def plot_example_snr(ax, SNR):
time_bin = 500
colors = misc.make_N_colors('Blues', 5)
colors = ['g', 'r', colors[1], colors[2], colors[3]]
signal = SNR.signals['spikes']
signal.my_firing_rate(id_list=[SNR[0]],
bin=time_bin,
display=ax,
kwargs={'color': colors[0]})
signal.my_firing_rate(id_list=[SNR[1]],
bin=time_bin,
display=ax,
kwargs={'color': colors[1]})
signal.my_firing_rate(id_list=[SNR[2]],
bin=time_bin,
display=ax,
kwargs={'color': colors[2]})
#signal.my_firing_rate(id_list=[SNR[3]], bin=time_bin, display=ax,
# kwargs={'color':colors[3]})
#signal.my_firing_rate(id_list=[SNR[4]], bin=time_bin, display=ax,
# kwargs={'color':colors[4]})
ax.my_set_no_ticks(yticks=4, xticks=4)
ax.set_ylim([0, 40])
ax.text(7000, 33, 'Weak', **{'color': colors[0]})
ax.text(4000, 7, 'Strong', **{'color': colors[1]})
ax.text(7000, 24, 'Set 1', **{'color': colors[2]})
#ax.text( 7000, 20, 'Set 1+2' , **{ 'color' : colors[3] })
#ax.text( 7000, 16, 'Set 2' , **{ 'color' : colors[4] })
#ax.legend(('Weak','Strong','Data 1','Data 1+2','Data 2'), loc='best')
ax.set_title('bin=%i' % (time_bin), **{'fontsize': 12})
ax.set_ylabel('Frequency SNr (Hz)')
def plot_selection_vs_neurons(ax, GPEmeanRates, SNRmeanRates):
colors = misc.make_N_colors('Blues', 5)
colors = ['g', 'r', colors[1], colors[2], colors[3]]
ax.plot(GPEmeanRates, SNRmeanRates[0, :], **{
'label': 'Weak',
'color': colors[0]
})
ax.plot(GPEmeanRates, SNRmeanRates[1, :], **{
'label': 'Strong',
'color': colors[1]
})
ax.plot(GPEmeanRates, SNRmeanRates[2, :], **{
'label': 'Data 1',
'color': colors[2]
})
#ax.plot(GPEmeanRates,SNRmeanRates[3,:],**{'label':'Data 1+2','color':colors[3]})
#ax.plot(GPEmeanRates,SNRmeanRates[4,:],**{'label':'Data 3','color':colors[4]})
ax.set_ylabel('Frequency SNr (Hz)')
ax.set_xlabel('Frequency GPEs (Hz)')
ax.my_set_no_ticks(yticks=4, xticks=4)
ax.set_ylim([0, 40])
#ax.set_xlim([0,1.4])
ax.text(0.7, 0.85, 'Weak', transform=ax.transAxes, **{'color': colors[0]})
ax.text(0.7,
0.75,
'Strong',
transform=ax.transAxes,
**{'color': colors[1]})
ax.text(0.7, 0.65, 'Set 1', transform=ax.transAxes, **{'color': colors[2]})
def plot_example_SNR(ax, SNR_list):
time_bin = 100
colors = misc.make_N_colors('Blues', 5)
colors = ['g', colors[1]]
labels = ['static', 'plastic']
for color, label, SNR in zip(colors, labels, SNR_list):
signal = SNR.signals['spikes']
signal.my_firing_rate(bin=time_bin,
display=ax,
kwargs={'color': color})
ax.set_title('bin=%i' % (time_bin), **{'fontsize': 12})
ax.set_ylabel('Frequency SNr (Hz)')
ax.set_xlabel('Time (ms)')
ax.my_set_no_ticks(yticks=4, xticks=4)
ax.set_xlim([2000, 3500])
#ax.set_ylim([10,30])
#ax.text('Data 1')
ax.text(0.05,
0.45,
'static',
transform=ax.transAxes,
**{'color': colors[0]})
ax.text(0.05,
0.25,
