def run():
# Call the C NSAT
print('Begin %s:run()' % (os.path.splitext(os.path.basename(__file__))[0]))
cfg = nsat.ConfigurationNSAT.readfileb(nsat.fnames.pickled)
nsat.run_c_nsat()
# Load the results (read binary files)
c_nsat_reader = nsat.C_NSATReader(cfg, nsat.fnames)
states = c_nsat_reader.read_c_nsat_states()
time_core0, states_core0 = states[0][0], states[0][1]
out_spikelist = nsat.importAER(nsat.read_from_file(nsat.fnames.events +
'_core_0.dat'),
sim_ticks=sim_ticks,
id_list=[0])
# Plot the results
fig = plt.figure(figsize=(10, 10))
for i in range(1, 9):
ax = fig.add_subplot(8, 1, i)
ax.plot(states_core0[:, 0, i - 1], 'b', lw=2)
plt.savefig('/tmp/%s.png' %
(os.path.splitext(os.path.basename(__file__))[0]))
plt.close()
print('Begin %s:run()' % (os.path.splitext(os.path.basename(__file__))[0]))
def run(fnames):
# Call the C NSAT
print('Begin %s:run()' % (os.path.splitext(os.path.basename(__file__))[0]))
cfg = nsat.ConfigurationNSAT.readfileb(fnames.pickled)
nsat.run_c_nsat(fnames)
# Load the results (read binary files)
c_nsat_reader = nsat.C_NSATReader(cfg, fnames)
states = c_nsat_reader.read_c_nsat_states()
states_core0 = states[0][1]
# np.save('states', states_core0)
in_spikelist = SL
ifname = fnames.events + '_core_0.dat'
out_spikelist = nsat.importAER(nsat.read_from_file(ifname),
sim_ticks=sim_ticks,
id_list=[0])
# Plot the results
fig = plt.figure(figsize=(10, 10))
for i in range(1, 9):
ax = fig.add_subplot(8, 1, i)
ax.plot(states_core0[:-1, 15, i - 1], 'b', lw=1.5)
# fig = plt.figure(figsize=(10, 10))
# for i in range(1, 9):
# ax = fig.add_subplot(8, 1, i)
# ax.plot(states_core0[:-1, 16, i-1], 'b', lw=1.5)
# out_spikelist.raster_plot()
plt.savefig('/tmp/%s.png' %
(os.path.splitext(os.path.basename(__file__))[0]))
plt.close()
print('End %s:run()' % (os.path.splitext(os.path.basename(__file__))[0]))
def run():
# Call the C NSAT
print('Begin %s:run()' % (os.path.splitext(os.path.basename(__file__))[0]))
cfg = nsat.ConfigurationNSAT.readfileb(nsat.fnames.pickled)
nsat.run_c_nsat()
# Load the results (read binary files)
c_nsat_reader = nsat.C_NSATReader(cfg, nsat.fnames)
states = c_nsat_reader.read_c_nsat_states()
states_core0 = states[0][1]
# wt = c_nsat_reader.read_c_nsat_weights_evo(0)[:, 1, 1]
wt, pids = c_nsat_reader.read_synaptic_weights_history(post=[0])
in_spikelist = SL
out_spikelist = nsat.importAER(nsat.read_from_file(nsat.fnames.events+'_core_0.dat'),
sim_ticks=sim_ticks,
id_list=[0])
# Plot the results
fig = plt.figure(figsize=(10, 10))
i = 1
ax = fig.add_subplot(4, 1, i)
ax.plot(states_core0[:-1, 0, i - 1], 'b', lw=3, label='$V_m$')
ax.set_ylabel('$V_m$')
ax.set_xticks([])
plt.locator_params(axis='y', nbins=4)
i = 2
ax = fig.add_subplot(4, 1, i)
ax.plot(states_core0[:-1, 0, i - 1], 'b', lw=3, label='$I_{syn}$')
ax.set_ylabel('$I_{syn}$')
ax.set_xticks([])
plt.locator_params(axis='y', nbins=4)
for t in SL[0].spike_times:
plt.axvline(t, color='k')
i = 4
ax = fig.add_subplot(4, 1, i)
for t in SL[0].spike_times:
plt.axvline(t, color='k')
ax.plot(states_core0[:-1, 0, 2], 'b', lw=3, label='$x_m$')
ax.set_ylabel('$x_m$')
plt.axhline(0, color='b', alpha=.5, linewidth=3)
plt.locator_params(axis='y', nbins=4)
