def get_equilibration_points(df, column_name=None):
"""
directly uses pymbar's timeseries utility!
source: https://github.com/choderalab/pymbar
https://pymbar.readthedocs.io/en/master/timeseries.html
and references therein
"""
equilibration_data = dict([])
for name in df.columns.values:
"""
t - t_0 starting point of equilibrated part of series
g - the statistical innefficency = 2*correlationtime +1
N_effmax - effective number of uncorrelated samples
"""
if isinstance(df.loc[0, name], complex):
[t, g, Neff_max] = timeseries.detectEquilibration(
np.array([x.real for x in df.loc[:, name]]))
equilibration_data[name + "_real"] = [t, g, Neff_max]
[t, g, Neff_max] = timeseries.detectEquilibration(
np.array([x.imag for x in df.loc[:, name]]))
equilibration_data[name + "_imag"] = [t, g, Neff_max]
else:
[t, g, Neff_max] = timeseries.detectEquilibration(df.loc[:, name])
equilibration_data[name] = [t, g, Neff_max]
print(equilibration_data)
return equilibration_data
def test_detectEquil_constant_trailing():
# This explicitly tests issue #122, see https://github.com/choderalab/pymbar/issues/122
x = np.random.normal(size=100) * 0.01
x[50:] = 3.0
# The input data is some MCMC chain where the trailing end of the chain is a constant sequence.
(t, g, Neff_max) = timeseries.detectEquilibration(x)
"""
def test_detectEquil_constant_trailing():
# This explicitly tests issue #122, see https://github.com/choderalab/pymbar/issues/122
x = np.random.normal(size=100) * 0.01
x[50:] = 3.0
# The input data is some MCMC chain where the trailing end of the chain is a constant sequence.
(t, g, Neff_max) = timeseries.detectEquilibration(x)
"""
def is_converged(series: Series, frac_min=0.5):
'''
Determine whether a time series has converged or not
Parameters
----------
series : Series
Time series
frac_min : float
Consider this time series is converged only if the fraction of converged parts relative to the full series
is larger than this threshold
Returns
-------
converged : bool
Converged or not
when : float
From when this times series converged
'''
from pymbar import timeseries
n_points = len(series)
array = np.array(series)
t0, g, Neff_max = timeseries.detectEquilibration(array,
nskip=max(
1, n_points // 100))
if t0 > n_points * (1 - frac_min):
return False, series.index[t0]
return True, series.index[t0]
def calc_df(u_kln):
"""
u_kln should be (nstates) x (nstates) x (nframes)
note that u_kln should be normalized by kT already
where each element is
a config from frame `n` of a trajectory conducted with state `k`
with energy recalculated using parameters of state `l`
"""
dims = u_kln.shape
if dims[0] != dims[1]:
raise ValueError(
"dimensions {} of u_kln should be square in the first two indices".
format(dims))
nstates = dims[0]
N_k = np.zeros([nstates], np.int32) # number of uncorrelated samples
for k in range(nstates):
[nequil, g, Neff_max] = timeseries.detectEquilibration(u_kln[k, k, :])
indices = timeseries.subsampleCorrelatedData(u_kln[k, k, :], g=g)
N_k[k] = len(indices)
u_kln[k, :, 0:N_k[k]] = u_kln[k, :, indices].T
# Compute free energy differences and statistical uncertainties
mbar = MBAR(u_kln, N_k)
[DeltaF_ij, dDeltaF_ij, Theta_ij] = mbar.getFreeEnergyDifferences()
# save data?
return DeltaF_ij, dDeltaF_ij
def get_decorrelated_samples(replica_positions, replica_energies,
temperature_list):
"""
Given a set of replica exchange trajectories, energies, and associated temperatures, this function returns decorrelated samples, as obtained from pymbar with timeseries.subsampleCorrelatedData.
:param replica_positions: Positions array for the replica exchange data for which we will write PDB files
:type replica_positions: `Quantity() <http://docs.openmm.org/development/api-python/generated/simtk.unit.quantity.Quantity.html>`_ ( np.array( [n_replicas,cgmodel.num_beads,3] ), simtk.unit )
:param replica_energies: List of dimension num_replicas X simulation_steps, which gives the energies for all replicas at all simulation steps
:type replica_energies: List( List( float * simtk.unit.energy for simulation_steps ) for num_replicas )
:param temperature_list: List of temperatures for the simulation data.
:type temperature_list: List( float * simtk.unit.temperature )
:returns:
- configurations ( List( `Quantity() <http://docs.openmm.org/development/api-python/generated/simtk.unit.quantity.Quantity.html>`_ (n_decorrelated_samples,cgmodel.num_beads,3), simtk.unit ) ) - A list of decorrelated samples
- energies ( List( `Quantity() <http://docs.openmm.org/development/api-python/generated/simtk.unit.quantity.Quantity.html>`_ ) ) - The energies for the decorrelated samples (configurations)
"""
all_poses = []
all_energies = []
for replica_index in range(len(replica_positions)):
energies = replica_energies[replica_index][replica_index]
[t0, g, Neff_max] = timeseries.detectEquilibration(energies)
energies_equil = energies[t0:]
poses_equil = replica_positions[replica_index][t0:]
indices = timeseries.subsampleCorrelatedData(energies_equil)
for index in indices:
all_energies.append(energies_equil[index])
all_poses.append(poses_equil[index])
all_energies = np.array([float(energy) for energy in all_energies])
return (all_poses, all_energies)
def collatedata(dictionary):
#I want this function to read in my huge data dictionaries
#Then I want it to work out the approximate final value (U or N) for each markov chain and simulation step
#Then i want to average each of these approximate final values across parallel markov chains
simsteps = list(dictionary.keys())
print(simsteps)
timesteps = list(dictionary[simsteps[0]].keys())
print(timesteps)
parallelsims = len(dictionary[simsteps[0]][timesteps[0]])
print(dictionary[simsteps[0]][timesteps[0]])
resultstot = {}
# datalist=np.zeros(len(EDicts[simsteps[0]].keys()))
for t in simsteps:
resultstot[t] = {'Ave': [], 'Std': []}
for q in range(parallelsims):
print(q)
for r in simsteps:
#print(len(datalist))
placeholder = []
for s in timesteps:
placeholder.append(dictionary[r][s][q])
#print(placeholder)
datalist = np.asarray(placeholder, dtype=float)
#print(datalist)
[t0, g, Neff_max] = timeseries.detectEquilibration(datalist)
#print('t0={0}'.format(t0))
#print(datalist[t0:])
avg = np.mean(datalist[t0:])
sdv = np.std(datalist[t0:])
resultstot[r]['Ave'].append(round(avg, 3))
resultstot[r]['Std'].append(round(sdv, 3))
return resultstot
def calcTension(energy_data, verbose=False):
dE1 = energy_data[:, 1] - energy_data[:, 0]
dE2 = energy_data[:, 2] - energy_data[:, 0]
BdE1 = dE1 / kTkJmol
BdE2 = dE2 / kTkJmol
nstates = 2
nframes = len(dE1)
u_kln = np.zeros([nstates, nstates, nframes], np.float64)
u_kln[0, 1, :] = BdE1
u_kln[1, 0, :] = BdE2
N_k = np.zeros([nstates], np.int32) # number of uncorrelated samples
for k in range(nstates):
[nequil, g, Neff_max] = timeseries.detectEquilibration(u_kln[k, k, :])
indices = timeseries.subsampleCorrelatedData(u_kln[k, k, :], g=g)
N_k[k] = len(indices)
u_kln[k, :, 0:N_k[k]] = u_kln[k, :, indices].T
if verbose:
print("...found {} uncorrelated samples out of {} total samples...".
format(N_k, nframes))
if verbose: print("=== Computing free energy differences ===")
mbar = MBAR(u_kln, N_k)
[DeltaF_ij, dDeltaF_ij, Theta_ij] = mbar.getFreeEnergyDifferences()
tension = DeltaF_ij[
0,
1] / da * 1e18 * kT #(in J/m^2). note da already has a factor of two for the two areas!
tensionError = dDeltaF_ij[0, 1] / da * 1e18 * kT
if verbose:
print('tension (pymbar): {} +/- {}N/m'.format(tension, tensionError))
return tension, tensionError
def production(self):
if os.path.exists(self.production_dcd_filename) and os.path.exists(self.production_data_filename):
return
prmtop = app.AmberPrmtopFile(self.prmtop_filename)
pdb = app.PDBFile(self.equil_pdb_filename)
system = prmtop.createSystem(nonbondedMethod=app.PME, nonbondedCutoff=CUTOFF, constraints=app.HBonds)
integrator = mm.LangevinIntegrator(self.temperature, FRICTION, TIMESTEP)
system.addForce(mm.MonteCarloBarostat(PRESSURE, self.temperature, BAROSTAT_FREQUENCY))
simulation = app.Simulation(prmtop.topology, system, integrator)
simulation.context.setPositions(pdb.positions)
simulation.context.setPeriodicBoxVectors(*pdb.topology.getPeriodicBoxVectors())
simulation.context.setVelocitiesToTemperature(self.temperature)
print('Production.')
simulation.reporters.append(app.DCDReporter(self.production_dcd_filename, OUTPUT_FREQUENCY))
simulation.reporters.append(app.StateDataReporter(self.production_data_filename, OUTPUT_DATA_FREQUENCY, step=True, potentialEnergy=True, temperature=True, density=True))
converged = False
while not converged:
simulation.step(N_STEPS)
d = pd.read_csv(self.production_data_filename, names=["step", "U", "Temperature", "Density"], skiprows=1)
density_ts = np.array(d.Density)
[t0, g, Neff] = ts.detectEquilibration(density_ts, nskip=1000)
density_ts = density_ts[t0:]
density_mean_stderr = density_ts.std() / np.sqrt(Neff)
if density_mean_stderr < STD_ERROR_TOLERANCE:
converged = True
def compute_timeseries(reduced_potentials):
"""
Use pymbar timeseries to compute the uncorrelated samples in an array of reduced potentials. Returns the uncorrelated sample indices.
Arguments
---------
reduced_potentials : np.array of floats
reduced potentials from which a timeseries is to be extracted
Returns
-------
t0 : int
production region index
g : float
statistical inefficiency
Neff_max : int
effective number of samples in production region
full_uncorrelated_indices : list of ints
uncorrelated indices
"""
from pymbar import timeseries
t0, g, Neff_max = timeseries.detectEquilibration(
reduced_potentials) #computing indices of uncorrelated timeseries
A_t_equil = reduced_potentials[t0:]
uncorrelated_indices = timeseries.subsampleCorrelatedData(A_t_equil, g=g)
A_t = A_t_equil[uncorrelated_indices]
full_uncorrelated_indices = [i + t0 for i in uncorrelated_indices]
return [t0, g, Neff_max, A_t, full_uncorrelated_indices]
def equilibrium_detection(df, series=None, lower=None, upper=None, step=None):
"""Subsample a DataFrame using automated equilibrium detection on a timeseries.
