def test_insert_lag_time(self):
lags = [1, 3, 5]
its = timescales_msm(self.dtraj2, lags=lags, errors='bayes', nsamples=10, show_progress=False)
new_lags = np.concatenate((lags, [2, 4]+list(range(6, 9))), axis=0)
its.lags = new_lags
np.testing.assert_equal(its._lags, new_lags)
its.estimate(self.dtraj2)
# compare with a one shot estimation
its_one_shot = timescales_msm(self.dtraj2, lags=new_lags, errors='bayes', nsamples=10, show_progress=False)
np.testing.assert_equal(its.timescales, its_one_shot.timescales)
self.assertEqual([m.lag for m in its.models],
[m.lag for m in its_one_shot.models])
# estimate with different data to trigger re-estimation
from pyemma.util.testing_tools import MockLoggingHandler
log_handler = MockLoggingHandler()
its.logger.addHandler(log_handler)
extended_new_lags = new_lags.tolist()
extended_new_lags.append(20)
its.estimate(self.dtraj4_2, lags=extended_new_lags)
np.testing.assert_equal(its.models[0].dtrajs_full[0], self.dtraj4_2)
self.assertIn("estimating from new data", log_handler.messages['warning'][0])
# remove a lag time and ensure the corresponding model is removed too
new_lags = new_lags[:-3]
new_lags_len = len(new_lags)
its.lags = new_lags
np.testing.assert_equal(its.lags, new_lags)
assert len(its.models) == new_lags_len
assert len(its.timescales) == new_lags_len
assert len(its.sample_mean) == new_lags_len
def test_inefficient_args_log(self):
from pyemma.util.testing_tools import MockLoggingHandler
m = MockLoggingHandler()
cl = cluster_kmeans(self.X, max_iter=1, keep_data=False)
cl.logger.addHandler(m)
cl.estimate(self.X, max_iter=1, clustercenters=cl.clustercenters)
found = False
for msg in m.messages['warning']:
if 'inefficient' in msg:
found = True
break
assert found
def test_too_short_traj_partial_fit(self):
data = [np.empty((20, 3)), np.empty((10, 3))]
lag = 11
tica_obj = tica(lag=lag)
from pyemma.util.testing_tools import MockLoggingHandler
log_handler = MockLoggingHandler()
tica_obj._covar.logger.addHandler(log_handler)
for x in data:
tica_obj.partial_fit(x)
self.assertEqual(tica_obj._used_data, 20 - lag)
self.assertEqual(len(log_handler.messages['warning']), 1)
self.assertIn("longer than lag", log_handler.messages['warning'][0])
def test_too_short_traj_partial_fit(self):
data = [np.empty((20, 3)), np.empty((10, 3))]
lag = 11
tica_obj = tica(lag=lag)
from pyemma.util.testing_tools import MockLoggingHandler
log_handler = MockLoggingHandler()
import logging
L = logging.getLogger('pyemma.coordinates.estimation.covariance')
L.addHandler(log_handler)
try:
for x in data:
tica_obj.partial_fit(x)
#self.assertEqual(tica_obj._used_data, 20 - lag)
self.assertEqual(len(log_handler.messages['warning']), 1)
self.assertIn("longer than lag",
log_handler.messages['warning'][0])
finally:
L.removeHandler(log_handler)