def subsample(N_k, U_kn, V_kn, N_kn, g, type):
K = len(N_k)
N_k_sampled = numpy.zeros(K)
tempspace = numpy.zeros(numpy.max(N_k))
for k in range(K):
if (type != 'volume') and (type != 'number'):
indices = timeseries.subsampleCorrelatedData(
U_kn[k, 0:N_k[k]], g[k])
tempspace = U_kn[k, indices].copy()
N_k_sampled[k] = numpy.size(indices)
U_kn[k, 0:N_k_sampled[k]] = tempspace[0:N_k_sampled[k]]
if (type in requireV):
indices = timeseries.subsampleCorrelatedData(
V_kn[k, 0:N_k[k]], g[k])
tempspace = V_kn[k, indices].copy()
N_k_sampled[k] = numpy.size(indices)
V_kn[k, 0:N_k_sampled[k]] = tempspace[0:N_k_sampled[k]]
if (type in requireN):
indices = timeseries.subsampleCorrelatedData(
N_kn[k, 0:N_k[k]], g[k])
tempspace = N_kn[k, indices].copy()
N_k_sampled[k] = numpy.size(indices)
N_kn[k, 0:N_k_sampled[k]] = tempspace[0:N_k_sampled[k]]
print "data has been subsampled using the statistical inefficiencies"
g[k] = 1.0
N_k[k] = N_k_sampled[k]
def subsample(Q_n,localQ):
print 'Subsampling the data'
g = timeseries.statisticalInefficiency(Q_n)
indices = numpy.array(timeseries.subsampleCorrelatedData(Q_n,g))
print '%i uncorrelated samples found of %i original samples' %(len(indices),len(Q_n))
localQ = localQ[:,indices]
return localQ
def getNkandUkln(do_dhdl=False):
"""Identifies uncorrelated samples and updates the arrays of the reduced potential energy and dhdlt retaining data entries of these samples only.
Assumes that 'dhdlt' and 'u_klt' are in memory, as well as proper values for 'sta' and 'fin', i.e. the starting and
final snapshot positions to be read, both are arrays of dimension K."""
u_kln = numpy.zeros([K,K,max(fin-sta)], numpy.float64) # u_kln[k,m,n] is the reduced potential energy of uncorrelated sample index n from state k evaluated at state m
N_k = numpy.zeros(K, int) # N_k[k] is the number of uncorrelated samples from state k
g = numpy.zeros(K,float) # autocorrelation times for the data
if do_dhdl:
dhdl = numpy.zeros([K,n_components,max(fin-sta)], float) #dhdl is value for dhdl for each component in the file at each time.
print "\n\nNumber of correlated and uncorrelated samples:\n\n%6s %12s %12s %12s\n" % ('State', 'N', 'N_k', 'N/N_k')
for k in range(K):
# Sum up over the energy components; notice, that only the relevant data is being used in the third dimension.
dhdl_sum = numpy.sum(dhdlt[k,:,sta[k]:fin[k]], axis=0)
# Determine indices of uncorrelated samples from potential autocorrelation analysis at state k
# (alternatively, could use the energy differences -- here, we will use total dhdl).
g[k] = timeseries.statisticalInefficiency(dhdl_sum)
indices = numpy.array(timeseries.subsampleCorrelatedData(dhdl_sum, g=g[k])) # indices of uncorrelated samples
N = len(indices) # number of uncorrelated samples
# Handle case where we end up with too few.
if N < 50:
if do_dhdl:
print "WARNING: Only %s uncorrelated samples found at lambda number %s; proceeding with analysis using correlated samples..." % (N, k)
indices = numpy.arange(len(dhdl_sum))
N = len(indices)
N_k[k] = N # Store the number of uncorrelated samples from state k.
for l in range(K):
u_kln[k,l,0:N] = u_klt[k,l,indices]
if do_dhdl:
print "%6s %12s %12s %12.2f" % (k, fin[k], N_k[k], g[k])
for n in range(n_components):
dhdl[k,n,0:N] = dhdlt[k,n,indices]
if do_dhdl:
return (dhdl, N_k, u_kln)
return (N_k, u_kln)
def subsample_series(series, g_t=None, return_g_t=False):
if g_t is None:
g_t = timeseries.statisticalInefficiency(series)
state_indices = timeseries.subsampleCorrelatedData(series, g = g_t, conservative=True)
N_k = len(state_indices)
transfer_series = series[state_indices]
if return_g_t:
return state_indices, transfer_series, g_t
else:
return state_indices, transfer_series
def subsample(N_k,U_kn,V_kn,N_kn,g,type):
K = len(N_k)
N_k_sampled = numpy.zeros(K, dtype=numpy.int)
tempspace = numpy.zeros(numpy.max(N_k))
for k in range(K):
if (type != 'volume') and (type != 'number'):
indices = timeseries.subsampleCorrelatedData(U_kn[k,0:N_k[k]],g[k])
tempspace = U_kn[k,indices].copy()
N_k_sampled[k] = numpy.size(indices)
U_kn[k,0:N_k_sampled[k]] = tempspace[0:N_k_sampled[k]]
if (type in requireV):
indices = timeseries.subsampleCorrelatedData(V_kn[k,0:N_k[k]],g[k])
tempspace = V_kn[k,indices].copy()
N_k_sampled[k] = numpy.size(indices)
V_kn[k,0:N_k_sampled[k]] = tempspace[0:N_k_sampled[k]]
if (type in requireN):
indices = timeseries.subsampleCorrelatedData(N_kn[k,0:N_k[k]],g[k])
tempspace = N_kn[k,indices].copy()
N_k_sampled[k] = numpy.size(indices)
N_kn[k,0:N_k_sampled[k]] = tempspace[0:N_k_sampled[k]]
print "data has been subsampled using the statistical inefficiencies"
g[k] = 1.0
N_k[k] = N_k_sampled[k]
def subsample(U_kn,Q_kn,K,N_max):
assume_uncorrelated = False
if assume_uncorrelated:
print 'Assuming data is uncorrelated'
N_k = numpy.zeros(K, numpy.int32)
N_k[:] = N_max
else:
print 'Subsampling the data...'
N_k = numpy.zeros(K,numpy.int32)
g = numpy.zeros(K,numpy.float64)
for k in range(K): # subsample the energies
g[k] = timeseries.statisticalInefficiency(Q_kn[k])#,suppress_warning=True)
indices = numpy.array(timeseries.subsampleCorrelatedData(Q_kn[k],g=g[k])) # indices of uncorrelated samples
N_k[k] = len(indices) # number of uncorrelated samplesadsf
U_kn[k,0:N_k[k]] = U_kn[k,indices]
Q_kn[k,0:N_k[k]] = Q_kn[k,indices]
return U_kn, Q_kn, N_k
def subsample(U_kn,Q_kn,K,N_max):
assume_uncorrelated = False
if assume_uncorrelated:
print 'Assuming data is uncorrelated'
N_k = numpy.zeros(K, numpy.int32)
N_k[:] = N_max
else:
print 'Subsampling the data...'
N_k = numpy.zeros(K,numpy.int32)
g = numpy.zeros(K,numpy.float64)
for k in range(K): # subsample the energies
g[k] = timeseries.statisticalInefficiency(Q_kn[k])#,suppress_warning=True)
indices = numpy.array(timeseries.subsampleCorrelatedData(Q_kn[k],g=g[k])) # indices of uncorrelated samples
N_k[k] = len(indices) # number of uncorrelated samplesadsf
U_kn[k,0:N_k[k]] = U_kn[k,indices]
Q_kn[k,0:N_k[k]] = Q_kn[k,indices]
return U_kn, Q_kn, N_k
def subsample1D(pos_kn,N_k,ineff):
''' Modifies pos_xkn,pos_ykn,N_k inplace
'''
logger.info("Subsampling using given ICATS")
K = pos_kn.shape[0]
for i in range(K):
indices = timeseries.subsampleCorrelatedData(pos_kn[i,0:N_k[i]], g = ineff[i])
newN = len(indices)
pos_kn[i,0:newN] = pos_kn[i,indices]
logger.debug("Original %s New %s",N_k[i],newN)
N_k[i] = newN
if newN < 10:
logger.warn("Very few independant samples %s",newN)
logger.info("Subsampled using given ICATS")
return pos_kn,N_k
def subsample(observ,maxIneff):
''' subsample according to largest inefff
Parameters
-------------
observ: list of arrays
ineff: array with ineff for each column of observ
Return
-----------
newObserv: list of arrays subsampled according to ineff
'''
logger.info("Subsampling using given ICATS")
newObserv = []
for i,sim in enumerate(observ):
indices = timeseries.subsampleCorrelatedData(sim[:,0], g = maxIneff[i])
newsim = sim[indices,...]
newObserv.append(newsim)
logger.debug("Original %s \nNew %s",[i.shape[0] for i in observ],[i.shape[0] for i in newObserv])
logger.info("Subsampled using given ICATS")
return newObserv
# infile.close()
# Parse data.
# n = 0
# for line in lines:
# if line[0] != '#' and line[0] != '@':
# tokens = line.split()
# u_kn[k,n] = beta_k[k] * (float(tokens[2]) - float(tokens[1])) # reduced potential energy without umbrella restraint
# n += 1
# Compute correlation times for potential energy and val
# timeseries. If the temperatures differ, use energies to determine samples; otherwise, use the cosine of val
if (DifferentTemperatures):
g_k[k] = timeseries.statisticalInefficiency(u_kn[k,:], u_kn[k,:])
print "Correlation time for set %5d is %10.3f" % (k,g_k[k])
indices = timeseries.subsampleCorrelatedData(u_kn[k,:])
else:
#g_k[k] = timeseries.statisticalInefficiency(val_kn[k,:], val_kn[k,:])
#print "Correlation time for set %5d is %10.3f" % (k,g_k[k])
indices = timeseries.subsampleCorrelatedData(val_kn[k,0:N_k[k]], fast=True, verbose=True)
# Subsample data.
N_k[k] = len(indices)
u_kn[k,0:N_k[k]] = u_kn[k,indices]
val_kn[k,0:N_k[k]] = val_kn[k,indices]
# print val_kn[k,0:N_k[k]]
# Set zero of u_kn -- this is arbitrary.
u_kn -= u_kn.min()
val_min = numpy.min([numpy.min(val_kn[k,0:N_k[k]]) for k in range(K)])
val_max = numpy.max([numpy.max(val_kn[k,0:N_k[k]]) for k in range(K)])
infile.close()
# Parse data.
n = 0
for line in lines:
if line[0] != '#' and line[0] != '@':
tokens = line.split()
u_kn[k,n] = beta_k[k] * (float(tokens[2]) - float(tokens[1])) # reduced potential energy without umbrella restraint
n += 1
# Compute correlation times for potential energy and chi
# timeseries. If the temperatures differ, use energies to determine samples; otherwise, use the cosine of chi
if (DifferentTemperatures):
g_k[k] = timeseries.statisticalInefficiency(u_kn[k,:], u_kn[k,:])
print "Correlation time for set %5d is %10.3f" % (k,g_k[k])
indices = timeseries.subsampleCorrelatedData(u_kn[k,:])
else:
g_k[k] = timeseries.statisticalInefficiency(numpy.cos(chi_kn[k,:]/(180.0/numpy.pi)),numpy.cos(chi_kn[k,:]/(180.0/numpy.pi)))
print "Correlation time for set %5d is %10.3f" % (k,g_k[k])
indices = timeseries.subsampleCorrelatedData(numpy.cos(chi_kn[k,:]/(180.0/numpy.pi)))
# Subsample data.
N_k[k] = len(indices)
u_kn[k,0:N_k[k]] = u_kn[k,indices]
chi_kn[k,0:N_k[k]] = chi_kn[k,indices]
# Set zero of u_kn -- this is arbitrary.
u_kn -= u_kn.min()
# Construct torsion bins
print "Binning data..."
delta = (chi_max - chi_min) / float(nbins)
raise "pymbar [https://simtk.org/home/pymbar] must be installed to complete analysis of free energies."
