def __init__(self, switch, bprobs):
half = len(bprobs) // 2
self.alloc = [0] * half + [1] * (len(bprobs) - half)
pprobs = numpy.zeros([max(self.alloc) + 1], Float)
for (b, p) in zip(self.alloc, bprobs):
pprobs[b] += p
self.bprobs = [p / pprobs[self.alloc[i]] for (i, p) in enumerate(bprobs)]
self.transition_matrix = SiteClassTransitionMatrix(switch, pprobs)
def Edge(seq1, seq2, length, bin_data, switch=1.0, bprobs=None):
# one sequence pair in, potentialy, a tree
bins = len(bin_data)
pair = pairwise.Pair(seq1, seq2)
EP = pair.make_reversible_emission_probs([(bin.mprobs, bin.Qd)
for bin in bin_data], length)
tms = [bin.indel.calc_transition_matrix(length) for bin in bin_data]
if bins == 1:
TM = tms[0]
else:
assert bprobs
R = SiteClassTransitionMatrix(switch, bprobs)
TM = R.nestTransitionMatricies(tms)
assert min(TM.Matrix.flat) >= 0, bin_data
return EP.make_pair_HMM(TM)
class PatchSiteDistribution(object):
def __init__(self, switch, bprobs):
half = len(bprobs) // 2
self.alloc = [0] * half + [1] * (len(bprobs) - half)
pprobs = numpy.zeros([max(self.alloc) + 1], Float)
for (b, p) in zip(self.alloc, bprobs):
pprobs[b] += p
self.bprobs = [p / pprobs[self.alloc[i]] for (i, p) in enumerate(bprobs)]
self.transition_matrix = SiteClassTransitionMatrix(switch, pprobs)
def get_weighted_sum_lhs(self, lhs):
result = numpy.zeros((2,) + lhs[0].shape, lhs[0].dtype.char)
temp = numpy.empty(lhs[0].shape, result.dtype.char)
for (patch, weight, lh) in zip(self.alloc, self.bprobs, lhs):
temp[:] = lh
temp *= weight
result[patch] += temp
return result
def __call__(self, root):
return SiteHmm(self, root)
def emit(self, length, random_series):
bprobs = [
[p for (patch, p) in zip(self.alloc, self.bprobs) if patch == a]
for a in [0, 1]
]
source = self.transition_matrix.emit(random_series)
result = numpy.zeros([length], int)
for i in range(length):
patch = next(source) - 1
result[i] = argpick(bprobs[patch], random_series)
return result
