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plotter.py
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plotter.py
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from brian import ms, mV, pA, second, Hz, siemens, nS
from matplotlib import pyplot, gridspec
import matplotlib
import numpy
import brian as bb
reload(bb)
import calc_spikes
from avalan import get_avalanches
from periodogram import periodogram
#from line_with_text import MyLine
################################################################################
def get_straightline(figure, sub_e, sub_r, c1x, c1y, c2x, c2y):
transFigure = figure.transFigure.inverted()
coord1 = transFigure.transform(sub_e.transData.transform([c1x, c1y]))
coord2 = transFigure.transform(sub_r.transData.transform([c2x, c2y]))
line = matplotlib.lines.Line2D((coord1[0], coord2[0]),
(coord1[1], coord2[1]), linestyle='--',# clip_on=False,
transform=figure.transFigure, color='gray', alpha=1, zorder=1)
return line
def plot_pop_raster(net, plot_inh=False):
"""plot rasterplot of whole E/I populations"""
pyplot.figure()
bb.raster_plot(net.mon_spike_e)
if plot_inh:
pyplot.figure()
bb.raster_plot(net.mon_spike_i)
def plot_ps_raster(net, chain_n=0, frac = 1., permutate=False, figure=0,
dummy_ass=False):
'''
plot a raster of the neurons in the phase sequence chain_n
frac is the fraction of neurons from each assembly to be plotted.
'''
a = calc_spikes.get_spike_times_ps(net, chain_n, frac, permutate,
True, dummy_ass)
if not figure:
figure = pyplot.figure()
bb.raster_plot_spiketimes(a)
pyplot.title('PS %d neuron firing'%chain_n)
def plot_pop_fr(net,w=10*ms):
"""plots E/I population FR"""
pyplot.figure()
pyplot.subplot(211)
pyplot.plot(net.mon_rate_e.smooth_rate(w))
pyplot.xlabel('t [ms]')
pyplot.ylabel('FR [sp/sec]')
pyplot.title('FR of E')
pyplot.subplot(212)
pyplot.plot(net.mon_rate_i.smooth_rate(w))
pyplot.xlabel('t [ms]')
pyplot.ylabel('FR [sp/sec]')
pyplot.title('FR of I')
def plot_voltage(net, plot_inh=False):
"""plot voltages of measured neurons"""
pyplot.figure()
for n in range(len(net.nrn_meas_e)):
pyplot.subplot(len(net.nrn_meas_e),1,n+1)
pyplot.plot(net.mon_volt_e.times/ms,
net.mon_volt_e[net.nrn_meas_e[n]]/mV)
pyplot.ylabel('V [mV]')
pyplot.xlabel('t [ms]')
pyplot.legend(['PY %d'%net.nrn_meas_e[n]])
if plot_inh:
pyplot.figure()
for n in range(len(net.nrn_meas_i)):
pyplot.subplot(len(net.nrn_meas_i),1,n+1)
pyplot.plot(net.mon_volt_i.times/ms,
net.mon_volt_i[net.nrn_meas_i[n]]/mV)
pyplot.ylabel('V [mV]')
pyplot.xlabel('t [ms]')
pyplot.legend(['IN %d'%net.nrn_meas_i[n]])
def plot_conductance(net, nrn = 0):
"""plot E/I conductances of measured E/I neurons"""
pyplot.figure()
for n in range(len(net.nrn_meas_e)):
pyplot.subplot(len(net.nrn_meas_e),1,n+1)
pyplot.plot(net.mon_econd_e.times/ms,
net.mon_econd_e[net.nrn_meas_e[n]]/nS)
pyplot.plot(net.mon_icond_e.times/ms,
net.mon_icond_e[net.nrn_meas_e[n]]/nS)
pyplot.ylabel('ge,gi,[nS]')
pyplot.xlabel('t [ms]')
pyplot.legend(['PY %d'%net.nrn_meas_e[n]])
pyplot.figure()
for n in range(len(net.nrn_meas_i)):
pyplot.subplot(len(net.nrn_meas_i),1,n+1)
pyplot.plot(net.mon_econd_i.times/ms,
net.mon_econd_i[net.nrn_meas_i[n]]/nS)
pyplot.plot(net.mon_icond_i.times/ms,
net.mon_icond_i[net.nrn_meas_i[n]]/nS)
pyplot.ylabel('ge,gi [nS]')
pyplot.xlabel('t [ms]')
pyplot.legend(['IN %d'%net.nrn_meas_i[n]])
def plot_currents(net, nrn = 0):
"""plot E/I current of measured E/I neurons"""
pyplot.figure()
for n in range(len(net.nrn_meas_e)):
pyplot.subplot(len(net.nrn_meas_e),1,n+1)
pyplot.plot(net.mon_ecurr_e.times/ms,
net.mon_ecurr_e[net.nrn_meas_e[n]]/pA)
pyplot.plot(net.mon_icurr_e.times/ms,
net.mon_icurr_e[net.nrn_meas_e[n]]/pA)
pyplot.plot(net.mon_icurr_e.times/ms,
(net.mon_ecurr_e[net.nrn_meas_e[n]]+
net.mon_icurr_e[net.nrn_meas_e[n]])/pA)
pyplot.ylabel('Ie,Ii,net [pA]')
pyplot.xlabel('t [ms]')
pyplot.legend(['PY %d'%net.nrn_meas_e[n]])
pyplot.figure()
for n in range(len(net.nrn_meas_i)):
pyplot.subplot(len(net.nrn_meas_i),1,n+1)
pyplot.plot(net.mon_ecurr_i.times/ms,
net.mon_ecurr_i[net.nrn_meas_i[n]]/pA)
pyplot.plot(net.mon_icurr_i.times/ms,
net.mon_icurr_i[net.nrn_meas_i[n]]/pA)
pyplot.ylabel('Ie,Ii [pA]')
pyplot.xlabel('t [ms]')
pyplot.legend(['IN %d'%net.nrn_meas_i[n]])
def plot_separatrix(net,t_list=[12,13],t_pre=.005,t_post=.025,ps=0):
'''
try to plot a separatrix
'''
pyplot.figure()
for t in t_list:
a,s = calc_spikes.get_alpha_sigma(net,t,t_pre,t_post)
pyplot.plot(s,a,'-*')
pyplot.xlabel('$\sigma$ [ms]')
pyplot.ylabel('$\\alpha$ [# spikes]')
def plot_separatrix_n(nn,t_list=[12,13],t_pre=.005,t_post=.025,ps=0,
n_spikes=False):
'''
try to plot a separatrix
'''
reload(calc_spikes)
figure = pyplot.figure(figsize=(12.,6.))