i = 3
ax = fig.add_subplot(4, 1, i)
for t in SL[0].spike_times:
plt.axvline(t, color='k')
ax.plot(wt[0][:, 1, 1], 'r', lw=3)
ax.set_ylabel('$w$')
ax.set_xticks([])
plt.locator_params(axis='y', nbins=4)
plt.savefig('/tmp/%s.png' % (os.path.splitext(os.path.basename(__file__))[0]))
plt.close()
print('End %s:run()' % (os.path.splitext(os.path.basename(__file__))[0]))
def read_spikelist(self, sim_ticks=None, id_list=None, core=0):
if sim_ticks is None:
sim_ticks = self.cfg.sim_ticks
if id_list is None:
id_list = self.cfg.spk_rec_mon[core]
filename = self.fname.events + '_core_{0}.dat'.format(core)
spikelist = nsat.importAER(self.read_events(core),
sim_ticks=sim_ticks,
id_list=id_list)
return spikelist
def run():
# Call the C NSAT
print('Begin %s:run()' % (os.path.splitext(os.path.basename(__file__))[0]))
cfg = nsat.ConfigurationNSAT.readfileb(nsat.fnames.pickled)
nsat.run_c_nsat()
# Load the results (read binary files)
c_nsat_reader = nsat.C_NSATReader(cfg, nsat.fnames)
states = c_nsat_reader.read_c_nsat_states()
states_core0 = states[0][1]
plt.figure()
for i in range(1, 5):
plt.subplot(4, 1, i)
plt.plot(states_core0[:, :, i - 1])
plt.figure()
S = np.maximum(states_core0[:, :, 0], 0)
plt.imshow(
S,
interpolation='spline36',
cmap=plt.get_cmap('gray'), #plt.cm.gray,
aspect='auto',
origin='lower')
spks = nsat.importAER(nsat.read_from_file(nsat.fnames.events +
'_core_0.dat'),
sim_ticks=sim_ticks,
id_list=list(range(cfg.core_cfgs[0].n_neurons)))
try:
raster = spks.raster_plot()
raster.savefig('/tmp/%s_raster.png' %
(os.path.splitext(os.path.basename(__file__))[0]))
raster.close()
except:
print('test_nf: Failed to generate /tmp/%s_raster.png' %
(os.path.splitext(os.path.basename(__file__))[0]))
# Plot the results
# events = spks.convert()
# mat = np.zeros((sim_ticks, N_NEURONS[0]))
# print(mat.shape)
# print(len(events[0]))
# print(len(events[1]))
# mat[events[0].astype('i'), events[1].astype('i')] = 1
plt.savefig('/tmp/%s.png' %
(os.path.splitext(os.path.basename(__file__))[0]))
plt.close()
print('End %s:run()' % (os.path.splitext(os.path.basename(__file__))[0]))
def SimSpikingStimulus(rates, t_sim=None):
m = np.shape(rates)[0]
n = int(m / 2)
C1 = (np.ones((n, n)) + np.random.uniform(0, 1, (n, n)) * 2)
np.fill_diagonal(C1, rates[:n])
C1 = np.maximum(C1, C1.T)
cor_spk1 = correlated_spikes(C1, rates, n)
cor_spk1.cox_process(time=t_sim)
spk1 = cor_spk1.extract_pyNCS_list()
tmp1 = nsat.importAER(spk1)
SL = pyST.SpikeList(id_list=list(range(m)))
for i in range(m):
if i < n:
SL[i] = tmp1[i]
if i >= n:
SL[i] = pyST.STCreate.poisson_generator(rates[i], t_stop=t_sim)
return SL
def run(fnames):
# Call the C NSAT
print('Begin %s:run()' % (os.path.splitext(os.path.basename(__file__))[0]))
cfg = nsat.ConfigurationNSAT.readfileb(fnames.pickled)
nsat.run_c_nsat(fnames)
# Load the results (read binary files)
c_nsat_reader = nsat.C_NSATReader(cfg, fnames)
ww = np.array(c_nsat_reader.read_c_nsat_synaptic_weights()[0])
spk = nsat.importAER(c_nsat_reader.read_events(0), sim_ticks=sim_ticks)
spk.raster_plot()
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(ww[:cfg.core_cfgs[0].n_inputs, cfg.core_cfgs[0].n_inputs, 1], 'k.')
