def plot_avg_firing_rate_spikes(spikes, tLimits, **kw):
'''Plot population-average firing rate from PopulationSpikes.'''
# keyword arguments
ax = kw.pop('ax', plt.gca())
sigmaTitle = kw.pop('sigmaTitle', False)
dt = kw.pop('dt', .5)
winLen = kw.pop('winLen', 2.0)
kw['xlabel'] = False
kw['ylabel'] = kw.get('ylabel', 'r (Hz)')
tStart = tLimits[0]
tEnd = tLimits[1]
rate, times = spikes.slidingFiringRate(tStart, tEnd, dt, winLen)
meanRate = np.mean(rate, axis=0)
signalPlot(times, meanRate, ax, **kw)
ax.set_ylim([0, None])
#ax.spines['left'].set_visible(False)
ax.spines['bottom'].set_visible(False)
ax.xaxis.set_visible(False)
#ax.yaxis.set_visible(False)
ax.yaxis.set_major_locator(ti.LinearLocator(2))
# Annotations
if (sigmaTitle):
ax.set_title('$\sigma$ = {0} pA'.format(int(noise_sigma)),
y=1.02,
va='bottom',
ha='center')
return ax
def plot_avg_firing_rate_spikes(spikes, tLimits, **kw):
'''Plot population-average firing rate from PopulationSpikes.'''
# keyword arguments
ax = kw.pop('ax', plt.gca())
sigmaTitle = kw.pop('sigmaTitle', False)
dt = kw.pop('dt', .5)
winLen = kw.pop('winLen', 2.0)
kw['xlabel'] = False
kw['ylabel'] = kw.get('ylabel', 'r (Hz)')
tStart = tLimits[0]
tEnd = tLimits[1]
rate, times = spikes.slidingFiringRate(tStart, tEnd, dt, winLen)
meanRate = np.mean(rate, axis=0)
signalPlot(times, meanRate, ax, **kw)
ax.set_ylim([0, None])
#ax.spines['left'].set_visible(False)
ax.spines['bottom'].set_visible(False)
ax.xaxis.set_visible(False)
#ax.yaxis.set_visible(False)
ax.yaxis.set_major_locator(ti.LinearLocator(2))
# Annotations
if (sigmaTitle):
ax.set_title('$\sigma$ = {0} pA'.format(int(noise_sigma)), y=1.02,
va='bottom', ha='center')
return ax
def plot(self, *args, **kwargs):
for n_theta_cycles in range(1, 9):
T = n_theta_cycles / self.theta_freq * 1e3
l, b, r, top = self.myc['bbox']
self.fig = self._get_final_fig(self.myc['fig_size'])
self.ax_theta = self.fig.add_subplot(3, 1, 1)
# Only theta
t = np.arange(0, T+self.dt, self.dt)
theta = self.const + .5 * (1. +
np.cos(2*np.pi*self.theta_freq*t*1e-3 - np.pi)) * (1 - self.const)
signalPlot(
t, theta,
ax=self.ax_theta,
color=self.myc['theta_color'],
zeroLine=False)
self.ax_theta.axis('off')
self.ax_theta.set_xlim([t[0], t[-1]])
self.ax_theta.set_ylim(-.2, 1.2)
self.ax_theta.set_title('A', x=0, y=.95, size=14, weight='bold',
ha='left', va='top')
# PAC only gamma
self.ax_pac = self.fig.add_subplot(3, 1, 2)
gamma = np.cos(2*np.pi*self.gamma_freq*t*1e-3 - np.pi) * theta
signalPlot(
t, gamma,
ax=self.ax_pac,
color=self.myc['gamma_color'],
zeroLine=False)
self.ax_pac.axis('off')
self.ax_pac.set_xlim([t[0], t[-1]])
self.ax_pac.set_ylim(-1.02, 1.02)
self.ax_pac.set_title('B', x=0, y=1, size=14, weight='bold',
ha='left', va='top')
