coverage = np.array(cov_list)
position = np.array(pos_list)
hda = list(chain.from_iterable(hdata))
start = min(position)
end = max(position)
pos_space = np.linspace(start, end, end-start)
kde = gaussian_kde(hda)
kde_pdf = kde.evaluate(pos_space)
fig = plt.figure()
# TODO: make this args
#fig.patch.set_alpha(0)
ax = fig.add_subplot(111)
# TODO: Make normed an args
hist, bis, patches = rhist(ax, np.array(hda), normed=True, facecolor="#dc322f", edgecolor='#dc322f', alpha=0.2)
# scale signal
new_max = max(hist)
kde_pdf = kde_pdf / max(kde_pdf) * new_max
plot(pos_space, kde_pdf, color='#268bd2', alpha=0.8)
ax.legend()
ax.set_xlabel('HSR1 nt')
ax.set_ylabel('Coverage')
ax.title.set_fontsize(18)
ax.fill_between(pos_space, kde_pdf, color="#268bd2", alpha=0.4)
rstyle(ax)
show()
fig = plt.figure()
if args.transparent:
fig.patch.set_alpha(0)
ax = fig.add_subplot(111)
defaults = {
'facecolor': '#dc322f',
'edgecolor': '#dc322f',
'alpha': 0.2,
}
if args.normed:
defaults.update({'normed': True,})
hist, bis, patches = rhist(ax, np.array(hdata), **defaults)
ax.legend()
ax.set_xlabel('nt')
ax.set_ylabel('Coverage')
ax.title.set_fontsize(18)
if args.kde:
kde = gaussian_kde(np.array(hdata))
kde_pdf = kde.evaluate(pos_space)
# TODO: Implement scaling as a separate funcion
new_max = max(hist)
kde_pdf = kde_pdf / max(kde_pdf) * new_max
plot(pos_space, kde_pdf, color='#268bd2', alpha=0.8)
ax.fill_between(pos_space, kde_pdf, color="#268bd2", alpha=0.4)
rstyle(ax)
show()