm=mpicomm) # initialize the replica-exchange simulation
simulation.number_of_iterations = 1000 # set the simulation to only run 2 iterations
simulation.timestep = 2.0 * units.femtoseconds # set the timestep for integration
simulation.nsteps_per_iteration = 500 # run 500 timesteps per iteration
simulation.collision_rate = 0.001 / units.picosecond # DEBUG: Use a low collision rate
simulation.platform = openmm.Platform.getPlatformByName('Reference') # use reference platform
simulation.verbose = True # DEBUG
# Run simulation.
simulation.run() # run the simulation
# Stop here if not root node.
if mpicomm and (mpicomm.rank != 0): return
# Analyze simulation to compute free energies.
analysis = simulation.analyze()
# TODO: Check if deviations exceed tolerance.
Delta_f_ij = analysis['Delta_f_ij']
dDelta_f_ij = analysis['dDelta_f_ij']
error = Delta_f_ij - Delta_f_ij_analytical
indices = numpy.where(dDelta_f_ij > 0.0)
nsigma = numpy.zeros([nstates,nstates], numpy.float32)
nsigma[indices] = error[indices] / dDelta_f_ij[indices]
MAX_SIGMA = 6.0 # maximum allowed number of standard errors
if numpy.any(nsigma > MAX_SIGMA):
print "Delta_f_ij"
print Delta_f_ij
print "Delta_f_ij_analytical"
print Delta_f_ij_analytical
print "error"
print error
print "stderr"
print dDelta_f_ij
print "nsigma"
print nsigma
raise Exception("Dimensionless free energy difference exceeds MAX_SIGMA of %.1f" % MAX_SIGMA)
error = analysis['Delta_u_ij'] - Delta_u_ij_analytical
nsigma = numpy.zeros([nstates,nstates], numpy.float32)
nsigma[indices] = error[indices] / dDelta_f_ij[indices]
if numpy.any(nsigma > MAX_SIGMA):
print "Delta_u_ij"
print analysis['Delta_u_ij']
print "Delta_u_ij_analytical"
print Delta_u_ij_analytical
print "error"
print error
print "nsigma"
print nsigma
raise Exception("Dimensionless potential energy difference exceeds MAX_SIGMA of %.1f" % MAX_SIGMA)
if verbose: print "PASSED."
return
def __init__(self, temperature, nbins, store_filename, protocol=None, mm=None):
"""
Initialize a Hamiltonian exchange simulation object.
ARGUMENTS
store_filename (string) - name of NetCDF file to bind to for simulation output and checkpointing
OPTIONAL ARGUMENTS
protocol (dict) - Optional protocol to use for specifying simulation protocol as a dict. Provided keywords will be matched to object variables to replace defaults.
NOTES
von Mises distribution used for restraints
http://en.wikipedia.org/wiki/Von_Mises_distribution
"""
import simtk.pyopenmm.extras.testsystems as testsystems
# Create reference system and state.
[system, coordinates] = testsystems.AlanineDipeptideImplicit()
self.reference_system = system
self.reference_state = repex.ThermodynamicState(system=system, temperature=temperature)
self.nbins = nbins
self.kT = (repex.kB * temperature)
self.beta = 1.0 / self.kT
self.delta = 360.0 / float(nbins) * units.degrees # bin spacing (angular)
self.sigma = self.delta/3.0 # standard deviation (angular)
self.kappa = (self.sigma / units.radians)**(-2) # kappa parameter (unitless)
# Create list of thermodynamic states with different bias potentials.
states = list()
# Create a state without a biasing potential.
[system, coordinates] = testsystems.AlanineDipeptideImplicit()
state = repex.ThermodynamicState(system=system, temperature=temperature)
states.append(state)
# Create states with biasing potentials.
for phi_index in range(nbins):
for psi_index in range(nbins):
print "bin (%d,%d)" % (phi_index, psi_index)
# Create system.
[system, coordinates] = testsystems.AlanineDipeptideImplicit()
# Add biasing potentials.
phi0 = (float(phi_index) + 0.5) * self.delta - 180.0 * units.degrees
psi0 = (float(psi_index) + 0.5) * self.delta - 180.0 * units.degrees
force = openmm.CustomTorsionForce('-kT * kappa * cos(theta - theta0)')
force.addGlobalParameter('kT', self.kT / units.kilojoules_per_mole)
force.addPerTorsionParameter('kappa')
force.addPerTorsionParameter('theta0')
force.addTorsion(4, 6, 8, 14, [self.kappa, phi0 / units.radians])
force.addTorsion(6, 8, 14, 16, [self.kappa, psi0 / units.radians])
system.addForce(force)
# Add state.
state = repex.ThermodynamicState(system=system, temperature=temperature)
states.append(state)
# Initialize replica-exchange simlulation.
ReplicaExchange.__init__(self, states, coordinates, store_filename, protocol=protocol, mm=mm)
# Override title.
self.title = '2D umbrella sampling replica-exchange simulation created on %s' % time.asctime(time.localtime())
return
import tempfile
file = tempfile.NamedTemporaryFile() # use a temporary file for testing -- you will want to keep this file, since it stores output and checkpoint data
# Select platform: one of 'Reference' (CPU-only), 'Cuda' (NVIDIA Cuda), or 'OpenCL' (for OS X 10.6 with OpenCL OpenMM compiled)
platform = simtk.openmm.Platform.getPlatformByName("OpenCL")
platform = simtk.openmm.Platform.getPlatformByName("Cuda")
# Set up device to bind to.
print "Selecting MPI communicator and selecting a GPU device..."
from mpi4py import MPI # MPI wrapper
hostname = os.uname()[1]
ngpus = 6 # number of GPUs per system
comm = MPI.COMM_WORLD # MPI communicator
deviceid = comm.rank % ngpus # select a unique GPU for this node assuming block allocation (not round-robin)
platform.setPropertyDefaultValue('CudaDeviceIndex', '%d' % deviceid) # select Cuda device index
platform.setPropertyDefaultValue('OpenCLDeviceIndex', '%d' % deviceid) # select OpenCL device index
print "node '%s' deviceid %d / %d, MPI rank %d / %d" % (hostname, deviceid, ngpus, comm.rank, comm.size)
# Make sure random number generators have unique seeds.
seed = numpy.random.randint(sys.maxint - comm.size) + comm.rank
numpy.random.seed(seed)
# Set up replica exchange simulation.
simulation = ReplicaExchange(states, coordinates, file.name, mpicomm=comm) # initialize the replica-exchange simulation
simulation.verbose = True
simulation.number_of_iterations = 100 # set the simulation to only run 2 iterations
simulation.timestep = 2.0 * units.femtoseconds # set the timestep for integration
simulation.nsteps_per_iteration = 500 # run 500 timesteps per iteration
simulation.platform = platform
simulation.run() # run the simulation