If `series` is ``None``, then this function will behave the same as
:func:`slicing`.
Parameters
----------
df : DataFrame
DataFrame to subsample according to equilibrium detection on `series`.
series : Series
Series to detect equilibration on. If ``None``, no equilibrium
detection-based subsampling will be performed.
lower : float
Lower bound to pre-slice `series` data from.
upper : float
Upper bound to pre-slice `series` to (inclusive).
step : int
Step between `series` items to pre-slice by.
Returns
-------
DataFrame
`df` subsampled according to subsampled `series`.
See Also
--------
pymbar.timeseries.detectEquilibration : detailed background
"""
if _check_multiple_times(df):
raise KeyError("Duplicate time values found; equilibrium detection "
"is only meaningful for a single, contiguous, "
"and sorted timeseries.")
if not _check_sorted(df):
raise KeyError("Equilibrium detection only works as expected if "
"values are sorted by time, increasing.")
if series is not None:
series = slicing(series, lower=lower, upper=upper, step=step)
# calculate statistical inefficiency of series
statinef = statisticalInefficiency(series)
# calculate statistical inefficiency of series, with equilibrium detection
t, statinef, Neff_max = detectEquilibration(series.values)
# we round up
statinef = int(np.rint(statinef))
# subsample according to statistical inefficiency
series = series.iloc[t::statinef]
df = df.loc[series.index]
else:
df = slicing(df, lower=lower, upper=upper, step=step)
return df
def get_equilibration_data(timeseries_to_analyze):
"""
Compute equilibration method given a timeseries
See the ``pymbar.timeseries.detectEquilibration`` function for full documentation
"""
[n_equilibration, g_t,
n_effective_max] = timeseries.detectEquilibration(timeseries_to_analyze)
return n_equilibration, g_t, n_effective_max
def is_equilibrated(data, threshold_fraction=0.50, threshold_neff=50, nskip=1):
"""Check if a dataset is equilibrated based on a fraction of equil data.
Using `pymbar.timeseries` module, check if a timeseries dataset has enough
equilibrated data based on two threshold values. The threshold_fraction
value translates to the fraction of total data from the dataset 'a_t' that
can be thought of as being in the 'production' region. The threshold_neff
is the minimum amount of effectively uncorrelated samples to have in a_t to
consider it equilibrated.
The `pymbar.timeseries` module returns the starting index of the
'production' region from 'a_t'. The fraction of 'production' data is
then compared to the threshold value. If the fraction of 'production' data
is >= threshold fraction this will return a list of
[True, t0, g, Neff] and [False, None, None, None] otherwise.
Parameters
----------
data : numpy.typing.Arraylike
1-D time dependent data to check for equilibration.
threshold_fraction : float, optional, default=0.8
Fraction of data expected to be equilibrated.
threshold_neff : int, optional, default=100
Minimum amount of effectively correlated samples to consider a_t
'equilibrated'.
nskip : int, optional, default=1
Since the statistical inefficiency is computed for every time origin
in a call to timeseries.detectEquilibration, for larger datasets
(> few hundred), increasing nskip might speed this up, while
discarding more data.
Returns
-------
list : [True, t0, g, Neff]
If the data set is considered properly equilibrated
list : [False, None, None, None]
If the data set is not considered properly equilibrated
"""
if threshold_fraction < 0.0 or threshold_fraction > 1.0:
raise ValueError(
f"Passed 'threshold_fraction' value: {threshold_fraction}, "
"expected value between 0.0-1.0."
)
threshold_neff = int(threshold_neff)
if threshold_neff < 1:
raise ValueError(
f"Passed 'threshold_neff' value: {threshold_neff}, expected value "
"1 or greater."
)
[t0, g, Neff] = timeseries.detectEquilibration(data, nskip=nskip)
frac_equilibrated = 1.0 - (t0 / np.shape(data)[0])
if (frac_equilibrated >= threshold_fraction) and (Neff >= threshold_neff):
return [True, t0, g, Neff]
else:
return [False, None, None, None]
def is_converged(series: Series, frac_min=0.5) -> (bool, float):
from pymbar import timeseries
n_points = len(series)
array = np.array(series)
t0, g, Neff_max = timeseries.detectEquilibration(array, nskip=max(1, n_points // 100))
if t0 > n_points * (1 - frac_min):
return False, series.index[t0]
return True, series.index[t0]
def subsample(enthalpies):
"""
Subsamples the enthalpies using John Chodera's code.
This is probably better than the simple cutoff we normally use.
No output -- it modifies the lists directly
"""
# Use automatic equilibration detection and pymbar.timeseries to subsample
[t0, g, Neff_max] = timeseries.detectEquilibration(enthalpies)
enthalpies = enthalpies[t0:]
return timeseries.subsampleCorrelatedData(enthalpies, g=g)
def production(self):
utils.make_path('production/')
self.production_dcd_filename = "production/"+self.identifier +"_production.dcd"
self.production_pdb_filename = "production/"+self.identifier +"_production.pdb"
self.production_data_filename = "production/"+self.identifier +"_production.csv"
utils.make_path(self.production_dcd_filename)
if os.path.exists(self.production_pdb_filename):
return
if self.ran_equilibrate:
pdb = app.PDBFile(self.equil_pdb_filename)
topology = pdb.topology
positions = pdb.positions
else:
positions = self.packed_trj.openmm_positions(0)
topology = self.packed_trj.top.to_openmm()
topology.setUnitCellDimensions(mm.Vec3(*self.packed_trj.unitcell_lengths[0]) * u.nanometer)
ff = self.ffxml
system = ff.createSystem(topology, nonbondedMethod=app.PME, nonbondedCutoff=self.cutoff, constraints=app.HBonds)
integrator = mm.LangevinIntegrator(self.temperature, self.friction, self.timestep)
system.addForce(mm.MonteCarloBarostat(self.pressure, self.temperature, self.barostat_frequency))
simulation = app.Simulation(topology, system, integrator)
simulation.context.setPositions(positions)
if not self.ran_equilibrate:
print('Minimizing.')
simulation.minimizeEnergy()
simulation.context.setVelocitiesToTemperature(self.temperature)
print('Production.')
simulation.reporters.append(app.DCDReporter(self.production_dcd_filename, self.output_frequency))
simulation.reporters.append(app.StateDataReporter(self.production_data_filename, self.output_data_frequency, step=True, potentialEnergy=True, temperature=True, density=True))
converged = False
while not converged:
simulation.step(self.n_steps)
d = pd.read_csv(self.production_data_filename, names=["step", "U", "Temperature", "Density"], skiprows=1)
density_ts = np.array(d.Density)
[t0, g, Neff] = ts.detectEquilibration(density_ts, nskip=1000)
density_ts = density_ts[t0:]
density_mean_stderr = density_ts.std() / np.sqrt(Neff)
if density_mean_stderr < self.stderr_tolerance:
converged = True
del(simulation)
if self.ran_equilibrate:
traj = md.load(self.production_dcd_filename, top=self.equil_pdb_filename)[-1]
else:
traj = md.load(self.production_dcd_filename, top=self.box_pdb_filename)[-1]
traj.save(self.production_pdb_filename)
def calc_statistics(_data):
t0, g, Neff = timeseries.detectEquilibration(_data)
data_equil = _data[t0:]
indices_subsampled = timeseries.subsampleCorrelatedData(data_equil, g=g)
sub_data = data_equil[indices_subsampled]
avg = sub_data.mean()
std = sub_data.std()
err = sub_data.std() / np.sqrt(len(indices_subsampled))
summary = [avg, std, err, t0, g, Neff]
return summary
def _construct_decorrelation_mask(self, sim_collection, rep, skip):
enes = sim_collection.reps_energies[rep]
ops = sim_collection.reps_order_params[rep]
steps = enes.steps
rpots = utility.calc_reduced_potentials(enes, ops,
sim_collection.conditions)
start_i, g, Neff = timeseries.detectEquilibration(rpots, nskip=skip)
template = '{:<8} {:<8} {:<3} {:<4.1f} {:<.1f}'
print(template.format(sim_collection.conditions.fileformat, steps,
start_i, g, Neff))
indices = (timeseries.subsampleCorrelatedData(rpots[start_i:], g=skip*g))
return [i + start_i for i in indices]
def decorrelate_data(self, dHdl_data=None, u_nk_data=None):
dHdl, u_nk = [], []
if dHdl_data is not None:
dHdl.append(equilibrium_detection(dHdl_data, dHdl_data.iloc[:, 0]))
#logger(f'Subsampling dHdl data of the {ordinal(self.n_state)} state ...')
_, g1, _ = detectEquilibration(dHdl_data.iloc[:, 0].values)
dHdl = pd.concat(dHdl)
setattr(dHdl, 'statineff', g1)
if u_nk_data is not None:
u_nk.append(equilibrium_detection(u_nk_data, u_nk_data.iloc[:, 0]))
#logger(f'Subsampling u_nk data of the {ordinal(self.n_state)} state ...\n')
t2, g2, N2 = detectEquilibration(u_nk_data.iloc[:, 0].values)
u_nk = pd.concat(u_nk)
setattr(u_nk, 'statineff', g2)
logger("Data preprocessing completed!\n")
if os.path.isfile('temporary.xvg') is True:
os.system("rm temporary.xvg")
return dHdl, u_nk
def compute_timeseries(reduced_potentials: np.array) -> list:
"""
Use pymbar timeseries to compute the uncorrelated samples in an array of reduced potentials. Returns the uncorrelated sample indices.
"""
from pymbar import timeseries
t0, g, Neff_max = timeseries.detectEquilibration(
reduced_potentials) #computing indices of uncorrelated timeseries
A_t_equil = reduced_potentials[t0:]
uncorrelated_indices = timeseries.subsampleCorrelatedData(A_t_equil, g=g)
A_t = A_t_equil[uncorrelated_indices]
full_uncorrelated_indices = [i + t0 for i in uncorrelated_indices]
return [t0, g, Neff_max, A_t, full_uncorrelated_indices]
def _calc_stat_neff(self):
"""
Estimate the statistical inefficiency of the salt occupancy.