# =============================================================================
# Subsample correlated samples to generate uncorrelated subsample.
# =============================================================================
print "Subsampling data to remove correlation..."
K = nlambda # number of states
N_k = nprod_iterations * numpy.ones(
[K],
numpy.int32) # N_k[k] is the number of uncorrelated samples at state k
u_kln_subsampled = numpy.zeros([K, K, nprod_iterations],
numpy.float64) # subsampled data
for k in range(K):
# Get indices of uncorrelated samples.
indices = subsampleCorrelatedData(u_kln[k, k, :])
# Store only uncorrelated data.
N_k[k] = len(indices)
for l in range(K):
u_kln_subsampled[k, l, 0:len(indices)] = u_kln[k, l, indices]
print "Number of uncorrelated samples per state:"
print N_k
# =============================================================================
# Analyze with MBAR to compute free energy differences and statistical errors.
# =============================================================================
print "Analyzing with MBAR..."
mbar = MBAR(u_kln_subsampled, N_k)
[Deltaf_ij, dDeltaf_ij] = mbar.getFreeEnergyDifferences()
print "Free energy differences (in kT)"
if len(fep_columns) > 0:
for i in range(len(fep_columns)):
reduced_fep_data.append(numpy.zeros([K, N_samples], numpy.float64))
for k in range(K):
# Extract timeseries.
A_t = biasing_variable_kt[0][k, :]
# Compute statistical inefficiency.
try:
g = timeseries.statisticalInefficiency(A_t)
except Exception as e:
print str(e)
print A_t
# Subsample data.
if subsample_trajectories:
indices = timeseries.subsampleCorrelatedData(A_t, g=g)
else:
indices = timeseries.subsampleCorrelatedData(A_t, g=1)
N = len(indices) # number of uncorrelated samples
print "k = %5d : g = %.1f, N = %d" % (k, g, N)
for i in range(nbiases):
biasing_variable_kn[i][k, 0:N] = biasing_variable_kt[i][k, indices]
for i in range(nperturbations + 1):
U_kn[i][k, 0:N] = U_kt[i][k, indices]
if not cluster_binning:
pmf_variable_kn_1[k, 0:N] = pmf_variable_kt_1[k, indices]
if ndim == 2:
pmf_variable_kn_2[k, 0:N] = pmf_variable_kt_2[k, indices]
if cluster_binning:
cluster_bin_kn[k, 0:N] = cluster_bin_kt[k, indices]
if len(expectation_columns) > 0:
def _subsample_kln(self, u_kln):
#Try to load in the data
if self.save_equil_data: #Check if we want to save/load equilibration data
try:
equil_data = numpy.load(
os.path.join(
self.source_directory, self.save_prefix + self.phase +
'_equil_data_%s.npz' % self.subsample_method))
if self.nequil is None:
self.nequil = equil_data['nequil']
elif type(self.nequil
) is int and self.subsample_method == 'per-state':
print "WARRNING: Per-state subsampling requested with only single value for equilibration..."
try:
self.nequil = equil_data['nequil']
print "Loading equilibration from file with %i states read" % self.nstates
except:
print "Assuming equal equilibration per state of %i" % self.nequil
self.nequil = numpy.array([self.nequil] * self.nstates)
self.g_t = equil_data['g_t']
Neff_max = equil_data['Neff_max']
#Do equilibration if we have not already
if self.subsample_method == 'per-state' and (
len(self.g_t) < self.nstates
or len(self.nequil) < self.nstates):
equil_loaded = False
raise IndexError
else:
equil_loaded = True
except:
if self.subsample_method == 'per-state':
self.nequil = numpy.zeros([self.nstates],
dtype=numpy.int32)
self.g_t = numpy.zeros([self.nstates])
Neff_max = numpy.zeros([self.nstates])
for k in xrange(self.nstates):
if self.verbose:
print "Computing timeseries for state %i/%i" % (
k, self.nstates - 1)
self.nequil[k] = 0
self.g_t[k] = timeseries.statisticalInefficiency(
u_kln[k, k, :])
Neff_max[k] = (u_kln[k, k, :].size + 1) / self.g_t[k]
#[self.nequil[k], self.g_t[k], Neff_max[k]] = self._detect_equilibration(u_kln[k,k,:])
else:
if self.nequil is None:
[self.nequil, self.g_t,
Neff_max] = self._detect_equilibration(self.u_n)
else:
[self.nequil_timeseries, self.g_t,
Neff_max] = self._detect_equilibration(self.u_n)
equil_loaded = False
if not equil_loaded:
numpy.savez(os.path.join(
self.source_directory, self.save_prefix + self.phase +
'_equil_data_%s.npz' % self.subsample_method),
nequil=self.nequil,
g_t=self.g_t,
Neff_max=Neff_max)
elif self.nequil is None:
if self.subsample_method == 'per-state':
self.nequil = numpy.zeros([self.nstates], dtype=numpy.int32)
self.g_t = numpy.zeros([self.nstates])
Neff_max = numpy.zeros([self.nstates])
for k in xrange(self.nstates):
[self.nequil[k], self.g_t[k],
Neff_max[k]] = self._detect_equilibration(u_kln[k, k, :])
if self.verbose:
print "State %i equilibrated with %i samples" % (
k, int(Neff_max[k]))
else:
[self.nequil, self.g_t,
Neff_max] = self._detect_equilibration(self.u_n)
if self.verbose: print[self.nequil, Neff_max]
# 1) Discard equilibration data
# 2) Subsample data to obtain uncorrelated samples
self.N_k = numpy.zeros(self.nstates, numpy.int32)
if self.subsample_method == 'per-state':
# Discard samples
nsamples_equil = self.niterations - self.nequil
self.u_kln = numpy.zeros(
[self.nstates, self.nstates,
nsamples_equil.max()])
for k in xrange(self.nstates):
self.u_kln[k, :, :nsamples_equil[k]] = u_kln[k, :,
self.nequil[k]:]
#Subsample
transfer_retained_indices = numpy.zeros(
[self.nstates, nsamples_equil.max()], dtype=numpy.int32)
for k in xrange(self.nstates):
state_indices = timeseries.subsampleCorrelatedData(
self.u_kln[k, k, :], g=self.g_t[k])
self.N_k[k] = len(state_indices)
transfer_retained_indices[k, :self.N_k[k]] = state_indices
transfer_kln = numpy.zeros(
[self.nstates, self.nstates,
self.N_k.max()])
self.retained_indices = numpy.zeros(
[self.nstates, self.N_k.max()], dtype=numpy.int32)
for k in xrange(self.nstates):
self.retained_indices[
k, :self.N_k[k]] = transfer_retained_indices[
k, :self.N_k[k]] #Memory reduction
transfer_kln[k, :, :self.N_k[k]] = self.u_kln[
k, :, self.retained_indices[k, :self.N_k[
k]]].T #Have to transpose since indexing in this way causes issues
#Cut down on memory, once function is done, transfer_kln should be released
self.u_kln = transfer_kln
self.retained_iters = self.N_k
else:
#Discard Samples
self.u_kln = u_kln[:, :, self.nequil:]
self.u_n = self.u_n[self.nequil:]
#Subsamples
indices = timeseries.subsampleCorrelatedData(
self.u_n, g=self.g_t) # indices of uncorrelated samples
self.u_kln = self.u_kln[:, :, indices]
self.N_k[:] = len(indices)
self.retained_indices = indices
self.retained_iters = len(indices)
return
from timeseries import subsampleCorrelatedData
from pymbar import MBAR
except:
raise "pymbar [https://simtk.org/home/pymbar] must be installed to complete analysis of free energies."
# =============================================================================
# Subsample correlated samples to generate uncorrelated subsample.
# =============================================================================
print "Subsampling data to remove correlation..."
K = nlambda # number of states
N_k = nprod_iterations*numpy.ones([K], numpy.int32) # N_k[k] is the number of uncorrelated samples at state k
u_kln_subsampled = numpy.zeros([K,K,nprod_iterations], numpy.float64) # subsampled data
for k in range(K):
# Get indices of uncorrelated samples.
indices = subsampleCorrelatedData(u_kln[k,k,:])
# Store only uncorrelated data.
N_k[k] = len(indices)
for l in range(K):
u_kln_subsampled[k,l,0:len(indices)] = u_kln[k,l,indices]
print "Number of uncorrelated samples per state:"
print N_k
# =============================================================================
# Analyze with MBAR to compute free energy differences and statistical errors.
# =============================================================================
print "Analyzing with MBAR..."
mbar = MBAR(u_kln_subsampled, N_k)
[Deltaf_ij, dDeltaf_ij] = mbar.getFreeEnergyDifferences()
print "Free energy differences (in kT)"
g_k = zeros([K], float64)
for k in range(K):
# Compute statistical inefficiency for extension timeseries
g = timeseries.statisticalInefficiency(x_kt[k,0:T_k[k]], x_kt[k,0:T_k[k]])
# store statistical inefficiency
g_k[k] = g
print "timeseries %d : g = %.1f, %.0f uncorrelated samples (of %d total samples)" % (k+1, g, floor(T_k[k] / g), T_k[k])
N_max = max(N_max, ceil(T_k[k] / g) + 1)
# Subsample trajectory position data.
x_kn = zeros([K, N_max], float64)
bin_kn = zeros([K, N_max], int32)
N_k = zeros([K], int32)
for k in range(K):
# Compute correlation times for potential energy and chi timeseries.
indices = timeseries.subsampleCorrelatedData(x_kt[k,0:T_k[k]])
# Store subsampled positions.
N_k[k] = len(indices)
x_kn[k,0:N_k[k]] = x_kt[k,indices]
bin_kn[k,0:N_k[k]] = bin_kt[k,indices]
# Set arbitrary zeros for external biasing potential.
x0_k = zeros([K], float64) # x position corresponding to zero of potential
for k in range(K):
x0_k[k] = x_kn[k,0:N_k[k]].mean()
print "x0_k = "
print x0_k
# Compute bias energies in units of kT.
u_kln = zeros([K,K,N_max], float64) # u_kln[k,l,n] is the reduced (dimensionless) relative potential energy of snapshot n from umbrella simulation k evaluated at umbrella l
for k in range(K):
def estimate_enthalpies(ncfile, ndiscard=0, nuse=None):
"""Estimate enthalpies of all alchemical states.
