def setUp(self):
self.tmpdir = tempfile.mkdtemp('test_random_access')
self.dim = 5
self.data = [
np.random.random((100, self.dim)).astype(np.float32),
np.random.random((20, self.dim)).astype(np.float32),
np.random.random((20, self.dim)).astype(np.float32)
]
self.stride = np.asarray([[0, 1], [0, 3], [0, 3], [0, 5], [0, 6],
[0, 7], [2, 1], [2, 1]])
self.stride2 = np.asarray([[2, 0]])
self.topfile = pkg_resources.resource_filename(__name__,
'data/test.pdb')
trajfile1, xyz1, n_frames1 = create_traj(self.topfile,
dir=self.tmpdir,
format=".binpos",
length=100)
trajfile2, xyz2, n_frames2 = create_traj(self.topfile,
dir=self.tmpdir,
format=".binpos",
length=20)
trajfile3, xyz3, n_frames3 = create_traj(self.topfile,
dir=self.tmpdir,
format=".binpos",
length=20)
self.data_feature_reader = [trajfile1, trajfile2, trajfile3]
def test_fragmented_xtc(self):
from pyemma.coordinates.tests.test_featurereader import create_traj
top_file = pkg_resources.resource_filename(__name__, 'data/test.pdb')
trajfiles = []
for _ in range(3):
f, _, _ = create_traj(top_file)
trajfiles.append(f)
try:
# three trajectories: one consisting of all three, one consisting of the first,
# one consisting of the first and the last
source = coor.source(
[trajfiles, [trajfiles[0]], [trajfiles[0], trajfiles[2]]],
top=top_file)
source.chunksize = 1000
out = source.get_output(stride=1)
trajs = [
mdtraj.load(trajfiles[i], top=top_file).xyz.reshape(-1, 9)
for i in range(0, 3)
]
np.testing.assert_equal(out[0], np.vstack(trajs))
np.testing.assert_equal(out[1], trajs[0])
np.testing.assert_equal(out[2], np.vstack((trajs[0], trajs[2])))
finally:
for t in trajfiles:
try:
os.unlink(t)
except EnvironmentError:
pass
def test_feature_reader_random_access(self):
from pyemma.coordinates.tests.test_featurereader import create_traj
topfile = pkg_resources.resource_filename(__name__, 'data/test.pdb')
trajfiles = []
for _ in range(3):
f, _, _ = create_traj(topfile)
trajfiles.append(f)
try:
source = coor.source(trajfiles, top=topfile)
source.chunksize = 2
out = source.get_output(stride=self.stride)
keys = np.unique(self.stride[:, 0])
for i, coords in enumerate(out):
if i in keys:
traj = mdtraj.load(trajfiles[i], top=topfile)
np.testing.assert_equal(
coords, traj.xyz[np.array(
self.stride[self.stride[:, 0] == i][:,
1])].reshape(
-1, 9))
finally:
for t in trajfiles:
try:
os.unlink(t)
except EnvironmentError:
pass
def test_fragmented_reader(self):
top_file = pkg_resources.resource_filename(__name__, 'data/test.pdb')
trajfiles = []
nframes = []
with TemporaryDirectory() as wd:
for _ in range(3):
f, _, l = create_traj(top_file, dir=wd)
trajfiles.append(f)
nframes.append(l)
# three trajectories: one consisting of all three, one consisting of the first,
# one consisting of the first and the last
reader = api.source(
[trajfiles, [trajfiles[0]], [trajfiles[0], trajfiles[2]]], top=top_file)
np.testing.assert_equal(reader.trajectory_lengths(),
[sum(nframes), nframes[0], nframes[0] + nframes[2]])