def plotSingleTuningCurve(dataPath, gaussianDistance, ax1=None):
b.rcParams['font.size'] = 20
averagingWindowSize = 1
nE = 1600
ymax = 1
if ax1==None:
b.figure(figsize=(8,6.5))
fig_axis = b.subplot(1,1,1)
else:
fig_axis = ax1
b.sca(ax1)
path = dataPath + '/dist'+str(gaussianDistance)+'/' +'activity/'
spikeCount = np.load(path + 'spikeCountPerExample.npy')
spikeCountSingleNeuron = (spikeCount[:,nE/2-2,0])
numMeasurements = len(spikeCount[:,0,0])
spikeCount = movingaverage(spikeCountSingleNeuron,averagingWindowSize)
spikeCount /= np.max(spikeCountSingleNeuron)
b.plot(spikeCount, color='deepskyblue', marker='o', alpha=0.6, linewidth=0, label='Output')#alpha=0.5+(0.5*float(i)/float(len(lower_peaks))),
fig_axis.set_xticks([0., numMeasurements/2, numMeasurements])
fig_axis.set_xticklabels(['0', '0.5', '1'])
fig_axis.set_yticks([0., ymax/2, ymax])
fig_axis.spines['top'].set_visible(False)
fig_axis.spines['right'].set_visible(False)
fig_axis.get_xaxis().tick_bottom()
fig_axis.get_yaxis().tick_left()
b.ylabel('Normalized response')
b.ylim(0,ymax+0.05)
# b.title('spikes:' + str(sum(spikeCount)) + ', pop. value: ' + str(computePopVector(spikeCount)))
if ax1==None:
b.legend(loc='upper left', fancybox=True, framealpha=0.0)
b.savefig(dataPath + '/tuningCurveSingleNeuron.png', dpi=300, bbox_inches='tight')
def plotActivity(dataPath, gaussianDistances, ax1=None):
averagingWindowSize = 32
if ax1==None:
b.figure()
else:
b.sca(ax1)
linewidth = 2
for i,dist in enumerate(gaussianDistances[:]):
path = dataPath + '/dist'+str(dist)+'/' +'activity/'
spikeCountTemp = np.load(path + 'spikeCountPerExample.npy')
spikeCount = spikeCountTemp[25,:,0]#np.loadtxt(path + 'spikeCountAe.txt')
# spikeCount = np.roll(spikeCount, 400)
inputSpikeCount = np.loadtxt(path + 'spikeCountXe.txt')
spikeCount = movingaverage(spikeCount,averagingWindowSize)
inputSpikeCount = movingaverage(inputSpikeCount,averagingWindowSize)
if i==len(gaussianDistances)-1:
b.plot(spikeCount, 'b', alpha=0.6, linewidth=linewidth, label='Max. dist output')#alpha=0.5+(0.5*float(i)/float(len(lower_peaks))),
b.plot(inputSpikeCount, 'r', alpha=1., linewidth=linewidth, label='Max. dist. input')
elif i==0:
b.plot(spikeCount, 'k--', alpha=0.6, linewidth=linewidth, label='Min. dist output')
b.plot(inputSpikeCount, 'r--', alpha=1., linewidth=linewidth, label='Min. dist. input')
else:
b.plot(spikeCount, 'k', alpha=0.2+(0.4*float(i)/float(len(gaussianDistances))), linewidth=0.6)
b.plot(inputSpikeCount, 'r', alpha=0.2+(0.4*float(i)/float(len(gaussianDistances))), linewidth=0.6)
b.ylim(0,35)
# b.title('spikes:' + str(sum(spikeCount)) + ', pop. value: ' + str(computePopVector(spikeCount)))
if ax1==None:
b.legend(loc='upper left', fancybox=True, framealpha=0.0)
b.savefig(dataPath + '/multipleAnglesActivity.png', dpi=300)
def plotHeatmap(dataPath, gaussianDistances, ax1=None):
b.rcParams['font.size'] = 20
if ax1==None:
b.figure(figsize=(8,15))
else:
b.sca(ax1)
nE = 1600
averagingWindowSize = 32
spikeCount = np.zeros((len(gaussianDistances), nE))
for i,dist in enumerate(gaussianDistances[:]):
path = dataPath + '/dist'+str(dist)+'/' +'activity/'