ARGUMENTS
ncfile (NetCDF) - input YANK netcdf file
OPTIONAL ARGUMENTS
ndiscard (int) - number of iterations to discard to equilibration
nuse (int) - number of iterations to use (after discarding)
TODO: Automatically determine 'ndiscard'.
TODO: Combine some functions with estimate_free_energies.
"""
# Get current dimensions.
niterations = ncfile.variables['energies'].shape[0]
nstates = ncfile.variables['energies'].shape[1]
natoms = ncfile.variables['energies'].shape[2]
# Extract energies.
print "Reading energies..."
energies = ncfile.variables['energies']
u_kln_replica = zeros([nstates, nstates, niterations], float64)
for n in range(niterations):
u_kln_replica[:, :, n] = energies[n, :, :]
print "Done."
# Deconvolute replicas
print "Deconvoluting replicas..."
u_kln = zeros([nstates, nstates, niterations], float64)
for iteration in range(niterations):
state_indices = ncfile.variables['states'][iteration, :]
u_kln[state_indices, :, iteration] = energies[iteration, :, :]
print "Done."
# Compute total negative log probability over all iterations.
u_n = zeros([niterations], float64)
for iteration in range(niterations):
u_n[iteration] = sum(diagonal(u_kln[:, :, iteration]))
#print u_n
# DEBUG
# outfile = open('u_n.out', 'w')
# for iteration in range(niterations):
# outfile.write("%8d %24.3f\n" % (iteration, u_n[iteration]))
# outfile.close()
# Discard initial data to equilibration.
u_kln_replica = u_kln_replica[:, :, ndiscard:]
u_kln = u_kln[:, :, ndiscard:]
u_n = u_n[ndiscard:]
# Truncate to number of specified conformations to use
if (nuse):
u_kln_replica = u_kln_replica[:, :, 0:nuse]
u_kln = u_kln[:, :, 0:nuse]
u_n = u_n[0:nuse]
# Subsample data to obtain uncorrelated samples
N_k = zeros(nstates, int32)
indices = timeseries.subsampleCorrelatedData(
u_n) # indices of uncorrelated samples
#indices = range(0,u_n.size) # DEBUG - assume samples are uncorrelated
N = len(indices) # number of uncorrelated samples
N_k[:] = N
u_kln[:, :, 0:N] = u_kln[:, :, indices]
print "number of uncorrelated samples:"
print N_k
print ""
# Compute average enthalpies.
H_k = zeros([nstates], float64) # H_i[i] is estimated enthalpy of state i
dH_k = zeros([nstates], float64)
for k in range(nstates):
H_k[k] = u_kln[k, k, :].mean()
dH_k[k] = u_kln[k, k, :].std() / sqrt(N)
return (H_k, dH_k)
# Estimate the statistical inefficiency of the simulation by analyzing the timeseries of interest.
# We use the max of cos and sin of the phi and psi timeseries because they are periodic angles.
# The
print "Computing statistical inefficiencies..."
g_cosphi = timeseries.statisticalInefficiencyMultiple(numpy.cos(phi_kt_replica * numpy.pi / 180.0))
print "g_cos(phi) = %.1f" % g_cosphi
g_sinphi = timeseries.statisticalInefficiencyMultiple(numpy.sin(phi_kt_replica * numpy.pi / 180.0))
print "g_sin(phi) = %.1f" % g_sinphi
g_cospsi = timeseries.statisticalInefficiencyMultiple(numpy.cos(psi_kt_replica * numpy.pi / 180.0))
print "g_cos(psi) = %.1f" % g_cospsi
g_sinpsi = timeseries.statisticalInefficiencyMultiple(numpy.sin(psi_kt_replica * numpy.pi / 180.0))
print "g_sin(psi) = %.1f" % g_sinpsi
# Subsample data with maximum of all correlation times.
print "Subsampling data..."
g = numpy.max(numpy.array([g_cosphi, g_sinphi, g_cospsi, g_sinpsi]))
indices = timeseries.subsampleCorrelatedData(U_kt[k,:], g = g)
print "Using g = %.1f to obtain %d uncorrelated samples per temperature" % (g, len(indices))
N_max = int(numpy.ceil(T / g)) # max number of samples per temperature
U_kn = numpy.zeros([K, N_max], numpy.float64)
phi_kn = numpy.zeros([K, N_max], numpy.float64)
psi_kn = numpy.zeros([K, N_max], numpy.float64)
N_k = N_max * numpy.ones([K], numpy.int32)
for k in range(K):
U_kn[k,:] = U_kt[k,indices]
phi_kn[k,:] = phi_kt[k,indices]
psi_kn[k,:] = psi_kt[k,indices]
print "%d uncorrelated samples per temperature" % N_max
#===================================================================================================
# Generate a list of indices of all configurations in kn-indexing
#===================================================================================================
def estimate_enthalpies(ncfile, ndiscard = 0, nuse = None):
"""Estimate enthalpies of all alchemical states.
ARGUMENTS
ncfile (NetCDF) - input YANK netcdf file
OPTIONAL ARGUMENTS
ndiscard (int) - number of iterations to discard to equilibration
nuse (int) - number of iterations to use (after discarding)
TODO: Automatically determine 'ndiscard'.
TODO: Combine some functions with estimate_free_energies.
"""
# Get current dimensions.
niterations = ncfile.variables['energies'].shape[0]
nstates = ncfile.variables['energies'].shape[1]
natoms = ncfile.variables['energies'].shape[2]
# Extract energies.
print "Reading energies..."
energies = ncfile.variables['energies']
u_kln_replica = zeros([nstates, nstates, niterations], float64)
for n in range(niterations):
u_kln_replica[:,:,n] = energies[n,:,:]
print "Done."
# Deconvolute replicas
print "Deconvoluting replicas..."
u_kln = zeros([nstates, nstates, niterations], float64)
for iteration in range(niterations):
state_indices = ncfile.variables['states'][iteration,:]
u_kln[state_indices,:,iteration] = energies[iteration,:,:]
print "Done."
# Compute total negative log probability over all iterations.
u_n = zeros([niterations], float64)
for iteration in range(niterations):
u_n[iteration] = sum(diagonal(u_kln[:,:,iteration]))
#print u_n
# DEBUG
# outfile = open('u_n.out', 'w')
# for iteration in range(niterations):
# outfile.write("%8d %24.3f\n" % (iteration, u_n[iteration]))
# outfile.close()
# Discard initial data to equilibration.
u_kln_replica = u_kln_replica[:,:,ndiscard:]
u_kln = u_kln[:,:,ndiscard:]
u_n = u_n[ndiscard:]
# Truncate to number of specified conformations to use
if (nuse):
u_kln_replica = u_kln_replica[:,:,0:nuse]
u_kln = u_kln[:,:,0:nuse]
u_n = u_n[0:nuse]
# Subsample data to obtain uncorrelated samples
N_k = zeros(nstates, int32)
indices = timeseries.subsampleCorrelatedData(u_n) # indices of uncorrelated samples
#indices = range(0,u_n.size) # DEBUG - assume samples are uncorrelated
N = len(indices) # number of uncorrelated samples
N_k[:] = N
u_kln[:,:,0:N] = u_kln[:,:,indices]
print "number of uncorrelated samples:"
print N_k
print ""
# Compute average enthalpies.
H_k = zeros([nstates], float64) # H_i[i] is estimated enthalpy of state i
dH_k = zeros([nstates], float64)
for k in range(nstates):
H_k[k] = u_kln[k,k,:].mean()
dH_k[k] = u_kln[k,k,:].std() / sqrt(N)
return (H_k, dH_k)
def estimate_free_energies(ncfile, ndiscard = 0, nuse = None):
"""Estimate free energies of all alchemical states.
ARGUMENTS
ncfile (NetCDF) - input YANK netcdf file
OPTIONAL ARGUMENTS
ndiscard (int) - number of iterations to discard to equilibration
nuse (int) - maximum number of iterations to use (after discarding)
TODO: Automatically determine 'ndiscard'.
"""
# Get current dimensions.
niterations = ncfile.variables['energies'].shape[0]
nstates = ncfile.variables['energies'].shape[1]
natoms = ncfile.variables['energies'].shape[2]
# Extract energies.
print "Reading energies..."
energies = ncfile.variables['energies']
u_kln_replica = zeros([nstates, nstates, niterations], float64)
for n in range(niterations):
u_kln_replica[:,:,n] = energies[n,:,:]
print "Done."
# Deconvolute replicas
print "Deconvoluting replicas..."
u_kln = zeros([nstates, nstates, niterations], float64)
for iteration in range(niterations):
state_indices = ncfile.variables['states'][iteration,:]
u_kln[state_indices,:,iteration] = energies[iteration,:,:]
print "Done."
# Compute total negative log probability over all iterations.
u_n = zeros([niterations], float64)
for iteration in range(niterations):
u_n[iteration] = sum(diagonal(u_kln[:,:,iteration]))
#print u_n
# DEBUG
outfile = open('u_n.out', 'w')
for iteration in range(niterations):
outfile.write("%8d %24.3f\n" % (iteration, u_n[iteration]))
outfile.close()
# Discard initial data to equilibration.
u_kln_replica = u_kln_replica[:,:,ndiscard:]
u_kln = u_kln[:,:,ndiscard:]
u_n = u_n[ndiscard:]
# Truncate to number of specified conforamtions to use
if (nuse):
u_kln_replica = u_kln_replica[:,:,0:nuse]
u_kln = u_kln[:,:,0:nuse]
u_n = u_n[0:nuse]
# Subsample data to obtain uncorrelated samples
N_k = zeros(nstates, int32)
indices = timeseries.subsampleCorrelatedData(u_n) # indices of uncorrelated samples
#indices = range(0,u_n.size) # DEBUG - assume samples are uncorrelated
N = len(indices) # number of uncorrelated samples
N_k[:] = N
u_kln[:,:,0:N] = u_kln[:,:,indices]
print "number of uncorrelated samples:"
print N_k
print ""
#===================================================================================================
# Estimate free energy difference with MBAR.
#===================================================================================================
# Initialize MBAR (computing free energy estimates, which may take a while)
print "Computing free energy differences..."
mbar = MBAR(u_kln, N_k, verbose = False, method = 'adaptive', maximum_iterations = 50000) # use slow self-consistent-iteration (the default)
#mbar = MBAR(u_kln, N_k, verbose = False, method = 'self-consistent-iteration', maximum_iterations = 50000) # use slow self-consistent-iteration (the default)
#mbar = MBAR(u_kln, N_k, verbose = True, method = 'Newton-Raphson') # use faster Newton-Raphson solver
# Get matrix of dimensionless free energy differences and uncertainty estimate.
print "Computing covariance matrix..."