'plastic',
transform=ax.transAxes,
**{'color': colors[1]})
def plot_example_3x_freq(ax, times, spk_mean, hzs=[10, 20, 40]):
spk_mean = spk_mean
colors = misc.make_N_colors('YlOrBr', 5)[2:]
labels = ['%i Hz' % hzs[0], '%i Hz' % hzs[1], '%i Hz' % hzs[2]]
coords = [[0.4, 0.52], [0.05, 0.22], [0.1, 0.74]]
for i, col in enumerate(colors):
ax.plot(times,
misc.convolve(spk_mean[i, :], 50, 'triangle', single=True)[0],
color=col)
#ax.plot(times, spk_mean[i,:], col)
lines = ax.lines
for line in lines:
line.set_xdata(line.get_xdata() - ADJUST_XDATA_MS)
misc.slice_line(line, xlim=[0, 1490])
#ax.set_ylim([0,40])
for label, coord, color in zip(labels, coords, colors):
ax.text(coord[0],
coord[1],
label,
transform=ax.transAxes,
fontsize=font_size_text,
**{'color': color})
ax.set_ylabel('Rate SNr (spikes/s)')
ax.set_xlabel('Time (ms)')
ax.my_set_no_ticks(yticks=6, xticks=8)
ax.set_xlim(misc.adjust_limit([0, 1500]))
ax.set_ylim(misc.adjust_limit([0, 40]))
def plot_example_SNR(ax, SNR_list):
time_bin = 5
colors = misc.make_N_colors('Blues', 5)
colors = ['g', 'm']
labels = ['Ref', 'Dyn GPe']
SNR_list = [SNR_list[0], SNR_list[2]]
for color, label, SNR in zip(colors, labels, SNR_list):
signal = SNR.signals['spikes']
signal.my_firing_rate(bin=time_bin,
display=ax,
kwargs={'color': color})
lines = ax.lines
lines[0].set_xdata(lines[0].get_xdata() - ADJUST_XDATA_MS)
lines[1].set_xdata(lines[1].get_xdata() - ADJUST_XDATA_MS)
ax.set_title('bin=%i' % (time_bin), **{'fontsize': 12})
ax.set_ylabel('Frequency SNr (Hz)')
ax.set_xlabel('Time (ms)')
#ax.set_ylim([0,55])
ax.my_set_no_ticks(yticks=4, xticks=4)
ax.set_xlim([0, 500])
t_a = ax.transAxes
ax.text(0.1, 0.45, 'Ref', transform=t_a, **{'color': colors[0]})
#ax.text( 0.05, 0.25, 'Strong' , transform=t_a, **{ 'color' : colors[1] })
ax.text(0.1, 0.35, 'Dyn GPe', transform=t_a, **{'color': 'm'})
def plot_example_firing_rate(ax, groups, name, color='b', ylim=[], ylabel=True, xlabel=False):
time_bin=20
colors=misc.make_N_colors('gist_rainbow', len(groups))
#signal.my_firing_rate(bin=time_bin, display=ax,
# kwargs={'color':color})
for group, col in zip(groups, colors):
signal=group.signals['spikes']
print name, 'CV:', numpy.mean(signal.cv_isi())
print name, 'mean:', signal.mean_rate()
print name, 'std:', signal.mean_rate_std()
hist=signal.spike_histogram(time_bin=1, normalized=True)
spk_mean=numpy.mean(hist, axis=0)
spk_mean=misc.convolve(spk_mean, 100, 'triangle',single=True)[0]
print spk_mean.shape
time=numpy.arange(1,len(spk_mean)+1)
ax.plot(time,spk_mean, **{'color':col})
ax.set_ylabel('Firing rate '+name+'\n (spikes/s)', fontsize=12.,multialignment='center')
ax.set_xlabel('Time (ms)')
ax.my_set_no_ticks( yticks=6, xticks = 8 )
ax.set_ylim(ylim)
ax.set_xlim([time[0],time[-1]])
