def run(MinLagtime, MaxLagtime, Interval, NumEigen, AssignmentsFn, symmetrize, nProc, output):
arglib.die_if_path_exists(output)
# Setup some model parameters
try:
Assignments = io.loadh(AssignmentsFn, "arr_0")
except KeyError:
Assignments = io.loadh(AssignmentsFn, "Data")
NumStates = max(Assignments.flatten()) + 1
if NumStates <= NumEigen - 1:
NumEigen = NumStates - 2
logger.warning(
"Number of requested eigenvalues exceeds the rank of the transition matrix! Defaulting to the maximum possible number of eigenvalues."
)
del Assignments
logger.info("Getting %d eigenvalues (timescales) for each lagtime...", NumEigen)
lagTimes = range(MinLagtime, MaxLagtime + 1, Interval)
logger.info("Building MSMs at the following lag times: %s", lagTimes)
# Get the implied timescales (eigenvalues)
impTimes = msm_analysis.get_implied_timescales(
AssignmentsFn, lagTimes, n_implied_times=NumEigen, sliding_window=True, symmetrize=symmetrize, n_procs=nProc
)
numpy.savetxt(output, impTimes)
return
def run(MinLagtime, MaxLagtime, Interval, NumEigen, AssignmentsFn, symmetrize,
nProc, output):
arglib.die_if_path_exists(output)
# Setup some model parameters
try:
Assignments = io.loadh(AssignmentsFn, 'arr_0')
except KeyError:
Assignments = io.loadh(AssignmentsFn, 'Data')
NumStates = max(Assignments.flatten()) + 1
if NumStates <= NumEigen - 1:
NumEigen = NumStates - 2
logger.warning(
"Number of requested eigenvalues exceeds the rank of the transition matrix! Defaulting to the maximum possible number of eigenvalues."
)
del Assignments
logger.info("Getting %d eigenvalues (timescales) for each lagtime...",
NumEigen)
lagTimes = range(MinLagtime, MaxLagtime + 1, Interval)
logger.info("Building MSMs at the following lag times: %s", lagTimes)
# Get the implied timescales (eigenvalues)
impTimes = msm_analysis.get_implied_timescales(AssignmentsFn,
lagTimes,
n_implied_times=NumEigen,
sliding_window=True,
symmetrize=symmetrize,
n_procs=nProc)
numpy.savetxt(output, impTimes)
return
def run(MinLagtime, MaxLagtime, Interval, NumEigen, AssignmentsFn, trimming,
symmetrize, nProc):
logger.info(
"Getting %d eigenvalues (timescales) for each lagtime...", NumEigen)
lagTimes = range(MinLagtime, MaxLagtime + 1, Interval)
logger.info("Building MSMs at the following lag times: %s", lagTimes)
# Get the implied timescales (eigenvalues)
impTimes = msm_analysis.get_implied_timescales(
AssignmentsFn, lagTimes, n_implied_times=NumEigen, sliding_window=True,
trimming=trimming, symmetrize=symmetrize, n_procs=nProc)
return impTimes
def run(MinLagtime, MaxLagtime, Interval, NumEigen, AssignmentsFn, trimming,
symmetrize, nProc):
logger.info("Getting %d eigenvalues (timescales) for each lagtime...",
NumEigen)
lagTimes = range(MinLagtime, MaxLagtime + 1, Interval)
logger.info("Building MSMs at the following lag times: %s", lagTimes)
assert np.all(np.array(lagTimes) > 0
), "Please specify a range of positive lag times."
# Get the implied timescales (eigenvalues)
impTimes = msm_analysis.get_implied_timescales(AssignmentsFn,
lagTimes,
n_implied_times=NumEigen,
sliding_window=True,
trimming=trimming,
symmetrize=symmetrize,
n_procs=nProc)
return impTimes