plt.savefig('/tmp/%s.png' %
(os.path.splitext(os.path.basename(__file__))[0]))
plt.close()
print('End %s:run()' % (os.path.splitext(os.path.basename(__file__))[0]))
def run(fnames):
# Call the C NSAT
print('Begin %s:run()' % (os.path.splitext(os.path.basename(__file__))[0]))
cfg = nsat.ConfigurationNSAT.readfileb(fnames.pickled)
nsat.run_c_nsat(fnames)
print("Plotting data")
# Load the results (read binary files)
c_nsat_reader = nsat.C_NSATReader(cfg, fnames)
states = c_nsat_reader.read_c_nsat_states()
time_core0, states_core0 = states[0][0], states[0][1]
pip = nsat.importAER(c_nsat_reader.read_events(0),
sim_ticks=sim_ticks,
id_list=list(range(
cfg.core_cfgs[0].n_neurons))).time_slice(
1000, sim_ticks - 1000).mean_rates()
x = np.arange(pip.shape[0])
from scipy.optimize import curve_fit
# define "to-fit" function
def sigmoid(x, a, b, c):
return a / (1 + np.exp(-b * x / a) * (a - c) / a)
# Fit data to function sigmoid
popt, pcov = curve_fit(sigmoid, x, pip)
print(("Sigmoid's parameters: a = {}, b = {}, c = {}".format(
popt[0], popt[1], popt[2])))
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(x, pip, 'k', lw=2)
ax.plot(x, sigmoid(x, popt[0], popt[1], popt[2]), 'r--', lw=2)
plt.savefig('/tmp/%s.png' %
(os.path.splitext(os.path.basename(__file__))[0]))
plt.close()
print('End %s:run()' % (os.path.splitext(os.path.basename(__file__))[0]))
def run(fnames):
# Call the C NSAT
print('Begin %s:run()' % (os.path.splitext(os.path.basename(__file__))[0]))
cfg = nsat.ConfigurationNSAT.readfileb(fnames.pickled)
nsat.run_c_nsat(fnames)
# Load the results (read binary files)
c_nsat_reader = nsat.C_NSATReader(cfg, fnames)
states = c_nsat_reader.read_c_nsat_states()
_, states_core0 = states[0][0], states[0][1]
_, states_core1 = states[1][0], states[1][1]
# Plot the results
fig = plt.figure(figsize=(10, 10))
for i in range(1, states_core0.shape[2] + 1):
ax = fig.add_subplot(2, 1, i)
ax.plot(states_core0[:-1, 0, i - 1], 'b', lw=3)
plt.savefig('/tmp/%s_0.png' %
(os.path.splitext(os.path.basename(__file__))[0]))
fig = plt.figure(figsize=(10, 10))
for i in range(1, states_core0.shape[2] + 1):
ax = fig.add_subplot(2, 1, i)
ax.plot(states_core1[:-1, 0, i - 1], 'b', lw=3)
plt.savefig('/tmp/%s_1.png' %
(os.path.splitext(os.path.basename(__file__))[0]))
plt.close()
