def plot_filtered(filt, first, aver, incmap, figno=666, err=1, smerr=1):
plt.figure(figno)
plt.clf()
plt.axes([0.1, 0.3, 0.8, 0.65])
plt.hold(1)
ax = plt.gca()
plot_iq(ax, first.T, smerr=smerr, label="First rep")
plot_iq(ax, aver.T, smerr=smerr, label="All reps")
plot_iq(ax, filt.T, smerr=smerr, label="Filtered")
plt.legend()
plt.axis('tight')
plt.axes([0.1, 0.05, 0.8, 0.15])
plt.imshow(incmap, interpolation='nearest', aspect='auto')
plt.plot(np.sum(incmap, axis=0)-0.5, 'y')
plt.axis('tight')
av = plt.axis()
plt.axis([av[0], av[1], av[3], av[2]])
plt.hold(0)
plt.show()
def plot_outliers(filtered, first, aver, inclist, cdm, threshold):
plt.clf()
plt.subplot(221)
sm = 1
ax = plt.gca()
plot_iq(ax, first.T, smerr=sm, label="First rep")
plot_iq(ax, aver.T, smerr=sm, label="All reps")
plot_iq(ax, filtered.T, smerr=sm, label="Filtered, %d reps" % len(inclist))
plt.legend()
plt.subplot(222)
plot_distmat(cdm)
plt.subplot(223)
N = filtered.shape[-1]
plot_clusterhist(cdm, inclist, N, threshold)
plt.subplot(224)
plot_distmat_marginal(cdm, threshold, sublist=inclist)
plt.show()
def plot_clustering(filtered, first, aver, inclist, cdm, links, threshold):
plt.clf()
plt.subplot(221)
sm = 1
ax = plt.gca()
plot_iq(ax, first.T, smerr=sm, label="First rep", color='blue')
plot_iq(ax, aver.T, smerr=sm, label="All reps", color='red')
plot_iq(ax, filtered.T, smerr=sm, label="Largest cluster, %d reps" % len(inclist), color='lawngreen')
plt.legend()
plt.subplot(222)
plot_distmat(cdm)
# plot_distmat_marginal(cdm)
plt.subplot(223)
N = filtered.shape[-1]
plot_clusterhist(cdm, inclist, N, threshold)
plt.subplot(224)
plot_dendrogram(links, threshold)
plt.show()
