def save_and_plot_results(): global fig_num print '...saving results' if not test_mode: save_theta() if not test_mode: save_connections() else: np.save(top_level_path + 'activity/conv_patch_connectivity_activity/results_' + str(num_examples) + '_' + ending, result_monitor) np.save(top_level_path + 'activity/conv_patch_connectivity_activity/input_numbers_' + str(num_examples) + '_' + ending, input_numbers) if do_plot: 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) 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) if spike_counters: b.figure(fig_num) fig_num += 1 for i, name in enumerate(spike_counters): b.subplot(len(spike_counters), 1, i + 1) b.plot(np.asarray(spike_counters['Ae'].count[:])) b.title('Spike count of population ' + name) plot_2d_input_weights() plot_patch_weights() b.ioff() b.show()
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): b.subplot(len(spike_counters), 1, i + 1) b.plot(spike_counters['Ae'].count[:]) b.title('Spike count of population ' + name) b.savefig('../plots/rate_monitors_' + str(n_e) + '_' + stdp_input + '_input_exc_weights_norm.png') plot_2d_input_weights() b.savefig('../plots/input_to_exc_weights' + str(n_e) + '_' + stdp_input + '_input_exc_weights_norm.png') b.ioff() b.show()
if rate_monitors: b.figure(fig_num) fig_num += 1 for i, name in enumerate(rate_monitors): b.subplot(len(rate_monitors), 1, i) b.plot(rate_monitors[name].times/b.second, rate_monitors[name].rate, '.') b.title('Rates of population ' + name) if spike_monitors: b.figure(fig_num) fig_num += 1 for i, name in enumerate(spike_monitors): b.subplot(len(spike_monitors), 1, i) b.raster_plot(spike_monitors[name]) b.title('Spikes of population ' + name) if spike_counters: b.figure(fig_num) fig_num += 1 for i, name in enumerate(spike_counters): b.subplot(len(spike_counters), 1, i) b.plot(spike_counters['Ae'].count[:]) b.title('Spike count of population ' + name) plot_2d_input_weights() b.ioff() b.show()
def plotResults(self): #------------------------------------------------------------------------------ # plot results #------------------------------------------------------------------------------ if self.rateMonitors: b.figure() for i, name in enumerate(self.rateMonitors): b.subplot(len(self.rateMonitors), 1, i) b.plot(self.rateMonitors[name].times/b.second, self.rateMonitors[name].rate, '.') b.title('rates of population ' + name) if self.spikeMonitors: b.figure() for i, name in enumerate(self.spikeMonitors): b.subplot(len(self.spikeMonitors), 1, i) b.raster_plot(self.spikeMonitors[name]) b.title('spikes of population ' + name) if name=='Ce': timePoints = np.linspace(0+(self.singleExampleTime+self.restingTime)/(2*b.second)*1000, self.runtime/b.second*1000-(self.singleExampleTime+self.restingTime)/(2*b.second)*1000, self.numExamples) b.plot(timePoints, self.resultMonitor[:,0]*self.nE, 'g') b.plot(timePoints, self.resultMonitor[:,1]*self.nE, 'r') if self.stateMonitors: b.figure() for i, name in enumerate(self.stateMonitors): b.plot(self.stateMonitors[name].times/b.second, self.stateMonitors[name]['v'][0], label = name + ' v 0') b.legend() b.title('membrane voltages of population ' + name) b.figure() for i, name in enumerate(self.stateMonitors): b.plot(self.stateMonitors[name].times/b.second, self.stateMonitors[name]['ge'][0], label = name + ' v 0') b.legend() b.title('conductances of population ' + name) plotWeights = [ # 'XeAe', # 'XeAi', # 'AeAe', # 'AeAi', # 'AiAe', # 'AiAi', # 'BeBe', # 'BeBi', # 'BiBe', # 'BiBi', # 'CeCe', # 'CeCi', 'CiCe', # 'CiCi', # 'HeHe', # 'HeHi', # 'HiHe', # 'HiHi', 'AeHe', # 'BeHe', # 'CeHe', 'HeAe', # 'HeBe', # 'HeCe', ] for name in plotWeights: b.figure() # my_cmap = matplotlib.colors.LinearSegmentedColormap.from_list('own2',['#f4f4f4', '#000000']) # my_cmap2 = matplotlib.colors.LinearSegmentedColormap.from_list('own2',['#000000', '#f4f4f4']) if name[1]=='e': nSrc = self.nE else: nSrc = self.nI if name[3]=='e': nTgt = self.nE else: nTgt = self.nI w_post = np.zeros((nSrc, nTgt)) connMatrix = self.connections[name][:] for i in xrange(nSrc): w_post[i, connMatrix.rowj[i]] = connMatrix.rowdata[i] im2 = b.imshow(w_post, interpolation="nearest", vmin = 0, cmap=cm.get_cmap('gist_ncar')) #my_cmap b.colorbar(im2) b.title('weights of connection' + name) if self.plotError: error = np.abs(self.resultMonitor[:,1] - self.resultMonitor[:,0]) correctionIdxs = np.where(error > 0.5)[0] correctedError = [1 - error[i] if (i in correctionIdxs) else error[i] for i in xrange(len(error))] correctedErrorSum = np.average(correctedError) b.figure() b.scatter(self.resultMonitor[:,1], self.resultMonitor[:,0], c=range(len(error)), cmap=cm.get_cmap('gray')) b.title('Error: ' + str(correctedErrorSum)) b.xlabel('Desired activity') b.ylabel('Population activity') b.figure() error = np.abs(self.resultMonitor[:,1] - self.resultMonitor[:,0]) correctionIdxs = np.where(error > 0.5)[0] correctedError = [1 - error[i] if (i in correctionIdxs) else error[i] for i in xrange(len(error))] correctedErrorSum = np.average(correctedError) b.scatter(self.resultMonitor[:,1], self.resultMonitor[:,0], c=self.resultMonitor[:,2], cmap=cm.get_cmap('gray')) b.title('Error: ' + str(correctedErrorSum)) b.xlabel('Desired activity') b.ylabel('Population activity') b.ioff() b.show()