# PAC Theta + gamma
self.ax_pac_all = self.fig.add_subplot(3, 1, 3)
self.ax_pac_all.hold('on')
signalPlot(
t, theta,
ax=self.ax_pac_all,
color=self.myc['theta_color'],
zeroLine=False)
gamma = .25 * np.cos(2*np.pi*self.gamma_freq*t*1e-3 - np.pi) * theta
signalPlot(
t, theta + gamma,
ax=self.ax_pac_all,
color=self.myc['gamma_color'],
zeroLine=False)
bar_x = 1 - 5. / 8 / n_theta_cycles
xScaleBar(50, x=bar_x, y=.1, ax=self.ax_pac_all, size='small')
self.ax_pac_all.axis('off')
self.ax_pac_all.set_xlim([t[0], t[-1]])
self.ax_pac_all.set_ylim(-.02, 1.5)
self.ax_pac_all.set_title('C', x=0, y=1, size=14, weight='bold',
ha='left', va='top')
self.fig.subplots_adjust(left=l, bottom=b, right=r, top=top)
fname = self.config['output_dir'] + "/pac_example_{0}.pdf"
self.fig.savefig(fname.format(n_theta_cycles), dpi=300,
transparent=True)
plt.close(self.fig)
def plot(self, *args, **kwargs):
for n_theta_cycles in range(1, 9):
T = n_theta_cycles / self.theta_freq * 1e3
l, b, r, top = self.myc['bbox']
self.fig = self._get_final_fig(self.myc['fig_size'])
self.ax_theta = self.fig.add_subplot(3, 1, 1)
# Only theta
t = np.arange(0, T + self.dt, self.dt)
theta = self.const + .5 * (
1. + np.cos(2 * np.pi * self.theta_freq * t * 1e-3 -
np.pi)) * (1 - self.const)
signalPlot(t,
theta,
ax=self.ax_theta,
color=self.myc['theta_color'],
zeroLine=False)
self.ax_theta.axis('off')
self.ax_theta.set_xlim([t[0], t[-1]])
self.ax_theta.set_ylim(-.2, 1.2)
self.ax_theta.set_title('A',
x=0,
y=.95,
size=14,
weight='bold',
ha='left',
va='top')
# PAC only gamma
self.ax_pac = self.fig.add_subplot(3, 1, 2)
gamma = np.cos(2 * np.pi * self.gamma_freq * t * 1e-3 -
np.pi) * theta
signalPlot(t,
gamma,
ax=self.ax_pac,
color=self.myc['gamma_color'],
zeroLine=False)
self.ax_pac.axis('off')
self.ax_pac.set_xlim([t[0], t[-1]])
self.ax_pac.set_ylim(-1.02, 1.02)
self.ax_pac.set_title('B',
x=0,
y=1,
size=14,
weight='bold',
ha='left',
va='top')
# PAC Theta + gamma
self.ax_pac_all = self.fig.add_subplot(3, 1, 3)
self.ax_pac_all.hold('on')
signalPlot(t,
theta,
ax=self.ax_pac_all,
color=self.myc['theta_color'],
zeroLine=False)
gamma = .25 * np.cos(2 * np.pi * self.gamma_freq * t * 1e-3 -
np.pi) * theta
signalPlot(t,
theta + gamma,
ax=self.ax_pac_all,
color=self.myc['gamma_color'],
zeroLine=False)
bar_x = 1 - 5. / 8 / n_theta_cycles
xScaleBar(50, x=bar_x, y=.1, ax=self.ax_pac_all, size='small')
self.ax_pac_all.axis('off')
self.ax_pac_all.set_xlim([t[0], t[-1]])
self.ax_pac_all.set_ylim(-.02, 1.5)
self.ax_pac_all.set_title('C',
x=0,
y=1,
size=14,
weight='bold',
ha='left',
va='top')
self.fig.subplots_adjust(left=l, bottom=b, right=r, top=top)
fname = self.config['output_dir'] + "/pac_example_{0}.pdf"
self.fig.savefig(fname.format(n_theta_cycles),
dpi=300,
transparent=True)
plt.close(self.fig)
