def generate_hsm_sequence(hsm, sequence_length=100):
"""Generate a Hidden Semi Markov Sequence given an input transition matrix
Used by :meth:`~openalea.stocatree.sequences.generate_sequence`
:param hsm: A hidden semi markov instance of HiddenSemiMarkov class from VPlants.Sequence_analysis
:param sequence_length: a length of sequence set to 100 by default
:returns: a sequence
"""
# Generate a Markov sequence
#from openalea.sequence_analysis._sequence_analysis import _SemiMarkovIterator, _HiddenSemiMarkov
#from openalea.sequence_analysis import HiddenSemiMarkov
if type(hsm) == HiddenSemiMarkov or type(hsm) == _HiddenSemiMarkov:
iterator = _SemiMarkovIterator(hsm)
simulation = iterator.simulation(sequence_length, True)
else:
print "expected hsm datatype. Got %s" % type(hsm)
return None
#processes = hsm.nb_output_process() + 1
#used to free memory in the c++ code
i = 0
sequence = []
for i in range(0, sequence_length):
if simulation[0][i] == 6:
break
sequence.append([simulation[0][i], simulation[1][i]])
sequence.reverse()
return sequence
def hsm_iterator(fn):
hsm = HiddenSemiMarkov(fn)
it = sa._SemiMarkovIterator(hsm)
return it
def hsm_iterator(fn):
hsm = HiddenSemiMarkov(fn)
it = sa._SemiMarkovIterator(hsm)
return it
def test_semi_markov_iterator():
hsm = HiddenSemiMarkov(get_shared_data('test_hidden_semi_markov.dat'))
smi = sa._SemiMarkovIterator(hsm)
sim = smi.simulation(N, True)
def test_semi_markov_iterator():
hsm = HiddenSemiMarkov(get_shared_data('test_hidden_semi_markov.dat'))
smi = sa._SemiMarkovIterator(hsm)
sim = smi.simulation(N, True)