"""
stat_ineff = []
for counts in self.nsalt:
t, g, Neff = ts.detectEquilibration(counts, fast=True)
stat_ineff.append(g)
stat_ineff = np.array(stat_ineff)
# Correcting the statistical inefficieny has returned a value of 1.0, when there were no acceptances.
stat_ineff[np.where(stat_ineff == 1.0)] = np.inf
return stat_ineff
def gather_dg(self, u_kln, nstates):
# Subsample data to extract uncorrelated equilibrium timeseries
N_k = np.zeros([nstates], np.int32) # number of uncorrelated samples
for k in range(nstates):
[_, g, __] = timeseries.detectEquilibration(u_kln[k, k, :])
indices = timeseries.subsampleCorrelatedData(u_kln[k, k, :], g=g)
N_k[k] = len(indices)
u_kln[k, :, 0:N_k[k]] = u_kln[k, :, indices].T
# Compute free energy differences and statistical uncertainties
mbar = MBAR(u_kln, N_k)
[DeltaF_ij, dDeltaF_ij, _] = mbar.getFreeEnergyDifferences()
print("Number of uncorrelated samples per state: {}".format(N_k))
return DeltaF_ij, dDeltaF_ij
def get_stats(data):
"""
later, can generalize, to use one column for decorrelating and getting reference indices
"""
[t0, g, Neff] = timeseries.detectEquilibration(data)
data_equil = data[t0:]
indices = timeseries.subsampleCorrelatedData(data_equil, g=g)
sub_data = data_equil[indices]
avg = sub_data.mean()
std = sub_data.std()
err = sub_data.std()/np.sqrt( len(indices) )
return avg,std,err, t0,g,Neff, sub_data
def test_analyze_time_series():
"""Compare the output of the ``analyze_time_series`` utility with ``pymbar``."""
np.random.seed(4)
random_array = np.random.rand(10)
statistics = analyze_time_series(random_array, minimum_samples=3)
expected_index, expected_value, _ = detectEquilibration(random_array,
fast=False)
assert expected_index == statistics.equilibration_index
assert np.isclose(statistics.statistical_inefficiency, expected_value)
assert statistics.n_total_points == 10
assert 0 < statistics.n_uncorrelated_points <= 10
assert 0 <= statistics.equilibration_index < 10
def production(in_top, in_pdb, out_dcd, out_csv, temperature):
temperature = temperature * u.kelvin # TODO: recycle John's simtk.unit parser
pdb = app.PDBFile(in_pdb)
top = app.GromacsTopFile(in_top)
top.topology.setPeriodicBoxVectors(pdb.topology.getPeriodicBoxVectors())
system = top.createSystem(nonbondedMethod=app.PME,
nonbondedCutoff=CUTOFF,
constraints=app.HBonds)
integrator = mm.LangevinIntegrator(temperature, FRICTION, TIMESTEP)
system.addForce(
mm.MonteCarloBarostat(PRESSURE, temperature, BAROSTAT_FREQUENCY))
simulation = app.Simulation(top.topology, system, integrator)
simulation.context.setPositions(pdb.positions)
simulation.context.setPeriodicBoxVectors(
*pdb.topology.getPeriodicBoxVectors())
simulation.context.setVelocitiesToTemperature(temperature)
print('Production.')
simulation.reporters.append(app.DCDReporter(out_dcd, OUTPUT_FREQUENCY))
simulation.reporters.append(
app.StateDataReporter(out_csv,
OUTPUT_DATA_FREQUENCY,
step=True,
potentialEnergy=True,
temperature=True,
density=True))
converged = False
while not converged:
simulation.step(N_STEPS)
d = pd.read_csv(out_csv,
names=["step", "U", "Temperature", "Density"],
skiprows=1)
density_ts = np.array(d.Density)
[t0, g, Neff] = ts.detectEquilibration(density_ts, nskip=1000)
density_ts = density_ts[t0:]
density_mean_stderr = density_ts.std() / np.sqrt(Neff)
if density_mean_stderr < STD_ERROR_TOLERANCE:
converged = True
def find_equilibrium(all_timeseries, column_name = None):
"""
directly uses pymbar's timeseries utility!
source: https://github.com/choderalab/pymbar
https://pymbar.readthedocs.io/en/master/timeseries.html
and references therein
"""
equilibration_data=dict([])
for name in all_timeseries:
[t, g, Neff_max] = timeseries.detectEquilibration(np.array(all_timeseries[name]) )
"""
t - t_0 starting point of equilibrated part of series
g - the statistical innefficency = 2*correlationtime +1
N_effmax - effective number of uncorrelated samples
"""
equilibration_data[name] = [t,g,Neff_max]
return equilibration_data
def read_concentration(files, discard=10, fast=False):
"""
Calculate the mean concentration and standard error from numerous numerous simulations, where each simulation has
a fixed chemical potential. Timeseries analysis is used to determine equilibrium properties.
Parameters
----------
files: list of str
the path to each results file that will be analysed.
discard: int
the initial amount of data to throw away
fast: bool
whether to perform the fast varient of the time series analysis
"""
concentration = np.zeros(len(files))
standard_error = np.zeros(len(files))
delta_mu = np.zeros(len(files))
lower = np.zeros(len(files))
upper = np.zeros(len(files))
for i in range(len(files)):
ncfile = Dataset(files[i], 'r')
volume = ncfile.groups['Sample state data']['volume'][:]
#ncations = ncfile.groups['Sample state data']['species counts'][:, 1]
nsalt = np.min(ncfile.groups['Sample state data']['species counts'][:, 1:2], axis=1)
delta_mu[i] = ncfile.groups['Control parameters']['delta_chem'][0]
ncfile.close()
# Get the concentration in Molarity
c = 1.0 * nsalt / volume * 1.66054
# Estimate the mean and standard error with timeseries analysis
t_equil, stat_ineff, n_eff = timeseries.detectEquilibration(c[discard:], fast=fast)
#mu, sigma, num_batches, conf_width = misc_tools.batch_estimate_2(c[(discard + t_equil):], stat_ineff)
#print("{0} batches for {1}".format(num_batches, files[i]))
c_equil = c[(discard + t_equil):]
concentration[i] = np.mean(c_equil)
independent_inds = timeseries.subsampleCorrelatedData(c_equil, g=stat_ineff, conservative=True)
mu_samps = misc_tools.bootstrap_estimates(c_equil[independent_inds])
lower[i] = np.percentile(mu_samps, 2.5)
upper[i] = np.percentile(mu_samps, 97.5)
standard_error[i] = mu_samps.std()
return concentration, standard_error, delta_mu, lower, upper
def equilibrate(traj, verbose=False, name=None):
traj = np.array(traj)
if traj.ndim == 1:
t0, g, n_eff = timeseries.detectEquilibration(traj)
if t0 == 0 and traj.size > 10:
# See https://github.com/choderalab/pymbar/issues/277
t0x, gx, n_effx = timeseries.detectEquilibration(traj[10:])
if t0x != 0:
t0 = t0x + 10
n = traj.size
res = traj[t0:]
elif traj.ndim == 2 and traj.shape[0] == 2:
t01, g1, n_eff1 = timeseries.detectEquilibration(traj[0])
t02, g2, n_eff2 = timeseries.detectEquilibration(traj[1])
t0 = max(t01, t02)
if t0 == 0 and traj.shape[1] > 10:
# See https://github.com/choderalab/pymbar/issues/277
t01x, g1x, n_eff1x = timeseries.detectEquilibration(traj[0, 10:])
t02x, g2x, n_eff2x = timeseries.detectEquilibration(traj[1, 10:])
t0x = max(t01x, t02x)
if t0x != 0:
t0 = t0x + 10
n = traj.shape[1]
res = traj[:, t0:]
elif traj.ndim == 2:
raise NotImplementedError(
'trajectory.equilibrate() in 2 dimensions is only '
'implemented for exactly two timeseries.')
else:
raise NotImplementedError(
'trajectory.equilibrate() is not implemented for '
'trajectories with more than 2 dimensions.')
if verbose:
if not name:
name = 'Trajectory'
if t0 == 0:
print(
'{:s} equilibration: No frames discarded for burn-in.'.format(
name))
elif t0 == 1:
print('{:s} equilibration: First frame ({:.1%} of '
'trajectory) discarded for burn-in.'.format(name, 1 / n))
else:
print('{:s} equilibration: First {:d} frames ({:.1%} of '
'trajectory) discarded for burn-in.'.format(
name, t0, t0 / n))
return res
def __init__(self,
ani_model: AlchemicalANI,
ani_trajs: list,
potential_energy_trajs: list,
lambdas,
max_snapshots_per_window=50,
):
K = len(lambdas)
assert (len(ani_trajs) == K)
assert (len(potential_energy_trajs) == K)
self.ani_model = ani_model
self.ani_trajs = ani_trajs
self.potential_energy_trajs = potential_energy_trajs
self.lambdas = lambdas
# thin each based automatic equilibration detection
N_k = []
snapshots = []
for i in range(K):
traj = self.ani_trajs[i]
equil, g = detectEquilibration(self.potential_energy_trajs[i])[:2]
thinning = int(g)
if len(traj[equil::thinning]) > max_snapshots_per_window:
# what thinning will give me len(traj[equil::thinning]) == max_snapshots_per_window?
thinning = int((len(traj) - equil) / max_snapshots_per_window)
new_snapshots = list(traj[equil::thinning].xyz * unit.nanometer)[:max_snapshots_per_window]
N_k.append(len(new_snapshots))
snapshots.extend(new_snapshots)
self.snapshots = snapshots
N = len(snapshots)
u_kn = np.zeros((K, N))
for k in range(K):
lamb = lambdas[k]
self.ani_model.lambda_value = lamb
for n in range(N):
u_kn[k, n] = self.ani_model.calculate_energy(snapshots[n]) / kT
self.mbar = MBAR(u_kn, N_k)
def test_compare_detectEquil(show_hist=False):
"""
compare detectEquilibration implementations (with and without binary search + fft)
"""
t_res = []
N=100
for _ in xrange(100):
A_t = testsystems.correlated_timeseries_example(N=N, tau=5.0) + 2.0
B_t = testsystems.correlated_timeseries_example(N=N, tau=5.0) + 1.0
C_t = testsystems.correlated_timeseries_example(N=N*2, tau=5.0)
D_t = np.concatenate([A_t, B_t, C_t, np.zeros(20)]) #concatenate and add flat region to one end (common in MC data)
bs_de = timeseries.detectEquilibration_binary_search(D_t, bs_nodes=10)
std_de = timeseries.detectEquilibration(D_t, fast=False, nskip=1)
t_res.append(bs_de[0]-std_de[0])
t_res_mode = float(stats.mode(t_res)[0][0])
eq(t_res_mode,0.,decimal=1)
if show_hist:
import matplotlib.pyplot as plt
plt.hist(t_res)
plt.show()
def test_compare_detectEquil(show_hist=False):
"""
compare detectEquilibration implementations (with and without binary search + fft)
"""
t_res = []
N = 100
for _ in xrange(100):
A_t = testsystems.correlated_timeseries_example(N=N, tau=5.0) + 2.0
B_t = testsystems.correlated_timeseries_example(N=N, tau=5.0) + 1.0
C_t = testsystems.correlated_timeseries_example(N=N * 2, tau=5.0)
D_t = np.concatenate([A_t, B_t, C_t])
bs_de = timeseries.detectEquilibration_binary_search(D_t, bs_nodes=10)
std_de = timeseries.detectEquilibration(D_t, fast=False, nskip=1)
t_res.append(bs_de[0] - std_de[0])
t_res_mode = float(stats.mode(t_res)[0][0])
eq(t_res_mode, 0., decimal=1)
if show_hist:
import matplotlib.pyplot as plt
plt.hist(t_res)
plt.show()
def individual_analysis_procedure(temperature):