ax.my_remove_axis( xaxis=not xlabel, yaxis=not ylabel )
def plot_max_freq(ax, frequencies, relativeFacilitation ):
colors = misc.make_N_colors('Blues', 5)
ax.plot(frequencies,relativeFacilitation[0,:],**{'label':'Data 1',
'color':colors[1]})
ax.set_ylabel('Pmax/P1')
ax.set_xlabel('Frequency (Hz)')
ax.my_set_no_ticks( yticks=4, xticks = 4 )
ax.text( 0.4, 0.4,'Set 1' , transform=ax.transAxes, **{ 'color' : colors[1] })
def plot_recovery(ax, revoceryTimes, relativeRecovery ):
colors = misc.make_N_colors('Blues', 5)
ax.plot(revoceryTimes,relativeRecovery[0,:],**{'label':'Data 1',
'color':colors[1]})
ax.set_ylabel('Prec/P1')
ax.set_xlabel('Time (ms)')
ax.my_set_no_ticks( yticks=4, xticks = 4 )
ax.text( 0.4, 0.5,'Set 1' , transform=ax.transAxes, **{ 'color' : colors[1] })
ax.text( 0.4, 0.4, 'Set 1+2' , transform=ax.transAxes, **{ 'color' : colors[2] })
ax.text( 0.4, 0.3, 'Set 2' , transform=ax.transAxes, **{ 'color' : colors[3] })
def plot_selection(ax, data, legend=True):
#! Colors for spike voltage traces fo AMPA, NMDA and GANA (color 0, 1 and 2 in
#! ``color_list``).
color_map = 'Set1'
no_colors = 6
color_list = misc.make_N_colors(cmap_name=color_map, N=no_colors)
sel_dic = {0: [], 1: [], 2: []}
for d in zip(*data):
sel_dic[d[4]].append((d[0], d[1]))
pprint.pprint(sel_dic)
plot_prop = [(sel_dic[2], 'k.', 'No selection'),
(sel_dic[0], 'ob', 'Selection'),
(sel_dic[1], 'ys', 'Intermediate')]
#! Create figure where figsize(width,height) and figure dimenstions window
#! width = figsize(width) x dpi and window hight = figsize(hight) x dpi
#fig=pylab.figure(figsize=(11, 10), dpi=50)
#! [left, bottom, width, hight]
#ax=pylab.axes([0.05, .25, .9, .7])
iter = 0
for cords, color_marker, sel in plot_prop:
if cords != []:
cords_zip = zip(*cords)
ax.plot(cords_zip[0],
cords_zip[1],
color_marker,
markersize=20,
label=sel,
zorder=2)
iter += 1
# beautify
#ax.axis([0.0, 48.0, 0, 48])
#ax.set_xlim(misc.adjust_limit([15, 80]))
#ax.set_ylim(misc.adjust_limit([15,50]))
#ax.set_aspect('equal', 'box')
#ax.set_axes([0.1, 0.5, 0.7,0.1])
#ax.set_xticks((0, 4, 8, 12 ,16, 20, 24, 28,32,36,40))
#ax.set_yticks((0, 4, 8, 12 ,16, 20, 24, 28,32,36,40))
ax.grid(True)
ax.set_ylabel('Burst rate (Spikes/s)')
ax.set_xlabel('Bursting $MSN_{D1}$ (%)')
if legend:
ax.legend(numpoints=1, ncol=1, loc=[-1.3, 0.5], borderpad=.75)
ax.my_set_no_ticks(yticks=8, xticks=4)
def plot_example_SNR(ax, SNR_list, flag):
time_bin=20
colors=misc.make_N_colors('cool',3)
colors=['m','c',colors[1]]
#labels=[r'$\delta_{fac}^{MSN}$' , r'$\delta_{dep}^{GPe}$',
# r'$\delta_{fac}^{MSN}$+$\delta_{dep}^{GPe}$']
coords=[[0.05, 0.2], [ 0.05, 0.65], [0.05, 0.8]]
SNR_list=[SNR_list[0], SNR_list[1], SNR_list[2]]
for color, SNR in zip(colors, SNR_list):
