def subtest_mcmc_expectation(testsystem, move_set):
if debug:
print testsystem.__class__.__name__
print str(move_set)
# Test settings.
temperature = 298.0 * units.kelvin
pressure = 1.0 * units.atmospheres
nequil = 10 # number of equilibration iterations
niterations = 20 # number of production iterations
# Retrieve system and positions.
[system, positions] = [testsystem.system, testsystem.positions]
platform_name = 'Reference'
from simtk.openmm import Platform
platform = Platform.getPlatformByName(platform_name)
# Compute properties.
kB = units.BOLTZMANN_CONSTANT_kB * units.AVOGADRO_CONSTANT_NA
kT = kB * temperature
ndof = 3*system.getNumParticles() - system.getNumConstraints()
# Create thermodynamic state
from repex.thermodynamics import ThermodynamicState
thermodynamic_state = ThermodynamicState(system=testsystem.system, temperature=temperature, pressure=pressure)
# Create MCMC sampler.
from repex.mcmc import MCMCSampler
sampler = MCMCSampler(thermodynamic_state, move_set=move_set, platform=platform)
# Create sampler state.
from repex.mcmc import SamplerState
sampler_state = SamplerState(system=testsystem.system, positions=testsystem.positions, platform=platform)
# Equilibrate
for iteration in range(nequil):
#print "equilibration iteration %d / %d" % (iteration, nequil)
# Update sampler state.
sampler_state = sampler.run(sampler_state, 1)
# Accumulate statistics.
x_n = np.zeros([niterations], np.float64) # x_n[i] is the x position of atom 1 after iteration i, in angstroms
potential_n = np.zeros([niterations], np.float64) # potential_n[i] is the potential energy after iteration i, in kT
kinetic_n = np.zeros([niterations], np.float64) # kinetic_n[i] is the kinetic energy after iteration i, in kT
temperature_n = np.zeros([niterations], np.float64) # temperature_n[i] is the instantaneous kinetic temperature from iteration i, in K
volume_n = np.zeros([niterations], np.float64) # volume_n[i] is the volume from iteration i, in K
for iteration in range(niterations):
if debug: print "iteration %d / %d" % (iteration, niterations)
# Update sampler state.
sampler_state = sampler.run(sampler_state, 1)
# Get statistics.
potential_energy = sampler_state.potential_energy
kinetic_energy = sampler_state.kinetic_energy
total_energy = sampler_state.total_energy
instantaneous_temperature = kinetic_energy * 2.0 / ndof / (units.BOLTZMANN_CONSTANT_kB * units.AVOGADRO_CONSTANT_NA)
volume = sampler_state.volume
#print "potential %8.1f kT | kinetic %8.1f kT | total %8.1f kT | volume %8.3f nm^3 | instantaneous temperature: %8.1f K" % (potential_energy/kT, kinetic_energy/kT, total_energy/kT, volume/(units.nanometers**3), instantaneous_temperature/units.kelvin)
# Accumulate statistics.
x_n[iteration] = sampler_state.positions[0,0] / units.angstroms
potential_n[iteration] = potential_energy / kT
kinetic_n[iteration] = kinetic_energy / kT
temperature_n[iteration] = instantaneous_temperature / units.kelvin
volume_n[iteration] = volume / (units.nanometers**3)
# Compute expected statistics.
if ('get_potential_expectation' in dir(testsystem)):
# Skip this check if the std dev is zero.
skip_test = False
if (potential_n.std() == 0.0):
skip_test = True
if debug: print "Skipping potential test since variance is zero."
if not skip_test:
potential_expectation = testsystem.get_potential_expectation(thermodynamic_state) / kT
potential_mean = potential_n.mean()
g = timeseries.statisticalInefficiency(potential_n, fast=True)
dpotential_mean = potential_n.std() / np.sqrt(niterations / g)
potential_error = potential_mean - potential_expectation
nsigma = abs(potential_error) / dpotential_mean
test_passed = True
if (nsigma > NSIGMA_CUTOFF):
test_passed = False
if debug or (test_passed is False):
print "Potential energy expectation"
print "observed %10.5f +- %10.5f kT | expected %10.5f | error %10.5f +- %10.5f (%.1f sigma)" % (potential_mean, dpotential_mean, potential_expectation, potential_error, dpotential_mean, nsigma)
if test_passed:
print "TEST PASSED"
else:
print "TEST FAILED"
print "----------------------------------------------------------------------------"
if ('get_volume_expectation' in dir(testsystem)):
# Skip this check if the std dev is zero.
skip_test = False
if (volume_n.std() == 0.0):
skip_test = True
if debug: print "Skipping volume test."