###
#
# This subroutine analyzes a timeseries for 'temperature',
# and generates a set of decorrelated sample energies and distances,
# which are used in later sampling to generate a free energy surface.
#
###
if (search_for_existing_data and not (os.path.exists(
str(output_dir + str(temperature) + "/uncorrelated_distances.dat"))
)) or not (search_for_existing_data):
output_obj = open(str(output_dir + str(temperature) + "/sim_data.dat"),
'r')
E_total_all_temp = np.array(
[l.split(',')[3] for l in output_obj.readlines()]
) # E_total_all_temp temporarily stores the total energies from NaCl simulation output
output_obj.close()
distances = util.get_distances(
str(output_dir + str(temperature) + "/coordinates.pdb"),
simulation_steps) # Read in the distances
E_total_all = np.array(
np.delete(E_total_all_temp, 0, 0), dtype=float
) # E_total_all stores total energies from NaCl simulation output, after re-typing
[t0, g, Neff_max] = timeseries.detectEquilibration(
E_total_all, nskip=nskip
) # Identify the indices of samples with high statistical efficiency (g)
E_total_equil = E_total_all[
t0:] # Using the index for the equilibration time (t0), truncate the time-series data before this index
uncorrelated_energy_indices = timeseries.subsampleCorrelatedData(
E_total_equil, g=g) # Determine indices of uncorrelated samples
np.savetxt(
str(output_dir + str(temperature) +
'/uncorrelated_total_energies.dat'),
E_total_equil[uncorrelated_energy_indices]
) # Write uncorrelated total energies to file
np.savetxt(
str(output_dir + str(temperature) + '/uncorrelated_distances.dat'),
distances[uncorrelated_energy_indices]
) # Write uncorrelated Na-Cl distances to file
return
def gather_dg(self, u_kln, nstates):
u_kln = np.vstack(u_kln)
# Subsample data to extract uncorrelated equilibrium timeseries
N_k = np.zeros([nstates], np.int32) # number of uncorrelated samples
for k in range(nstates):
[_, g, __] = timeseries.detectEquilibration(u_kln[k, k, :])
indices = timeseries.subsampleCorrelatedData(u_kln[k, k, :], g=g)
N_k[k] = len(indices)
u_kln[k, :, 0:N_k[k]] = u_kln[k, :, indices].T
# Compute free energy differences and statistical uncertainties
mbar = MBAR(u_kln, N_k)
[DeltaF_ij, dDeltaF_ij, _] = mbar.getFreeEnergyDifferences()
logger.debug(
"Number of uncorrelated samples per state: {}".format(N_k))
logger.debug("Relative free energy change for {0} = {1} +- {2}".format(
self.name, DeltaF_ij[0, nstates - 1] * self.kTtokcal,
dDeltaF_ij[0, nstates - 1] * self.kTtokcal))
return DeltaF_ij[0, nstates -
1] * self.kTtokcal, dDeltaF_ij[0, nstates -
1] * self.kTtokcal
def equilibrate(traj, verbose=False, name=None):
traj = np.array(traj)
if traj.ndim == 1:
t0, g, n_eff = timeseries.detectEquilibration(traj)
if t0 == 0 and traj.size > 10:
# See https://github.com/choderalab/pymbar/issues/277
t0x, gx, n_effx = timeseries.detectEquilibration(traj[10:])
if t0x != 0:
t0 = t0x + 10
n = traj.size
res = traj[t0:]
elif traj.ndim == 2 and traj.shape[0] == 2:
t01, g1, n_eff1 = timeseries.detectEquilibration(traj[0])
t02, g2, n_eff2 = timeseries.detectEquilibration(traj[1])
t0 = max(t01, t02)
if t0 == 0 and traj.shape[1] > 10:
# See https://github.com/choderalab/pymbar/issues/277
t01x, g1x, n_eff1x = timeseries.detectEquilibration(traj[0, 10:])
t02x, g2x, n_eff2x = timeseries.detectEquilibration(traj[1, 10:])
t0x = max(t01x, t02x)
if t0x != 0:
t0 = t0x + 10
n = traj.shape[1]
res = traj[:, t0:]
elif traj.ndim == 2:
raise NotImplementedError('trajectory.equilibrate() in 2 dimensions is only '
'implemented for exactly two timeseries.')
else:
raise NotImplementedError('trajectory.equilibrate() is not implemented for '
'trajectories with more than 2 dimensions.')
if verbose:
if not name:
name = 'Trajectory'
if t0 == 0:
print('{:s} equilibration: No frames discarded for burn-in.'.format(name))
elif t0 == 1:
print('{:s} equilibration: First frame ({:.1%} of '
'trajectory) discarded for burn-in.'.format(name, 1 / n))
else:
print('{:s} equilibration: First {:d} frames ({:.1%} of '
'trajectory) discarded for burn-in.'.format(name, t0, t0 / n))
return res
def run_endpoint_perturbation(lambda_thermodynamic_state, nonalchemical_thermodynamic_state, initial_hybrid_sampler_state, mc_move, n_iterations, factory,
lambda_index=0, print_work=False, write_system=False, write_state=False, write_trajectories=False):
"""
Parameters
----------
lambda_thermodynamic_state : ThermodynamicState
The thermodynamic state corresponding to the hybrid system at a lambda endpoint
nonalchemical_thermodynamic_state : ThermodynamicState
The nonalchemical thermodynamic state for the relevant endpoint
initial_hybrid_sampler_state : SamplerState
Starting positions for the sampler. Must be compatible with lambda_thermodynamic_state
mc_move : MCMCMove
The MCMove that will be used for sampling at the lambda endpoint
n_iterations : int
The number of iterations
factory : HybridTopologyFactory
The hybrid topology factory
lambda_index : int, optional, default=0
The index, 0 or 1, at which to retrieve nonalchemical positions
print_work : bool, optional, default=False
If True, will print work values
write_system : bool, optional, default=False
If True, will write alchemical and nonalchemical System XML files
write_state : bool, optional, default=False
If True, write alchemical (hybrid) State XML files each iteration
write_trajectories : bool, optional, default=False
If True, will write trajectories
Returns
-------
df : float
Free energy difference between alchemical and nonalchemical systems, estimated with EXP
ddf : float
Standard deviation of estimate, corrected for correlation, from EXP estimator.
"""
import mdtraj as md
#run an initial minimization:
mcmc_sampler = mcmc.MCMCSampler(lambda_thermodynamic_state, initial_hybrid_sampler_state, mc_move)
mcmc_sampler.minimize(max_iterations=20)
new_sampler_state = mcmc_sampler.sampler_state
if write_system:
with open(f'hybrid{lambda_index}-system.xml', 'w') as outfile:
outfile.write(openmm.XmlSerializer.serialize(lambda_thermodynamic_state.system))
with open(f'nonalchemical{lambda_index}-system.xml', 'w') as outfile:
outfile.write(openmm.XmlSerializer.serialize(nonalchemical_thermodynamic_state.system))
#initialize work array
w = np.zeros([n_iterations])
non_potential = np.zeros([n_iterations])
hybrid_potential = np.zeros([n_iterations])
#run n_iterations of the endpoint perturbation:
hybrid_trajectory = unit.Quantity(np.zeros([n_iterations, lambda_thermodynamic_state.system.getNumParticles(), 3]), unit.nanometers) # DEBUG
nonalchemical_trajectory = unit.Quantity(np.zeros([n_iterations, nonalchemical_thermodynamic_state.system.getNumParticles(), 3]), unit.nanometers) # DEBUG
for iteration in range(n_iterations):
# Generate a new sampler state for the hybrid system
mc_move.apply(lambda_thermodynamic_state, new_sampler_state)
# Compute the hybrid reduced potential at the new sampler state
hybrid_context, integrator = cache.global_context_cache.get_context(lambda_thermodynamic_state)
new_sampler_state.apply_to_context(hybrid_context, ignore_velocities=True)
hybrid_reduced_potential = lambda_thermodynamic_state.reduced_potential(hybrid_context)
if write_state:
state = hybrid_context.getState(getPositions=True, getParameters=True)
state_xml = openmm.XmlSerializer.serialize(state)
with open(f'state{iteration}_l{lambda_index}.xml', 'w') as outfile:
outfile.write(state_xml)
# Construct a sampler state for the nonalchemical system
if lambda_index == 0:
nonalchemical_positions = factory.old_positions(new_sampler_state.positions)
elif lambda_index == 1:
nonalchemical_positions = factory.new_positions(new_sampler_state.positions)
else:
raise ValueError("The lambda index needs to be either one or zero for this to be meaningful")
nonalchemical_sampler_state = SamplerState(nonalchemical_positions, box_vectors=new_sampler_state.box_vectors)
if write_trajectories:
state = hybrid_context.getState(getPositions=True)
hybrid_trajectory[iteration,:,:] = state.getPositions(asNumpy=True)
nonalchemical_trajectory[iteration,:,:] = nonalchemical_positions
# Compute the nonalchemical reduced potential
nonalchemical_context, integrator = cache.global_context_cache.get_context(nonalchemical_thermodynamic_state)
nonalchemical_sampler_state.apply_to_context(nonalchemical_context, ignore_velocities=True)
nonalchemical_reduced_potential = nonalchemical_thermodynamic_state.reduced_potential(nonalchemical_context)
# Compute and store the work
w[iteration] = nonalchemical_reduced_potential - hybrid_reduced_potential
non_potential[iteration] = nonalchemical_reduced_potential
hybrid_potential[iteration] = hybrid_reduced_potential
if print_work:
print(f'{iteration:8d} {hybrid_reduced_potential:8.3f} {nonalchemical_reduced_potential:8.3f} => {w[iteration]:8.3f}')
if write_trajectories:
if lambda_index == 0:
nonalchemical_mdtraj_topology = md.Topology.from_openmm(factory._topology_proposal.old_topology)
elif lambda_index == 1:
nonalchemical_mdtraj_topology = md.Topology.from_openmm(factory._topology_proposal.new_topology)
md.Trajectory(hybrid_trajectory / unit.nanometers, factory.hybrid_topology).save(f'hybrid{lambda_index}.pdb')
md.Trajectory(nonalchemical_trajectory / unit.nanometers, nonalchemical_mdtraj_topology).save(f'nonalchemical{lambda_index}.pdb')
# Analyze data and return results
[t0, g, Neff_max] = timeseries.detectEquilibration(w)
w_burned_in = w[t0:]
[df, ddf] = pymbar.EXP(w_burned_in)
ddf_corrected = ddf * np.sqrt(g)
results = [df, ddf_corrected, t0, Neff_max]
return results, non_potential, hybrid_potential
def overlap_check(reference_system, positions, platform_name=None, precision=None, nsteps=50, nsamples=200, factory_args=None, cached_trajectory_filename=None):
"""
Test overlap between reference system and alchemical system by running a short simulation.
