def plotStateSignal(ax,
t,
sig,
leftSpineOn=True,
labely="",
labelyPos=-0.2,
color='black',
scaleBar=None,
scaleX=0.75,
scaleY=0.05,
scaleText='ms',
scaleTextSize='small'):
setSignalAxes(ax, leftSpineOn)
if (sig is not None):
ax.plot(t, sig, color=color)
#ax.set_ylabel(labely)
ax.text(labelyPos,
0.5,
labely,
verticalalignment='center',
horizontalalignment='right',
transform=ax.transAxes,
rotation=90)
ax.set_xlim([t[0], t[-1]])
if (scaleBar is not None):
xScaleBar(scaleBar,
x=scaleX,
y=scaleY,
ax=ax,
size=scaleTextSize,
unitsText=scaleText)
def plotGammaExample(ps, r, c, trialNum, tStart, tEnd, **kw):
'''Plot examples of gamma activity.'''
ax = kw.pop('ax', plt.gca())
noise_sigma = kw.pop('noise_sigma', None)
noise_sigma_xy = kw.pop('noise_sigma_xy', (0.95, 0.85))
xscale_kw = kw.pop('xscale_kw', None)
yscale_kw = kw.pop('yscale_kw', None)
monIdx_e = kw.pop('monIdx_e', 1)
monIdx_i = kw.pop('monIdx_i', 0)
globalAxesSettings(ax)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['bottom'].set_visible(False)
data = ps[r][c][trialNum].data
monEList = data['stateMon_e']
t, Isyn_e = extractSummedSignals(monEList, ['I_clamp_GABA_A'], tStart,
tEnd, monIdx=monIdx_e)
monIList = data['stateMon_i']
t, Isyn_i = extractSummedSignals(monIList,
['I_clamp_AMPA', 'I_clamp_NMDA'],
tStart, tEnd,
monIdx=monIdx_i)
Isyn_e *= 1e-3
Isyn_i *= 1e-3
plt.plot(t, Isyn_e, color='red')
plt.plot(t, Isyn_i, color='blue')
max = np.max(Isyn_e)
min = np.min(Isyn_i)
absmax = np.max([np.abs(max), np.abs(min)])
ax.set_xlim([tStart, tEnd])
ax.xaxis.set_ticks_position('none')
ax.yaxis.set_ticks_position('none')
ax.set_xticks([])
ax.set_yticks([])
ax.set_ylim([-absmax, absmax])
if (noise_sigma is not None):
txt = '$\sigma$ = {0} pA'.format(noise_sigma)
ns_x, ns_y = noise_sigma_xy[0], noise_sigma_xy[1]
ax.text(ns_x, ns_y, txt, transform=ax.transAxes, va='bottom', ha='right',
size='x-small')
# Scale bars
if xscale_kw is not None:
xscale_kw = dict(xscale_kw)
xscale_kw.update(ax=ax)
xScaleBar(**xscale_kw)
if yscale_kw is not None:
yscale_kw = dict(yscale_kw)
yscale_kw.update(ax=ax)
yScaleBar(**yscale_kw)
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 plotEIRaster(ESpikes, ISpikes, tLimits, ylabel=None, **kw):
'''Plot a raster plot E and I spikes.'''
# kw arguments
ax = kw.pop('ax', plt.gca())
yticks = kw.pop('yticks', True)
yticks_style = kw.pop('yticks_style', 'separate')
ylabelPos = kw.pop('ylabelPos', -0.22)
EColor = kw.pop('ecolor', 'red')
IColor = kw.pop('icolor', 'blue')
title = kw.pop('title', True)
ann = kw.pop('ann', False)
ann_EI = kw.pop('ann_EI', False)
sigmaTitle = kw.pop('sigmaTitle', True)
noise_sigma = kw.pop('noise_sigma', None)
scaleBar = kw.pop('scaleBar', None)
scaleX = kw.pop('scaleX', 0.75)
scaleY = kw.pop('scaleY', 0.05)
scaleText = kw.pop('scaleText', 'ms')
scaleTextYOffset = kw.pop('scaleTextYOffset', .075)
scaleHeight = kw.pop('scaleHeight', .02)
scaleTextSize= kw.pop('scaleTextSize', 'small')
reshape_senders = kw.pop('reshape_senders', True)
kw['markersize'] = kw.get('markersize', 1.0)
if ylabel is None:
ylabel = 'Neuron #'
ESpikes = ESpikes.windowed(tLimits)
ISpikes = ISpikes.windowed(tLimits)
ESenders, ETimes = ESpikes.rasterData()
ISenders, ITimes = ISpikes.rasterData()
if reshape_senders:
# TO REMOVE: this is to transform the neuron number from row-wise to
# column wise indexes. A better solution has to be devised in the
# future
logger.warn('Nx, Ny are fixed in the code. Make sure the torus size '
'is the same as specified here.')