gs = gridspec.GridSpec(20,20)
gs.update(left=.1,wspace=.05,hspace=.05)
subp = pyplot.subplot(gs[0:11,0:6])
xlim = [-.5,16.5]
x_ticks = [0,8,16]
y_ticks = [0,.500,1]
#y_ticks = [0,100,200,300,400,500,600]
y_ticks = [0,.1,.2,.3,.4,.5,.6,.7,.8,.9,1.]
y_tick_lab = ['0','','','','','50','','','','','100']
ylim = [0,1.1]
tas_l = [] # t, alpha, sigma list
#tas_l.append([17,500,0])
#tas_l.append([18,500,0])
#tas_l.append([19,500,0])
#tms_sig_l = [[0,0],[.200,5],[.400,10],[.600,20],[.800,30]]
#tms_alpha_l = [[20,400],[21,300],[22,200],[23,100],[24,50]]
tms_alpha_l = [[17.,1.],[18.,.8],[19.,.6],[20.,.4],[21.,.2],[22.,.1]]
tms_sig_l = [[0,0],[.200,4],[.400,8],[.600,12],[.800,16]]
for tms_a in tms_alpha_l:
for tms_s in tms_sig_l:
tas_l.append([tms_a[0]+tms_s[0], tms_a[1], tms_s[1]])
#print tas_l
for tas in tas_l:
#reload(calc_spikes)
a,s = calc_spikes.get_alpha_sigma(nn,tas[0],t_pre,t_post,
n_spikes=n_spikes)
#print tas,a,s
#a=a/nn.s_ass
print a
a.insert(0,tas[1])
s.insert(0,tas[2])
na=numpy.array(a)
ns=numpy.array(s)
if a[-1]>.6: color = 'r'
elif a[-1]>.4: color = 'b'
else: color= 'g'
#color= 'g'
#pyplot.plot(s,a,'-*',color=color)
pyplot.quiver(ns[:-1],na[:-1],ns[1:]-ns[:-1],na[1:]-na[:-1],
width=.015, scale_units = 'xy',angles='xy',scale=1.,color=color)
pyplot.plot(tas[2],tas[1],'o',color=color)
#pyplot.plot(s[1:],a[1:],'*',color=color)
pyplot.plot(s[-1],a[-1],'o',color='black')
#pyplot.plot(numpy.array(tas_l)[:,2],numpy.array(tas_l)[:,1],'o')
subp.set_xlabel('$\sigma$ [ms]',size=20)
subp.set_ylabel('$\\alpha$ [% neurons]',size=20)
subp.set_xticks(x_ticks)
subp.set_xticklabels(x_ticks,size=20)
subp.set_yticks(y_ticks)
subp.set_yticklabels(y_tick_lab,size=20)
subp.set_xlim(xlim)
subp.set_ylim(ylim)
figure.savefig('tfigs/separau_pr'+str(nn.pr_ee)+'pf'+str(nn.pf_ee)+
'tp'+str(t_pre)+'_'+str(t_post)+'.png', format = 'png')
#pyplot.show()
return a,s
def remove_spines(ax,xlim,ylim,xticks,yticks,ylabel,legend,keepx=False):
for loc, spine in ax.spines.items():
if loc in ['left']:
spine.set_position(('outward',0)) # outward by 10 points
elif loc in ['right','top']:
spine.set_color('none') # don't draw spine
elif loc in ['bottom']:
spine.set_position(('outward',10)) # outward by 10 points
if not keepx: spine.set_color('none') # don't draw spine
else:
raise ValueError('unknown spine location: %s'%loc)
pyplot.xlim(xlim)
pyplot.xticks(xticks)
pyplot.yticks(yticks)
pyplot.ylim(ylim)
pyplot.ylabel(ylabel)
if legend<>[]: pyplot.legend(legend)
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
def remove_spines_only(ax, keepx=False):
for loc, spine in ax.spines.items():
if loc in ['left']:
spine.set_position(('outward', 0)) # outward by 10 points
elif loc in ['right','top']:
spine.set_color('none') # don't draw spine
elif loc in ['bottom']:
spine.set_position(('outward', 0)) # outward by 10 points
if not keepx: spine.set_color('none') # don't draw spine
else:
raise ValueError('unknown spine location: %s'%loc)
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
def plot_rast_V_I_sep(net,nrn=-2,xlim = [16500,17500]):
""" plots a ratser of a reduced PS,
conductances, currents, voltage of a neuron in a single figure"""
tres = .1 # time resolution for conductance
cxlim = [xlim[0]/tres,xlim[1]/tres] # lim for the vectors(with tres in mind)
xticks=numpy.arange(xlim[0],xlim[1]+1,200)
xticks_lab = numpy.arange(0,xlim[1]-xlim[0]+1,200)
figure = pyplot.figure(figsize=(12.,8.))
gs = gridspec.GridSpec(20,12)
gs.update(left=.1,wspace=.0,hspace=.0)
ylab_xposit = xlim[0]-130
################################################################
#sub_rast = pyplot.subplot(gs[0:6,0:12])
sub_rast = pyplot.subplot(gs[14:20,1:12])
m = bb.Monitor()
m.source = []
m.spikes = calc_spikes.get_spike_times_ps(net,0,.1)
bb.raster_plot(m,color=(0,0,0))#, #showgrouplines=True,
#spacebetweengroups=0.1, grouplinecol=(0.5, 0.5, 0.5))
# spikes of measured neuron in red o.,
# now it works automatically just with neurons from the last group
nrn_spikes_all = net.mon_spike_e[net.nrn_meas_e[nrn]]
nrn_spikes = nrn_spikes_all[numpy.logical_and(
nrn_spikes_all >= xlim[0]/1000., nrn_spikes_all <=xlim[1]/1000.)]
