def output(I, forward, reverse):
D = (list(), list())
model = BIC.get_best_model(I, penality , diff_threshold)
print model.k
for rv in model.rvs:
print rv
for i,FH in enumerate((forward, reverse)):
for line in FH:
chrom,start, stop, cov = line.strip("\n").split("\t")
pos = (float(stop) + float(start ) ) /2.
if pos > I.stop:
break
elif I.start<=pos <=I.stop:
D[i].append((pos, float(cov)))
minX,maxX = min([x for d in D for x,y in d]), max([x for d in D for x,y in d])
xs = np.linspace(0, (maxX-minX)/100., 1000 )
bins = 500
counts,edges = np.histogram([(x-minX)/100. for x,y in D[0]], weights=[y for x,y in D[0]], bins=bins, normed=1)
edges = (edges[:-1] + edges[1:])/2.
F = plt.figure(figsize=(15,10))
plt.bar(edges, counts,width=(edges[-1]-edges[0])/bins,alpha=0.5)
plt.plot(xs, map(lambda x: model.pdf(x, 1) , xs), linewidth=2.)
counts,edges = np.histogram([(x-minX)/100. for x,y in D[1]], weights=[y for x,y in D[1]], bins=bins, normed=1)
edges = (edges[:-1] + edges[1:])/2.
plt.bar(edges, -counts, width=(edges[-1]-edges[0])/bins, color="red",alpha=0.5)
plt.plot(xs, map(lambda x: -model.pdf(x, -1) ,xs ), linewidth=2.)
plt.show()
def output(I, G): model = BIC.get_best_model(I, penality , diff_threshold) bidirs = [rv for rv in model.rvs if check_bidir_component(rv,si_thresh=si_thresh, l_thresh=l_thresh, w_thresh=w_thresh, pi_thresh=pi_thresh)] for c in bidirs: G["mu"].append(c.mu) G["si"].append(c.si) G["l"].append(c.l) G["pi"].append(c.pi) pass
def ouput(I, FHW):
model = BIC.get_best_model(I, penality, diff_threshold)
bidirs = [
rv
for rv in model.rvs
if check_bidir_component(rv, si_thresh=si_thresh, l_thresh=l_thresh, w_thresh=w_thresh, pi_thresh=pi_thresh)
]
if bidirs:
FHW.write("#" + I.chrom + ":" + str(I.start) + "-" + str(I.stop) + "\n")
for N in bidirs:
XS = bin_ChIP_signal(N, I)
FHW.write(N.__str__() + "\n")
for X, data_type, peak in XS:
FHW.write(data_type + "," + str(peak) + "," + ",".join([str(x) + "-" + str(y) for x, y in X]) + "\n")
def output(I, FHW, penality,diff_threshold ):
model = BIC.get_best_model(I, penality , diff_threshold)
FHW.write("#" + I.chrom + ":" + str(I.start) + "-" + str(I.stop) + "," + str(I.annotation_N) + "\n")
for rv in model.rvs:
FHW.write(rv.__str__()+"\n")