print('End %s:run()' % (os.path.splitext(os.path.basename(__file__))[0]))
out_spikelist = nsat.importAER(c_nsat_reader.read_events(0),
sim_ticks=100,
id_list=[0])
out_spikelist.id_slice([0]).raster_plot(kwargs={'color': 'b'})
plt.show()
def run(fnames):
# Call the C NSAT
print('Begin %s:run()' % (os.path.splitext(os.path.basename(__file__))[0]))
cfg = nsat.ConfigurationNSAT.readfileb(fnames.pickled)
nsat.run_c_nsat(fnames)
# Load the results (read binary files)
c_nsat_reader = nsat.C_NSATReader(cfg, fnames)
states = c_nsat_reader.read_c_nsat_states()
states_core0 = states[0][1]
in_spikelist = SL
out_spikelist = nsat.importAER(c_nsat_reader.read_events(0),
sim_ticks=sim_ticks,
id_list=[0])
spks = out_spikelist.convert("[times,ids]")
spks = np.vstack([spks[0], spks[1]]).astype('int')
pretty_fig(spks, states_core0, t_stop=t_stop)
plt.savefig('/tmp/%s.png' %
(os.path.splitext(os.path.basename(__file__))[0]))
plt.close()
print('End %s:run()' % (os.path.splitext(os.path.basename(__file__))[0]))
# Write C NSAT parameters binary files
c_nsat_writer = nsat.C_NSATWriter(cfg, path='/tmp',
prefix='test_eight_states_neuron')
c_nsat_writer.write()
# Write Intel FPGA parameters hex files
# intel_fpga_writer = nsat.IntelFPGAWriter(cfg, path='.',
# prefix='test_eight_states_neuron')
# intel_fpga_writer.write()
# intel_fpga_writer.write_globals()
# Call the C NSAT
print("Running C NSAT!")
nsat.run_c_nsat(c_nsat_writer.fname)
# Load the results (read binary files)
c_nsat_reader = nsat.C_NSATReader(cfg, c_nsat_writer.fname)
states = c_nsat_reader.read_c_nsat_states()
time_core0, states_core0 = states[0][0], states[0][1]
out_spikelist = nsat.importAER(nsat.read_from_file(c_nsat_writer.fname.events+'_core_0.dat'),
sim_ticks=sim_ticks,
id_list=[0])
# Plot the results
fig = plt.figure(figsize=(10, 10))
for i in range(1, 9):
ax = fig.add_subplot(8, 1, i)
ax.plot(states_core0[:, 0, i-1], 'b', lw=2)
plt.show()
cfg.set_ext_events(ext_evts_data)
# Write C NSAT parameters binary files
c_nsat_writer = nsat.C_NSATWriter(cfg, path='/tmp', prefix='test_wta')
c_nsat_writer.write()
# Write Intel FPGA parameters hex files
## intel_fpga_writer = nsat.IntelFPGAWriter(cfg, path='.',
## prefix='test_wta')
## intel_fpga_writer.write()
## intel_fpga_writer.write_globals()
# Call the C NSAT
print("Running C NSAT!")