Nx, Ny = 34, 30
N = Nx * Ny
if (N != ESpikes.N or N != ISpikes.N):
raise ValueError("Fix the number of neurons in plotEIRaster")
Ex = ESenders % Nx
Ey = ESenders // Ny
ESenders = Ey + Ex * Ny
Ix = ISenders % Nx
Iy = ISenders // Ny
ISenders = Iy + Ix * Ny
ISenders += ESpikes.N
globalAxesSettings(ax)
ax.minorticks_on()
ax.xaxis.set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['bottom'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.yaxis.set_major_locator(ti.LinearLocator(2))
ax.yaxis.set_minor_locator(ti.NullLocator())
ax.plot(ETimes, ESenders+1, '.', color='red', mec='none', **kw)
ax.plot(ITimes, ISenders+1, '.', color='blue', mec='none', **kw)
ax.set_xlim(tLimits)
ax.set_ylim([1, ESpikes.N+ISpikes.N])
if (yticks_style == 'separate'):
ax.set_yticks([1, ESpikes.N, ESpikes.N+ISpikes.N])
ax.invert_yaxis()
ax.text(ylabelPos, 0.5, ylabel, va='center', ha='center',
transform=ax.transAxes, rotation=90)
if (not yticks):
ax.yaxis.set_ticklabels([])
# Annotations
if (sigmaTitle):
ax.set_title('$\sigma$ = {0} pA'.format(int(noise_sigma)), y=1.02,
va='bottom', ha='center')
if (ann):
Y, X = aggr.computeYX(space, iterList, r=r, c=c)
gE = Y[r, c]
gI = X[r, c]
txt = '$g_E$ = {0} nS\n$g_I$ = {1} nS'.format(gE, gI)
ax.text(0.99, 1.02, txt, va='bottom', ha='right', size='small',
transform=ax.transAxes)
if (ann_EI):
ax.text(-0.05, 0.75, 'E', va='center', ha='center', size='small',
transform=ax.transAxes, color='red', weight='bold')
ax.text(-0.05, 0.25, 'I', va='center', ha='center', size='small',
transform=ax.transAxes, color='blue', weight='bold')
if (scaleBar is not None):
xScaleBar(scaleBar, x=scaleX, y=scaleY, ax=ax, size=scaleTextSize,
unitsText=scaleText, textYOffset=scaleTextYOffset,
height=scaleHeight)
return ax
def plotEIRaster(ESpikes, ISpikes, tLimits, ylabel=None, **kw):
'''Plot a raster plot E and I spikes.'''
# kw arguments
ax = kw.pop('ax', plt.gca())
yticks = kw.pop('yticks', True)
yticks_style = kw.pop('yticks_style', 'separate')
ylabelPos = kw.pop('ylabelPos', -0.22)
EColor = kw.pop('ecolor', 'red')
IColor = kw.pop('icolor', 'blue')
title = kw.pop('title', True)
ann = kw.pop('ann', False)
ann_EI = kw.pop('ann_EI', False)
sigmaTitle = kw.pop('sigmaTitle', True)
noise_sigma = kw.pop('noise_sigma', None)
scaleBar = kw.pop('scaleBar', None)
scaleX = kw.pop('scaleX', 0.75)
scaleY = kw.pop('scaleY', 0.05)
scaleText = kw.pop('scaleText', 'ms')
scaleTextYOffset = kw.pop('scaleTextYOffset', .075)
scaleHeight = kw.pop('scaleHeight', .02)
scaleTextSize = kw.pop('scaleTextSize', 'small')
reshape_senders = kw.pop('reshape_senders', True)
kw['markersize'] = kw.get('markersize', 1.0)
if ylabel is None:
ylabel = 'Neuron #'
ESpikes = ESpikes.windowed(tLimits)
ISpikes = ISpikes.windowed(tLimits)
ESenders, ETimes = ESpikes.rasterData()
ISenders, ITimes = ISpikes.rasterData()
if reshape_senders:
# TO REMOVE: this is to transform the neuron number from row-wise to
# column wise indexes. A better solution has to be devised in the
# future
logger.warn('Nx, Ny are fixed in the code. Make sure the torus size '
'is the same as specified here.')
Nx, Ny = 34, 30
N = Nx * Ny
if (N != ESpikes.N or N != ISpikes.N):
raise ValueError("Fix the number of neurons in plotEIRaster")
Ex = ESenders % Nx
Ey = ESenders // Ny
ESenders = Ey + Ex * Ny
Ix = ISenders % Nx
Iy = ISenders // Ny
ISenders = Iy + Ix * Ny
ISenders += ESpikes.N
globalAxesSettings(ax)
ax.minorticks_on()
ax.xaxis.set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['bottom'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.yaxis.set_major_locator(ti.LinearLocator(2))
ax.yaxis.set_minor_locator(ti.NullLocator())
ax.plot(ETimes, ESenders + 1, '.', color='red', mec='none', **kw)
ax.plot(ITimes, ISenders + 1, '.', color='blue', mec='none', **kw)
ax.set_xlim(tLimits)
ax.set_ylim([1, ESpikes.N + ISpikes.N])
if (yticks_style == 'separate'):
ax.set_yticks([1, ESpikes.N, ESpikes.N + ISpikes.N])
ax.invert_yaxis()
ax.text(ylabelPos,
0.5,
ylabel,
va='center',
ha='center',
transform=ax.transAxes,
rotation=90)
if (not yticks):
ax.yaxis.set_ticklabels([])
# Annotations
if (sigmaTitle):
ax.set_title('$\sigma$ = {0} pA'.format(int(noise_sigma)),
y=1.02,
va='bottom',
ha='center')
if (ann):
Y, X = aggr.computeYX(space, iterList, r=r, c=c)
gE = Y[r, c]
gI = X[r, c]
txt = '$g_E$ = {0} nS\n$g_I$ = {1} nS'.format(gE, gI)
ax.text(0.99,
1.02,
txt,
va='bottom',
ha='right',
size='small',
transform=ax.transAxes)
if (ann_EI):
ax.text(-0.05,
0.75,
'E',
va='center',
ha='center',
size='small',
transform=ax.transAxes,
color='red',
weight='bold')
ax.text(-0.05,
0.25,
'I',
va='center',
ha='center',
size='small',
transform=ax.transAxes,
color='blue',
weight='bold')
if (scaleBar is not None):
xScaleBar(scaleBar,
x=scaleX,
y=scaleY,
ax=ax,
size=scaleTextSize,
unitsText=scaleText,
textYOffset=scaleTextYOffset,
height=scaleHeight)
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)