if not skip_test:
volume_expectation = testsystem.get_volume_expectation(thermodynamic_state) / (units.nanometers**3)
volume_mean = volume_n.mean()
g = timeseries.statisticalInefficiency(volume_n, fast=True)
dvolume_mean = volume_n.std() / np.sqrt(niterations / g)
volume_error = volume_mean - volume_expectation
nsigma = abs(volume_error) / dvolume_mean
test_passed = True
if (nsigma > NSIGMA_CUTOFF):
test_passed = False
if debug or (test_passed is False):
print "Volume expectation"
print "observed %10.5f +- %10.5f kT | expected %10.5f | error %10.5f +- %10.5f (%.1f sigma)" % (volume_mean, dvolume_mean, volume_expectation, volume_error, dvolume_mean, nsigma)
if test_passed:
print "TEST PASSED"
else:
print "TEST FAILED"
print "----------------------------------------------------------------------------"
def subtest_mcmc_expectation(testsystem, move_set):
if debug:
print testsystem.__class__.__name__
print str(move_set)
# Test settings.
temperature = 298.0 * units.kelvin
pressure = 1.0 * units.atmospheres
nequil = 10 # number of equilibration iterations
niterations = 20 # number of production iterations
# Retrieve system and positions.
[system, positions] = [testsystem.system, testsystem.positions]
platform_name = 'Reference'
from simtk.openmm import Platform
platform = Platform.getPlatformByName(platform_name)
# Compute properties.
kB = units.BOLTZMANN_CONSTANT_kB * units.AVOGADRO_CONSTANT_NA
kT = kB * temperature
ndof = 3 * system.getNumParticles() - system.getNumConstraints()
# Create thermodynamic state
from repex.thermodynamics import ThermodynamicState
thermodynamic_state = ThermodynamicState(system=testsystem.system,
temperature=temperature,
pressure=pressure)
# Create MCMC sampler.
from repex.mcmc import MCMCSampler
sampler = MCMCSampler(thermodynamic_state,
move_set=move_set,
platform=platform)
# Create sampler state.
from repex.mcmc import SamplerState
sampler_state = SamplerState(system=testsystem.system,
positions=testsystem.positions,
platform=platform)
# Equilibrate
for iteration in range(nequil):
#print "equilibration iteration %d / %d" % (iteration, nequil)
# Update sampler state.
sampler_state = sampler.run(sampler_state, 1)
# Accumulate statistics.
x_n = np.zeros(
[niterations], np.float64
) # x_n[i] is the x position of atom 1 after iteration i, in angstroms
potential_n = np.zeros(
[niterations], np.float64
) # potential_n[i] is the potential energy after iteration i, in kT
kinetic_n = np.zeros(
[niterations], np.float64
) # kinetic_n[i] is the kinetic energy after iteration i, in kT
temperature_n = np.zeros(
[niterations], np.float64
) # temperature_n[i] is the instantaneous kinetic temperature from iteration i, in K
volume_n = np.zeros(
[niterations],
np.float64) # volume_n[i] is the volume from iteration i, in K
for iteration in range(niterations):
if debug: print "iteration %d / %d" % (iteration, niterations)
# Update sampler state.
sampler_state = sampler.run(sampler_state, 1)
# Get statistics.
potential_energy = sampler_state.potential_energy
kinetic_energy = sampler_state.kinetic_energy
total_energy = sampler_state.total_energy
instantaneous_temperature = kinetic_energy * 2.0 / ndof / (
units.BOLTZMANN_CONSTANT_kB * units.AVOGADRO_CONSTANT_NA)
volume = sampler_state.volume
#print "potential %8.1f kT | kinetic %8.1f kT | total %8.1f kT | volume %8.3f nm^3 | instantaneous temperature: %8.1f K" % (potential_energy/kT, kinetic_energy/kT, total_energy/kT, volume/(units.nanometers**3), instantaneous_temperature/units.kelvin)
# Accumulate statistics.
x_n[iteration] = sampler_state.positions[0, 0] / units.angstroms
potential_n[iteration] = potential_energy / kT
kinetic_n[iteration] = kinetic_energy / kT
temperature_n[iteration] = instantaneous_temperature / units.kelvin
volume_n[iteration] = volume / (units.nanometers**3)
# Compute expected statistics.
if ('get_potential_expectation' in dir(testsystem)):
# Skip this check if the std dev is zero.
skip_test = False
if (potential_n.std() == 0.0):
skip_test = True
if debug: print "Skipping potential test since variance is zero."