Parameters
----------
reference_system : simtk.openmm.System
The reference System object to compare with
positions : simtk.unit.Quantity with units compatible with nanometers
The positions to assess energetics for.
platform_name : str, optional, default=None
The name of the platform to use for benchmarking.
nsteps : int, optional, default=50
Number of molecular dynamics steps between samples.
nsamples : int, optional, default=100
Number of samples to collect.
factory_args : dict(), optional, default=None
Arguments passed to AbsoluteAlchemicalFactory.
cached_trajectory_filename : str, optional, default=None
If specified, attempt to cache (or reuse) trajectory.
"""
# Create a fully-interacting alchemical state.
factory = AbsoluteAlchemicalFactory(reference_system, **factory_args)
alchemical_state = AlchemicalState()
alchemical_system = factory.createPerturbedSystem(alchemical_state)
temperature = 300.0 * unit.kelvin
collision_rate = 5.0 / unit.picoseconds
timestep = 2.0 * unit.femtoseconds
kT = (kB * temperature)
# Select platform.
platform = None
if platform_name:
platform = openmm.Platform.getPlatformByName(platform_name)
# Create integrators.
reference_integrator = openmm.LangevinIntegrator(temperature, collision_rate, timestep)
alchemical_integrator = openmm.VerletIntegrator(timestep)
# Create contexts.
if platform:
reference_context = openmm.Context(reference_system, reference_integrator, platform)
alchemical_context = openmm.Context(alchemical_system, alchemical_integrator, platform)
else:
reference_context = openmm.Context(reference_system, reference_integrator)
alchemical_context = openmm.Context(alchemical_system, alchemical_integrator)
ncfile = None
if cached_trajectory_filename:
cache_mode = 'write'
# Try reading from cache
from netCDF4 import Dataset
if os.path.exists(cached_trajectory_filename):
try:
ncfile = Dataset(cached_trajectory_filename, 'r')
if (ncfile.variables['positions'].shape == (nsamples, reference_system.getNumParticles(), 3)):
# Read the cache if everything matches
cache_mode = 'read'
except:
pass
if cache_mode == 'write':
# If anything went wrong, create a new cache.
try:
(pathname, filename) = os.path.split(cached_trajectory_filename)
if not os.path.exists(pathname): os.makedirs(pathname)
ncfile = Dataset(cached_trajectory_filename, 'w', format='NETCDF4')
ncfile.createDimension('samples', 0)
ncfile.createDimension('atoms', reference_system.getNumParticles())
ncfile.createDimension('spatial', 3)
ncfile.createVariable('positions', 'f4', ('samples', 'atoms', 'spatial'))
except Exception as e:
logger.info(str(e))
logger.info('Could not create a trajectory cache (%s).' % cached_trajectory_filename)
ncfile = None
# Collect simulation data.
reference_context.setPositions(positions)
du_n = np.zeros([nsamples], np.float64) # du_n[n] is the
print()
import click
with click.progressbar(range(nsamples)) as bar:
for sample in bar:
if cached_trajectory_filename and (cache_mode == 'read'):
# Load cached frames.
positions = unit.Quantity(ncfile.variables['positions'][sample,:,:], unit.nanometers)
reference_context.setPositions(positions)
else:
# Run dynamics.
reference_integrator.step(nsteps)
# Get reference energies.
reference_state = reference_context.getState(getEnergy=True, getPositions=True)
reference_potential = reference_state.getPotentialEnergy()
if np.isnan(reference_potential/kT):
raise Exception("Reference potential is NaN")
# Get alchemical energies.
alchemical_context.setPositions(reference_state.getPositions(asNumpy=True))
alchemical_state = alchemical_context.getState(getEnergy=True)
alchemical_potential = alchemical_state.getPotentialEnergy()
if np.isnan(alchemical_potential/kT):
raise Exception("Alchemical potential is NaN")
du_n[sample] = (alchemical_potential - reference_potential) / kT
if cached_trajectory_filename and (cache_mode == 'write') and (ncfile is not None):
ncfile.variables['positions'][sample,:,:] = reference_state.getPositions(asNumpy=True) / unit.nanometers
# Clean up.
del reference_context, alchemical_context
if cached_trajectory_filename and (ncfile is not None):
ncfile.close()
# Discard data to equilibration and subsample.
from pymbar import timeseries
[t0, g, Neff] = timeseries.detectEquilibration(du_n)
indices = timeseries.subsampleCorrelatedData(du_n, g=g)
du_n = du_n[indices]
# Compute statistics.
from pymbar import EXP
[DeltaF, dDeltaF] = EXP(du_n)
# Raise an exception if the error is larger than 3kT.
MAX_DEVIATION = 3.0 # kT
if (dDeltaF > MAX_DEVIATION):
report = "DeltaF = %12.3f +- %12.3f kT (%5d samples, g = %6.1f)" % (DeltaF, dDeltaF, Neff, g)
raise Exception(report)
return
from pymbar import timeseries # determine equilibrated region [t, g, Neff_max] = timeseries.detectEquilibration(A_t) # extract equilibrated region A_t_equilibrated = A_t[t:]
print "%16s %8d" % (dimension_name, len(ncfile.dimensions[dimension_name]))
# Read dimensions.
niterations = ncfile.variables['positions'].shape[0]
nstates = ncfile.variables['positions'].shape[1]
natoms = ncfile.variables['positions'].shape[2]
print "Read %(niterations)d iterations, %(nstates)d states" % vars()
# Read reference PDB file.
reference_pdb_filename = os.path.join(source_directory, "complex.pdb")
atoms = read_pdb(reference_pdb_filename)
# Choose number of samples to discard to equilibration
u_n = extract_u_n(ncfile)
if numpy.any(numpy.isnan(u_n)): continue
nskip = int(len(u_n) / 100.0)
[nequil, g_t, Neff_max] = timeseries.detectEquilibration(u_n, nskip)
print [nequil, Neff_max]
# Resample configurations for state 0.
state = 0
nsamples = 5000
output_pdb_filename = os.path.join(source_directory, 'resampled.pdb')
write_pdb_resampled(ncfile, atoms, output_pdb_filename, state, nequil, nsamples)
# Close input NetCDF file.
ncfile.close()
#except Exception as e:
# print str(e)
# pass
kT = unit.AVOGADRO_CONSTANT_NA * unit.BOLTZMANN_CONSTANT_kB * integrator.getTemperature()
for k in range(nstates):
for iteration in range(niterations):
print('state %5d iteration %5d / %5d' % (k, iteration, niterations))
# Set alchemical state
context.setParameter('lambda', lambdas[k])
# Run some dynamics
integrator.step(nsteps)
# Compute energies at all alchemical states
for l in range(nstates):
context.setParameter('lambda', lambdas[l])
u_kln[k,l,iteration] = context.getState(getEnergy=True).getPotentialEnergy() / kT
# Estimate free energy of Lennard-Jones particle insertion
from pymbar import MBAR, timeseries
# Subsample data to extract uncorrelated equilibrium timeseries
N_k = np.zeros([nstates], np.int32) # number of uncorrelated samples
for k in range(nstates):
[nequil, g, Neff_max] = timeseries.detectEquilibration(u_kln[k,k,:])
indices = timeseries.subsampleCorrelatedData(u_kln[k,k,:], g=g)
N_k[k] = len(indices)
u_kln[k,:,0:N_k[k]] = u_kln[k,:,indices].T
# Compute free energy differences and statistical uncertainties
mbar = MBAR(u_kln, N_k)
[DeltaF_ij, dDeltaF_ij, Theta_ij] = mbar.getFreeEnergyDifferences()
print('DeltaF_ij (kT):')
print(DeltaF_ij)
print('dDeltaF_ij (kT):')
print(dDeltaF_ij)
def analyze(source_directory):
"""
Analyze contents of store files to compute free energy differences.
Parameters
----------
source_directory : string
The location of the NetCDF simulation storage files.