raster_sp_x = nrn_spikes*1000.
raster_sp_y = (491+nrn)*numpy.ones(len(nrn_spikes))
pyplot.plot(raster_sp_x,raster_sp_y,'o',color='red')
pyplot.plot(raster_sp_x,raster_sp_y,'.',color='red')
sub_rast.set_xlim(xlim)
sub_rast.set_xticks(xticks)
sub_rast.set_xticklabels([])
sub_rast.set_xlabel('')
# put some lines between groups
for i in range(net.n_ass):
sub_rast.plot(numpy.arange(xlim[0],xlim[1]),
(i+1)*50.*numpy.ones(1000),'gray')
# put an arrow for the neuron we record from
#pyplot.text(xlim[1]+2,490+nrn,'$\leftarrow$',size=18)
#pyplot.text(xlim[0]+500-18,-60,'$\lightning$',size=25)
pyplot.text(xlim[0]+500-18,-60,'$\Uparrow$',size=25)
#pyplot.text(xlim[0]+500+23,-80,'Stimulation',size=18)
yticks=[0,100,200,300,400,500]
yticks=[0,250,500]
sub_rast.set_ylabel('',size=20)
sub_rast.set_yticks(yticks)
sub_rast.set_yticklabels(yticks,size=20)
sub_rast.xaxis.set_tick_params(width=1,length=5)
sub_rast.yaxis.set_tick_params(width=1,length=5)
#sub_rast.set_ylabel('Neurons',size=18)
sub_rast.text(ylab_xposit,375,'Neuron #',rotation = 'vertical',size=20)
sub_rast.set_xticklabels(xticks_lab, size=20)
pyplot.xlabel('Time [ms]',size=20)
sub_rast.text(xticks[0]-200,500.,'C',size=28,color='black')
################################################################
#sub_curr = pyplot.subplot(gs[9:13,0:12])
sub_curr = pyplot.subplot(gs[0:5,1:12])
yticks=[-200,0,200,400]
curr_e= net.mon_ecurr_e[net.nrn_meas_e[nrn]]/pA+200
curr_i= net.mon_icurr_e[net.nrn_meas_e[nrn]]/pA
volt_e= net.mon_volt_e[net.nrn_meas_e[nrn]]/mV # measured voltage here
curr_leak = 10.*nS*(-60.-volt_e)*mV/pA
curr_maxe=(curr_e[cxlim[0]:cxlim[1]]).max()
curr_maxi=(curr_i[cxlim[0]:cxlim[1]]).max()
curr_mine=(curr_e[cxlim[0]:cxlim[1]]).min()
curr_mini=(curr_i[cxlim[0]:cxlim[1]]).min()
pyplot.plot(net.mon_ecurr_e.times/ms, curr_e,'r')
pyplot.plot(net.mon_ecurr_e.times/ms, curr_i,'b')
pyplot.plot(net.mon_ecurr_e.times/ms, curr_e+curr_i+curr_leak,'black')
ylim=[1.2*min(curr_mine,curr_mini),1.2*max(curr_maxe,curr_maxi)]
remove_spines(sub_curr,xlim=xlim,ylim=ylim,xticks=[],yticks=yticks,
#ylabel='I [pA]',legend= ['$I_{exc}$','$I_{inh}$'])
ylabel='I [pA]',legend= [])
sub_curr.set_ylabel('',size=20)
sub_curr.text(ylab_xposit,370,'Current [pA]',rotation = 'vertical',size=20)
sub_curr.set_yticks(yticks)
sub_curr.set_yticklabels(yticks,size=20)
sub_curr.yaxis.set_tick_params(width=1,length=5)
#sub_curr.text(xticks[-1]-00,-200.,'$I_{inh}$',color='red',size=18)
#sub_curr.text(xticks[-1]-00,50.,'$I_{tot}$',color='black',size=18)
#sub_curr.text(xticks[-1]-00,250.,'$I_{exc}$',color='green',size=18)
sub_curr.text(xticks[-2]-5,-320.,'Inhibition',size=20,color='b',zorder=3)
sub_curr.text(xticks[-2]-5,80.,'Total',size=20,zorder=3)
sub_curr.text(xticks[-2]-5,300.,'Excitation',size=20,color='r',zorder=3)
sub_curr.text(xticks[0]-200,500.,'A',size=28,color='black')
################################################################
#raster_sp_x = nrn_spikes*1000
#raster_sp_y = (491+nrn)*numpy.ones(len(nrn_spikes))
#sub_volt = pyplot.subplot(gs[16:20,0:12])
sub_volt = pyplot.subplot(gs[7:12,1:12])
for i,v in enumerate(volt_e):
if v<-59 and volt_e[i-1]>-55:
#pyplot.vlines((i-1)*tres,-50,-40)
volt_e[i-1] = -40
#volt_e[i] = -40
#volt_e[i+1] = -40
#print i
pyplot.plot(net.mon_volt_e.times/ms, volt_e,'black')
volt_sp_x = nrn_spikes*1000
volt_sp_y = -40*numpy.ones(len(volt_sp_x))
pyplot.plot(volt_sp_x,volt_sp_y,'o',color='red')
#for i,v in enumerate(volt_e):
#if volt_e[i-1]>-45:
#pyplot.plot((i-1)*tres,-40,'o',color='red')
ylim=[-62,-39]
yticks=[-60,-50,-40]
#remove_spines(sub_volt,xlim=xlim,ylim=ylim,
#xticks=xticks,yticks=yticks,
#ylabel='V [mV]',legend= [],keepx=True)
remove_spines(sub_volt,xlim=xlim,ylim=ylim,
xticks=[],yticks=yticks,
ylabel='V [mV]',legend= [])
#sub_volt.set_xticklabels(xticks_lab, size=18)
#pyplot.xlabel('Time [ms]',size=18)
sub_volt.set_ylabel('',size=20)
sub_volt.text(ylab_xposit,-40,'Voltage [mV]',rotation = 'vertical',size=20)
sub_volt.set_yticks(yticks)
sub_volt.set_yticklabels(yticks,size=20)
sub_volt.yaxis.set_tick_params(width=1,length=5)
sub_volt.plot(numpy.arange(xlim[0],xlim[1]),
-50.*numpy.ones(1000),'-.',color = 'gray')
sub_volt.text(xticks[0]-200,-40.,'B',size=28,color='black')
#sub_volt.text(xticks[-1]-00,-55.,'$V_{m}$',color='black',size=18)
# draw vertical lines between spikes in raster and voltage trace
linii_nekvi=[]
curr_sp_y = 400.