nsat.run_c_nsat(c_nsat_writer.fname)
# Load the results (read binary files)
c_nsat_reader = nsat.C_NSATReader(cfg, c_nsat_writer.fname)
states = c_nsat_reader.read_c_nsat_states()
states_core0 = states[0][1]
in_apikelist = SL
out_spikelist = nsat.importAER(c_nsat_reader.read_c_nsat_raw_events()[0],
sim_ticks=sim_ticks,
id_list=[0])
spks = out_spikelist.convert("[times,ids]")
spks = np.vstack([spks[0], spks[1]]).astype('int')
pretty_fig(spks, states_core0, t_stop=t_stop)
plt.show()
print("Epoch #: ", i)
c_nsat_writer_train.fname.stats_nsat = \
'/tmp/test_eCD_stats_nsat_full'+str(i)
nsat.run_c_nsat(c_nsat_writer_train.fname)
if n_epoch > 1:
shutil.copyfile(
fname_train.shared_mem + '_core_0.dat',
fname_train.syn_wgt_table + '_core_0.dat',
)
shutil.copyfile(
fname_train.shared_mem + '_core_0.dat',
fname_test.syn_wgt_table + '_core_0.dat',
)
nsat.run_c_nsat(c_nsat_writer_test.fname)
test_spk = nsat.importAER(
nsat.read_from_file(c_nsat_writer_test.fname.events +
'_core_0.dat'),
sim_ticks=sim_ticks_test)
test_spk = nsat.importAER(c_nsat_reader_test.read_events(0),
sim_ticks=sim_ticks_test)
# train_spk = nsat.importAER(nsat.read_from_file(
# c_nsat_writer_train.fname.events+'_core_0.dat'),
# sim_ticks=sim_ticks_train)
sl = test_spk.id_slice([16, 17])
mm = np.argmax(sl.firing_rate(time_bin=test_duration), axis=0)[::2]
print(100 * sum(labels[::2] != mm) / 32.0)
e.append(sum(labels[::2] != mm) / 32.0)
e = np.array(e)
np.save('/tmp/error_full', e)
# fig = plt.figure()
# ax = fig.add_subplot(111)
c_nsat_writer.write()
# Call the C NSAT
print("Running C NSAT!")
nsat.run_c_nsat(c_nsat_writer.fname)
# Load the results (read binary files)
c_nsat_reader = nsat.C_NSATReader(cfg, c_nsat_writer.fname)
states = c_nsat_reader.read_c_nsat_states()
states_core0 = states[0][1]
# np.save('states', states_core0)
in_spikelist = SL
ifname = c_nsat_writer.fname.events + '_core_0.dat'
out_spikelist = nsat.importAER(nsat.read_from_file(ifname),
sim_ticks=sim_ticks,
id_list=[0])
# Plot the results
fig = plt.figure(figsize=(10, 10))
for i in range(1, 9):
ax = fig.add_subplot(8, 1, i)
ax.plot(states_core0[:-1, 15, i - 1], 'b', lw=1.5)
# fig = plt.figure(figsize=(10, 10))
# for i in range(1, 9):
# ax = fig.add_subplot(8, 1, i)
# ax.plot(states_core0[:-1, 16, i-1], 'b', lw=1.5)
# out_spikelist.raster_plot()
plt.show()
cfg.core_cfgs[0].latex_print_parameters(1)
print("Running C NSAT!")
nsat.run_c_nsat(c_nsat_writer.fname)
c_nsat_reader = nsat.C_NSATReader(cfg, c_nsat_writer.fname)
states = c_nsat_reader.read_c_nsat_states()
states_core0 = states[0][1]
np.save('states_vmem_', states_core0)
fW = c_nsat_reader.read_c_nsat_synaptic_weights()
# Load results
# Output spikes
test_spikelist = nsat.importAER(
nsat.read_from_file(c_nsat_writer.fname.events + '_core_0.dat'),
sim_ticks=sim_ticks,
id_list=range(N_NEURONS))
test_spikelist.save('nsat_spikes')
# # Plot data
figure(figsize=(8, 4))
gs = gridspec.GridSpec(4 + 5, 1)
ax1 = plt.subplot(gs[0:3, 0])
np.save('times_vmem_', tp)
for t in tp:
ax1.fill_between([t - 25, t + pdict['tp'] - 25], [0, 0],
[N_INPUTS, N_INPUTS],
linewidth=0,
alpha=0.5,
facecolor='b')
# Call the C NSAT
print("Running C NSAT!")