spikeCountTemp = np.load(path + 'spikeCountPerExample.npy')
spikeCount[i,:] = spikeCountTemp[25,:,0]#np.loadtxt(path + 'spikeCountAe.txt')
# spikeCount[i,:] = np.roll(spikeCount[i,:], int(0.25*len(spikeCount[i,:])))
spikeCount[i,:] = movingaverage(spikeCount[i,:], averagingWindowSize)
spikeCount[i,:] /= np.max(spikeCount[i,:])
b.imshow(spikeCount[:,:], aspect='auto', extent=[0,1,2,0])
b.colorbar()
b.xlabel('Neuron number (resorted)')
b.xlabel('Neuron number (resorted)')
if ax1==None:
b.savefig(dataPath + '/multipleAnglesHeatmap.png', dpi=300, bbox_inches='tight')
def plotActivity(dataPath, lower_peaks, ax1=None):
averagingWindowSize = 32
if ax1==None:
b.figure()
else:
b.sca(ax1)
for i,gaussian_peak_low in enumerate(lower_peaks[:]):
path = dataPath + '/peak_'+str(gaussian_peak_low)+'/' +'activity/'
spikeCount = np.loadtxt(path + 'spikeCountAe.txt')
inputSpikeCount = np.loadtxt(path + 'spikeCountXe.txt')
spikeCount = movingaverage(spikeCount,averagingWindowSize)
inputSpikeCount = movingaverage(inputSpikeCount,averagingWindowSize)
if i==len(lower_peaks)-1:
b.plot(spikeCount, 'k', alpha=1., linewidth=3, label='Output')#alpha=0.5+(0.5*float(i)/float(len(lower_peaks))),
b.plot(inputSpikeCount, 'r', alpha=1., linewidth=3, label='Input')
elif i==0:
b.plot(spikeCount, 'k', alpha=0.6, linewidth=3)#
b.plot(inputSpikeCount, 'r', alpha=0.6, linewidth=3)
else:
b.plot(spikeCount, 'k', alpha=0.2+(0.4*float(i)/float(len(lower_peaks))), linewidth=0.6)
b.plot(inputSpikeCount, 'r', alpha=0.2+(0.4*float(i)/float(len(lower_peaks))), linewidth=0.6)
b.legend()
b.ylim(0,35)
# b.title('spikes:' + str(sum(spikeCount)) + ', pop. value: ' + str(computePopVector(spikeCount)))
if ax1==None:
b.savefig(dataPath + 'CueIntegration.png', dpi=900)
def plotActivity(dataPath, inputStrengths, ax1=None):
averagingWindowSize = 1
nE = 1600
ymax = 30
# b.rcParams['lines.color'] = 'w'
# b.rcParams['text.color'] = 'w'
# b.rcParams['xtick.color'] = 'w'
# b.rcParams['ytick.color'] = 'w'
# b.rcParams['axes.labelcolor'] = 'w'
# b.rcParams['axes.edgecolor'] = 'w'
b.rcParams['font.size'] = 20
if ax1==None:
b.figure(figsize=(8,6.5))
fig_axis = b.subplot(1,1,1)
else:
fig_axis = ax1
b.sca(ax1)
for i,inputStrength in enumerate(inputStrengths[:]):
path = dataPath + '/peak_'+str(inputStrength)+'/' +'activity/'
spikeCount = np.loadtxt(path + 'spikeCountAe.txt')
inputSpikeCount = np.loadtxt(path + 'spikeCountXe.txt')
spikeCount = movingaverage(spikeCount,averagingWindowSize)
inputSpikeCount = movingaverage(inputSpikeCount,averagingWindowSize)
if i==len(inputStrengths)-1:
b.plot(inputSpikeCount, 'r', alpha=1., linewidth=3, label='Input')
b.plot(spikeCount, 'deepskyblue', alpha=0.6, linewidth=3, label='Output')#alpha=0.5+(0.5*float(i)/float(len(numPeaks))),
# elif i==0:
# b.plot(spikeCount, 'k--', alpha=1., linewidth=3)#
# b.plot(inputSpikeCount, 'r--', alpha=1., linewidth=3)
else:
b.plot(spikeCount, 'k', alpha=0.2+(0.4*float(i)/float(len(inputStrength))), linewidth=0.6)
b.plot(inputSpikeCount, 'r', alpha=0.2+(0.4*float(i)/float(len(inputStrength))), linewidth=0.6)
fig_axis.set_xticks([0., nE/2, nE])
fig_axis.set_xticklabels(['0', '0.5', '1'])