"""
# Storage for different phases.
data = dict()
phase_prefixes = ['solvent', 'complex']
suffixes = ['explicit', 'implicit']
DeltaF_restraints = None
# Process each netcdf file.
netcdf_files_found = 0
for phase in phase_prefixes:
# Read reference PDB file.
#from simtk.openmm import app
#reference_pdb_filename = os.path.join(source_directory, phase + '.pdb')
#reference_pdb = app.PDBFile(reference_pdb_filename)
#if phase in ['vacuum', 'solvent']:
# reference_pdb_filename = os.path.join(source_directory, "ligand.pdb")
#else:
# reference_pdb_filename = os.path.join(source_directory, "complex.pdb")
#atoms = read_pdb(reference_pdb_filename)
for suffix in suffixes:
# Construct full path to NetCDF file.
fullpath = os.path.join(source_directory, '%s-%s.nc' % (phase, suffix))
logger.debug("Attempting to open %s..." % fullpath)
# Skip if the file doesn't exist.
if (not os.path.exists(fullpath)): continue
# Open NetCDF file for reading.
logger.info("Opening NetCDF trajectory file '%(fullpath)s' for reading..." % vars())
try:
ncfile = netcdf.Dataset(fullpath, 'r')
except Exception as e:
logger.error(e.message)
raise Exception("Error opening NetCDF trajectory file '%(fullpath)s' for reading..." % vars())
# DEBUG
logger.info("dimensions:")
for dimension_name in ncfile.dimensions.keys():
logger.info("%16s %8d" % (dimension_name, len(ncfile.dimensions[dimension_name])))
# Read dimensions.
niterations = ncfile.variables['positions'].shape[0]
nstates = ncfile.variables['positions'].shape[1]
natoms = ncfile.variables['positions'].shape[2]
logger.info("Read %(niterations)d iterations, %(nstates)d states" % vars())
# Increment number of netcdf files found.
netcdf_files_found += 1
# Read standard state correction free energy.
if phase == 'complex':
DeltaF_restraints = ncfile.groups['metadata'].variables['standard_state_correction'][0]
# Read reference PDB file.
#if phase in ['vacuum', 'solvent']:
# reference_pdb_filename = os.path.join(source_directory, "ligand.pdb")
#else:
# reference_pdb_filename = os.path.join(source_directory, "complex.pdb")
#atoms = read_pdb(reference_pdb_filename)
# Check to make sure no self-energies go nan.
#check_energies(ncfile, atoms)
# Check to make sure no positions are nan
#check_positions(ncfile)
# Choose number of samples to discard to equilibration
MIN_ITERATIONS = 10 # minimum number of iterations to use automatic detection
if niterations > MIN_ITERATIONS:
from pymbar import timeseries
u_n = extract_u_n(ncfile)
u_n = u_n[1:] # discard initial frame of zero energies TODO: Get rid of initial frame of zero energies
[nequil, g_t, Neff_max] = timeseries.detectEquilibration(u_n)
nequil += 1 # account for initial frame of zero energies
logger.info([nequil, Neff_max])
else:
nequil = 1 # discard first frame
g_t = 1
Neff_max = niterations
# Examine acceptance probabilities.
show_mixing_statistics(ncfile, cutoff=0.05, nequil=nequil)
# Estimate free energies.
(Deltaf_ij, dDeltaf_ij) = estimate_free_energies(ncfile, ndiscard = nequil, g=g_t)
# Estimate average enthalpies
(DeltaH_i, dDeltaH_i) = estimate_enthalpies(ncfile, ndiscard = nequil, g=g_t)
# Accumulate free energy differences
entry = dict()
entry['DeltaF'] = Deltaf_ij[0,nstates-1]
entry['dDeltaF'] = dDeltaf_ij[0,nstates-1]
entry['DeltaH'] = DeltaH_i[nstates-1] - DeltaH_i[0]
entry['dDeltaH'] = np.sqrt(dDeltaH_i[0]**2 + dDeltaH_i[nstates-1]**2)
data[phase] = entry
# Get temperatures.
ncvar = ncfile.groups['thermodynamic_states'].variables['temperatures']
temperature = ncvar[0] * units.kelvin
kT = kB * temperature
# Close input NetCDF file.
ncfile.close()
# Give the user a useful warning if no NetCDF files found.
if netcdf_files_found == 0:
raise Exception("No YANK output files were found in the specified store directory (%s)" % source_directory)
# Compute hydration free energy (free energy of transfer from vacuum to water)
#DeltaF = data['vacuum']['DeltaF'] - data['solvent']['DeltaF']
#dDeltaF = numpy.sqrt(data['vacuum']['dDeltaF']**2 + data['solvent']['dDeltaF']**2)
#print "Hydration free energy: %.3f +- %.3f kT (%.3f +- %.3f kcal/mol)" % (DeltaF, dDeltaF, DeltaF * kT / units.kilocalories_per_mole, dDeltaF * kT / units.kilocalories_per_mole)
# Compute enthalpy of transfer from vacuum to water
#DeltaH = data['vacuum']['DeltaH'] - data['solvent']['DeltaH']
#dDeltaH = numpy.sqrt(data['vacuum']['dDeltaH']**2 + data['solvent']['dDeltaH']**2)
#print "Enthalpy of hydration: %.3f +- %.3f kT (%.3f +- %.3f kcal/mol)" % (DeltaH, dDeltaH, DeltaH * kT / units.kilocalories_per_mole, dDeltaH * kT / units.kilocalories_per_mole)
if DeltaF_restraints is None:
raise Exception("DeltaF_restraints not found.")
# Compute binding free energy.
DeltaF = data['solvent']['DeltaF'] - DeltaF_restraints - data['complex']['DeltaF']
dDeltaF = np.sqrt(data['solvent']['dDeltaF']**2 + data['complex']['dDeltaF']**2)
logger.info("")
logger.info("Binding free energy : %16.3f +- %.3f kT (%16.3f +- %.3f kcal/mol)" % (DeltaF, dDeltaF, DeltaF * kT / units.kilocalories_per_mole, dDeltaF * kT / units.kilocalories_per_mole))
logger.info("")
#logger.info("DeltaG vacuum : %16.3f +- %.3f kT" % (data['vacuum']['DeltaF'], data['vacuum']['dDeltaF']))
logger.info("DeltaG solvent : %16.3f +- %.3f kT" % (data['solvent']['DeltaF'], data['solvent']['dDeltaF']))
logger.info("DeltaG complex : %16.3f +- %.3f kT" % (data['complex']['DeltaF'], data['complex']['dDeltaF']))
logger.info("DeltaG restraint : %16.3f kT" % DeltaF_restraints)
logger.info("")
# Compute binding enthalpy
DeltaH = data['solvent']['DeltaH'] - DeltaF_restraints - data['complex']['DeltaH']
dDeltaH = np.sqrt(data['solvent']['dDeltaH']**2 + data['complex']['dDeltaH']**2)
logger.info("Binding enthalpy : %16.3f +- %.3f kT (%16.3f +- %.3f kcal/mol)" % (DeltaH, dDeltaH, DeltaH * kT / units.kilocalories_per_mole, dDeltaH * kT / units.kilocalories_per_mole))
def test_detectEquil():
x = np.random.normal(size=10000)
(t, g, Neff_max) = timeseries.detectEquilibration(x)
def generate_simulation_data(database, parameters, cid):
"""
Regenerate simulation data for given parameters.
ARGUMENTS
database (dict) - database of molecules
parameters (dict) - dictionary of GBSA parameters keyed on GBSA atom types
"""
platform = openmm.Platform.getPlatformByName("Reference")
from pymbar import timeseries
entry = database[cid]
molecule = entry["molecule"]
iupac_name = entry["iupac"]
# Retrieve vacuum system.
vacuum_system = copy.deepcopy(entry["system"])
# Retrieve OpenMM System.
solvent_system = copy.deepcopy(entry["system"])
# Get nonbonded force.
forces = {
solvent_system.getForce(index).__class__.__name__: solvent_system.getForce(index)
for index in range(solvent_system.getNumForces())
}
nonbonded_force = forces["NonbondedForce"]
# Add GBSA term
gbsa_force = openmm.GBSAOBCForce()
gbsa_force.setNonbondedMethod(openmm.GBSAOBCForce.NoCutoff) # set no cutoff
gbsa_force.setSoluteDielectric(1)
gbsa_force.setSolventDielectric(78)
# Build indexable list of atoms.
atoms = [atom for atom in molecule.GetAtoms()]
natoms = len(atoms)
# Assign GBSA parameters.
for (atom_index, atom) in enumerate(atoms):
[charge, sigma, epsilon] = nonbonded_force.getParticleParameters(atom_index)
atomtype = atom.GetStringData("gbsa_type") # GBSA atomtype
radius = parameters["%s_%s" % (atomtype, "radius")] * units.angstroms
scalingFactor = parameters["%s_%s" % (atomtype, "scalingFactor")]
gbsa_force.addParticle(charge, radius, scalingFactor)
# Add the force to the system.
solvent_system.addForce(gbsa_force)
# Create context for solvent system.
timestep = 2.0 * units.femtosecond
collision_rate = 20.0 / units.picoseconds
temperature = entry["temperature"]
integrator = openmm.LangevinIntegrator(temperature, collision_rate, timestep)
context = openmm.Context(vacuum_system, integrator, platform)
# Set the coordinates.
positions = entry["positions"]
context.setPositions(positions)
# Minimize.
openmm.LocalEnergyMinimizer.minimize(context)
# Simulate, saving periodic snapshots of configurations.
kT = kB * temperature
beta = 1.0 / kT
initial_time = time.time()
nsteps_per_iteration = 2500
niterations = 200
x_n = np.zeros([niterations, natoms, 3], np.float32) # positions, in nm
u_n = np.zeros([niterations], np.float64) # energy differences, in kT
for iteration in range(niterations):
integrator.step(nsteps_per_iteration)
state = context.getState(getEnergy=True, getPositions=True)
x_n[iteration, :, :] = state.getPositions(asNumpy=True) / units.nanometers
u_n[iteration] = beta * state.getPotentialEnergy()
if np.any(np.isnan(u_n)):
raise Exception("Encountered NaN for molecule %s | %s" % (cid, iupac_name))
final_time = time.time()
elapsed_time = final_time - initial_time
# Clean up.
del context, integrator
# Discard initial transient to equilibration.
[t0, g, Neff_max] = timeseries.detectEquilibration(u_n)
x_n = x_n[t0:, :, :]
u_n = u_n[t0:]
# Subsample to remove correlation.
indices = timeseries.subsampleCorrelatedData(u_n, g=g)
x_n = x_n[indices, :, :]
u_n = u_n[indices]
# Store data.
entry["x_n"] = x_n
entry["u_n"] = u_n
print "%48s | %64s | simulation %12.3f s | %5d samples discarded | %5d independent samples remain" % (
cid,
iupac_name,
elapsed_time,
t0,
len(indices),
)
return [cid, entry]
def analyze(source_directory, verbose=False):
"""
Analyze contents of store files to compute free energy differences.
Parameters
----------
source_directory : string
The location of the NetCDF simulation storage files.
verbose : bool, optional, default=False
If True, verbose output will be generated.
