import sys
import numpy as np
import scipy.sparse
import matplotlib.pyplot as pp
from msmbuilder import io, msm_analysis, MSMLib
from bayesmutant import SimpleMutantSampler
wt_tprob = np.loadtxt('cayleytree_tprob_wildtype.dat')
mutant_tprob = np.loadtxt('cayleytree_tprob_mutant.dat')
base_counts = np.zeros_like(wt_tprob)
for i in range(base_counts.shape[0]):
base_counts[i] = np.random.multinomial(200, wt_tprob[i])
print 'base counts'
print base_counts
ms = SimpleMutantSampler(base_counts, mutant_tprob)
ms.step(5000)
#print ms.eff_counts()
#print 'observed counts'
#print ms.counts
io.saveh('sampling2.h5', base_counts=base_counts, samples=ms.samples,
observed_counts=ms.counts, scores=ms.scores,
effective_counts=ms.eff_counts(),
transition_matrix=mutant_tprob)
import os
import sys
import numpy as np
import scipy.sparse
from msmbuilder import io, msm_analysis, MSMLib
from bayesmutant import SimpleMutantSampler
P = np.loadtxt('base_transition_matrix.dat')
mutant_transition_matrix = P + 0.2*scipy.sparse.rand(P.shape[0], P.shape[1], density=0.1).todense()
mutant_transition_matrix /= np.sum(mutant_transition_matrix, axis=1)
trajectory = np.array(msm_analysis.sample(P, 0, 5000))
base_counts = MSMLib.get_counts_from_traj(trajectory).todense()
print 'base counts'
print base_counts
ms = SimpleMutantSampler(base_counts, mutant_transition_matrix)
ms.step(5000)
print 'observed counts'
print ms.counts
io.saveh('sampling.h5', base_counts=base_counts, samples=ms.samples,
observed_counts=ms.counts, scores=ms.scores,
transition_matrix=mutant_transition_matrix)