signal=SNR.signals['spikes']
signal.my_firing_rate(bin=time_bin, display=ax, kwargs={'color':color})
lines = ax.lines
for line in lines:
line.set_xdata(line.get_xdata()-ADJUST_XDATA_MS)
misc.slice_line(line, xlim=[0,1490])
if flag==0:
leg=ax.legend(lines,['Burst in MSN subpopulation' , 'Burst in GPe subpopulation',
'Burst in both MSN- and GPe subpopulations'], loc=2)
if flag==1:
leg=ax.legend(lines,['Burst in MSN subpopulation' , 'Pause in GPe subpopulation',
'Burst in MSN- and pause in GPe subpopulations'], loc=2)
frame = leg.get_frame()
#frame.set_visible(False)
pylab.setp(frame, edgecolor='w')
ltext = leg.get_texts() # all the text.Text instance in the legend
pylab.setp(ltext, fontsize=12., backgroundcolor='w')
ax.plot([0, 1490],[SELECTION_THR]*len([0, 1490]),**{'color':[0.6,0.6,0.6],
'label':'', 'linewidth':1,
'linestyle':'--'})
ax.text( 0.9, 0.11,'Thr' , transform=ax.transAxes, **{ 'color' : [0.6,0.6,0.6]})
#ax.set_title('bin=%i'%(time_bin),**{'fontsize':12})
ax.set_ylabel('Firing rate SNr (spikes/s)')
ax.set_xlabel('Time (ms)')
ax.my_set_no_ticks( yticks=7, xticks=8 )
ax.set_xlim(misc.adjust_limit([0,1500]))
ax.set_ylim(misc.adjust_limit([0,115]))
def plot_selection_vs_neurons_full(ax, hzs, data):
colors = misc.make_N_colors('Blues', 5)
colors = ['g', 'r', colors[1], colors[2], colors[3]]
syn = SYNAPSE_MODELS
ax.plot(hzs, data[syn[0]]['thrVec'], **{
'label': 'Weak',
'color': colors[0]
})
ax.plot(hzs, data[syn[1]]['thrVec'], **{
'label': 'Strong',
'color': colors[1]
})
ax.plot(hzs, data[syn[2]]['thrVec'], **{
'label': 'Set 1',
'color': colors[2]
})
ax.plot(hzs, data[syn[3]]['thrVec'], **{
'label': 'Set 1+2',
'color': colors[3]
})
ax.plot(hzs, data[syn[4]]['thrVec'], **{
'label': 'Set 2',
'color': colors[4]
})
ax.set_xlabel('Frequency GPE (Hz)')
ax.set_ylabel('GPEs at thr (#)')
ax.my_set_no_ticks(yticks=4, xticks=5)
ax.set_xlim([0, 51])
ax.set_ylim([0, 75])
#ax.text('Data 1')
ax.text(0.7, 0.85, 'Weak', transform=ax.transAxes, **{'color': colors[0]})
ax.text(0.7,
0.75,
'Strong',
transform=ax.transAxes,
**{'color': colors[1]})
ax.text(0.7, 0.65, 'Set 1', transform=ax.transAxes, **{'color': colors[2]})
ax.text(0.7,
0.55,
'Set 1+2',
transform=ax.transAxes,
**{'color': colors[3]})
ax.text(0.7, 0.45, 'Set 2', transform=ax.transAxes, **{'color': colors[4]})
def plot_SNr_rate_std_vs_syn_event_x3_late(ax, hzs):
colors = misc.make_N_colors('YlOrBr', 5)[2:]
labels = ['%i Hz' % hzs[0], '%i Hz' % hzs[1], '%i Hz' % hzs[2]]
coords = [[0.4, 0.52], [0.05, 0.22], [0.1, 0.74]]
i_inverval = 1 # 0-100 ms
i_syn = 2 # Plastic
for hz, col in zip(hzs, colors):
nb, rate_data, r_std_data, n_max_sel, s = simulate_selection_vs_neurons(
SEL_INTERVALS, msn_burst_rate=hz, load=True)