for i in range(len(volt_sp_x)):
#linii_nekvi.append(get_straightline(figure,sub_rast,sub_volt,
# raster_sp_x[i],raster_sp_y[i],volt_sp_x[i],volt_sp_y[i]))
linii_nekvi.append(get_straightline(figure,sub_rast,sub_curr,
raster_sp_x[i],raster_sp_y[i],volt_sp_x[i],curr_sp_y))
'''
transFigure = figure.transFigure.inverted()
c1x = raster_sp_x[0]
c1y = raster_sp_y[0]
c2x = volt_sp_x[0]
c2y = volt_sp_y[0]
coord1 = transFigure.transform(sub_rast.transData.transform([c1x,c1y]))
coord2 = transFigure.transform(sub_volt.transData.transform([c2x,c2y]))
line = matplotlib.lines.Line2D((coord1[0],coord2[0]),(coord1[1],coord2[1]),
transform=figure.transFigure,color='gray',alpha=.9)
print c1x,c1y,c2x,c2y
print coord1, coord2
'''
figure.lines = linii_nekvi
loc='tfigs/rcv/'
prefix = 'rccv_'
fname = 'prf'+str(net.pr_ee) + str(net.pf_ee)+str(-nrn)+'_int'+str(xlim[0])
figure.savefig(loc+prefix+ fname +'.eps', format = 'eps')
figure.savefig(loc+prefix+ fname +'.png', format = 'png')
figure.savefig(loc+prefix+ fname +'.pdf', format = 'pdf')
pyplot.show()
def plot_rast_V_I_disc(net, nrn=-2, xlim0=[19375,19625],
xlim1 = [25625,26375], xlim2=[38625,36375], disc_only=False):
"""
For plotting Fig 2
- plots currents, voltage of a neuron in a single figure
- plots a ratser of a PS with a discontinious x axis for
showing off the dummpy plasticity
xlimX : gives the limits of the x axes for the different plots
X=0 : limits of pre-pff-boost
X=1 : limits of discrete sequence sfter boost
X=1 : limits of continuous sequence after boost
puts an extra raster of a continuous sequence replay
"""
from figure_subplots import draw_evoked_rasta2
tres = .1 # time resolution for conductance
subpzorder = 2
figure = pyplot.figure(figsize=(18., 12.))
grid_y = 26
if disc_only:
grid_y = 18
gs = gridspec.GridSpec(grid_y, 13)
gs.update(left=.04, wspace=.15, hspace=.0)
ylab_xposit = xlim0[0] - 80
# label size
labsize = 20
################################################################
dx_tick = 50
xticks0 = numpy.arange(xlim0[0], xlim0[1]+1, dx_tick)
xticks0_lab = numpy.arange(0, xlim0[1]-xlim0[0]+1, dx_tick)
# first tick in second plot in plotting time
shift_xtick = dx_tick - (xlim0[1]-xlim0[0]) % dx_tick
# last tick
first_xtick= ((xlim0[1]-xlim0[0]+dx_tick)//dx_tick)*dx_tick
last_xtick = first_xtick + xlim1[1]-xlim1[0] + 1
xticks1 = numpy.arange(xlim1[0] + shift_xtick, xlim1[1]+1, dx_tick)
xticks1_lab = numpy.arange(first_xtick, last_xtick, dx_tick)
#xticks=numpy.arange(xlim[0], xlim[1]+1, 200)
#xticks_lab = numpy.arange(0, xlim[1]-xlim[0]+1, 200)
################################################################
sub_rast0 = pyplot.subplot(gs[12:18, 1:4], zorder = subpzorder )
m = bb.Monitor()
m.source = []
frac = .1
m.spikes = calc_spikes.get_spike_times_ps(net, 0, frac)
bb.raster_plot(m, color=(0, 0, 0))
#sub_rast0.plot(numpy.array(m.spikes)[:,0],numpy.array(m.spikes)[:,1], '.')
# spikes of measured neuron in red o.,
# now it works only with neurons from the last group
nrn_spikes_all = net.mon_spike_e[net.nrn_meas_e[nrn]]
nrn_spikes0 = nrn_spikes_all[numpy.logical_and(
nrn_spikes_all >= xlim0[0]/1000., nrn_spikes_all <= xlim0[1]/1000.)]
nrn_spikes1 = nrn_spikes_all[numpy.logical_and(
nrn_spikes_all >= xlim1[0]/1000., nrn_spikes_all <= xlim1[1]/1000.)]
raster_sp_x0 = nrn_spikes0*1000.
