def load(cls, tica_fn):
"""
load a tICA solution to use in projecting data.
Parameters:
-----------
tica_fn : str
filename pointing to tICA solutions
"""
# the only variables we need to save are the two matrices
# and the eigenvectors / values as well as the lag time
logger.warn("NOTE: You can only use the tICA solution, you will "
"not be able to continue adding data")
f = io.loadh(tica_fn)
metric = cPickle.loads(f["metric_string"][0])
tica_obj = cls(f['lag'][0], prep_metric=metric)
# lag entry is an array... with a single item
tica_obj.timelag_corr_mat = f['timelag_corr_mat']
tica_obj.cov_mat = f['cov_mat']
tica_obj.vals = f['vals']
tica_obj.vecs = f['vecs']
tica_obj._sort()
return tica_obj
def load(cls, tica_fn):
"""
load a tICA solution to use in projecting data.
Parameters:
-----------
tica_fn : str
filename pointing to tICA solutions
"""
# the only variables we need to save are the two matrices
# and the eigenvectors / values as well as the lag time
logger.warn("NOTE: You can only use the tICA solution, you will "
"not be able to continue adding data")
f = io.loadh(tica_fn)
metric = cPickle.loads(f["metric_string"][0])
tica_obj = cls(f['lag'][0], prep_metric=metric)
# lag entry is an array... with a single item
tica_obj.timelag_corr_mat = f['timelag_corr_mat']
tica_obj.cov_mat = f['cov_mat']
tica_obj.vals = f['vals']
tica_obj.vecs = f['vecs']
tica_obj._sort()
return tica_obj
def test_pickle():
# test pickling of topology (bug #391)
cPickle.loads(cPickle.dumps(md.load(get_fn('bpti.pdb')).topology))