fig_axis.set_yticks([0., ymax/2, ymax])
fig_axis.spines['top'].set_visible(False)
fig_axis.spines['right'].set_visible(False)
fig_axis.get_xaxis().tick_bottom()
fig_axis.get_yaxis().tick_left()
b.ylabel('Firing Rate [Hz]')
b.ylim(0,ymax)
# b.title('spikes:' + str(sum(spikeCount)) + ', pop. value: ' + str(computePopVector(spikeCount)))
if ax1==None:
b.xlabel('Neuron number (resorted)')
b.legend(fancybox=True, framealpha=0.0, loc='upper left')
b.savefig(dataPath + 'CueIntegration_single.png', dpi=900, transparent=True)
def plotPopulationTuningCurve(dataPath, gaussianDistance, ax1=None):
b.rcParams['font.size'] = 20
averagingWindowSize = 1
nE = 1600
ymax = 1
if ax1==None:
b.figure(figsize=(8,6.5))
fig_axis = b.subplot(1,1,1)
else:
fig_axis = ax1
b.sca(ax1)
path = dataPath + '/dist'+str(gaussianDistance)+'/' +'activity/'
spikeCount = np.load(path + 'spikeCountPerExample.npy')
numMeasurements = len(spikeCount[:,0,0])
measurementSpace = np.arange(numMeasurements)
populationSpikeCount = np.zeros((numMeasurements))
for i in xrange(nE):
populationSpikeCount += np.roll(spikeCount[:,i,0], int(-1.*i/nE*numMeasurements+ numMeasurements/2))
populationSpikeCount = movingaverage(populationSpikeCount,averagingWindowSize)
populationSpikeCount /= np.max(populationSpikeCount)
if gaussianDistance==0.:
mean = sum(measurementSpace*populationSpikeCount)/numMeasurements #note this correction
sigma = sum(populationSpikeCount*(measurementSpace-mean)**2)/numMeasurements #note this correction
popt, _ = curve_fit(gaus,measurementSpace,populationSpikeCount,p0=[1,mean,sigma])
print 'Gaussian amplitude: ', popt[0], ', mean: ', popt[1], ', std.: ', popt[2]**2
b.plot(measurementSpace,gaus(measurementSpace,*popt),'k')
b.plot(populationSpikeCount, color='deepskyblue', marker='o', alpha=0.6, linewidth=0, label='Output')#alpha=0.5+(0.5*float(i)/float(len(lower_peaks))),
fig_axis.set_xticks([0., numMeasurements/2, numMeasurements])
fig_axis.set_xticklabels(['0', '0.5', '1'])
fig_axis.set_yticks([0., ymax/2, ymax])
fig_axis.spines['top'].set_visible(False)
fig_axis.spines['right'].set_visible(False)
fig_axis.get_xaxis().tick_bottom()
fig_axis.get_yaxis().tick_left()
# b.ylabel('Normalized response')
b.ylim(0,ymax+0.05)
# b.title('spikes:' + str(sum(spikeCount)) + ', pop. value: ' + str(computePopVector(spikeCount)))
if ax1==None:
b.legend(loc='upper left', fancybox=True, framealpha=0.0)
b.savefig(dataPath + '/tuningCurvePopulation' + str(gaussianDistance) + '.png', dpi=300, bbox_inches='tight')
def plotActivity(dataPath, ax1=None):
averagingWindowSize = 1
nE = 1600
ymax = 40
# b.rcParams['lines.color'] = 'w'
# b.rcParams['text.color'] = 'w'
# b.rcParams['xtick.color'] = 'w'
# b.rcParams['ytick.color'] = 'w'
# b.rcParams['axes.labelcolor'] = 'w'
# b.rcParams['axes.edgecolor'] = 'w'
b.rcParams['font.size'] = 20
if ax1==None:
fig = b.figure(figsize=(8,6.5))
fig_axis=b.subplot(1,1,1)
else:
fig_axis = ax1
b.sca(ax1)
path = dataPath + 'activity/'
spikeCount = np.loadtxt(path + 'spikeCountCe.txt')
popVecs = np.loadtxt(path + 'popVecs1.txt')
desiredResult = (popVecs[0] + popVecs[1])%1.*1600
