def plot_cne(name, scores, valid):
lookahead = len(scores) / 50
scores = np.array([float(x) for x in scores])
signal = smooth(scores, window_len=40, window='bartlett')
maxima = peakdetect(signal, lookahead=lookahead)[0]
m_x = np.array([m[0] for m in maxima])
m_y = np.array([m[1] for m in maxima])
plt.plot(range(len(signal)), signal, 'k', m_x, m_y, 'bo')
plt.axvspan(valid[0], valid[1], facecolor='r', alpha=0.4)
plt.xlabel('alignment')
plt.ylabel('score')
plt.title(name)
fig = plt.gcf()
fig.set_size_inches(8, 5)
plt.savefig(name + '.png', dpi=140)
def ranked_peaks(cne_dict, extra):
def dist(cne, i):
v_avg = (extra[cne]['dr_valid_co']['start'] + extra[cne]['dr_valid_co']['end']) / 2.0
v_i = v_avg - extra[cne]['dr_co']['start']
return abs(i - v_i)
results = defaultdict(dict)
for cne, scores in cne_dict.iteritems():
lookahead = int(len(scores) / 50) + 1
scores = np.array([float(x) for x in scores])
signal = smooth(scores, window_len=40, window='bartlett')
maxima = peakdetect(signal, lookahead=lookahead)[0]
maxima.sort(key=itemgetter(1), reverse=True)
if not maxima:
print cne
continue
hit_rank = np.argmin(np.array([dist(cne, i) for i, score in maxima]))
results[cne]['rank'] = hit_rank + 1
results[cne]['places'] = len(maxima)
return results
