def test_iterator(self):
c = self.ass
for itraj, chunk in c:
assert types.is_int(itraj)
assert types.is_int_matrix(chunk)
assert chunk.shape[0] <= c.chunksize or c.chunksize == 0
assert chunk.shape[1] == c.dimension()
def test_iterator(self):
for c in self.cl:
for itraj, chunk in c:
assert types.is_int(itraj)
assert types.is_int_matrix(chunk)
assert chunk.shape[0] <= c.chunksize
assert chunk.shape[1] == c.dimension()
def test_get_output(self):
c = self.ass
O = c.get_output()
assert types.is_list(O)
assert len(O) == 1
assert types.is_int_matrix(O[0])
assert O[0].shape[0] == self.T
assert O[0].shape[1] == 1
def test_get_output(self):
for c in self.cl:
O = c.get_output()
assert types.is_list(O)
assert len(O) == 1
assert types.is_int_matrix(O[0])
assert O[0].shape[0] == self.T
assert O[0].shape[1] == 1
def _apply_offsets_to_samples(indices, offsets):
r""" This private function applies the given offsets returned by the milestone_counting function to
the results of the sample_indexes_by_* functions.
This is necessary in order to obtain the right order in the input files.
Notes
-----
Operates in place.
Parameters
----------
indices : list of ndarray( (N, 2) )
trajectory and corresponding frame indices of original input
offsets: dict {itraj, offset}
"""
from pyemma.util import types
assert types.is_int_matrix(indices)
assert isinstance(offsets, list), offsets
# 1. restore itraj indices (the mapping is relative to the original oder)
last_valid = None
n_missing = 0
for itraj, offset in enumerate(offsets):
if offset is None: # this itraj is missing
n_missing += 1
else:
if n_missing > 0:
# shift subsequent indices by n_missing
indices[indices[:, 0] > last_valid, 0] += n_missing
last_valid = itraj
n_missing = 0
# 2. restore frame indices (relative to org order)
for itraj, offset in enumerate(offsets):
if offset is None:
continue
indices[indices[:, 0] == itraj, 1] += offset