"""
# Turn on debug info.
# TODO: Control verbosity of logging output using verbose optional flag.
logging.basicConfig(level=logging.DEBUG)
# Storage for different phases.
data = dict()
phase_prefixes = ['solvent', 'complex']
suffixes = ['explicit', 'implicit']
# Process each netcdf file.
for phase in phase_prefixes:
for suffix in suffixes:
# Construct full path to NetCDF file.
fullpath = os.path.join(source_directory, '%s-%s.nc' % (phase, suffix))
if verbose: print "Attempting to open %s..." % fullpath
# Skip if the file doesn't exist.
if (not os.path.exists(fullpath)): continue
# Open NetCDF file for reading.
logger.info("Opening NetCDF trajectory file '%(fullpath)s' for reading..." % vars())
ncfile = netcdf.Dataset(fullpath, 'r')
# DEBUG
logger.info("dimensions:")
for dimension_name in ncfile.dimensions.keys():
logger.info("%16s %8d" % (dimension_name, len(ncfile.dimensions[dimension_name])))
# Read dimensions.
niterations = ncfile.variables['positions'].shape[0]
nstates = ncfile.variables['positions'].shape[1]
natoms = ncfile.variables['positions'].shape[2]
logger.info("Read %(niterations)d iterations, %(nstates)d states" % vars())
# Read reference PDB file.
#if phase in ['vacuum', 'solvent']:
# reference_pdb_filename = os.path.join(source_directory, "ligand.pdb")
#else:
# reference_pdb_filename = os.path.join(source_directory, "complex.pdb")
#atoms = read_pdb(reference_pdb_filename)
# Check to make sure no self-energies go nan.
#check_energies(ncfile, atoms)
# Check to make sure no positions are nan
#check_positions(ncfile)
# Choose number of samples to discard to equilibration
# TODO: Switch to pymbar.timeseries module.
from pymbar import timeseries
u_n = extract_u_n(ncfile)
[nequil, g_t, Neff_max] = timeseries.detectEquilibration(u_n)
logger.info([nequil, Neff_max])
# Examine acceptance probabilities.
show_mixing_statistics(ncfile, cutoff=0.05, nequil=nequil)
# Estimate free energies.
(Deltaf_ij, dDeltaf_ij) = estimate_free_energies(ncfile, ndiscard = nequil)
# Estimate average enthalpies
(DeltaH_i, dDeltaH_i) = estimate_enthalpies(ncfile, ndiscard = nequil)
# Accumulate free energy differences
entry = dict()
entry['DeltaF'] = Deltaf_ij[0,nstates-1]
entry['dDeltaF'] = dDeltaf_ij[0,nstates-1]
entry['DeltaH'] = DeltaH_i[nstates-1] - DeltaH_i[0]
entry['dDeltaH'] = np.sqrt(dDeltaH_i[0]**2 + dDeltaH_i[nstates-1]**2)
data[phase] = entry
# Get temperatures.
ncvar = ncfile.groups['thermodynamic_states'].variables['temperatures']
temperature = ncvar[0] * units.kelvin
kT = kB * temperature
# Close input NetCDF file.
ncfile.close()
# Compute hydration free energy (free energy of transfer from vacuum to water)
#DeltaF = data['vacuum']['DeltaF'] - data['solvent']['DeltaF']
#dDeltaF = numpy.sqrt(data['vacuum']['dDeltaF']**2 + data['solvent']['dDeltaF']**2)
#print "Hydration free energy: %.3f +- %.3f kT (%.3f +- %.3f kcal/mol)" % (DeltaF, dDeltaF, DeltaF * kT / units.kilocalories_per_mole, dDeltaF * kT / units.kilocalories_per_mole)
# Compute enthalpy of transfer from vacuum to water
#DeltaH = data['vacuum']['DeltaH'] - data['solvent']['DeltaH']
#dDeltaH = numpy.sqrt(data['vacuum']['dDeltaH']**2 + data['solvent']['dDeltaH']**2)
#print "Enthalpy of hydration: %.3f +- %.3f kT (%.3f +- %.3f kcal/mol)" % (DeltaH, dDeltaH, DeltaH * kT / units.kilocalories_per_mole, dDeltaH * kT / units.kilocalories_per_mole)
# Read standard state correction free energy.
DeltaF_restraints = 0.0
phase = 'complex'
fullpath = os.path.join(source_directory, phase + '.nc')
ncfile = netcdf.Dataset(fullpath, 'r')
DeltaF_restraints = ncfile.groups['metadata'].variables['standard_state_correction'][0]
ncfile.close()
# Compute binding free energy.
DeltaF = data['solvent']['DeltaF'] - DeltaF_restraints - data['complex']['DeltaF']
dDeltaF = np.sqrt(data['solvent']['dDeltaF']**2 + data['complex']['dDeltaF']**2)
logger.info("")
logger.info("Binding free energy : %16.3f +- %.3f kT (%16.3f +- %.3f kcal/mol)" % (DeltaF, dDeltaF, DeltaF * kT / units.kilocalories_per_mole, dDeltaF * kT / units.kilocalories_per_mole))
logger.info("")
#logger.info("DeltaG vacuum : %16.3f +- %.3f kT" % (data['vacuum']['DeltaF'], data['vacuum']['dDeltaF']))
logger.info("DeltaG solvent : %16.3f +- %.3f kT" % (data['solvent']['DeltaF'], data['solvent']['dDeltaF']))
logger.info("DeltaG complex : %16.3f +- %.3f kT" % (data['complex']['DeltaF'], data['complex']['dDeltaF']))
logger.info("DeltaG restraint : %16.3f kT" % DeltaF_restraints)
logger.info("")
# Compute binding enthalpy
DeltaH = data['solvent']['DeltaH'] - DeltaF_restraints - data['complex']['DeltaH']
dDeltaH = np.sqrt(data['solvent']['dDeltaH']**2 + data['complex']['dDeltaH']**2)
logger.info("Binding enthalpy : %16.3f +- %.3f kT (%16.3f +- %.3f kcal/mol)" % (DeltaH, dDeltaH, DeltaH * kT / units.kilocalories_per_mole, dDeltaH * kT / units.kilocalories_per_mole))
def overlap_check(reference_system, positions, receptor_atoms, ligand_atoms, platform_name=None, annihilate_electrostatics=True, annihilate_sterics=False, precision=None, nsteps=50, nsamples=200):
"""
Test overlap between reference system and alchemical system by running a short simulation.
Parameters
----------
reference_system : simtk.openmm.System
The reference System object to compare with
positions : simtk.unit.Quantity with units compatible with nanometers
The positions to assess energetics for.
receptor_atoms : list of int
The list of receptor atoms.
ligand_atoms : list of int
The list of ligand atoms to alchemically modify.
platform_name : str, optional, default=None
The name of the platform to use for benchmarking.
annihilate_electrostatics : bool, optional, default=True
If True, electrostatics will be annihilated; if False, decoupled.
annihilate_sterics : bool, optional, default=False
If True, sterics will be annihilated; if False, decoupled.
nsteps : int, optional, default=50
Number of molecular dynamics steps between samples.
nsamples : int, optional, default=100
Number of samples to collect.
"""
# Create a fully-interacting alchemical state.
factory = AbsoluteAlchemicalFactory(reference_system, ligand_atoms=ligand_atoms)
alchemical_state = AlchemicalState()
alchemical_system = factory.createPerturbedSystem(alchemical_state)
temperature = 300.0 * units.kelvin
collision_rate = 5.0 / units.picoseconds
timestep = 2.0 * units.femtoseconds
kT = (kB * temperature)
# Select platform.
platform = None
if platform_name:
platform = openmm.Platform.getPlatformByName(platform_name)
# Create integrators.
reference_integrator = openmm.LangevinIntegrator(temperature, collision_rate, timestep)
alchemical_integrator = openmm.VerletIntegrator(timestep)
# Create contexts.
if platform:
reference_context = openmm.Context(reference_system, reference_integrator, platform)
alchemical_context = openmm.Context(alchemical_system, alchemical_integrator, platform)
else:
reference_context = openmm.Context(reference_system, reference_integrator)
alchemical_context = openmm.Context(alchemical_system, alchemical_integrator)
# Collect simulation data.
reference_context.setPositions(positions)
du_n = np.zeros([nsamples], np.float64) # du_n[n] is the
for sample in range(nsamples):
# Run dynamics.
reference_integrator.step(nsteps)
# Get reference energies.
reference_state = reference_context.getState(getEnergy=True, getPositions=True)
reference_potential = reference_state.getPotentialEnergy()
# Get alchemical energies.
alchemical_context.setPositions(reference_state.getPositions())
alchemical_state = alchemical_context.getState(getEnergy=True)
alchemical_potential = alchemical_state.getPotentialEnergy()
du_n[sample] = (alchemical_potential - reference_potential) / kT
# Clean up.
del reference_context, alchemical_context
# Discard data to equilibration and subsample.
from pymbar import timeseries
[t0, g, Neff] = timeseries.detectEquilibration(du_n)
indices = timeseries.subsampleCorrelatedData(du_n, g=g)
du_n = du_n[indices]
# Compute statistics.
from pymbar import EXP
[DeltaF, dDeltaF] = EXP(du_n)
# Raise an exception if the error is larger than 3kT.
MAX_DEVIATION = 3.0 # kT
if (dDeltaF > MAX_DEVIATION):
report = "DeltaF = %12.3f +- %12.3f kT (%5d samples, g = %6.1f)" % (DeltaF, dDeltaF, Neff, g)
raise Exception(report)
return
def analyze(source_directory):
"""
Analyze contents of store files to compute free energy differences.
Parameters
----------
source_directory : string
The location of the NetCDF simulation storage files.