raster_sp_y0 = (net.nrn_meas_e[nrn])*numpy.ones(len(nrn_spikes0))*frac
pyplot.plot(raster_sp_x0, raster_sp_y0, 'o', color='red', zorder=3)
pyplot.plot(raster_sp_x0, raster_sp_y0, '.', color='red', zorder=3)
sub_rast0.set_xlim(xlim0)
#sub_rast0.set_xticks([])
#sub_rast0.set_xticklabels([])
sub_rast0.set_xlabel('')
# put some lines between groups
for i in range(net.n_ass):
sub_rast0.plot(numpy.arange(xlim0[0], xlim0[1]),
(i+1)*frac*net.s_ass*numpy.ones(xlim0[1]-xlim0[0]),
'gray', zorder=1)
yticks=[0, 2500, 5000]
yticks=[0, 250, 500]
sub_rast0.set_ylabel('', size=20)
sub_rast0.set_yticks(yticks)
sub_rast0.set_yticklabels(yticks, size=labsize)
sub_rast0.xaxis.set_tick_params(width=1, length=5)
sub_rast0.yaxis.set_tick_params(width=1, length=5)
sub_rast0.set_xlim(xlim0)
sub_rast0.set_xticks(xticks0)
sub_rast0.set_xticks([])
sub_rast0.set_xticklabels(xticks0_lab, size=20)
sub_rast0.set_xticklabels([], size=20)
sub_rast0.text(xticks0[0]-130, yticks[-1], 'C', size=28, color='black')
sub_rast0.text(ylab_xposit, 375, 'Neuron #', rotation = 'vertical',
size=20)
pyplot.text(xlim0[0]+125-12, -60, '$\Uparrow$', size=25)
sub_rast0.yaxis.tick_right()
sub_rast0.spines['right'].set_visible(False)
###
sub_rast = pyplot.subplot(gs[12:18, 4:13], zorder = subpzorder)
m = bb.Monitor()
m.source = []
m.spikes = calc_spikes.get_spike_times_ps(net, 0, frac)
bb.raster_plot(m, color=(0, 0, 0))
#pyplot.plot(numpy.array(m.spikes)[:,0],numpy.array(m.spikes)[:,1], '.')
raster_sp_x1 = nrn_spikes1*1000.
raster_sp_y1 = (net.nrn_meas_e[nrn])*numpy.ones(len(nrn_spikes1))*frac
pyplot.plot(raster_sp_x1, raster_sp_y1, 'o', color='red')
pyplot.plot(raster_sp_x1, raster_sp_y1, '.', color='red')
# put some lines between groups
for i in range(net.n_ass):
sub_rast.plot(numpy.arange(xlim1[0], xlim1[1]),
(i+1)*frac*net.s_ass*numpy.ones(xlim1[1]-xlim1[0]), 'gray')
sub_rast.set_xlim(xlim1)
sub_rast.set_xticks(xticks1)
sub_rast.set_xticks([])
sub_rast.set_xlabel('')
sub_rast.spines['left'].set_visible(False)
sub_rast.set_ylabel('', size=20)
sub_rast.set_yticks([])
sub_rast.xaxis.set_tick_params(width=1, length=5)
sub_rast.yaxis.set_tick_params(width=1, length=5)
sub_rast.set_xticklabels([], size=20)
pyplot.text(xlim1[0]+375-12,-60, '$\Uparrow$', size=25)
#sub_rast.set_xticklabels(xticks1_lab, size=20)
#pyplot.xlabel('Time [ms]', size=20)
# size of the diagonal lines in axes coordinates
dx0 = .012
dy0 = .04
# the second subplot has different size than the prev
dx1 = dx0*3./8
dy1 = dy0*1.
kwargs = dict(transform=sub_rast0.transAxes, color='k', clip_on=False)
sub_rast0.yaxis.tick_left()
# top-right diagonal
sub_rast0.plot((1-dx0, 1+dx0), (1-dy0, 1+dy0), **kwargs)
# bottom-right diagonal
sub_rast0.plot((1-dx0, 1+dx0), (-dy0, +dy0), **kwargs)
kwargs.update(transform=sub_rast.transAxes) # switch to the bottom axes
# top-left diagonal
sub_rast.plot((-dx1, +dx1), (1-dy1, 1+dy1), **kwargs)
# bottom-left diagonal
sub_rast.plot((-dx1, dx1), (-dy1, +dy1), **kwargs)
################################################################
sub_curr0 = pyplot.subplot(gs[0:5, 1:4], zorder = subpzorder)
sub_curr0.patch.set_facecolor('None')
#lim for the vectors(with tres in mind)
cx0lim = [xlim0[0]/tres, xlim0[1]/tres]
cx1lim = [xlim1[0]/tres, xlim1[1]/tres]
yticks=[-200, 0, 200, 400]
curr_e= net.mon_ecurr_e[net.nrn_meas_e[nrn]]/pA+200
curr_i= net.mon_icurr_e[net.nrn_meas_e[nrn]]/pA
volt_e= net.mon_volt_e[net.nrn_meas_e[nrn]]/mV # measured voltage here
curr_leak = 10.*nS*(-60.-volt_e)*mV/pA
curr_maxe = max((curr_e[cx0lim[0]:cx0lim[1]]).max(),
(curr_e[cx1lim[0]:cx1lim[1]]).max())
curr_maxi = max((curr_i[cx0lim[0]:cx0lim[1]]).max(),
(curr_i[cx1lim[0]:cx1lim[1]]).max())
curr_mine = min((curr_e[cx0lim[0]:cx0lim[1]]).min(),
(curr_e[cx1lim[0]:cx1lim[1]]).min())
curr_mini = min((curr_i[cx0lim[0]:cx0lim[1]]).min(),
(curr_i[cx1lim[0]:cx1lim[1]]).min())
sub_curr0.plot(net.mon_ecurr_e.times/ms, curr_e, 'r', zorder=3)
sub_curr0.plot(net.mon_ecurr_e.times/ms, curr_i, 'b', zorder=1)
sub_curr0.plot(net.mon_ecurr_e.times/ms, curr_e+curr_i+curr_leak,
'black', zorder=2)
ylim=[1.02*min(curr_mine, curr_mini), 1.1*max(curr_maxe, curr_maxi)]
remove_spines(sub_curr0, xlim=xlim0, ylim=ylim, xticks=[], yticks=yticks,
ylabel='I [pA]', legend= [])