(Deltaf_ij, dDeltaf_ij) = mbar.getFreeEnergyDifferences(uncertainty_method='svd-ew')
# # Matrix of free energy differences
print "Deltaf_ij:"
for i in range(nstates):
for j in range(nstates):
print "%8.3f" % Deltaf_ij[i,j],
print ""
# print Deltaf_ij
# # Matrix of uncertainties in free energy difference (expectations standard deviations of the estimator about the true free energy)
print "dDeltaf_ij:"
for i in range(nstates):
for j in range(nstates):
print "%8.3f" % dDeltaf_ij[i,j],
print ""
# Return free energy differences and an estimate of the covariance.
return (Deltaf_ij, dDeltaf_ij)
#------------------------------------------------------------------------
# Read Data From File
#------------------------------------------------------------------------
print("")
print("Preparing data:")
T_from_file = read_simulation_temps(simulation,NumTemps)
E_from_file = read_total_energies(simulation,TE_COL_NUM)
K = len(T_from_file)
N_k = numpy.zeros(K,numpy.int32)
g = numpy.zeros(K,numpy.float64)
for k in range(K): # subsample the energies
g[k] = timeseries.statisticalInefficiency(E_from_file[k])
indices = numpy.array(timeseries.subsampleCorrelatedData(E_from_file[k],g=g[k])) # indices of uncorrelated samples
N_k[k] = len(indices) # number of uncorrelated samples
E_from_file[k,0:N_k[k]] = E_from_file[k,indices]
#------------------------------------------------------------------------
# Insert Intermediate T's and corresponding blank U's and E's
#------------------------------------------------------------------------
Temp_k = T_from_file
minT = T_from_file[0]
maxT = T_from_file[len(T_from_file) - 1]
#beta = 1/(k*BT)
#T = 1/(kB*beta)
if dtype == 'temperature':
minv = minT
maxv = maxT
elif dtype == 'beta': # actually going in the opposite direction as beta for logistical reasons
if len(fep_columns) > 0:
for i in range(len(fep_columns)):
reduced_fep_data.append(numpy.zeros([K,N_samples], numpy.float64))
for k in range(K):
# Extract timeseries.
A_t = biasing_variable_kt[0][k,:]
# Compute statistical inefficiency.
try:
g = timeseries.statisticalInefficiency(A_t)
except Exception as e:
print str(e)
print A_t
# Subsample data.
if subsample_trajectories:
indices = timeseries.subsampleCorrelatedData(A_t, g=g)
else:
indices = timeseries.subsampleCorrelatedData(A_t, g=1)
N = len(indices) # number of uncorrelated samples
print "k = %5d : g = %.1f, N = %d" % (k, g, N)
for i in range(nbiases):
biasing_variable_kn[i][k,0:N] = biasing_variable_kt[i][k,indices]
for i in range(nperturbations+1):
U_kn[i][k,0:N] = U_kt[i][k,indices]
if not cluster_binning:
pmf_variable_kn_1[k,0:N] = pmf_variable_kt_1[k,indices]
if ndim == 2:
pmf_variable_kn_2[k,0:N] = pmf_variable_kt_2[k,indices]
if cluster_binning:
cluster_bin_kn[k,0:N] = cluster_bin_kt[k,indices]
if len(expectation_columns) > 0:
for state in range(K):
# construct timeseries
Nstate = 0
for t in range(T):
if state_t[t] == state:
#u_t_singlestate[Nstate] = u_tk[t,state]
u_t_singlestate[Nstate] = u_t[t]
Nstate += 1
if Nstate > 0:
g_state = timeseries.statisticalInefficiency(
u_t_singlestate[0:Nstate], u_t_singlestate[0:Nstate])
print "state %5d : g = %16.8f, N = %6d" % (state, g_state, Nstate)
# Analyze timeseries to determine effectively uncorrelated snapshots.
indices = timeseries.subsampleCorrelatedData(
u_t) # indices of uncorrelated samples
N = len(indices) # number of uncorrelated samples
print "%d uncorrelated samples of %d snapshots." % (N, T)
# DEBUG: assume all samples are uncorrelated
# indices = range(0,T,20)
# for t in range(T):
# print "%8d %16.8f" % (t, u_t[t])
# N = len(indices)
# Count number of uncorrelated samples in each state.
N_k = zeros(K, int32)
for n in range(N):
t = indices[n]
state = state_t[t]
N_k[state] += 1
infile.close()
# Parse data.
n = 0
for line in lines:
if line[0] != '#' and line[0] != '@':
tokens = line.split()
u_kn[k,n] = beta_k[k] * (float(tokens[2]) - float(tokens[1])) # reduced potential energy without umbrella restraint
n += 1
# Compute correlation times for potential energy and chi
# timeseries. If the temperatures differ, use energies to determine samples; otherwise, use the cosine of chi
if (DifferentTemperatures):
g_k[k] = timeseries.statisticalInefficiency(u_kn[k,:], u_kn[k,0:N_k[k]])
print "Correlation time for set %5d is %10.3f" % (k,g_k[k])
indices = timeseries.subsampleCorrelatedData(u_kn[k,0:N_k[k]])
else:
chi_radians = chi_kn[k,0:N_k[k]]/(180.0/numpy.pi)
g_cos = timeseries.statisticalInefficiency(numpy.cos(chi_radians))
g_sin = timeseries.statisticalInefficiency(numpy.sin(chi_radians))
print "g_cos = %.1f | g_sin = %.1f" % (g_cos, g_sin)
g_k[k] = max(g_cos, g_sin)
print "Correlation time for set %5d is %10.3f" % (k,g_k[k])
indices = timeseries.subsampleCorrelatedData(chi_radians, g=g_k[k])
# Subsample data.
N_k[k] = len(indices)
u_kn[k,0:N_k[k]] = u_kn[k,indices]
chi_kn[k,0:N_k[k]] = chi_kn[k,indices]
N_max = numpy.max(N_k) # shorten the array size
u_kln = numpy.zeros([K,K,N_max], numpy.float64) # u_kln[k,l,n] is the reduced potential energy of snapshot n from umbrella simulation k evaluated at umbrella l
u_t_singlestate = zeros([T], float64)
for state in range(K):
# construct timeseries
Nstate = 0
for t in range(T):
if state_t[t] == state:
#u_t_singlestate[Nstate] = u_tk[t,state]
u_t_singlestate[Nstate] = u_t[t]
Nstate += 1
if Nstate > 0:
g_state = timeseries.statisticalInefficiency(u_t_singlestate[0:Nstate], u_t_singlestate[0:Nstate])
print "state %5d : g = %16.8f, N = %6d" % (state, g_state, Nstate)
# Analyze timeseries to determine effectively uncorrelated snapshots.
indices = timeseries.subsampleCorrelatedData(u_t) # indices of uncorrelated samples
N = len(indices) # number of uncorrelated samples
print "%d uncorrelated samples of %d snapshots." % (N, T)
# DEBUG: assume all samples are uncorrelated
# indices = range(0,T,20)
# for t in range(T):
# print "%8d %16.8f" % (t, u_t[t])
# N = len(indices)
# Count number of uncorrelated samples in each state.
N_k = zeros(K, int32)
for n in range(N):
t = indices[n]
state = state_t[t]
N_k[state] += 1
def main():
options = parse_args()
kB = 0.00831447/4.184 #Boltzmann constant (Gas constant) in kJ/(mol*K)
dT = 2.5 # Temperature increment for calculating Cv(T)
T = numpy.loadtxt(options.tfile)
print 'Initial temperature states are', T
K = len(T)
U_kn, Q_kn, N_max = read_data(options,T,K)
print 'Subsampling Q...'
N_k = numpy.zeros(K,numpy.int32)
g = numpy.zeros(K,numpy.float64)
for k in range(K): # subsample the energies
g[k] = timeseries.statisticalInefficiency(Q_kn[k])#,suppress_warning=True)
indices = numpy.array(timeseries.subsampleCorrelatedData(Q_kn[k],g=g[k])) # indices of uncorrelated samples
N_k[k] = len(indices) # number of uncorrelated samplesadsf
print '%i uncorrelated samples out of %i total samples' %(len(indices),options.N_max/options.skip)
U_kn[k,0:N_k[k]] = U_kn[k,indices]
Q_kn[k,0:N_k[k]] = Q_kn[k,indices]
insert = True
if insert:
#------------------------------------------------------------------------
# Insert Intermediate T's and corresponding blank U's and E's
#------------------------------------------------------------------------
# Set up variables
Temp_k = T
currentT = T[0] + dT
maxT = T[-1]
i = 1
print("--Inserting intermediate temperatures...")
# Loop, inserting T's at which we are interested in the properties
while (currentT < maxT) :
if (currentT < Temp_k[i]):
Temp_k = numpy.insert(Temp_k, i, currentT)
currentT = currentT + dT
else:
currentT = Temp_k[i] + dT
i = i + 1
# Update number of states
K = len(Temp_k)
print("--Inserting blank energies to match up with inserted temperatures...")
# Loop, inserting E's into blank matrix (leaving blanks only where new Ts are inserted)
Q_fromfile = Q_kn
Nall_k = numpy.zeros([K], numpy.int32) # Number of samples (n) for each state (k) = number of iterations/energies
E_kn = numpy.zeros([K, N_max], numpy.float64)
Q_kn = numpy.zeros([K, N_max], numpy.float64)
i = 0
for k in range(K):
if (Temp_k[k] == T[i]):
E_kn[k,0:N_k[i]] = U_kn[i,0:N_k[i]]
Q_kn[k,0:N_k[i]] = Q_fromfile[i,0:N_k[i]]
Nall_k[k] = N_k[i]
i = i + 1
else:
print 'Not inserting intermediate temperatures'
Temp_k = T
E_kn = U_kn
Nall_k = N_k
#------------------------------------------------------------------------
# Compute inverse temperatures
#------------------------------------------------------------------------
beta_k = 1 / (kB * Temp_k)
#------------------------------------------------------------------------
# Compute reduced potential energies
#------------------------------------------------------------------------
print "--Computing reduced energies..."
u_kln = numpy.zeros([K,K,N_max], numpy.float64) # u_kln is reduced pot. ener. of segment n of temp k evaluated at temp l
for k in range(K):
for l in range(K):
u_kln[k,l,0:Nall_k[k]] = beta_k[l] * E_kn[k,0:Nall_k[k]]
#------------------------------------------------------------------------
# Initialize MBAR
#------------------------------------------------------------------------
# Initialize MBAR with Newton-Raphson
print ""
print "Initializing MBAR:"
print "--K = number of Temperatures"
print "--L = number of Temperatures"
print "--N = number of Energies per Temperature"
# Use Adaptive Method (Both Newton-Raphson and Self-Consistent, testing which is better)
if insert:
mbar = pymbar.MBAR(u_kln, Nall_k, method = 'adaptive', verbose=True, relative_tolerance=1e-12)
else:
f_k = wham.histogram_wham(beta_k, U_kn, Nall_k, relative_tolerance = 1.0e-4)
mbar = pymbar.MBAR(u_kln, Nall_k, initial_f_k = f_k, verbose=True)
#------------------------------------------------------------------------
# Compute Expectations for E_kt and E2_kt as E_expect and E2_expect
#------------------------------------------------------------------------
print ""
print "Computing Expectations for E..."
