def cb(y, active_set, counter, current):
solution = np.empty(len(y))
for v, w, f, l in active_set:
solution[f:f + l] = max(v, 0) / w * g**np.arange(l)
plt.figure(figsize=(3, 3))
color = y.copy()
plt.plot(solution, c='k', zorder=-11, lw=1.2)
plt.scatter(np.arange(len(y)),
solution,
s=60,
cmap=plt.cm.Spectral,
c=color,
clip_on=False,
zorder=11)
plt.scatter([np.arange(len(y))[current]], [solution[current]],
s=200,
lw=2.5,
marker='+',
color='b',
clip_on=False,
zorder=11)
for a in active_set[::2]:
plt.axvspan(a[2], a[2] + a[3], alpha=0.1, color='k', zorder=-11)
for x in np.where(trueSpikes)[0]:
plt.plot([x, x], [0, 1.65], lw=1.5, c='r', zorder=-12)
plt.xlim((0, len(y) - .5))
plt.ylim((0, 1.65))
simpleaxis(plt.gca())
plt.xticks([])
plt.yticks([])
# plt.tight_layout()
if save_figs:
plt.savefig('fig/%d.pdf' % counter)
plt.show()
def plot_trace(n=0, lg=False):
plt.plot(trueC[n], c=col[2], clip_on=False, zorder=5, label='Truth')
plt.plot(solution, c=col[0], clip_on=False, zorder=7, label='Estimate')
plt.plot(y,
c=col[7],
alpha=.7,
lw=1,
clip_on=False,
zorder=-10,
label='Data')
if lg:
plt.legend(frameon=False, ncol=3, loc=(.1, .62), columnspacing=.8)
spks = np.append(0, solution[1:] - g * solution[:-1])
plt.text(800,
2.2,
'Correlation: %.3f' % (np.corrcoef(trueSpikes[n], spks)[0, 1]),
size=24)
plt.gca().set_xticklabels([])
simpleaxis(plt.gca())
plt.ylim(0, 2.85)
plt.xlim(0, 1500)
plt.yticks([0, 2], [0, 2])
plt.xticks([300, 600, 900, 1200], ['', ''])
def plotTrace(lg=False):
fig = plt.figure(figsize=(10, 9))
fig.add_axes([.13, .7, .86, .29])
plt.plot(c, c=col[0], label='L1')
plt.plot(c_t, c=col[1], label='Thresh.')
plt.plot(trueC[0], c=col[2], lw=3, label='Truth', zorder=-5)
plt.plot(y, c=col[7], lw=1.5, alpha=.7, zorder=-10, label='Data')
if lg:
plt.legend(frameon=False, ncol=4, loc=(.05, .82))
plt.gca().set_xticklabels([])
simpleaxis(plt.gca())
plt.yticks([0, int(y.max())], [0, int(y.max())])
plt.xticks(range(150, 500, 150), [''] * 3)
plt.ylabel('Fluor.')
plt.xlim(0, 452)
fig.add_axes([.13, .39, .86, .29])
for i, ss in enumerate(s[:500]):
if ss > 1e-2:
plt.plot([i, i], [2.5, 2.5 + ss], c=col[0], zorder=10)
plt.plot([0, 450], [2.5, 2.5], c=col[0], zorder=10)
for i, ss in enumerate(s_t[:500]):
if ss > 1e-2:
plt.plot([i, i], [1.25, 1.25 + ss], c=col[1], zorder=10)
plt.plot([0, 450], [1.25, 1.25], c=col[1], zorder=10)
for i, ss in enumerate(trueSpikes[0, :500]):
if ss > 1e-2:
plt.plot([i, i], [0, ss], c=col[2], clip_on=False, zorder=10)
plt.plot([0, 450], [0, 0], c=col[2], clip_on=False, zorder=10)
plt.gca().set_xticklabels([])
simpleaxis(plt.gca())
plt.yticks([0, 1.25, 2.5], ['Truth', 'Thresh.', 'L1'])
for tick in plt.gca().yaxis.get_major_ticks():
tick.label1.set_verticalalignment('bottom')
plt.xticks(range(150, 500, 150), [''] * 3)
plt.ylim(0, 3.5)
plt.xlim(0, 452)
fig.add_axes([.13, .08, .86, .29])
for i, r in enumerate(res):
for rr in r:
plt.plot([rr, rr], [.1 * i - .04, .1 * i + .04], c='k')
for rr in np.where(trueSpikes[0])[0]:
plt.plot([rr, rr], [-.08, -.16], c='r')
plt.gca().set_xticklabels([])
simpleaxis(plt.gca())