resultMonitor = np.loadtxt(path + 'resultPopVecs1.txt')
actualResult = resultMonitor[2]*1600
ax1.axvline(desiredResult, color='r', linewidth=3, ymax=ymax, label='Desired result')
ax1.axvline(actualResult, color='blue', linewidth=3, ymax=ymax, label='Population vector')
print 'desiredResult', desiredResult, ', actual result', actualResult
# spikeCount = np.roll(spikeCount, 800+int(-1*desiredResult))
spikeCount = movingaverage(spikeCount,averagingWindowSize)
b.plot(spikeCount, 'deepskyblue', alpha=0.6, linewidth=3, label='Population activity')
fig_axis.set_xticks([0., nE/2, nE])
fig_axis.set_xticklabels(['0', '0.5', '1'])
fig_axis.set_yticks([0., ymax/2, ymax])
fig_axis.spines['top'].set_visible(False)
fig_axis.spines['right'].set_visible(False)
fig_axis.get_xaxis().tick_bottom()
fig_axis.get_yaxis().tick_left()
b.ylabel('Firing Rate [Hz]')
b.ylim(0,ymax)
b.legend(fancybox=True, framealpha=0.0, loc='upper left')
# b.title('spikes:' + str(sum(spikeCount)) + ', pop. value: ' + str(computePopVector(spikeCount)))
if ax1==None:
b.xlabel('Neuron number (resorted)')
b.savefig(dataPath + 'SignalRestoration.png', dpi=900, transparent=True)
def plotSingleActivity(dataPath, gaussianDistance, ax1=None):
b.rcParams['font.size'] = 20
averagingWindowSize = 30
nE = 1600
ymax = 1
if ax1==None:
b.figure(figsize=(8,6.5))
fig_axis = b.subplot(1,1,1)
else:
fig_axis = ax1
b.sca(ax1)
linewidth = 3
path = dataPath + '/dist'+str(gaussianDistance)+'/' +'activity/'
spikeCountTemp = np.load(path + 'spikeCountPerExample.npy')
spikeCount = spikeCountTemp[25,:,0]#np.loadtxt(path + 'spikeCountAe.txt')
# spikeCount = np.roll(spikeCount, 400)
inputSpikeCount = np.roll(np.loadtxt(path + 'spikeCountXe.txt'), 400)
spikeCount = movingaverage(spikeCount,averagingWindowSize)
spikeCount /= np.max(spikeCount)
inputSpikeCount = movingaverage(inputSpikeCount,averagingWindowSize)
inputSpikeCount /= np.max(inputSpikeCount)
b.plot(spikeCount, 'deepskyblue', alpha=0.6, linewidth=linewidth, label='Output')#alpha=0.5+(0.5*float(i)/float(len(lower_peaks))),
b.plot(inputSpikeCount, 'r', alpha=1., linewidth=linewidth, label='Input')
fig_axis.set_xticks([0., nE/2, nE])
fig_axis.set_xticklabels(['0', '0.5', '1'])
fig_axis.set_yticks([0., ymax/2, ymax])
fig_axis.spines['top'].set_visible(False)
fig_axis.spines['right'].set_visible(False)
fig_axis.get_xaxis().tick_bottom()
fig_axis.get_yaxis().tick_left()
b.ylabel('Normalized response')
b.ylim(0,ymax)
# b.title('spikes:' + str(sum(spikeCount)) + ', pop. value: ' + str(computePopVector(spikeCount)))
if ax1==None:
b.legend(loc='upper left', fancybox=True, framealpha=0.0)
b.savefig(dataPath + '/multipleAnglesSingleActivity.png', dpi=300)
result_monitor)
np.save(data_path + 'activity/inputNumbers' + str(num_examples),
input_numbers)
#------------------------------------------------------------------------------
# plot results
#------------------------------------------------------------------------------
if rate_monitors:
b.figure(fig_num)
fig_num += 1
for i, name in enumerate(rate_monitors):
b.subplot(len(rate_monitors), 1, i + 1)
b.plot(rate_monitors[name].times / b.second, rate_monitors[name].rate,
'.')