sub_curr0.set_ylabel('', size=20)
sub_curr0.text(ylab_xposit, 330, 'Current [pA]',
rotation = 'vertical', size=20)
sub_curr0.text(xticks0[0]-130, 500., 'A', size=28, color='black')
sub_curr0.set_yticks(yticks)
sub_curr0.set_yticklabels(yticks, size=20)
sub_curr0.yaxis.set_tick_params(width=1, length=5)
sub_curr = pyplot.subplot(gs[0:5, 4:13], zorder = subpzorder)
sub_curr.patch.set_facecolor('None')
pyplot.plot(net.mon_ecurr_e.times/ms, curr_e, 'r')
pyplot.plot(net.mon_ecurr_e.times/ms, curr_i, 'b')
pyplot.plot(net.mon_ecurr_e.times/ms, curr_e+curr_i+curr_leak, 'black')
ylim=[1.2*min(curr_mine, curr_mini), 1.2*max(curr_maxe, curr_maxi)]
remove_spines(sub_curr, xlim=xlim1, ylim=ylim, xticks=[], yticks=[],
ylabel='', legend= [])
sub_curr.set_ylabel('', size=20)
sub_curr.yaxis.set_tick_params(width=1, length=5)
sub_curr.text(xlim1[1]-87, -320., 'Inhibition', size=20,
color='b', zorder=3)
sub_curr.text(xlim1[1]-45, 80., 'Total', size=20, zorder=3)
sub_curr.text(xlim1[1]-92, 300., 'Excitation', size=20,
color='r', zorder=3)
'''
'''
sub_curr0.spines['right'].set_visible(False)
sub_curr.spines['left'].set_visible(False)
sub_curr0.yaxis.tick_left()
'''
kwargs.update(transform=sub_curr0.transAxes) # switch to the bottom axes
sub_rast0.yaxis.tick_left()
# top-right diagonal
sub_rast0.plot((1-dx0, 1+dx0), (1-dy0, 1+dy0), **kwargs)
# bottom-right diagonal
sub_rast0.plot((1-dx0, 1+dx0), (-dy0, +dy0), **kwargs)
kwargs.update(transform=sub_curr.transAxes) # switch to the bottom axes
# top-left diagonal
sub_rast.plot((-dx1, +dx1), (1-dy1, 1+dy1), **kwargs)
# bottom-left diagonal
sub_rast.plot((-dx1, dx1), (-dy1, +dy1), **kwargs)
'''
################################################################
#raster_sp_x = nrn_spikes*1000
#raster_sp_y = (491+nrn)*numpy.ones(len(nrn_spikes))
#sub_volt = pyplot.subplot(gs[16:20,0:12])
sub_volt0 = pyplot.subplot(gs[6:11,1:4], zorder = subpzorder)
sub_volt0.patch.set_facecolor('None')
ylim=[-61,-39]
yticks=[-60,-50,-40]
for i,v in enumerate(volt_e):
if v<-59 and volt_e[i-1]>-55:
volt_e[i-1] = -40
volt_sp_x0 = nrn_spikes0*1000
volt_sp_y0 = -40*numpy.ones(len(volt_sp_x0))
pyplot.plot(net.mon_volt_e.times/ms, volt_e,'black')
pyplot.plot(volt_sp_x0, volt_sp_y0, 'o', color='red')
remove_spines(sub_volt0, xlim=xlim0, ylim=ylim,
xticks=[], yticks=yticks,
ylabel='V [mV]', legend= [])
sub_volt0.set_ylabel('', size=20)
sub_volt0.text(ylab_xposit, -41, 'Voltage [mV]',
rotation='vertical', size=20)
sub_volt0.set_yticks(yticks)
sub_volt0.set_yticklabels(yticks, size=20)
sub_volt0.yaxis.set_tick_params(width=1, length=5)
sub_volt0.plot(numpy.arange(xlim0[0],xlim0[1]),
-50.*numpy.ones(xlim0[1]-xlim0[0]), '-.', color='gray')
sub_volt0.text(xticks0[0]-130, -40., 'B', size=28, color='black')
sub_volt = pyplot.subplot(gs[6:11,4:13], zorder = subpzorder)
sub_volt.patch.set_facecolor('None')
volt_sp_x1 = nrn_spikes1*1000
volt_sp_y1 = -40*numpy.ones(len(volt_sp_x1))
pyplot.plot(net.mon_volt_e.times/ms, volt_e,'black')
pyplot.plot(volt_sp_x1, volt_sp_y1, 'o', color='red')
remove_spines(sub_volt, xlim=xlim1, ylim=ylim,
xticks=[], yticks=yticks,
ylabel='', legend= [])
sub_volt.set_yticks([])
sub_volt.plot(numpy.arange(xlim1[0],xlim1[1]),
-50.*numpy.ones(xlim1[1]-xlim1[0]), '-.', color='gray')
sub_volt0.spines['right'].set_visible(False)
sub_volt.spines['left'].set_visible(False)
# draw vertical lines between spikes in raster and voltage trace
linii_nekvi=[]
curr_sp_y = 400.
for i in range(len(volt_sp_x0)):
linii_nekvi.append(get_straightline(figure, sub_rast0, sub_curr0,
raster_sp_x0[i], raster_sp_y0[i], volt_sp_x0[i], curr_sp_y))
for i in range(len(volt_sp_x1)):
linii_nekvi.append(get_straightline(figure, sub_rast, sub_curr,
raster_sp_x1[i], raster_sp_y1[i], volt_sp_x1[i], curr_sp_y))
if disc_only:
bar_x = xlim1[-1] - 100
bar_y = -60
x_len = 100
sub_volt.text(bar_x+.15*x_len, bar_y+2, str(x_len)+' ms', size=20)
transFigure = figure.transFigure.inverted()
coord1 = transFigure.transform(
sub_volt.transData.transform([bar_x+0, bar_y]))
coord2 = transFigure.transform(
sub_volt.transData.transform([bar_x+0+x_len, bar_y]))
line_bar = matplotlib.lines.Line2D((coord1[0], coord2[0]),
(coord1[1], coord2[1]), transform=figure.transFigure,
color='black', linewidth=3.)