plt.yticks([0, .5, 1], [0, 0.5, 1.0])
plt.xticks(range(0, 500, 150), [0, 5, 10, ''])
plt.ylim(-.2, 1.1)
plt.xlim(0, 452)
plt.ylabel(r'$s_{\min}$')
plt.xlabel('Time [s]', labelpad=-10)
plt.show()
a[0] -= lam * (1 - g**len(a[2]))
a_s_tmp[-1][0] -= lam
for v, w, idx in a_s_tmp:
solution[idx] = v / w * g**np.arange(len(idx))
# plot to illustrate updating lambda
ax = fig.add_axes([ax0, .73, .08, .12])
plt.plot(np.linspace(0, 1.7),
[(res - x * tmp).dot(res - x * tmp) for x in np.linspace(0, 1.7)],
c=col[1])
plt.plot([-.1, ll], [sn**2 * T, sn**2 * T], c='k')
plt.plot([ll, ll], [100, sn**2 * T], c='k')
plt.scatter([0], [RSS], s=50, c=col[0], zorder=11, clip_on=False)
plt.text(.007, 3 + RSS, '$\lambda^-$', color=col[0])
plt.xticks([0, ll], [0, '$\lambda^*$'])
plt.yticks([sn**2 * T], ['$\sigma^2 T$'])
simpleaxis(plt.gca())
plt.xlim(0, 1.6)
plt.ylim(114, 138)
plt.xlabel('$\lambda$', labelpad=-40, x=1.1)
plt.ylabel('RSS', labelpad=-30, y=.42)
# plot result after updating lambda, but before rerunning oasis to fix violations
ax = fig.add_axes([ax1, .73, 1 - ax1, .12])
plot_trace(n)
# plot result after rerunning oasis to fix violations
solution, active_set = foo(active_set, g, ll)
ax = fig.add_axes([ax1, .59, 1 - ax1, .12])
plot_trace(n)
plt.ylabel('Fluorescence', y=0)
# solve for gamma
def cb(y, active_set, counter, current):
solution = np.empty(len(y))
for i, (v, w, f, l) in enumerate(active_set):
solution[f:f + l] = (v if i else max(v, 0)) / w * g**np.arange(l)
color = y.copy()
ax1.plot(solution, c='k', zorder=-11, lw=1.3, clip_on=False)
ax1.scatter(np.arange(len(y)),
solution,
s=40,
cmap=plt.cm.Spectral,
c=color,
clip_on=False,
zorder=11)
ax1.scatter([np.arange(len(y))[current]], [solution[current]],
s=120,
lw=2.5,
marker='+',
color='b',
clip_on=False,
zorder=11)
for a in active_set[::2]:
ax1.axvspan(a[2], a[2] + a[3], alpha=0.1, color='k', zorder=-11)
for x in np.where(trueSpikes)[0]:
ax1.plot([x, x], [0, 2.3], lw=1.5, c='r', zorder=-12)
ax1.set_xlim((0, len(y) - .5))
ax1.set_ylim((0, 2.3))
simpleaxis(ax1)
ax1.set_xticks([])
ax1.set_yticks([])
ax1.set_ylabel('Fluorescence')
for i, s in enumerate(np.r_[[0], solution[1:] - g * solution[:-1]]):
ax2.plot([i, i], [0, s], c='k', zorder=-11, lw=1.4, clip_on=False)
ax2.scatter(np.arange(len(y)),
np.r_[[0], solution[1:] - g * solution[:-1]],
s=40,
cmap=plt.cm.Spectral,
c=color,
clip_on=False,
zorder=11)
ax2.scatter([np.arange(len(y))[current]],
[np.r_[[0], solution[1:] - g * solution[:-1]][current]],
s=120,
lw=2.5,
marker='+',
color='b',
clip_on=False,
zorder=11)
for a in active_set[::2]:
ax2.axvspan(a[2], a[2] + a[3], alpha=0.1, color='k', zorder=-11)
for x in np.where(trueSpikes)[0]:
ax2.plot([x, x], [0, 1.55], lw=1.5, c='r', zorder=-12)
ax2.set_xlim((0, len(y) - .5))
ax2.set_ylim((0, 1.55))
simpleaxis(ax2)
ax2.set_xticks([])
ax2.set_yticks([])
ax2.set_xlabel('Time', labelpad=35, x=.5)
ax2.set_ylabel('Spikes')
plt.subplots_adjust(left=0.032,
right=.995,
top=.995,
bottom=0.19,
hspace=0.22)
# fig.canvas.draw()
if save_figs:
plt.savefig('video/%03d.pdf' % counter)
plt.pause(1e-9)
ax1.clear()
ax2.clear()