b.title('Rates of population ' + name)
b.savefig('../plots/rate_monitors_' + str(n_e) + '_' + stdp_input +
'_input_exc_weights_norm.png')
if spike_monitors:
b.figure(fig_num)
fig_num += 1
for i, name in enumerate(spike_monitors):
b.subplot(len(spike_monitors), 1, i + 1)
b.raster_plot(spike_monitors[name])
b.title('Spikes of population ' + name)
b.savefig('../plots/spike_monitors_' + str(n_e) + '_' + stdp_input +
'_input_exc_weights_norm.png')
if spike_counters:
b.figure(fig_num)
fig_num += 1
for i, name in enumerate(spike_counters):
fi = b.figure(figsize = (5.0,4.6))
ax = plt.subplot(1,1,1)
matplotlib.rcParams.update({'font.size': 22})
b.scatter(resultMonitor[start_time:end_time,1]*1600, resultMonitor[start_time:end_time,0]*1600, c='k', cmap=cmap.gray) #range(len(error))
# b.title('Error: ' + str(correctedErrorSum))
# b.xlabel('Desired activity')
# b.ylabel('Population activity')
ax.set_xticks([0,800,1600])
ax.set_xticklabels(['0', '800', '1600'])
ax.set_yticks([0,800,1600])
ax.set_yticklabels(['0', '800', '1600'], va='center')
b.xlim(xmin = 0, xmax = 1600)
b.ylim(ymin = 0, ymax = 1600)
b.savefig('evaluation' + ending, dpi = 300, transparent=True)
b.figure()
b.scatter(correctedDesDiff, correctedError[1:], c=resultMonitor[start_time+1:end_time,2], cmap=cmap.gray)
b.title('Error: ' + str(correctedErrorSum))
b.xlabel('Difference from last desired activity')
b.ylabel('Error')
b.figure()
b.scatter(correctedPopDiff, correctedError[1:], c=resultMonitor[start_time+1:end_time,2], cmap=cmap.gray)
b.title('Error: ' + str(correctedErrorSum))
b.xlabel('Difference from last population activity')
b.ylabel('Error')
def plotTuningHeatmap(dataPath, gaussianDistances, ax1=None):
b.rcParams['font.size'] = 20
# averagingWindowSize = 1
nE = 1600
if ax1==None:
b.figure(figsize=(8,15))
fig_axis = b.subplot(1,1,1)
else:
b.sca(ax1)
fig_axis=ax1
path = dataPath + '/dist'+str(gaussianDistances[0])+'/' +'activity/'
spikeCount = np.load(path + 'spikeCountPerExample.npy')
numMeasurements = len(spikeCount[:,0,0])
measurementSpace = np.arange(numMeasurements)
populationSpikeCount = np.zeros((numMeasurements, len(gaussianDistances)))
for i,gaussianDistance in enumerate(gaussianDistances):
# distanceAngle = gaussianDistance * 2 * 360
path = dataPath + '/dist'+str(gaussianDistance)+'/' +'activity/'
spikeCount = np.load(path + 'spikeCountPerExample.npy')
for j in xrange(nE):
populationSpikeCount[:,i] += np.roll(spikeCount[:,j,0], int(-1.*j/nE*numMeasurements+ numMeasurements/2))
# j = 0
# populationSpikeCount[:,i] += np.roll(spikeCount[:,j,0], int(-1.*j/nE*numMeasurements+ numMeasurements/2))
# populationSpikeCount[:,i] = movingaverage(populationSpikeCount[:,i],averagingWindowSize)
populationSpikeCount[:,i] /= np.max(populationSpikeCount[:,i])
if gaussianDistance==0.:
mean = sum(measurementSpace*populationSpikeCount[:,i])/numMeasurements #note this correction
sigma = sum(populationSpikeCount[:,i]*(measurementSpace-mean)**2)/numMeasurements #note this correction
popt, _ = curve_fit(gaus,measurementSpace,populationSpikeCount[:,i],p0=[1,mean,sigma])
# tuningWidth = abs(popt[2])*2
tuningWidth = np.sum(populationSpikeCount>=0.5)
tuningWidthAngle = tuningWidth/50.*360.
print 'Gaussian amplitude:', popt[0], 'mean:', popt[1], 'std.:', popt[2], \
', tuning width:', tuningWidth, ', tuning width angle:', tuningWidthAngle
minX = max(0,round(numMeasurements/2-tuningWidth*2))
maxX = min(numMeasurements, round(numMeasurements/2+tuningWidth*2))
minY = round(0)
maxY = round(tuningWidth/50.*len(gaussianDistances)*2*2)
print minX, maxX, minY, maxY
croppedCount = populationSpikeCount[minX:maxX,minY:maxY]
fig_axis.imshow(croppedCount.transpose(), aspect='auto',
extent=[minX, maxX, 2, 0])
# b.colorbar()
fig_axis.set_xlabel('Distance from preferred stimulus, \ndivided by tuning width')
fig_axis.set_xticks([numMeasurements/2-tuningWidth*2, numMeasurements/2-tuningWidth,
numMeasurements/2, numMeasurements/2+tuningWidth, numMeasurements/2+tuningWidth*2])
fig_axis.set_xticklabels(['-2', '-1', '0', '1', '2'])
fig_axis.set_yticks([0., 0.5, 1, 1.5, 2])
fig_axis.spines['top'].set_visible(False)
fig_axis.spines['right'].set_visible(False)
fig_axis.spines['bottom'].set_visible(False)
fig_axis.spines['left'].set_visible(False)
fig_axis.get_xaxis().tick_bottom()
fig_axis.get_yaxis().tick_left()
if ax1==None:
b.savefig(dataPath + '/tuningHeatmap.png', dpi=300, bbox_inches='tight')
return tuningWidthAngle