def test_featurizer_empty(self):
from pyemma.datasets import get_bpti_test_data
top = get_bpti_test_data()['top']
f = pyemma.coordinates.featurizer(top)
params = {}
params['topologyfile'] = top
self.compare(f, params)
def test_save_chain(self):
""" ensure a chain is correctly saved/restored"""
from pyemma.datasets import get_bpti_test_data
reader = pyemma.coordinates.source(get_bpti_test_data()['trajs'],
top=get_bpti_test_data()['top'])
tica = pyemma.coordinates.tica(reader)
cluster = pyemma.coordinates.cluster_uniform_time(tica, 10)
cluster.save(self.fn, save_streaming_chain=True)
restored = pyemma.load(self.fn)
self.assertIsInstance(restored, type(cluster))
self.assertIsInstance(restored.data_producer, type(tica))
self.assertIsInstance(restored.data_producer.data_producer,
type(reader))
cluster.save(self.fn, overwrite=True, save_streaming_chain=False)
restored = pyemma.load(self.fn)
assert restored.data_producer is None
def test_featurizer(self):
from pyemma.datasets import get_bpti_test_data
top = get_bpti_test_data()['top']
f = pyemma.coordinates.featurizer(top)
f.add_distances_ca()
params = {}
params['topologyfile'] = top
params['active_features'] = f.active_features
self.maxDiff = None
self.compare(f, params)
def setUpClass(cls):
from pyemma.datasets import get_bpti_test_data
d = get_bpti_test_data()
trajs, top = d['trajs'], d['top']
s = source(trajs, top=top)
t = tica(s, lag=1)
c = cluster_kmeans(t)
cls.model_file = tempfile.mktemp()
c.save(cls.model_file, save_streaming_chain=True)
def test_min_rmsd(self):
import pyemma.datasets as data
d = data.get_bpti_test_data()
reader = coor.source(d['trajs'], top=d['top'])
N_centers = 9
centers = np.asarray((reader.ra_itraj_jagged[0, [0, 1, 7]],
reader.ra_itraj_jagged[1, [32, 1, 23]],
reader.ra_itraj_jagged[2, [17, 8, 15]])
).reshape((N_centers, -1))
dtraj = coor.assign_to_centers(reader, centers=centers, metric='minRMSD', return_dtrajs=True)
num_assigned_states = len(np.unique(np.concatenate(dtraj)))
self.assertEqual(num_assigned_states, N_centers,
"assigned states=%s out of %s possible ones."
% (num_assigned_states, N_centers))
def test_assignment_multithread_minrsmd(self):
# re-do assignment with multiple threads and compare results
import pyemma.datasets as data
d = data.get_bpti_test_data()
reader = coor.source(d['trajs'], top=d['top'])
N_centers = 9
centers = np.asarray((reader.ra_itraj_jagged[0, [0, 1, 7]],
reader.ra_itraj_jagged[1, [32, 1, 23]],
reader.ra_itraj_jagged[2, [17, 8, 15]])
).reshape((N_centers, -1))
chunksize = 1000
assignment_mp = coor.assign_to_centers(reader, centers, n_jobs=2, chunksize=chunksize, metric='minRMSD')
assignment_sp = coor.assign_to_centers(reader, centers, n_jobs=1, chunksize=chunksize, metric='minRMSD')
np.testing.assert_equal(assignment_mp, assignment_sp)
def model_file():
file = None
try:
from pyemma.datasets import get_bpti_test_data
d = get_bpti_test_data()
trajs, top = d['trajs'], d['top']
s = source(trajs, top=top)
t = tica(s, lag=1)
c = cluster_kmeans(t)
file = tempfile.mktemp()
c.save(file, save_streaming_chain=True)
yield file
finally:
if file is not None:
shutil.rmtree(file, ignore_errors=True)
from pyemma.coordinates.data.util.traj_info_cache import TrajectoryInfoCache
from pyemma.coordinates.tests.test_featurereader import create_traj
from pyemma.datasets import get_bpti_test_data
from pyemma.util import config
from pyemma.util.files import TemporaryDirectory
import mdtraj
import pkg_resources
import pyemma
import numpy as np
if six.PY2:
import dumbdbm
else:
from dbm import dumb as dumbdbm
xtcfiles = get_bpti_test_data()['trajs']
pdbfile = get_bpti_test_data()['top']
class TestTrajectoryInfoCache(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls.work_dir = tempfile.mkdtemp("traj_cache_test")
def setUp(self):
self.tmpfile = tempfile.mktemp(dir=self.work_dir)
self.db = TrajectoryInfoCache(self.tmpfile)
assert len(self.db._database) == 1, len(self.db._database)
assert 'db_version' in self.db._database