if not skip_test:
potential_expectation = testsystem.get_potential_expectation(
thermodynamic_state) / kT
potential_mean = potential_n.mean()
g = timeseries.statisticalInefficiency(potential_n, fast=True)
dpotential_mean = potential_n.std() / np.sqrt(niterations / g)
potential_error = potential_mean - potential_expectation
nsigma = abs(potential_error) / dpotential_mean
test_passed = True
if (nsigma > NSIGMA_CUTOFF):
test_passed = False
if debug or (test_passed is False):
print "Potential energy expectation"
print "observed %10.5f +- %10.5f kT | expected %10.5f | error %10.5f +- %10.5f (%.1f sigma)" % (
potential_mean, dpotential_mean, potential_expectation,
potential_error, dpotential_mean, nsigma)
if test_passed:
print "TEST PASSED"
else:
print "TEST FAILED"
print "----------------------------------------------------------------------------"
if ('get_volume_expectation' in dir(testsystem)):
# Skip this check if the std dev is zero.
skip_test = False
if (volume_n.std() == 0.0):
skip_test = True
if debug: print "Skipping volume test."
if not skip_test:
volume_expectation = testsystem.get_volume_expectation(
thermodynamic_state) / (units.nanometers**3)
volume_mean = volume_n.mean()
g = timeseries.statisticalInefficiency(volume_n, fast=True)
dvolume_mean = volume_n.std() / np.sqrt(niterations / g)
volume_error = volume_mean - volume_expectation
nsigma = abs(volume_error) / dvolume_mean
test_passed = True
if (nsigma > NSIGMA_CUTOFF):
test_passed = False
if debug or (test_passed is False):
print "Volume expectation"
print "observed %10.5f +- %10.5f kT | expected %10.5f | error %10.5f +- %10.5f (%.1f sigma)" % (
volume_mean, dvolume_mean, volume_expectation,
volume_error, dvolume_mean, nsigma)
if test_passed:
print "TEST PASSED"
else:
print "TEST FAILED"
print "----------------------------------------------------------------------------"
#print context.getPlatform().getName()
# Create MCMC move set.
from repex.mcmc import HMCMove, GHMCMove, LangevinDynamicsMove, MonteCarloBarostatMove
#move_set = [ GHMCMove(nsteps=10), HMCMove(nsteps=10) ]
move_set = [ GHMCMove(), MonteCarloBarostatMove() ]
#move_set = [ GHMCMove() ]
#move_set = [ LangevinDynamicsMove() ]
# Create thermodynamic state
from repex.thermodynamics import ThermodynamicState
thermodynamic_state = ThermodynamicState(system=testsystem.system, temperature=temperature, pressure=pressure)
# Create MCMC sampler.
from repex.mcmc import MCMCSampler
sampler = MCMCSampler(thermodynamic_state, move_set=move_set, platform=platform)
# Create sampler state.
from repex.mcmc import MCMCSamplerState
sampler_state = MCMCSamplerState(system=testsystem.system, positions=testsystem.positions)
# Equilibrate
for iteration in range(nequil):
print "equilibration iteration %d / %d" % (iteration, nequil)
# Update sampler state.
sampler_state = sampler.run(sampler_state, 1)
# Accumulate statistics.
x_n = numpy.zeros([niterations], numpy.float64) # x_n[i] is the x position of atom 1 after iteration i, in angstroms
potential_n = numpy.zeros([niterations], numpy.float64) # potential_n[i] is the potential energy after iteration i, in kT
from repex.mcmc import HMCMove, GHMCMove, LangevinDynamicsMove, MonteCarloBarostatMove
#move_set = [ GHMCMove(nsteps=10), HMCMove(nsteps=10) ]
move_set = [GHMCMove(), MonteCarloBarostatMove()]
#move_set = [ GHMCMove() ]
#move_set = [ LangevinDynamicsMove() ]
# Create thermodynamic state
from repex.thermodynamics import ThermodynamicState
thermodynamic_state = ThermodynamicState(system=testsystem.system,
temperature=temperature,
pressure=pressure)
# Create MCMC sampler.
from repex.mcmc import MCMCSampler
sampler = MCMCSampler(thermodynamic_state,
move_set=move_set,
platform=platform)
# Create sampler state.
from repex.mcmc import MCMCSamplerState
sampler_state = MCMCSamplerState(system=testsystem.system,
positions=testsystem.positions)
# Equilibrate
for iteration in range(nequil):
print "equilibration iteration %d / %d" % (iteration, nequil)
# Update sampler state.
sampler_state = sampler.run(sampler_state, 1)
# Accumulate statistics.
