def plotDiffusion():
m = np.random.randn(100, 200)
random_walks = np.cumsum(m, axis=1)
fig = plt.figure()
###
ax = fig.add_subplot(231)
for i in xrange(random_walks.shape[0]):
ax.add_line(plt.Line2D(
np.arange(random_walks.shape[1]), random_walks[i, :],
color='k', alpha=.35))
misc.adjust_spines(ax, ['left', 'bottom'])
ax.tick_params(axis='both', direction='out')
ax.grid('on')
ax.set_xlabel('Time (a.u.)')
ax.set_ylabel('Magnitude (a.u.)')
ax.set_xlim(0, 100)
ax.set_ylim(-25, 25)
ax = fig.add_subplot(234)
ax.add_line(plt.Line2D(
np.arange(random_walks.shape[1]), np.var(random_walks, axis=0),
color='k'))
misc.adjust_spines(ax, ['left', 'bottom'])
ax.tick_params(axis='both', direction='out')
ax.grid('on')
ax.set_xlabel('Time (a.u.)')
ax.set_ylabel('Variance')
ax.set_xlim(0, 100)
ax.set_ylim(0, 125)
###
ax = fig.add_subplot(232)
for i in xrange(random_walks.shape[0]):
ax.add_line(plt.Line2D(
np.arange(random_walks.shape[1]), random_walks[i, :],
color='k', alpha=.35))
misc.adjust_spines(ax, ['left', 'bottom'])
ax.tick_params(axis='both', direction='out')
ax.grid('on')
ax.set_xlabel('Time (a.u.)')
ax.set_ylabel('Magnitude (a.u.)')
ax.set_xlim(0, 100)
ax.set_ylim(-25, 25)
colors = ['red', 'green', 'blue']
ax.axvline(15, color='black')
for i in np.arange(3):
ax.axvline(15 + (i + 1) * 20, color=colors[i])
ax = fig.add_subplot(235)
for i in np.arange(3)[::-1]:
ax.scatter(random_walks[:, 15],
random_walks[:, 15 + (i + 1) * 20], color=colors[i],
alpha=.2, marker='o')
ax.text(0.1, 0.9 - i * .05, 'r = %.2f' %
pearsonr(random_walks[:, 15],
random_walks[:, 15 + (i + 1) * 20])[0],
transform=ax.transAxes, color=colors[i])
misc.adjust_spines(ax, ['left', 'bottom'])
ax.tick_params(axis='both', direction='out')
ax.grid('on')
ax.set_xlabel('Magnitude (a.u.)')
ax.set_ylabel('Magnitude (a.u.)')
ax.set_xlim(-25, 25)
ax.set_ylim(-25, 25)
###
ax = fig.add_subplot(233)
for i in xrange(random_walks.shape[0]):
ax.add_line(plt.Line2D(
np.arange(random_walks.shape[1]), random_walks[i, :],
color='k', alpha=.35))
misc.adjust_spines(ax, ['left', 'bottom'])
ax.tick_params(axis='both', direction='out')
ax.grid('on')
ax.set_xlabel('Time (a.u.)')
ax.set_ylabel('Magnitude (a.u.)')
ax.set_xlim(0, 100)
ax.set_ylim(-25, 25)
ax.axvline(10, color='orange')
ax.axvline(40, color='orange')
ax.axvline(30, color='cyan')
ax.axvline(60, color='cyan')
ax = fig.add_subplot(236)
ax.scatter(random_walks[:, 10], random_walks[
:, 40], color='orange', alpha=.35, marker='o')
ax.scatter(random_walks[:, 30], random_walks[
:, 60], color='cyan', alpha=.35, marker='o')
ax.text(0.1, 0.9, 'r = %.2f' %
pearsonr(random_walks[:, 10], random_walks[:, 40])[0],
transform=ax.transAxes, color='orange')
ax.text(0.1, 0.8, 'r = %.2f' %
pearsonr(random_walks[:, 30], random_walks[:, 60])[0],
transform=ax.transAxes, color='cyan')
misc.adjust_spines(ax, ['left', 'bottom'])
ax.tick_params(axis='both', direction='out')
ax.grid('on')
ax.set_xlabel('Magnitude (a.u.)')
ax.set_ylabel('Magnitude (a.u.)')
ax.set_xlim(-25, 25)
ax.set_ylim(-25, 25)
plt.tight_layout()
def plotCorCE(g, n_bins=8):
fig = plt.figure()
# find the best phi
phis = findBestPhi(
g['spk_counts'][:, 0:n_bins],
g['count_means'][:, 0:n_bins],
np.float64(g['trial_counts'][:, 0:n_bins]),
min_trials=6)
g_var = computeVarCorCovCE(
g['spk_counts'][:, 0:n_bins],
g['count_means'][:, 0:n_bins],
g['trial_counts'][:, 0:n_bins],
phis)
ax = fig.add_subplot(121)
cax = ax.imshow(
g_var['corCE'], vmin=0, vmax=1,
interpolation='nearest', cmap='jet')
n_points = g_var['corCE'].shape[0]
ax.set_xticks(np.arange(n_points))
ax.set_yticks(np.arange(n_points))
ax.set_xticklabels(g['spk_counts_times'], rotation=45)
ax.set_yticklabels(g['spk_counts_times'])
ax.add_line(plt.Line2D(
np.arange(n_points)[1:], np.repeat(0, n_points)[1:],
color='k'))
ax.add_line(plt.Line2D(
np.arange(n_points)[0:-1] + 1, np.arange(n_points)[0:-1],
color='k', linestyle='--'))
# vertically oriented colorbar
cbar = fig.colorbar(cax, ticks=[0, 0.5, 1], shrink=0.2, aspect=4)
cbar.ax.set_yticklabels(['0.0', '0.5', '1.0'])
cbar.ax.set_ylabel('CorCE')
# comparison with theory
corCEPred = g_var['corCE']
n = corCEPred.shape[0]
corCETheory = corCEpartialSums(n)
ax = fig.add_subplot(122)
x = np.arange(2, n + 1)
ax.scatter(
x, np.diagonal(corCEPred, 1),
color='k', s=25, facecolors='none')
ax.scatter(x, corCEPred[0, 1:], color='k', s=25)
ax.add_line(
plt.Line2D(x, np.diagonal(corCETheory, 1),
color='k', linestyle='--'))
ax.add_line(plt.Line2D(x, corCETheory[0, 1:], color='k'))
misc.adjust_spines(ax, ['left', 'bottom'])
ax.tick_params(axis='both', direction='out')
ax.grid('on')
ax.set_xlabel('Time (ms)')
ax.set_ylabel('CorCE')
ax.set_xticklabels(g['spk_counts_times'])
ax.set_ylim(0.3, 1)
plt.tight_layout()
