def test_its_hmsm(self):
estimator = msm.timescales_hmsm([self.double_well_data.dtraj_T100K_dt10_n6good], 2, lags = [1, 10, 100], n_jobs=2)
ref = np.array([[ 222.0187561 ],
[ 339.47351559],
[ 382.39905462]])
assert np.allclose(estimator.timescales, ref, rtol=0.1, atol=10.0) # rough agreement
def test_its_hmsm(self):
estimator = msm.timescales_hmsm([self.double_well_data.dtraj_T100K_dt10_n6good], 2, lags = [1, 10, 100])
ref = np.array([[ 222.0641768 ],
[ 336.530405 ],
[ 369.57961198]])
assert np.allclose(estimator.timescales, ref, rtol=0.1, atol=10.0) # rough agreement
def plotTimescales(self,
lags=None,
errors=None,
nits=5,
results=False,
plot=True):
""" Plot the implied timescales of MSMs of various lag times
Parameters
----------
lags : list
The lag times at which to compute the timescales. By default it spreads out 25 lag times linearly from lag
10 until the mode length of the trajectories.
errors : errors
Calculate errors using Bayes (Refer to pyEMMA documentation)
nits : int
Number of implied timescales to calculate. Default: all
results : bool
If the method should return the calculated implied timescales
plot : bool
If the method should display the plot of implied timescales
Returns
-------
If given `results`=True this method will return the following data
its : np.ndarray
The calculated implied timescales. 2D array with dimensions (len(`lags`), `nits`)
lags : np.ndarray
A list of the lag times that were used to calculate the implied timescales
Examples
--------
>>> model = ModelHMM(data)
>>> model.plotTimescales()
>>> model.plotTimescales(lags=list(range(1,100,5)))
"""
import pyemma.plots as mplt
import pyemma.msm as msm
self._integrityCheck()
if lags is None:
lags = self._defaultLags()
from htmd.config import _config
its = msm.timescales_hmsm(self.data.St,
nits,
lags=lags,
errors='bayes',
nsamples=250,
n_jobs=_config['ncpus'])
if plot:
plt.ion()
plt.figure()
mplt.plot_implied_timescales(its, dt=self.data.fstep, units='ns')
plt.show()
if results:
return its.get_timescales(), its.lags
def plotTimescales(self, maxlag, numstates):
import pyemma.msm as msm
import pyemma.plots as mplt
its = msm.timescales_hmsm(self.data.St.tolist(),
numstates,
lags=maxlag)
mplt.plot_implied_timescales(its,
ylog=True,
units='ns',
dt=self.data.fstep,
linewidth=2)
def test_its_bhmm(self):
estimator = msm.timescales_hmsm([self.double_well_data.dtraj_T100K_dt10_n6good], 2, lags = [1, 10],
errors='bayes', nsamples=100)
ref = np.array([[ 222.02102829],
[ 342.49101981]])
# rough agreement with MLE
assert np.allclose(estimator.timescales, ref, rtol=0.1, atol=10.0)
# within left / right intervals. This test should fail only 1 out of 1000 times.
L, R = estimator.get_sample_conf(conf=0.999)
np.testing.assert_array_less(L, estimator.timescales)
np.testing.assert_array_less(estimator.timescales, R)
def plotTimescales(self, lags=None, errors=None, nits=5, results=False, plot=True):
""" Plot the implied timescales of MSMs of various lag times
Parameters
----------
lags : list
The lag times at which to compute the timescales. By default it spreads out 25 lag times linearly from lag
10 until the mode length of the trajectories.
errors : errors
Calculate errors using Bayes (Refer to pyEMMA documentation)
nits : int
Number of implied timescales to calculate. Default: all
results : bool
If the method should return the calculated implied timescales
plot : bool
If the method should display the plot of implied timescales
Returns
-------
If given `results`=True this method will return the following data
its : np.ndarray
The calculated implied timescales. 2D array with dimensions (len(`lags`), `nits`)
lags : np.ndarray
A list of the lag times that were used to calculate the implied timescales
Examples
--------
>>> model = ModelHMM(data)
>>> model.plotTimescales()
>>> model.plotTimescales(lags=list(range(1,100,5)))
"""
import pyemma.plots as mplt
import pyemma.msm as msm
self._integrityCheck()
if lags is None:
lags = self._defaultLags()
from htmd.config import _config
its = msm.timescales_hmsm(self.data.St, nits, lags=lags, errors='bayes', nsamples=250, n_jobs=_config['ncpus'])
if plot:
plt.ion()
plt.figure()
mplt.plot_implied_timescales(its, dt=self.data.fstep, units='ns')
plt.show()
if results:
return its.get_timescales(), its.lags
def plotTimescales(self, maxlag, numstates):
import pyemma.msm as msm
import pyemma.plots as mplt
its = msm.timescales_hmsm(self.data.St.tolist(), numstates, lags=maxlag)
mplt.plot_implied_timescales(its, ylog=True, units='ns', dt=self.data.fstep, linewidth=2)
def plotTimescales(self, maxlag):
import pyemma.msm as msm
import pyemma.plots as mplt
its = msm.timescales_hmsm(self.hmm.discrete_trajectories_full, self.macronum, lags=maxlag)
mplt.plot_implied_timescales(its, ylog=True, units='ns', dt=self.data.fstep, linewidth=2)