syn_events = nb * hz + (N_MSN - nb) * 0.1
ax.plot(
syn_events,
rate_data[i_inverval][i_syn, :] / r_std_data[i_inverval][i_syn, :],
**{'color': col})
for label, coord, color in zip(labels, coords, colors):
ax.text(coord[0],
coord[1],
label,
transform=ax.transAxes,
fontsize=font_size_text,
**{'color': color})
ax.set_ylabel('Signal to noise ratio')
ax.set_xlabel('Input SNr (events/s)')
lines = ax.lines
for line in lines:
misc.slice_line(line, xlim=[0, LIM_SYN_EVENTS])
ax.my_set_no_ticks(yticks=6, xticks=5)
ax.set_xlim(misc.adjust_limit([50, LIM_SYN_EVENTS]))
ax.set_ylim(misc.adjust_limit([0, 4]))
ax.text(0.1,
0.85,
'Last 100 ms of the burst',
transform=ax.transAxes,
fontsize=font_size_text,
**{'color': 'k'})
def plot_selection_vs_neurons2(ax, nbNeurons, meanRates):
colors = misc.make_N_colors('Blues', 5)
colors = ['g', 'r', colors[1], colors[2], colors[3]]
ax.plot(nbNeurons, meanRates[0, :], **{
'label': 'Weak',
'color': colors[0]
})
ax.plot(nbNeurons, meanRates[1, :], **{
'label': 'Strong',
'color': colors[1]
})
ax.plot(nbNeurons, meanRates[2, :], **{
'label': 'Set 1',
'color': colors[2]
})
ax.plot(nbNeurons, meanRates[3, :], **{
'label': 'Set 1+2',
'color': colors[3]
})
ax.plot(nbNeurons, meanRates[4, :], **{
'label': 'Set 2',
'color': colors[4]
})
ax.plot(nbNeurons, [SELECTIONTHR] * len(nbNeurons), '--k', **{'label': ''})
ax.set_ylabel('Frequency SNr (Hz)')
ax.set_xlabel('GPEs (#)')
ax.my_set_no_ticks(yticks=4, xticks=4)
ax.set_xlim([0, 61])
ax.text(0.7, 0.85, 'Weak', transform=ax.transAxes, **{'color': colors[0]})
ax.text(0.7,
0.75,
'Strong',
transform=ax.transAxes,
**{'color': colors[1]})
ax.text(0.7, 0.65, 'Set 1', transform=ax.transAxes, **{'color': colors[2]})
ax.text(0.7,
0.55,
'Set 1+2',
transform=ax.transAxes,
**{'color': colors[3]})
ax.text(0.7, 0.45, 'Set 2', transform=ax.transAxes, **{'color': colors[4]})
def plot_example_snr(ax, SNR):
time_bin=500
colors = misc.make_N_colors('Blues', 5)
colors=['g','r', colors[1], 'b', colors[3]]
signal=SNR.signals['spikes']
signal.my_firing_rate(id_list=[SNR[0]], bin=time_bin, display=ax,
kwargs={'color':colors[0]})
signal.my_firing_rate(id_list=[SNR[1]], bin=time_bin, display=ax,
kwargs={'color':colors[1]})
signal.my_firing_rate(id_list=[SNR[3]], bin=time_bin, display=ax,
kwargs={'color':colors[3]})
ax.my_set_no_ticks( yticks=4, xticks = 4 )
ax.set_ylim([0,40])
ax.text( 6500, 33, 'Ref 1' , **{ 'color' : colors[0] })
ax.text( 4000, 7, 'Ref 2' , **{ 'color' : colors[1] })
ax.text( 6500, 24, 'Dyn MSN' , **{ 'color' : colors[3] })
ax.set_title('bin=%i'%(time_bin),**{'fontsize':12})
ax.set_ylabel('Frequency SNr (Hz)')
def plot_selection(ax, data, legend=True):
#! Colors for spike voltage traces fo AMPA, NMDA and GANA (color 0, 1 and 2 in
#! ``color_list``).