linii_nekvi.append(line_bar)
figure.lines = linii_nekvi
from random import randint
loc='tfigs/rcv8/'
prefix = 'rccv_'
fname = 'prf' + str(net.pr_ee) + str(net.pf_ee_new) + '_' + \
str(-nrn) + '_int' + str(xlim1[0]) + '_' + str(randint(0, 99999))
figure.savefig(loc+prefix+fname+'rand_.eps', format='eps')
figure.savefig(loc+prefix+fname+'rand_.png', format='png')
figure.savefig(loc+prefix+fname+'rand_.pdf', format='pdf')
pyplot.show()
return 0
################################################################
# raster for continious ASS at the bottom of the figure
fname = 'contASS_pr0.1pfboost0.04frac0.1.npz'
#fname = 'contin_modified.npz'
xlim0 = [19375, 19625]
xlim2 = [28625, 29375]
#xticks=numpy.arange(x0, x1+1, 200)
#xticks_lab = numpy.arange(0, x1-x0+1, 200)
xticks=[]
# first tick in second plot in plotting time
shift_xtick = dx_tick - (xlim0[1]-xlim0[0]) % dx_tick
# last tick
first_xtick= ((xlim0[1]-xlim0[0]+dx_tick)//dx_tick)*dx_tick
last_xtick = first_xtick + xlim2[1]-xlim2[0] + 1
xticks2 = numpy.arange(xlim2[0] + shift_xtick, xlim2[1]+1, dx_tick)
sub_rast20 = pyplot.subplot(gs[20:26, 1:4], zorder = subpzorder)
draw_evoked_rasta2(fname, '', x0=xlim0[0], x1=xlim0[1])
yticks=[0, 100, 200, 300, 400, 500]
yticks=[0, 250, 500]
#yticks=[0, 2500, 5000]
sub_rast20.set_xticks(xticks0)
sub_rast20.set_xticks([])
sub_rast20.set_xticklabels([])
sub_rast20.set_yticks(yticks)
sub_rast20.set_yticklabels(yticks, size=20)
sub_rast20.text(xlim0[0]-80, 375, 'Neuron #',
rotation = 'vertical', size=20)
sub_rast20.text(xlim0[0]-130, yticks[-1], 'D', size=28, color='black')
sub_rast20.spines['right'].set_visible(False)
sub_rast20.xaxis.set_tick_params(width=1, length=5)
sub_rast20.yaxis.set_tick_params(width=1, length=5)
pyplot.text(xlim0[0]+125-12,-60, '$\Uparrow$', size=25)
kwargs = dict(transform=sub_rast20.transAxes, color='k', clip_on=False)
sub_rast20.yaxis.tick_left()
# top-right diagonal
sub_rast20.plot((1-dx0, 1+dx0), (1-dy0, 1+dy0), **kwargs)
# bottom-right diagonal
sub_rast20.plot((1-dx0, 1+dx0), (-dy0, +dy0), **kwargs)
#####
sub_rast2 = pyplot.subplot(gs[20:26, 4:13], zorder = subpzorder)
draw_evoked_rasta2(fname, '', x0=xlim2[0], x1=xlim2[1])
sub_rast2.xaxis.set_tick_params(width=1, length=5)
sub_rast2.yaxis.set_tick_params(width=1, length=5)
#sub_rast.set_ylabel('Neurons',size=18)
sub_rast2.set_xticks(xticks2)
sub_rast2.set_xticks([])
sub_rast2.set_xticklabels([])
#pyplot.xlabel('Time [ms]', size=20)
sub_rast2.spines['left'].set_visible(False)
kwargs.update(transform=sub_rast2.transAxes) # switch to the bottom axes
# top-left diagonal
sub_rast2.plot((-dx1, +dx1), (1-dy1, 1+dy1), **kwargs)
# bottom-left diagonal
sub_rast2.plot((-dx1, dx1), (-dy1, +dy1), **kwargs)
pyplot.text(xlim2[0]+375-12,-60, '$\Uparrow$', size=25)
'''
bar_x = xlim2[-1] - 100
bar_y = -100
x_len = 100
pyplot.text(bar_x+.15*x_len, bar_y+20, str(x_len)+' ms', size=20)
transFigure = figure.transFigure.inverted()
coord1 = transFigure.transform(
sub_rast2.transData.transform([bar_x+0, bar_y]))
coord2 = transFigure.transform(
sub_rast2.transData.transform([bar_x+0+x_len, bar_y]))
'''
bar_x = xlim1[-1] - 100
bar_y = -60
x_len = 100
sub_volt.text(bar_x+.15*x_len, bar_y+2, str(x_len)+' ms', size=20)
transFigure = figure.transFigure.inverted()
coord1 = transFigure.transform(
sub_volt.transData.transform([bar_x+0, bar_y]))
coord2 = transFigure.transform(
sub_volt.transData.transform([bar_x+0+x_len, bar_y]))
line_bar = matplotlib.lines.Line2D((coord1[0], coord2[0]),
(coord1[1], coord2[1]), transform=figure.transFigure,
color='black', linewidth=3.)
linii_nekvi.append(line_bar)
figure.lines = linii_nekvi
#1/0
#pyplot.show()
#return 0
from random import randint
loc='tfigs/rcv7/'
prefix = 'rccv_'
fname = 'prf' + str(net.pr_ee) + str(net.pf_ee) + '_' + \
str(-nrn) + '_int' + str(xlim1[0]) + '_' + str(randint(0, 99999))
figure.savefig(loc+prefix+fname+'.eps', format='eps')
figure.savefig(loc+prefix+fname+'.png', format='png')
figure.savefig(loc+prefix+fname+'.pdf', format='pdf')
#pyplot.close()
def plot_rast_V_I_contraster(net, nrn=-2, xlim = [16500,17500]):
"""
For plotting Fig 2
plots a ratser of a reduced PS,
conductances, currents, voltage of a neuron in a single figure
puts an extra raster of a continuous sequence replay
"""
from figure_subplots import draw_evoked_rasta2
tres = .1 # time resolution for conductance
figure = pyplot.figure(figsize=(18., 12.))
