def sanity_check():
G = 10000
config = [G/2]
mfl = 250
lamb = 1.0/mfl
num_frags = 10000
frags = concat([chip(G,config,mfl) for i in xrange(num_frags)])
min_seq_length = 75
sequenced_frags = filter(lambda (start,stop):stop - start > min_seq_length,frags)
fd_frags,bk_frags = separate(lambda x:random.random() < 0.5,sequenced_frags)
fd_reads = [('+',start,start+min_seq_length) for (start,stop) in fd_frags]
bk_reads = [('-',stop-min_seq_length,stop) for (start,stop) in bk_frags]
reads = fd_reads + bk_reads
inferred_frags = exp_reconstruction(reads,lamb,G)
plot_reads(reads,G=G)
plt.plot(frag_density(frags,G=G),label="all frags")
plt.plot(frag_density(sequenced_frags,G=G),label="seq frags")
plt.plot((inferred_frags),label="inferred frags")
plt.legend()
def recovery():
G = 10000
config = [G/2]
mfl = 250
lamb = 1/float(mfl)
num_frags = 1000
frags = concat([chip(G,config,mfl) for i in xrange(num_frags)])
min_seq_length = 75
sequenced_frags = filter(lambda (start,stop):stop - start > min_seq_length,frags)
fd_frags,bk_frags = separate(lambda x:random.random() < 0.5,sequenced_frags)
fd_reads = [('+',start,start+75) for (start,stop) in fd_frags]
bk_reads = [('-',stop-75,stop) for (start,stop) in bk_frags]
reads = fd_reads + bk_reads
hyp0 = [int(random.random() < 0.5) for i in range(G)]
def f(hyp):
return log_likelihood(reads,hyp,lamb,G)
def prop(hyp):
i = random.randrange(G)
hyp_copy = hyp[:]
hyp_copy[i] = 1 - hyp_copy[i]
return hyp_copy
chain = mh(f,prop,hyp0,use_log=True,verbose=True)