(E_expect, dE_expect) = mbar.computeExpectations(u_kln)*(beta_k)**(-1)
print "Computing Expectations for E^2..."
(E2_expect,dE2_expect) = mbar.computeExpectations(u_kln*u_kln)*(beta_k)**(-2)
print "Computing Expectations for Q..."
(Q,dQ) = mbar.computeExpectations(Q_kn)
#------------------------------------------------------------------------
# Compute Cv for NVT simulations as <E^2> - <E>^2 / (RT^2)
#------------------------------------------------------------------------
#print ""
#print "Computing Heat Capacity as ( <E^2> - <E>^2 ) / ( R*T^2 )..."
Cv_expect = numpy.zeros([K], numpy.float64)
dCv_expect = numpy.zeros([K], numpy.float64)
for i in range(K):
Cv_expect[i] = (E2_expect[i] - (E_expect[i]*E_expect[i])) / ( kB * Temp_k[i] * Temp_k[i])
dCv_expect[i] = 2*dE_expect[i]**2 / (kB *Temp_k[i]*Temp_k[i]) # from propagation of error
#print "Temperature dA <E> +/- d<E> <E^2> +/- d<E^2> Cv +/- dCv"
#print "-------------------------------------------------------------------------------"
#for k in range(K):
# print "%8.3f %8.3f %9.3f +/- %5.3f %9.1f +/- %5.1f %7.4f +/- %6.4f" % (Temp_k[k],mbar.f_k[k],E_expect[k],dE_expect[k],E2_expect[k],dE2_expect[k],Cv_expect[k], dCv_expect[k])
#numpy.savetxt('/home/edz3fz/Qsurf_int.txt',Q)
#numpy.savetxt('/home/edz3fz/dQsurf_int.txt',dQ)
#numpy.savetxt('/home/edz3fz/dQsol.txt',dQ)
#numpy.savetxt('/home/edz3fz/Qtemp.tt',Temp_k)
import matplotlib.pyplot as plt
#ncavg = numpy.average(Q_fromfile, axis=1)
plt.figure(1)
#plt.plot(T, ncavg, 'ko')
plt.plot(Temp_k,Q,'k')
plt.errorbar(Temp_k, Q, yerr=dQ)
plt.xlabel('Temperature (K)')
plt.ylabel('Q fraction native contacts')
#plt.title('Heat Capacity from Go like model MC simulation of 1BSQ')
plt.savefig(options.direc+'/foldingcurve.png')
numpy.save(options.direc+'/foldingcurve',numpy.array([Temp_k, Q, dQ]))
numpy.save(options.direc+'/heatcap',numpy.array([Temp_k, Cv_expect, dCv_expect]))
if options.show:
plt.show()
] #file=['/home/edz3fz/checkensemble_high/CE_high.txt','/home/edz3fz/checkensemble_low/CE_low.txt'] #file=[direc+'/energy426.txt',direc+'/energy442.txt'] #file = ['/home/edz3fz/surface_replica_exchange/replica0/energy300.txt', '/home/edz3fz/surface_replica_exchange/replica3/energy356.txt'] down = load(files[0]) up = load(files[1]) length = len(down) down = down[length / 2::] up = up[length / 2::] #up=up[-50000::] #down=down[-50000::] #up=up[::100] #down=down[::100] g_up = timeseries.statisticalInefficiency(up) indices_up = numpy.array(timeseries.subsampleCorrelatedData(up, g=g_up)) print len(indices_up), 'samples' g_down = timeseries.statisticalInefficiency(down) indices_down = numpy.array(timeseries.subsampleCorrelatedData(up, g=g_down)) print len(indices_down), 'samples' type = 'total' U_kn = zeros([2, len(up)]) U_kn[0, 0:len(indices_down)] = down[indices_down] U_kn[1, 0:len(indices_up)] = up[indices_up] #T_k=array([300.,336.8472786]) #T_k=array([426.81933819,442.13650313]) #T_k=array([424.67492585,450]) #T_k=array([437.99897735,450]) N_k = [len(indices_up), len(indices_down)]
files = ['%s/energy%i.npy' % (direc, T[-2]), '%s/energy%i.npy' % (direc, T[-1])] #file=['/home/edz3fz/checkensemble_high/CE_high.txt','/home/edz3fz/checkensemble_low/CE_low.txt'] #file=[direc+'/energy426.txt',direc+'/energy442.txt'] #file = ['/home/edz3fz/surface_replica_exchange/replica0/energy300.txt', '/home/edz3fz/surface_replica_exchange/replica3/energy356.txt'] down=load(files[0]) up=load(files[1]) length = len(down) down = down[length/2::] up = up[length/2::] #up=up[-50000::] #down=down[-50000::] #up=up[::100] #down=down[::100] g_up = timeseries.statisticalInefficiency(up) indices_up = numpy.array(timeseries.subsampleCorrelatedData(up,g=g_up)) print len(indices_up), 'samples' g_down = timeseries.statisticalInefficiency(down) indices_down = numpy.array(timeseries.subsampleCorrelatedData(up,g=g_down)) print len(indices_down), 'samples' type='total' U_kn=zeros([2,len(up)]) U_kn[0,0:len(indices_down)] = down[indices_down] U_kn[1,0:len(indices_up)] = up[indices_up] #T_k=array([300.,336.8472786]) #T_k=array([426.81933819,442.13650313]) #T_k=array([424.67492585,450])
# Calculate Reduced Potential
if aur == 'o':
if rfc[k,0] == 0:
tmp=np.ones([R],np.float64)*0.001
u[0:N[k]] = np.sum(beta*tmp[0:R]*((val[0:N[k],k,0:R])**2), axis=1)
else:
u[0:N[k]] = np.sum(beta*rfc[k,0:R]*((val[0:N[k],k,0:R])**2), axis=1)
else:
if rfc[k,0] == 0:
tmp=np.ones([R],np.float64)*0.001
u[0:N[k]] = np.sum(beta*tmp[0:R]*((val[0:N[k],k,0:R]-req[k,0:R])**2), axis=1)
else:
u[0:N[k]] = np.sum(beta*rfc[k,0:R]*((val[0:N[k],k,0:R]-req[k,0:R])**2), axis=1)
g[k] = calcg(u[0:N[k]])
subs = timeseries.subsampleCorrelatedData(np.zeros([N[k]]),g=g[k])
Nind[k] = len(subs)
if Nind[k] > 100000:
Neff[k] = 100000
else:
Neff[k] = Nind[k]
print "Processed Window %5.0f. N= %12.0f. g= %10.3f Nind= %12.0f Neff= %12.0f" % ( k, N[k], g[k], Nind[k], Neff[k] )
print "Max Neff= %.0f" % ( np.max(Neff) )
Upot = np.zeros([K,K,np.max(Neff)], np.float64)
# Calculate Restraint Energy
for k in range(K):
subs = timeseries.subsampleCorrelatedData(np.zeros([N[k]]),g=g[k])
def _subsample_kln(self, u_kln):
#Try to load in the data
if self.save_equil_data: #Check if we want to save/load equilibration data
try:
equil_data = numpy.load(os.path.join(self.source_directory, self.save_prefix + self.phase + '_equil_data_%s.npz' % self.subsample_method))
if self.nequil is None:
self.nequil = equil_data['nequil']
elif type(self.nequil) is int and self.subsample_method == 'per-state':
print "WARRNING: Per-state subsampling requested with only single value for equilibration..."
try:
self.nequil = equil_data['nequil']
print "Loading equilibration from file with %i states read" % self.nstates
except:
print "Assuming equal equilibration per state of %i" % self.nequil
self.nequil = numpy.array([self.nequil] * self.nstates)
self.g_t = equil_data['g_t']
Neff_max = equil_data['Neff_max']
#Do equilibration if we have not already
if self.subsample_method == 'per-state' and (len(self.g_t) < self.nstates or len(self.nequil) < self.nstates):
equil_loaded = False
raise IndexError
else:
equil_loaded = True
except:
if self.subsample_method == 'per-state':
self.nequil = numpy.zeros([self.nstates], dtype=numpy.int32)
self.g_t = numpy.zeros([self.nstates])
Neff_max = numpy.zeros([self.nstates])
for k in xrange(self.nstates):
if self.verbose: print "Computing timeseries for state %i/%i" % (k,self.nstates-1)
self.nequil[k] = 0
self.g_t[k] = timeseries.statisticalInefficiency(u_kln[k,k,:])
Neff_max[k] = (u_kln[k,k,:].size + 1 ) / self.g_t[k]
#[self.nequil[k], self.g_t[k], Neff_max[k]] = self._detect_equilibration(u_kln[k,k,:])
else:
if self.nequil is None:
[self.nequil, self.g_t, Neff_max] = self._detect_equilibration(self.u_n)
else:
[self.nequil_timeseries, self.g_t, Neff_max] = self._detect_equilibration(self.u_n)
equil_loaded = False
if not equil_loaded:
numpy.savez(os.path.join(self.source_directory, self.save_prefix + self.phase + '_equil_data_%s.npz' % self.subsample_method), nequil=self.nequil, g_t=self.g_t, Neff_max=Neff_max)
elif self.nequil is None:
if self.subsample_method == 'per-state':
self.nequil = numpy.zeros([self.nstates], dtype=numpy.int32)
self.g_t = numpy.zeros([self.nstates])
Neff_max = numpy.zeros([self.nstates])
for k in xrange(self.nstates):
[self.nequil[k], self.g_t[k], Neff_max[k]] = self._detect_equilibration(u_kln[k,k,:])
if self.verbose: print "State %i equilibrated with %i samples" % (k, int(Neff_max[k]))
else:
[self.nequil, self.g_t, Neff_max] = self._detect_equilibration(self.u_n)
if self.verbose: print [self.nequil, Neff_max]
# 1) Discard equilibration data
# 2) Subsample data to obtain uncorrelated samples
self.N_k = numpy.zeros(self.nstates, numpy.int32)
if self.subsample_method == 'per-state':
# Discard samples
nsamples_equil = self.niterations - self.nequil
self.u_kln = numpy.zeros([self.nstates,self.nstates,nsamples_equil.max()])
for k in xrange(self.nstates):
self.u_kln[k,:,:nsamples_equil[k]] = u_kln[k,:,self.nequil[k]:]
#Subsample
transfer_retained_indices = numpy.zeros([self.nstates,nsamples_equil.max()], dtype=numpy.int32)
for k in xrange(self.nstates):
state_indices = timeseries.subsampleCorrelatedData(self.u_kln[k,k,:], g = self.g_t[k])
self.N_k[k] = len(state_indices)
transfer_retained_indices[k,:self.N_k[k]] = state_indices
transfer_kln = numpy.zeros([self.nstates, self.nstates, self.N_k.max()])
self.retained_indices = numpy.zeros([self.nstates,self.N_k.max()], dtype=numpy.int32)
for k in xrange(self.nstates):
self.retained_indices[k,:self.N_k[k]] = transfer_retained_indices[k,:self.N_k[k]] #Memory reduction
transfer_kln[k,:,:self.N_k[k]] = self.u_kln[k,:,self.retained_indices[k,:self.N_k[k]]].T #Have to transpose since indexing in this way causes issues
#Cut down on memory, once function is done, transfer_kln should be released
self.u_kln = transfer_kln
self.retained_iters = self.N_k
else:
#Discard Samples
self.u_kln = u_kln[:,:,self.nequil:]
self.u_n = self.u_n[self.nequil:]
#Subsamples
indices = timeseries.subsampleCorrelatedData(self.u_n, g=self.g_t) # indices of uncorrelated samples
self.u_kln = self.u_kln[:,:,indices]
self.N_k[:] = len(indices)
self.retained_indices = indices
self.retained_iters = len(indices)
return
def estimate_free_energies(ncfile, ndiscard=0, nuse=None):
"""Estimate free energies of all alchemical states.