gs = gridspec.GridSpec(25, 12)
gs.update(left=.04, wspace=.0, hspace=.0)
ylab_xposit = xlim[0] - 80
################################################################
# raster for continious ASS at the bottom of the figure
x0 = 20500
x1 = 21500
xticks=numpy.arange(x0, x1+1, 200)
xticks_lab = numpy.arange(0, x1-x0+1, 200)
sub_rast2 = pyplot.subplot(gs[19:25, 1:12])
fname = 'contin_ass_pf0.06pr0.06.npz'
draw_evoked_rasta2(fname, '', x0=x0, x1=x1)
yticks=[0, 100, 200, 300, 400, 500]
yticks=[0, 2500, 5000]
sub_rast2.set_yticks(yticks)
sub_rast2.set_yticklabels(yticks, size=20)
sub_rast2.xaxis.set_tick_params(width=1, length=5)
sub_rast2.yaxis.set_tick_params(width=1, length=5)
#sub_rast.set_ylabel('Neurons',size=18)
sub_rast2.text(x0-80, 3750, 'Neuron #', rotation = 'vertical', size=20)
sub_rast2.set_xticks(xticks)
sub_rast2.set_xticklabels(xticks_lab, size=20)
pyplot.xlabel('Time [ms]', size=20)
sub_rast2.text(xticks[0]-130, 5000., 'D', size=28, color='black')
################################################################
xticks=numpy.arange(xlim[0], xlim[1]+1, 200)
xticks_lab = numpy.arange(0, xlim[1]-xlim[0]+1, 200)
cxlim = [xlim[0]/tres, xlim[1]/tres] #lim for the vectors(with tres in mind)
################################################################
sub_rast = pyplot.subplot(gs[12:18, 1:12])
m = bb.Monitor()
m.source = []
frac = 1.
m.spikes = calc_spikes.get_spike_times_ps(net, 0, frac)
bb.raster_plot(m, color=(0, 0, 0))#, #showgrouplines=True,
#spacebetweengroups=0.1, grouplinecol=(0.5, 0.5, 0.5))
# spikes of measured neuron in red o.,
# now it works automatically just with neurons from the last group
nrn_spikes_all = net.mon_spike_e[net.nrn_meas_e[nrn]]
nrn_spikes = nrn_spikes_all[numpy.logical_and(
nrn_spikes_all >= xlim[0]/1000., nrn_spikes_all <= xlim[1]/1000.)]
raster_sp_x = nrn_spikes*1000.
raster_sp_y = (net.nrn_meas_e[nrn])*numpy.ones(len(nrn_spikes))
pyplot.plot(raster_sp_x, raster_sp_y, 'o', color='red')
pyplot.plot(raster_sp_x, raster_sp_y, '.', color='red')
sub_rast.set_xlim(xlim)
sub_rast.set_xticks(xticks)
sub_rast.set_xticklabels([])
sub_rast.set_xlabel('')
# put some lines between groups
for i in range(net.n_ass):
sub_rast.plot(numpy.arange(xlim[0],xlim[1]),
(i+1)*frac*net.s_ass*numpy.ones(1000),'gray')
# put an arrow for the neuron we record from
#pyplot.text(xlim[0]+500-18, -60, '$\Uparrow$', size=25)
yticks=[0, 100, 200, 300, 400, 500]
yticks=[0, 2500, 5000]
sub_rast.set_ylabel('', size=20)
sub_rast.set_yticks(yticks)
sub_rast.set_yticklabels(yticks, size=20)
sub_rast.xaxis.set_tick_params(width=1, length=5)
sub_rast.yaxis.set_tick_params(width=1, length=5)
sub_rast.text(ylab_xposit, 3750, 'Neuron #', rotation = 'vertical', size=20)
#sub_rast.set_xticklabels(xticks_lab, size=20)
#pyplot.xlabel('Time [ms]', size=20)
sub_rast.text(xticks[0]-130, 5000., 'C', size=28, color='black')
################################################################
#sub_curr = pyplot.subplot(gs[9:13,0:12])
sub_curr = pyplot.subplot(gs[0:5,1:12])
yticks=[-200,0,200,400]
curr_e= net.mon_ecurr_e[net.nrn_meas_e[nrn]]/pA+200
curr_i= net.mon_icurr_e[net.nrn_meas_e[nrn]]/pA
volt_e= net.mon_volt_e[net.nrn_meas_e[nrn]]/mV # measured voltage here
curr_leak = 10.*nS*(-60.-volt_e)*mV/pA
curr_maxe=(curr_e[cxlim[0]:cxlim[1]]).max()
curr_maxi=(curr_i[cxlim[0]:cxlim[1]]).max()
curr_mine=(curr_e[cxlim[0]:cxlim[1]]).min()
curr_mini=(curr_i[cxlim[0]:cxlim[1]]).min()
pyplot.plot(net.mon_ecurr_e.times/ms, curr_e,'r')
pyplot.plot(net.mon_ecurr_e.times/ms, curr_i,'b')
pyplot.plot(net.mon_ecurr_e.times/ms, curr_e+curr_i+curr_leak,'black')
ylim=[1.2*min(curr_mine,curr_mini),1.2*max(curr_maxe,curr_maxi)]
remove_spines(sub_curr,xlim=xlim,ylim=ylim,xticks=[],yticks=yticks,
#ylabel='I [pA]',legend= ['$I_{exc}$','$I_{inh}$'])
ylabel='I [pA]',legend= [])
sub_curr.set_ylabel('',size=20)
sub_curr.text(ylab_xposit, 370, 'Current [pA]',
rotation = 'vertical', size=20)
sub_curr.set_yticks(yticks)
sub_curr.set_yticklabels(yticks,size=20)
sub_curr.yaxis.set_tick_params(width=1, length=5)
#sub_curr.text(xticks[-1]-00,-200.,'$I_{inh}$',color='red',size=18)
#sub_curr.text(xticks[-1]-00,50.,'$I_{tot}$',color='black',size=18)
#sub_curr.text(xticks[-1]-00,250.,'$I_{exc}$',color='green',size=18)
sub_curr.text(xticks[-2]-5, -320., 'Inhibition', size=20,
color='b', zorder=3)
sub_curr.text(xticks[-2]-5, 80., 'Total', size=20, zorder=3)
sub_curr.text(xticks[-2]-5, 300., 'Excitation', size=20,
color='r', zorder=3)