def test_pos(self):
r = DataInMemory(self.d)
r.chunksize = 17
it = r.iterator()
t = 0
for itraj, X in it:
assert t == it.pos
t += len(X)
if it.last_chunk_in_traj:
t = 0
def estimate(self, X, chunksize=None, **kwargs):
# ensure the input is able to provide a stream
if not isinstance(X, Iterable):
from pyemma.util import types
array_list = types.ensure_traj_list(X)
X = DataInMemory(array_list, chunksize=chunksize)
# Because we want to use pipelining methods like get_output, we have to set a data producer.
self.data_producer = X
X.chunksize = chunksize
# run estimation
try:
super(StreamingEstimator, self).estimate(X, **kwargs)
except NotConvergedWarning as ncw:
self.logger.info("Presumably finished estimation. Message: %s" %
ncw)
return self