ARGUMENTS
ncfile (NetCDF) - input YANK netcdf file
OPTIONAL ARGUMENTS
ndiscard (int) - number of iterations to discard to equilibration
nuse (int) - maximum number of iterations to use (after discarding)
TODO: Automatically determine 'ndiscard'.
"""
# Get current dimensions.
niterations = ncfile.variables['energies'].shape[0]
nstates = ncfile.variables['energies'].shape[1]
natoms = ncfile.variables['energies'].shape[2]
# Extract energies.
print "Reading energies..."
energies = ncfile.variables['energies']
u_kln_replica = zeros([nstates, nstates, niterations], float64)
for n in range(niterations):
u_kln_replica[:, :, n] = energies[n, :, :]
print "Done."
# Deconvolute replicas
print "Deconvoluting replicas..."
u_kln = zeros([nstates, nstates, niterations], float64)
for iteration in range(niterations):
state_indices = ncfile.variables['states'][iteration, :]
u_kln[state_indices, :, iteration] = energies[iteration, :, :]
print "Done."
# Compute total negative log probability over all iterations.
u_n = zeros([niterations], float64)
for iteration in range(niterations):
u_n[iteration] = sum(diagonal(u_kln[:, :, iteration]))
#print u_n
# DEBUG
outfile = open('u_n.out', 'w')
for iteration in range(niterations):
outfile.write("%8d %24.3f\n" % (iteration, u_n[iteration]))
outfile.close()
# Discard initial data to equilibration.
u_kln_replica = u_kln_replica[:, :, ndiscard:]
u_kln = u_kln[:, :, ndiscard:]
u_n = u_n[ndiscard:]
# Truncate to number of specified conforamtions to use
if (nuse):
u_kln_replica = u_kln_replica[:, :, 0:nuse]
u_kln = u_kln[:, :, 0:nuse]
u_n = u_n[0:nuse]
# Subsample data to obtain uncorrelated samples
N_k = zeros(nstates, int32)
indices = timeseries.subsampleCorrelatedData(
u_n) # indices of uncorrelated samples
#indices = range(0,u_n.size) # DEBUG - assume samples are uncorrelated
N = len(indices) # number of uncorrelated samples
N_k[:] = N
u_kln[:, :, 0:N] = u_kln[:, :, indices]
print "number of uncorrelated samples:"
print N_k
print ""
#===================================================================================================
# Estimate free energy difference with MBAR.
#===================================================================================================
# Initialize MBAR (computing free energy estimates, which may take a while)
print "Computing free energy differences..."
mbar = MBAR(u_kln,
N_k,
verbose=False,
method='adaptive',
maximum_iterations=50000
) # use slow self-consistent-iteration (the default)
#mbar = MBAR(u_kln, N_k, verbose = False, method = 'self-consistent-iteration', maximum_iterations = 50000) # use slow self-consistent-iteration (the default)
#mbar = MBAR(u_kln, N_k, verbose = True, method = 'Newton-Raphson') # use faster Newton-Raphson solver
# Get matrix of dimensionless free energy differences and uncertainty estimate.
print "Computing covariance matrix..."
(Deltaf_ij,
dDeltaf_ij) = mbar.getFreeEnergyDifferences(uncertainty_method='svd-ew')
# # Matrix of free energy differences
print "Deltaf_ij:"
for i in range(nstates):
for j in range(nstates):
print "%8.3f" % Deltaf_ij[i, j],
print ""
# print Deltaf_ij
# # Matrix of uncertainties in free energy difference (expectations standard deviations of the estimator about the true free energy)
print "dDeltaf_ij:"
for i in range(nstates):
for j in range(nstates):
print "%8.3f" % dDeltaf_ij[i, j],
print ""
# Return free energy differences and an estimate of the covariance.
return (Deltaf_ij, dDeltaf_ij)
def call_mbar(aur, temp, phase):
### Arguments
aur = aur # t or u or r or d
temp = float(temp) # temp
kB = 1.381e-23 * 6.022e23 / (4.184 * 1000.0
) # Boltzmann constant in kJ/mol/K
beta = 1 / (kB * temp) # beta
N_max = 2000000 # Max frames for any simulation window, you should check this if you did some long runs
sys.stdout = open('subs-' + aur + '.log', 'w')
### Determine Number of umbrellas
K = 0
filename = './' + aur + '%02.0f/restraints.dat' % K
while os.path.isfile(filename):
K = K + 1
filename = './' + aur + '%02.0f/restraints.dat' % K
R = 1
print "K= %5.0f R= %5.0f" % (K, R)
### Allocate storage for simulation data
N = np.zeros(
[K], np.int32
) # N_k[k] is the number of snapshots to be used from umbrella simulation k
Neff = np.zeros([K], np.int32)
Nind = np.zeros([K], np.int32)
Nprg = np.zeros([K], np.int32)
rty = ['d'] * R # restraint type (distance or angle)
rfc = np.zeros([K, R], np.float64) # restraint force constant
rfc2 = np.zeros([K, R], np.float64) # restraint force constant
fcmax = np.zeros(
[R], np.float64
) # full force constant value used during umbrella portion of work
req = np.zeros([K, R], np.float64) # restraint target value
req2 = np.zeros([K, R], np.float64) # restraint target value
val = np.zeros(
[N_max, K, R],
np.float64) # value of the restrained variable at each frame n
val2 = np.zeros(
[N_max, K, R],
np.float64) # value of the restrained variable at each frame n
g = np.zeros([K], np.float64)
### Tmp type arrays for energy and spline fitting/integration
u = np.zeros([N_max], np.float64)
x = np.zeros([K], np.float64)
y = np.zeros([K], np.float64)
m = np.zeros([K], np.float64)
s = np.zeros([K], np.float64)
print "Done with array setup\n"
### Read the simulation data
r = 0
for k in range(K):
# Read Equilibrium Value and Force Constant
if aur == 't':
with open('./' + aur + '%02.0f/colvar.in' % k, 'r') as f:
for line in f:
if 'posit2' in line:
for line in f:
cols = line.split()
if len(cols) != 0 and (cols[0] == "centers"):
req[k, r] = float(cols[1])
if len(cols) != 0 and (cols[0] == "forceConstant"):
rfc[k, r] = float(cols[1]) / 2
break
if 'posit3' in line:
for line in f:
cols = line.split()
if len(cols) != 0 and (cols[0] == "centers"):
req2[k, r] = float(cols[1])
if len(cols) != 0 and (cols[0] == "forceConstant"):
rfc2[k, r] = float(cols[1]) / 2
break
elif aur == 'o':
with open('./' + aur + '%02.0f/colvar.in' % k, 'r') as f:
for line in f:
if 'orient2' in line:
for line in f:
cols = line.split()
if len(cols) != 0 and (cols[0] == "centers"):
str = cols[1][1:-1]
req[k, r] = float(str)
if len(cols) != 0 and (cols[0] == "forceConstant"):
rfc[k, r] = float(cols[1]) / 2
break
elif aur == 'r' or aur == 'l':
with open('./' + aur + '%02.0f/colvar.in' % k, 'r') as f:
for line in f:
if 'rmsd2' in line:
for line in f:
cols = line.split()
if len(cols) != 0 and (cols[0] == "centers"):
req[k, r] = float(cols[1])
if len(cols) != 0 and (cols[0] == "forceConstant"):
rfc[k, r] = float(cols[1]) / 2
break
elif aur == 'p' or aur == 'b':
with open('./' + aur + '%02.0f/colvar.in' % k, 'r') as f:
for line in f:
if 'rmsd1' in line:
for line in f:
cols = line.split()
if len(cols) != 0 and (cols[0] == "centers"):
req[k, r] = float(cols[1])
if len(cols) != 0 and (cols[0] == "forceConstant"):
rfc[k, r] = float(cols[1]) / 2
break
elif aur == 'u':
with open('./' + aur + '%02.0f/colvar.in' % k, 'r') as f:
for line in f:
if 'posit3' in line:
for line in f:
cols = line.split()
if len(cols) != 0 and (cols[0] == "centers"):
req[k, r] = float(cols[1])
if len(cols) != 0 and (cols[0] == "forceConstant"):
rfc[k, r] = float(cols[1]) / 2
break
else:
sys.exit("not sure about restraint type!")