"""
analysis_script_path = os.path.join(source_directory, 'analysis.yaml')
if not os.path.isfile(analysis_script_path):
err_msg = 'Cannot find analysis.yaml script in {}'.format(source_directory)
logger.error(err_msg)
raise RuntimeError(err_msg)
with open(analysis_script_path, 'r') as f:
analysis = yaml.load(f)
phases = [phase_name for phase_name, sign in analysis]
# Storage for different phases.
data = dict()
# Process each netcdf file.
for phase in phases:
ncfile_path = os.path.join(source_directory, phase + '.nc')
# Open NetCDF file for reading.
logger.info("Opening NetCDF trajectory file %(ncfile_path)s for reading..." % vars())
try:
ncfile = netcdf.Dataset(ncfile_path, 'r')
logger.debug("dimensions:")
for dimension_name in ncfile.dimensions.keys():
logger.debug("%16s %8d" % (dimension_name, len(ncfile.dimensions[dimension_name])))
# Read dimensions.
niterations = ncfile.variables['positions'].shape[0]
nstates = ncfile.variables['positions'].shape[1]
logger.info("Read %(niterations)d iterations, %(nstates)d states" % vars())
DeltaF_restraints = 0.0
if 'metadata' in ncfile.groups:
# Read phase direction and standard state correction free energy.
# Yank sets correction to 0 if there are no restraints
DeltaF_restraints = ncfile.groups['metadata'].variables['standard_state_correction'][0]
# Choose number of samples to discard to equilibration
MIN_ITERATIONS = 10 # minimum number of iterations to use automatic detection
if niterations > MIN_ITERATIONS:
from pymbar import timeseries
u_n = extract_u_n(ncfile)
u_n = u_n[1:] # discard initial frame of zero energies TODO: Get rid of initial frame of zero energies
[nequil, g_t, Neff_max] = timeseries.detectEquilibration(u_n)
nequil += 1 # account for initial frame of zero energies
logger.info([nequil, Neff_max])
else:
nequil = 1 # discard first frame
g_t = 1
Neff_max = niterations
# Examine acceptance probabilities.
show_mixing_statistics(ncfile, cutoff=0.05, nequil=nequil)
# Extract equilibrated, decorrelated energies, check for fully interacting state
(u_kln, N_k, u_n) = extract_ncfile_energies(ncfile, ndiscard=nequil, g=g_t)
# Create MBAR object to use for free energy and entropy states
mbar = initialize_MBAR(ncfile, u_kln=u_kln, N_k=N_k)
# Estimate free energies, use fully interacting state if present
(Deltaf_ij, dDeltaf_ij) = estimate_free_energies(ncfile, mbar=mbar)
# Estimate average enthalpies
(DeltaH_i, dDeltaH_i) = estimate_enthalpies(ncfile, mbar=mbar)
# Accumulate free energy differences
entry = dict()
entry['DeltaF'] = Deltaf_ij[0, -1]
entry['dDeltaF'] = dDeltaf_ij[0, -1]
entry['DeltaH'] = DeltaH_i[0, -1]
entry['dDeltaH'] = dDeltaH_i[0, -1]
entry['DeltaF_restraints'] = DeltaF_restraints
data[phase] = entry
# Get temperatures.
ncvar = ncfile.groups['thermodynamic_states'].variables['temperatures']
temperature = ncvar[0] * units.kelvin
kT = kB * temperature
finally:
ncfile.close()
# Compute free energy and enthalpy
DeltaF = 0.0
dDeltaF = 0.0
DeltaH = 0.0
dDeltaH = 0.0
for phase, sign in analysis:
DeltaF -= sign * (data[phase]['DeltaF'] + data[phase]['DeltaF_restraints'])
dDeltaF += data[phase]['dDeltaF']**2
DeltaH -= sign * (data[phase]['DeltaH'] + data[phase]['DeltaF_restraints'])
dDeltaH += data[phase]['dDeltaH']**2
dDeltaF = np.sqrt(dDeltaF)
dDeltaH = np.sqrt(dDeltaH)
# Attempt to guess type of calculation
calculation_type = ''
for phase in phases:
if 'complex' in phase:
calculation_type = ' of binding'
elif 'solvent1' in phase:
calculation_type = ' of solvation'
# Print energies
logger.info("")
logger.info("Free energy{}: {:16.3f} +- {:.3f} kT ({:16.3f} +- {:.3f} kcal/mol)".format(
calculation_type, DeltaF, dDeltaF, DeltaF * kT / units.kilocalories_per_mole,
dDeltaF * kT / units.kilocalories_per_mole))
logger.info("")
for phase in phases:
logger.info("DeltaG {:<25} : {:16.3f} +- {:.3f} kT".format(phase, data[phase]['DeltaF'],
data[phase]['dDeltaF']))
if data[phase]['DeltaF_restraints'] != 0.0:
logger.info("DeltaG {:<25} : {:25.3f} kT".format('restraint',
data[phase]['DeltaF_restraints']))
logger.info("")
logger.info("Enthalpy{}: {:16.3f} +- {:.3f} kT ({:16.3f} +- {:.3f} kcal/mol)".format(
calculation_type, DeltaH, dDeltaH, DeltaH * kT / units.kilocalories_per_mole,
dDeltaH * kT / units.kilocalories_per_mole))
def extract_trajectory(output_path, nc_path, state_index=None, replica_index=None,
start_frame=0, end_frame=-1, skip_frame=1, keep_solvent=True,
discard_equilibration=False, image_molecules=False):
"""Extract phase trajectory from the NetCDF4 file.
Parameters
----------
output_path : str
Path to the trajectory file to be created. The extension of the file
determines the format.
nc_path : str
Path to the NetCDF4 file containing the trajectory.
state_index : int, optional
The index of the alchemical state for which to extract the trajectory.
One and only one between state_index and replica_index must be not None
(default is None).
replica_index : int, optional
The index of the replica for which to extract the trajectory. One and
only one between state_index and replica_index must be not None (default
is None).
start_frame : int, optional
Index of the first frame to include in the trajectory (default is 0).
end_frame : int, optional
Index of the last frame to include in the trajectory. If negative, will
count from the end (default is -1).
skip_frame : int, optional
Extract one frame every skip_frame (default is 1).
keep_solvent : bool, optional
If False, solvent molecules are ignored (default is True).
discard_equilibration : bool, optional
If True, initial equilibration frames are discarded (see the method
pymbar.timeseries.detectEquilibration() for details, default is False).
"""
# Check correct input
if (state_index is None) == (replica_index is None):
raise ValueError('One and only one between "state_index" and '
'"replica_index" must be specified.')
if not os.path.isfile(nc_path):
raise ValueError('Cannot find file {}'.format(nc_path))
# Import simulation data
try:
nc_file = netcdf.Dataset(nc_path, 'r')
# Extract topology and system serialization
serialized_system = nc_file.groups['metadata'].variables['reference_system'][0]
serialized_topology = nc_file.groups['metadata'].variables['topology'][0]
# Determine if system is periodic
from simtk import openmm
reference_system = openmm.XmlSerializer.deserialize(str(serialized_system))
is_periodic = reference_system.usesPeriodicBoundaryConditions()
logger.info('Detected periodic boundary conditions: {}'.format(is_periodic))
# Get dimensions
n_iterations = nc_file.variables['positions'].shape[0]
n_atoms = nc_file.variables['positions'].shape[2]
logger.info('Number of iterations: {}, atoms: {}'.format(n_iterations, n_atoms))
# Determine frames to extract
if start_frame <= 0:
# TODO yank saves first frame with 0 energy!
start_frame = 1
if end_frame < 0:
end_frame = n_iterations + end_frame + 1
frame_indices = range(start_frame, end_frame, skip_frame)
if len(frame_indices) == 0:
raise ValueError('No frames selected')
logger.info('Extracting frames from {} to {} every {}'.format(
start_frame, end_frame, skip_frame))
# Discard equilibration samples
if discard_equilibration:
u_n = extract_u_n(nc_file)[frame_indices]
n_equil, g, n_eff = timeseries.detectEquilibration(u_n)
logger.info(("Discarding initial {} equilibration samples (leaving {} "
"effectively uncorrelated samples)...").format(n_equil, n_eff))
frame_indices = frame_indices[n_equil:-1]
# Extract state positions and box vectors
positions = np.zeros((len(frame_indices), n_atoms, 3))
if is_periodic:
box_vectors = np.zeros((len(frame_indices), 3, 3))
if state_index is not None:
logger.info('Extracting positions of state {}...'.format(state_index))
# Deconvolute state indices
state_indices = np.zeros(len(frame_indices))
for i, iteration in enumerate(frame_indices):
replica_indices = nc_file.variables['states'][iteration, :]
state_indices[i] = np.where(replica_indices == state_index)[0][0]
# Extract state positions and box vectors
for i, iteration in enumerate(frame_indices):
replica_index = state_indices[i]
positions[i, :, :] = nc_file.variables['positions'][iteration, replica_index, :, :].astype(np.float32)
if is_periodic:
box_vectors[i, :, :] = nc_file.variables['box_vectors'][iteration, replica_index, :, :].astype(np.float32)
else: # Extract replica positions and box vectors
logger.info('Extracting positions of replica {}...'.format(replica_index))
for i, iteration in enumerate(frame_indices):
positions[i, :, :] = nc_file.variables['positions'][iteration, replica_index, :, :].astype(np.float32)
if is_periodic:
box_vectors[i, :, :] = nc_file.variables['box_vectors'][iteration, replica_index, :, :].astype(np.float32)
finally:
nc_file.close()
# Create trajectory object
logger.info('Creating trajectory object...')
topology = utils.deserialize_topology(serialized_topology)
trajectory = mdtraj.Trajectory(positions, topology)
if is_periodic:
trajectory.unitcell_vectors = box_vectors
# Force periodic boundary conditions to molecules positions
if image_molecules:
logger.info('Applying periodic boundary conditions to molecules positions...')
trajectory.image_molecules(inplace=True)
# Remove solvent
if not keep_solvent:
logger.info('Removing solvent molecules...')
trajectory = trajectory.remove_solvent()
# Detect format
extension = os.path.splitext(output_path)[1][1:] # remove dot
try:
save_function = getattr(trajectory, 'save_' + extension)
except AttributeError:
raise ValueError('Cannot detect format from extension of file {}'.format(output_path))
# Create output directory and save trajectory
logger.info('Creating trajectory file: {}'.format(output_path))
output_dir = os.path.dirname(output_path)
if output_dir != '' and not os.path.isdir(output_dir):
os.makedirs(output_dir)
save_function(output_path)
out = open("logd_bayes.txt", 'w')
used_samples = open("mcmc_sampling_details.txt","w")
out.write("Molecule, Log D +/-, HPD95%[low, high]\n")
debug.write("Molecule mean - median = difference")
used_samples.write("Molecule, equilibration, N samples")
# curdir = os.getcwd()
# os.makedirs("plots", )
# os.chdir("plots")
for mol in sorted(list(x.logd.keys())):
print("Processing {}".format(mol))
# sns.plt.figure()
trace = numpy.asarray(mc.trace("LogD_{}".format(mol))[:])
# Burn in and thinning estimated using pymbar
burnin = detectEquilibration(trace)[0]
trace= trace[burnin:]
uncorrelated_indices = subsampleCorrelatedData(trace)
trace=trace[uncorrelated_indices]
median = pymc.utils.quantiles(trace)[50]
mean = numpy.mean(trace)
lower, upper = pymc.utils.hpd(trace, 0.05)
lower_s = to_precision(lower,2) # string of number with 2 sig digits
upper_s = to_precision(upper,2)
logd = ufloat(mean, numpy.std(trace))
# Formats the mean and error by the correct amount of significant digits
out.write("{0}, {1:.1u}, [{2}, {3}]\n".format(mol, logd, lower_s, upper_s ))
debug.write("{}: {} - {} = {}".format(mol, mean, median, mean-median))
used_samples.write("{}, {}, {}".format(mol, burnin, len(uncorrelated_indices)))