def uniform_motif(N,
L,
desired_ic,
epsilon=0.1,
beta=None,
ps=None,
count_sampler=None,
verbose=False):
if verbose: print "uniform motif accept reject:", N, L, desired_ic, beta
correction_per_col = 3 / (2 * log(2) * N)
desired_ic_for_beta = desired_ic + L * correction_per_col
if desired_ic_for_beta == 2 * L: # if we reach the upper limit, things break down
cols = [sample_col_from_count((0, 0, 0, N)) for _ in range(L)]
motif_p = map(lambda site: "".join(site), transpose(cols))
return motif_p
if beta is None:
beta = find_beta_for_mean_motif_ic(N, L, desired_ic_for_beta)
if verbose:
print "beta:", beta
if ps is None:
ps = count_ps_from_beta(N, beta)
if count_sampler is None:
count_sampler = inverse_cdf_sampler(enumerate_counts(N), ps)
def rQ_raw():
counts = [count_sampler() for i in range(L)]
cols = [sample_col_from_count(count) for count in counts]
motif_p = map(lambda site: "".join(site), transpose(cols))
return motif_p
def rQ():
return sample_until(lambda M: inrange(M, desired_ic, epsilon),
rQ_raw,
1,
progress_bar=False)[0]
def dQhat(motif):
return exp(beta * motif_ic(motif))
Imin = desired_ic - epsilon
Imax = desired_ic + epsilon
log_M = -beta * Imin
if verbose: print "Imin, Imax, log_M:", Imin, Imax, log_M
def dQ(motif):
return exp(beta * motif_ic(motif) + log_M)
def AR(motif):
return 1.0 / dQ(motif)
#M = exp(-beta*(desired_ic - epsilon)) # which ic? +/- correction
trials = 0
while True:
trials += 1
motif = rQ()
r = random.random()
if r < AR(motif):
return motif
if verbose and trials % 100 == 0:
print trials, AR(motif)
def uniform_motif(N,L,desired_ic,epsilon=0.1,beta=None,ps=None,count_sampler=None,verbose=False):
if verbose: print "uniform motif accept reject:",N,L,desired_ic,beta
correction_per_col = 3/(2*log(2)*N)
desired_ic_for_beta = desired_ic + L * correction_per_col
if desired_ic_for_beta == 2*L: # if we reach the upper limit, things break down
cols = [sample_col_from_count((0,0,0,N)) for _ in range(L)]
motif_p = map(lambda site:"".join(site),transpose(cols))
return motif_p
if beta is None:
beta = find_beta_for_mean_motif_ic(N,L,desired_ic_for_beta)
if verbose:
print "beta:",beta
if ps is None:
ps = count_ps_from_beta(N,beta)
if count_sampler is None:
count_sampler = inverse_cdf_sampler(enumerate_counts(N),ps)
def rQ_raw():
counts = [count_sampler() for i in range(L)]
cols = [sample_col_from_count(count) for count in counts]
motif_p = map(lambda site:"".join(site),transpose(cols))
return motif_p
def rQ():
return sample_until(lambda M:inrange(M,desired_ic,epsilon),rQ_raw,1,progress_bar=False)[0]
def dQhat(motif):
return exp(beta*motif_ic(motif))
Imin = desired_ic - epsilon
Imax = desired_ic + epsilon
log_M = -beta*Imin
if verbose: print "Imin, Imax, log_M:",Imin, Imax, log_M
def dQ(motif):
return exp(beta*motif_ic(motif) + log_M)
def AR(motif):
return 1.0/dQ(motif)
#M = exp(-beta*(desired_ic - epsilon)) # which ic? +/- correction
trials = 0
while True:
trials +=1
motif = rQ()
r = random.random()
if r < AR(motif):
return motif
if verbose and trials % 100 == 0:
print trials, AR(motif)
def rQ_raw():
counts = [count_sampler() for i in range(L)]
cols = [sample_col_from_count(count) for count in counts]
motif_p = map(lambda site:"".join(site),transpose(cols))
return motif_p
def rQ_raw():
counts = [count_sampler() for i in range(L)]
cols = [sample_col_from_count(count) for count in counts]
motif_p = map(lambda site: "".join(site), transpose(cols))
return motif_p