color_map = 'Set1'
no_colors = 6
color_list = misc.make_N_colors(cmap_name=color_map, N=no_colors)
sel_dic = {0: [], 1: [], 2: []}
for d in zip(*data):
sel_dic[d[4]].append((d[0], d[1]))
pprint.pprint(sel_dic)
plot_prop = [(sel_dic[2], 'k.', 'No selection'),
(sel_dic[0], 'ob', 'Selection'),
(sel_dic[1], 'ys', 'Intermediate')]
iter = 0
for cords, color_marker, sel in plot_prop:
if cords != []:
cords_zip = zip(*cords)
ax.plot(cords_zip[0],
cords_zip[1],
color_marker,
markersize=20,
label=sel,
zorder=2)
iter += 1
ax.grid(True)
ax.set_ylabel('Bursting $MSN_{D2}$ (%)')
ax.set_xlabel('Bursting $MSN_{D1}$ (%)')
if legend:
ax.legend(numpoints=1, ncol=1, loc=[-1.3, 0.5], borderpad=.75)
ax.my_set_no_ticks(yticks=8, xticks=4)
def plot_max_freq(ax, frequencies, relativeFacilitation):
colors = misc.make_N_colors('Blues', 5)
ax.plot(frequencies, relativeFacilitation[0, :], **{
'label': 'Data 1',
'color': colors[1]
})
ax.plot(frequencies, relativeFacilitation[1, :], **{
'label': 'Data 1+2',
'color': colors[2]
})
ax.plot(frequencies, relativeFacilitation[2, :], **{
'label': 'Data 2',
'color': colors[3]
})
ax.set_ylabel('Pmax/P1')
ax.set_xlabel('Firing rate (spikes/s)')
ax.my_set_no_ticks(yticks=4, xticks=4)
ax.text(0.4, 0.4, 'Set 1', transform=ax.transAxes, **{'color': colors[1]})
ax.text(0.4,
0.3,
'Set 1+2',
transform=ax.transAxes,
**{'color': colors[2]})
ax.text(0.4, 0.2, 'Set 2', transform=ax.transAxes, **{'color': colors[3]})
def plot_example_raster(ax, groups, name, ylabel=True, xlabel=False):
global ADJUST_XDATA_MS
colors=misc.make_N_colors('gist_rainbow', len(groups))
for group, col in zip(groups, colors):
group.signals['spikes'].raster_plot(display=ax,
kwargs={'color':col, 'zorder':1})
lines = ax.lines
ax.set_ylabel(name+' id')
ax.my_set_no_ticks( yticks=6, xticks = 5 )
ax.set_ylim([groups[0].ids[0],groups[-1].ids[-1]])
for line in lines:
line.set_xdata(line.get_xdata()-ADJUST_XDATA_MS)
ax.set_xlim([0.,10500])
#ax.set_xlim(misc.adjust_limit([0,1500]))
#if not ylabel: ax.set_ylabel('')
#if not xlabel: ax.set_xlabel('')
ax.my_remove_axis( xaxis=not xlabel, yaxis=not ylabel )
# MSN mean rate
ax = ax_list[2]
my_nest.ResetKernel()
MSN = create_input_population(nInput, nRep=nRep, simTime=simTime)
plot_input_mean_rate(ax,
binSize=binSize,
Input=MSN,
nRep=nRep,
nYticks=nb_yticks_sr,
simTime=simTime)
ax.set_xlim(tLim)
# Example plots
axes = [ax_list[3], ax_list[4], ax_list[5], ax_list[6], ax_list[7]]
my_nest.ResetKernel()
colors = misc.make_N_colors('Blues', 5)
colors = ['g', 'r', colors[1], colors[2], colors[3]]
plot_output_example(axes, colors, SNR_example_plot, useSynapses)
# SNR mean rate
ax = ax_list[8]
labels = [
'Weak static', 'Strong static', 'Plastic data I', 'Plastic data I+II',
'Plastic data II'
]
plot_output_mean_rate(ax, binSize, colors, labels, nRep, SNR_mean_rate,
nOutputPerSynapse, simTime, synapses)
ax.legend(numpoints=1, loc=[1.1, 1.6])
ax = ax_list[8]
ax.set_xlim(tLim)