# Read in Values for restrained variables for each simulation
filename = './' + aur + '%02.0f/restraints.dat' % k
infile = open(filename, 'r')
restdat = infile.readlines(
) # slice off first 20 lines readlines()[20:]
infile.close()
# Parse Data
n = 0
s = 0
from_line = 0
if int(phase) == 0:
from_line = 500
for line in restdat:
s += 1 #so ira analizar o arquivo a partir da linha 500!
if line[0] != '#' and line[0] != '@' and s > from_line:
cols = line.split()
if aur == 'o':
val[n, k, r] = math.acos(float(cols[2]))
elif aur == 'u' or aur == 'l':
val[n, k, r] = float(cols[2])
elif aur == 't':
val[n, k, r] = float(cols[1])
val2[n, k, r] = float(cols[2])
else:
val[n, k, r] = float(cols[1])
n += 1
N[k] = n
# Calculate Reduced Potential
if aur == 'o':
if rfc[k, 0] == 0:
tmp = np.ones([R], np.float64) * 0.001
u[0:N[k]] = np.sum(beta * tmp[0:R] *
((val[0:N[k], k, 0:R])**2),
axis=1) #->slicing syntax [0:N[k]]
else:
u[0:N[k]] = np.sum(beta * rfc[k, 0:R] *
((val[0:N[k], k, 0:R])**2),
axis=1)
elif aur == 't':
if rfc[k, 0] == 0 and rfc2[k, 0] != 0:
tmp = np.ones([R], np.float64) * 0.001
u[0:N[k]] = np.sum(beta * (tmp[0:R] * ((
(val[0:N[k], k, 0:R] - req[k, 0:R])**2)) + rfc2[k, 0:R] * (
(val2[0:N[k], k, 0:R] - req2[k, 0:R])**2)),
axis=1) #-> (1/k_bT)*Kx**2
elif rfc[k, 0] != 0 and rfc2[k, 0] == 0:
tmp = np.ones([R], np.float64) * 0.001
u[0:N[k]] = np.sum(
beta *
(rfc[k, 0:R] *
(((val[0:N[k], k, 0:R] - req[k, 0:R])**2)) + tmp[0:R] *
((val2[0:N[k], k, 0:R] - req2[k, 0:R])**2)),
axis=1)
elif rfc[k, 0] == 0 and rfc2[k, 0] == 0:
tmp = np.ones([R], np.float64) * 0.001
u[0:N[k]] = np.sum(
beta *
(tmp[0:R] *
(((val[0:N[k], k, 0:R] - req[k, 0:R])**2)) + tmp[0:R] *
((val2[0:N[k], k, 0:R] - req2[k, 0:R])**2)),
axis=1)
else:
u[0:N[k]] = np.sum(beta * (rfc[k, 0:R] * ((
(val[0:N[k], k, 0:R] - req[k, 0:R])**2)) + rfc2[k, 0:R] * (
(val2[0:N[k], k, 0:R] - req2[k, 0:R])**2)),
axis=1)
else:
if rfc[k, 0] == 0:
tmp = np.ones([R], np.float64) * 0.001
u[0:N[k]] = np.sum(beta * tmp[0:R] *
((val[0:N[k], k, 0:R] - req[k, 0:R])**2),
axis=1) #-> (1/k_bT)*Kx**2
else:
u[0:N[k]] = np.sum(beta * rfc[k, 0:R] *
((val[0:N[k], k, 0:R] - req[k, 0:R])**2),
axis=1)
g[k] = calcg(u[0:N[k]])
subs = timeseries.subsampleCorrelatedData(np.zeros([N[k]]), g=g[k])
Nind[k] = len(subs)
if Nind[k] > 100000:
Neff[k] = 100000
else:
Neff[k] = Nind[k]
print "Processed Window %5.0f. N= %12.0f. g= %10.3f Nind= %12.0f Neff= %12.0f" % (
k, N[k], g[k], Nind[k], Neff[k])
print "Max Neff= %.0f" % (np.max(Neff))
Upot = np.zeros([K, K, np.max(Neff)], np.float64)
# Calculate Restraint Energy
for k in range(K):
# subs = timeseries.subsampleCorrelatedData(np.zeros([N[k]]),g=g[k])
for l in range(K):
if aur == 'o':
Upot[k, l, 0:Neff[k]] = np.sum(beta * rfc[l, 0:R] *
((val[0:Neff[k], k, 0:R])**2),
axis=1)
elif aur == 't':
Upot[k, l, 0:Neff[k]] = np.sum(
beta * (rfc[l, 0:R] *
((val[0:Neff[k], k, 0:R] - req[l, 0:R])**2) +
rfc2[l, 0:R] *
((val2[0:Neff[k], k, 0:R] - req2[l, 0:R])**2)),
axis=1)
else:
Upot[k, l, 0:Neff[k]] = np.sum(
beta * rfc[l, 0:R] *
((val[0:Neff[k], k, 0:R] - req[l, 0:R])**2),
axis=1)
val = []
prg = [100]
for p in range(len(prg)):
Nprg = Neff * prg[p] / 100 ## Test integers out only
print "Running MBAR on %.0f percent of the data ... " % (prg[p])
mbar = pymbar.MBAR(Upot,
Nprg,
verbose=True,
method='adaptive',
initialize='BAR')
print "Calculate Free Energy Differences Between States"
[Deltaf, dDeltaf] = mbar.getFreeEnergyDifferences()
min = np.argmin(Deltaf[0])
# Write to file
print "Free Energy Differences (in units of kcal/mol)"
print "%9s %8s %8s %12s %12s" % ('bin', 'f', 'df', 'deq', 'dfc')
datfile = open('subs-' + aur + '.%03.0f.dat' % prg[p], 'w')
for k in range(K):
if aur == 'r' or aur == 'o' or aur == 'p' or aur == 'b' or aur == 'l':
print "%10.5f %10.5f %10.5f %12.7f %12.7f" % (
rfc[k, 0] / rfc[-1, 0], Deltaf[0, k] / beta,
dDeltaf[0, k] / beta, req[k, 0], rfc[k, 0])
datfile.write("%10.5f %10.5f %10.5f %12.7f %12.7f\n" %
(rfc[k, 0] / rfc[-1, 0], Deltaf[0, k] / beta,
dDeltaf[0, k] / beta, req[k, 0], rfc[k, 0]))
elif aur == 't':
print "%10.5f %10.5f %10.5f %12.7f %12.7f %12.7f %12.7f" % (
rfc[k, 0] / rfc[-1, 0], Deltaf[0, k] / beta, dDeltaf[0, k]
/ beta, req[k, 0], req2[k, 0], rfc[k, 0], rfc2[k, 0])
datfile.write("%10.5f %10.5f %10.5f %12.7f %12.7f\n" %
(rfc[k, 0] / rfc[-1, 0], Deltaf[0, k] / beta,
dDeltaf[0, k] / beta, req[k, 0], rfc[k, 0]))
elif aur == 'd':
print "%9.0f %10.5f %10.5f %12.7f %12.7f" % (
k, Deltaf[0, k] / beta, dDeltaf[0, k] / beta, req[k, 0],
rfc[k, 0] / rfc[-1, 0])
datfile.write("%9.0f %10.5f %10.5f %12.7f %12.7f\n" %
(k, Deltaf[0, k] / beta, dDeltaf[0, k] / beta,
req[k, 0], rfc[k, 0] / rfc[-1, 0]))
else: # 'u'
print "%10.5f %10.5f %10.5f %12.7f %12.7f" % (
req[k, 0], Deltaf[0, k] / beta, dDeltaf[0, k] / beta,
req[k, 0], rfc[k, 0])
datfile.write("%10.5f %10.5f %10.5f %12.7f %12.7f\n" %
(req[k, 0], Deltaf[0, k] / beta,
dDeltaf[0, k] / beta, req[k, 0], rfc[k, 0]))
datfile.close()
print "\n\n"
for iteration in range(niterations):
state_indices = ncfile.variables['states'][iteration,:]
u_kln[state_indices,:,iteration] = ncfile.variables['energies'][iteration,:,:]
ncfile.close()
# Extract log probability history.
u_n = numpy.zeros([niterations], numpy.float64)
for iteration in range(niterations):
u_n[iteration] = 0.0
for state in range(nstates):
u_n[iteration] += u_kln[state,state,iteration]
# Detect equilibration.
[nequil, g, Neff] = detect_equilibration(u_n)
u_n = u_n[nequil:]
u_kln = u_kln[:,:,nequil:]
# Subsample data.
indices = timeseries.subsampleCorrelatedData(u_n, g=g)
u_n = u_n[indices]
u_kln = u_kln[:,:,indices]
N_k = len(indices) * numpy.ones([nstates], numpy.int32)
# Analyze with MBAR.
mbar = pymbar.MBAR(u_kln, N_k)
[Delta_f_ij, dDelta_f_ij] = mbar.getFreeEnergyDifferences()
# Compare with analytical.
f_i_analytical = numpy.zeros([nstates], numpy.float64)
for (state_index, state) in enumerate(simulation.states):
values = computeHarmonicOscillatorExpectations(K, mass, state.temperature)
f_i_analytical[state_index] = values['free energies']['potential']
Delta_f_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]
def analyze_data(store_filename, phipsi_outfile=None):
"""
Analyze output from parallel tempering simulations.
"""
temperature = 300.0 * units.kelvin # temperature
ndiscard = 100 # number of samples to discard to equilibration
# Allocate storage for results.
results = dict()
# Compute kappa
nbins = 10
kB = units.BOLTZMANN_CONSTANT_kB * units.AVOGADRO_CONSTANT_NA # Boltzmann constant
kT = (kB * temperature) # thermal energy
beta = 1.0 / kT # inverse temperature
delta = 360.0 / float(nbins) * units.degrees # bin spacing
sigma = delta/3.0 # standard deviation
kappa = (sigma / units.radians)**(-2) # kappa parameter (unitless)
# Open NetCDF file.
ncfile = netcdf.Dataset(store_filename, 'r', version=2)
# Get dimensions.
[niterations, nstates, natoms, ndim] = ncfile.variables['positions'][:,:,:,:].shape
print "%d iterations, %d states, %d atoms" % (niterations, nstates, natoms)
# Discard initial configurations to equilibration.
print "First %d iterations will be discarded to equilibration." % ndiscard
niterations -= ndiscard
# Print summary statistics about mixing in state space.
[tau2, dtau2] = show_mixing_statistics_with_error(ncfile)
# Compute correlation time of state index.
states = ncfile.variables['states'][:,:].copy()
A_kn = [ states[:,k].copy() for k in range(nstates) ]
g_states = timeseries.statisticalInefficiencyMultiple(A_kn)
tau_states = (g_states-1.0)/2.0
# Compute statistical error.
nblocks = 10
blocksize = int(niterations) / int(nblocks)
g_states_i = numpy.zeros([nblocks], numpy.float64)
tau_states_i = numpy.zeros([nblocks], numpy.float64)
for block_index in range(nblocks):
# Extract block
states = ncfile.variables['states'][(blocksize*block_index):(blocksize*(block_index+1)),:].copy()
A_kn = [ states[:,k].copy() for k in range(nstates) ]
g_states_i[block_index] = timeseries.statisticalInefficiencyMultiple(A_kn)
tau_states_i[block_index] = (g_states_i[block_index]-1.0)/2.0
dg_states = g_states_i.std() / numpy.sqrt(float(nblocks))
dtau_states = tau_states_i.std() / numpy.sqrt(float(nblocks))
# Print.
print "g_states = %.3f+-%.3f iterations" % (g_states, dg_states)
print "tau_states = %.3f+-%.3f iterations" % (tau_states, dtau_states)
del states, A_kn
# Compute end-to-end time.
states = ncfile.variables['states'][:,:].copy()
[tau_end, dtau_end] = average_end_to_end_time(states)
# Compute statistical inefficiency for reduced potential
energies = ncfile.variables['energies'][ndiscard:,:,:].copy()
states = ncfile.variables['states'][ndiscard:,:].copy()
u_n = numpy.zeros([niterations], numpy.float64)
for iteration in range(niterations):
u_n[iteration] = 0.0
for replica in range(nstates):
state = states[iteration,replica]
u_n[iteration] += energies[iteration,replica,state]
del energies, states
g_u = timeseries.statisticalInefficiency(u_n)
print "g_u = %8.1f iterations" % g_u
# Compute x and y umbrellas.
print "Computing torsions..."
positions = ncfile.variables['positions'][ndiscard:,:,:,:]
coordinates = units.Quantity(numpy.zeros([natoms,ndim], numpy.float32), units.angstroms)
phi_it = units.Quantity(numpy.zeros([nstates,niterations], numpy.float32), units.radians)
psi_it = units.Quantity(numpy.zeros([nstates,niterations], numpy.float32), units.radians)
for iteration in range(niterations):
for replica in range(nstates):
coordinates[:,:] = units.Quantity(positions[iteration,replica,:,:].copy(), units.angstroms)
phi_it[replica,iteration] = compute_torsion(coordinates, 4, 6, 8, 14)
psi_it[replica,iteration] = compute_torsion(coordinates, 6, 8, 14, 16)
# Run MBAR.
print "Grouping torsions by state..."
phi_state_it = numpy.zeros([nstates,niterations], numpy.float32)
psi_state_it = numpy.zeros([nstates,niterations], numpy.float32)
states = ncfile.variables['states'][ndiscard:,:].copy()
for iteration in range(niterations):
replicas = numpy.argsort(states[iteration,:])
for state in range(1,nstates):
replica = replicas[state]
phi_state_it[state,iteration] = phi_it[replica,iteration] / units.radians
psi_state_it[state,iteration] = psi_it[replica,iteration] / units.radians
print "Evaluating reduced potential energies..."
N_k = numpy.ones([nstates], numpy.int32) * niterations
u_kln = numpy.zeros([nstates, nstates, niterations], numpy.float32)
for l in range(1,nstates):
phi0 = ((numpy.floor((l-1)/nbins) + 0.5) * delta - 180.0 * units.degrees) / units.radians
psi0 = ((numpy.remainder((l-1), nbins) + 0.5) * delta - 180.0 * units.degrees) / units.radians
u_kln[:,l,:] = - kappa * numpy.cos(phi_state_it[:,:] - phi0) - kappa * numpy.cos(psi_state_it[:,:] - psi0)
# print "Running MBAR..."
# #mbar = pymbar.MBAR(u_kln, N_k, verbose=True, method='self-consistent-iteration')
# mbar = pymbar.MBAR(u_kln[1:,1:,:], N_k[1:], verbose=True, method='adaptive', relative_tolerance=1.0e-2) # only use biased samples
# f_k = mbar.f_k
# mbar = pymbar.MBAR(u_kln[1:,1:,:], N_k[1:], verbose=True, method='Newton-Raphson', initial_f_k=f_k) # only use biased samples
# #mbar = pymbar.MBAR(u_kln, N_k, verbose=True, method='Newton-Raphson', initialize='BAR')
# print "Getting free energy differences..."
# [df_ij, ddf_ij] = mbar.getFreeEnergyDifferences(uncertainty_method='svd-ew')
# print df_ij
# print ddf_ij
# print "ln(Z_ij / Z_55):"
# reference_bin = 4*nbins+4
# for psi_index in range(nbins):
# print " [,%2d]" % (psi_index+1),
# print ""
# for phi_index in range(nbins):
# print "[%2d,]" % (phi_index+1),
# for psi_index in range(nbins):
# print "%8.3f" % (-df_ij[reference_bin, phi_index*nbins+psi_index]),
# print ""
# print ""
# print "dln(Z_ij / Z_55):"
# reference_bin = 4*nbins+4
# for psi_index in range(nbins):
# print " [,%2d]" % (psi_index+1),
# print ""
# for phi_index in range(nbins):
# print "[%2d,]" % (phi_index+1),
# for psi_index in range(nbins):
# print "%8.3f" % (ddf_ij[reference_bin, phi_index*nbins+psi_index]),
# print ""
# print ""
# Compute statistical inefficiencies of various functions of the timeseries data.
print "Computing statistical infficiencies of cos(phi), sin(phi), cos(psi), sin(psi)..."
cosphi_kn = [ numpy.cos(phi_it[replica,:] / units.radians).copy() for replica in range(1,nstates) ]
sinphi_kn = [ numpy.sin(phi_it[replica,:] / units.radians).copy() for replica in range(1,nstates) ]
cospsi_kn = [ numpy.cos(psi_it[replica,:] / units.radians).copy() for replica in range(1,nstates) ]
sinpsi_kn = [ numpy.sin(psi_it[replica,:] / units.radians).copy() for replica in range(1,nstates) ]
g_cosphi = timeseries.statisticalInefficiencyMultiple(cosphi_kn)
g_sinphi = timeseries.statisticalInefficiencyMultiple(sinphi_kn)
g_cospsi = timeseries.statisticalInefficiencyMultiple(cospsi_kn)
g_sinpsi = timeseries.statisticalInefficiencyMultiple(sinpsi_kn)
tau_cosphi = (g_cosphi-1.0)/2.0
tau_sinphi = (g_sinphi-1.0)/2.0
tau_cospsi = (g_cospsi-1.0)/2.0
tau_sinpsi = (g_sinpsi-1.0)/2.0
# Compute relaxation times in each torsion.
print "Relaxation times for transitions among phi or psi bins alone:"
phibin_it = ((phi_it + 180.0 * units.degrees) / (delta + 0.1*units.degrees)).astype(numpy.int16)
tau_phi = compute_relaxation_time(phibin_it, nbins)
psibin_it = ((psi_it + 180.0 * units.degrees) / (delta + 0.1*units.degrees)).astype(numpy.int16)
tau_psi = compute_relaxation_time(psibin_it, nbins)
print "tau_phi = %8.1f iteration" % tau_phi
print "tau_psi = %8.1f iteration" % tau_psi
# Compute statistical error.
nblocks = 10
blocksize = int(niterations) / int(nblocks)
g_cosphi_i = numpy.zeros([nblocks], numpy.float64)
g_sinphi_i = numpy.zeros([nblocks], numpy.float64)
g_cospsi_i = numpy.zeros([nblocks], numpy.float64)
g_sinpsi_i = numpy.zeros([nblocks], numpy.float64)
tau_cosphi_i = numpy.zeros([nblocks], numpy.float64)
tau_sinphi_i = numpy.zeros([nblocks], numpy.float64)
tau_cospsi_i = numpy.zeros([nblocks], numpy.float64)
tau_sinpsi_i = numpy.zeros([nblocks], numpy.float64)
for block_index in range(nblocks):
# Extract block
slice_indices = range(blocksize*block_index,blocksize*(block_index+1))
cosphi_kn = [ numpy.cos(phi_it[replica,slice_indices] / units.radians).copy() for replica in range(1,nstates) ]
sinphi_kn = [ numpy.sin(phi_it[replica,slice_indices] / units.radians).copy() for replica in range(1,nstates) ]
cospsi_kn = [ numpy.cos(psi_it[replica,slice_indices] / units.radians).copy() for replica in range(1,nstates) ]
sinpsi_kn = [ numpy.sin(psi_it[replica,slice_indices] / units.radians).copy() for replica in range(1,nstates) ]
g_cosphi_i[block_index] = timeseries.statisticalInefficiencyMultiple(cosphi_kn)
g_sinphi_i[block_index] = timeseries.statisticalInefficiencyMultiple(sinphi_kn)
g_cospsi_i[block_index] = timeseries.statisticalInefficiencyMultiple(cospsi_kn)
g_sinpsi_i[block_index] = timeseries.statisticalInefficiencyMultiple(sinpsi_kn)
tau_cosphi_i[block_index] = (g_cosphi_i[block_index]-1.0)/2.0
tau_sinphi_i[block_index] = (g_sinphi_i[block_index]-1.0)/2.0
tau_cospsi_i[block_index] = (g_cospsi_i[block_index]-1.0)/2.0
tau_sinpsi_i[block_index] = (g_sinpsi_i[block_index]-1.0)/2.0
dtau_cosphi = tau_cosphi_i.std() / numpy.sqrt(float(nblocks))
dtau_sinphi = tau_sinphi_i.std() / numpy.sqrt(float(nblocks))
dtau_cospsi = tau_cospsi_i.std() / numpy.sqrt(float(nblocks))
dtau_sinpsi = tau_sinpsi_i.std() / numpy.sqrt(float(nblocks))
del cosphi_kn, sinphi_kn, cospsi_kn, sinpsi_kn
print "Integrated autocorrelation times"
print "tau_cosphi = %8.1f+-%.1f iterations" % (tau_cosphi, dtau_cosphi)
print "tau_sinphi = %8.1f+-%.1f iterations" % (tau_sinphi, dtau_sinphi)
print "tau_cospsi = %8.1f+-%.1f iterations" % (tau_cospsi, dtau_cospsi)
print "tau_sinpsi = %8.1f+-%.1f iterations" % (tau_sinpsi, dtau_sinpsi)
# Print LaTeX line.
print ""
print "%(store_filename)s & %(tau2).2f $\pm$ %(dtau2).2f & %(tau_states).2f $\pm$ %(dtau_states).2f & %(tau_end).2f $\pm$ %(dtau_end).2f & %(tau_cosphi).2f $\pm$ %(dtau_cosphi).2f & %(tau_sinphi).2f $\pm$ %(dtau_sinphi).2f & %(tau_cospsi).2f $\pm$ %(dtau_cospsi).2f & %(tau_sinpsi).2f $\pm$ %(dtau_sinpsi).2f \\\\" % vars()
print ""
if phipsi_outfile is not None:
# Write uncorrelated (phi,psi) data
outfile = open(phipsi_outfile, 'w')
outfile.write('# alanine dipeptide 2d umbrella sampling data\n')
# Write umbrella restraints
nbins = 10 # number of bins per torsion
outfile.write('# %d x %d grid of restraints\n' % (nbins, nbins))
outfile.write('# Each state was sampled from p_i(x) = Z_i^{-1} q(x) q_i(x) where q_i(x) = exp[kappa*cos(phi(x)-phi_i) + kappa*cos(psi(x)-psi_i)]\n')
outfile.write('# phi(x) and psi(x) are periodic torsion angles on domain [-180, +180) degrees.\n')
outfile.write('# kappa = %f\n' % kappa)
outfile.write('# phi_i = [-180 + (floor(i / nbins) + 0.5) * delta] degrees\n')
outfile.write('# psi_i = [-180 + ( (i % nbins) + 0.5) * delta] degrees\n')
outfile.write('# where i = 0...%d, nbins = %d, and delta = %f degrees\n' % (nbins*nbins-1, nbins, delta / units.degrees))
outfile.write('# Data has been subsampled to generate approximately uncorrelated samples.\n')
outfile.write('#\n')
# write data header
outfile.write('# ')
for replica in range(nstates):
outfile.write('state %06d ' % replica)
outfile.write('\n')
# write data
indices = timeseries.subsampleCorrelatedData(u_n, g=g_u) # indices of uncorrelated iterations
states = ncfile.variables['states'][ndiscard:,:].copy()
for iteration in indices:
outfile.write(' ')
replicas = numpy.argsort(states[iteration,:])
for state in range(1,nstates):
replica = replicas[state]
outfile.write('%+6.1f %+6.1f ' % (phi_it[replica,iteration] / units.degrees, psi_it[replica,iteration] / units.degrees))
outfile.write('\n')
outfile.close()
return results