nsat.run_c_nsat(c_nsat_writer.fname)
# Load the results (read binary files)
c_nsat_reader = nsat.C_NSATReader(cfg, c_nsat_writer.fname)
states = c_nsat_reader.read_c_nsat_states()
time_core0, states_core0 = states[0][0], states[0][1]
wt = c_nsat_reader.read_c_nsat_weights_evo([0, 1])[0]
# cfg.write_hex_weights_monitors(wt[:, 0, 1])
# out_spikelist = nsat.importAER(nsat.read_from_file(c_nsat_writer.fname.events+'_core_0.dat'),
# sim_ticks=sim_ticks, id_list=[0])
out_spikelist = nsat.importAER(
nsat.read_from_file(c_nsat_writer.fname.events +
'_core_0.dat'.encode('utf-8')),
sim_ticks=sim_ticks)
# Plot the results
fig = plt.figure(figsize=(15, 15))
ax = fig.add_subplot(4, 2, 1)
ax.plot(states_core0[:-1, 0, 0], 'b', lw=3, label='$x_m$')
ax.set_ylabel('$V_m$')
ax = fig.add_subplot(4, 2, 2)
ax.plot(states_core0[:-1, 1, 0], 'b', lw=3, label='$x_m$')
ax.set_ylabel('$V_m$')
ax = fig.add_subplot(4, 2, 3)
ax.plot(states_core0[:-1, 0, 1], 'b', lw=3, label='$I_{syn}$')
print("############# Running simulation #####################")
pip = []
for i in range(nepochs):
nsat.run_c_nsat(fname_train)
copyfile(fname_train.shared_mem+'_core_0.dat',
fname_test.syn_wgt_table+'_core_0.dat',)
# cfg_test.core_cfgs[0].W = c_nsat_reader_train.read_c_nsat_weights()[0]
# cfg_train.core_cfgs[0].W = cfg_test.core_cfgs[0].W.copy()
# c_nsat_writer_test.write_L0connectivity()
# c_nsat_writer_train.write_L0connectivity()
if test_every>0:
if i % test_every == test_every-1:
nsat.run_c_nsat(fname_test)
test_spikelist = nsat.importAER(c_nsat_reader_test.read_c_nsat_raw_events()[0],
sim_ticks=sim_ticks_test,
id_list=np.arange(sP, sP+Np))
pip .append([i, float(sum(np.argmax(test_spikelist.id_slice(range(sP,sP+Np)).firing_rate(t_sample_test).T,axis=1) == targets_classify[:N_test]))/N_test*100])
print exp_name
print pip
copyfile(fname_train.shared_mem+'_core_0.dat',
fname_train.syn_wgt_table+'_core_0.dat',)
try:
import experimentTools as et
d=et.mksavedir()
et.save(cfg_test, 'cfg_test.pkl')
et.save(cfg_train, 'cfg_train.pkl')
et.save(pip, 'pip.pkl')
def run():
# Call the C NSAT
global spk0
print('Begin %s:run()' % (os.path.splitext(os.path.basename(__file__))[0]))
cfg = nsat.ConfigurationNSAT.readfileb(nsat.fnames.pickled)
nsat.run_c_nsat()
# Load the results (read binary files)
c_nsat_reader = nsat.C_NSATReader(cfg, nsat.fnames)
#ww = np.array(c_nsat_reader.read_c_nsat_synaptic_weights()[0])
states = c_nsat_reader.read_c_nsat_states()
time_core0, states_core0 = states[0][0], states[0][1]
# wt = c_nsat_reader.read_c_nsat_syn_evo()[0][0]
wt, pids = c_nsat_reader.read_synaptic_weights_history()
wt = wt[0]
spk = nsat.importAER(c_nsat_reader.read_events(0), sim_ticks=sim_ticks)
spk.raster_plot()
print((SL[0].spike_times))
print((SL[1].spike_times))
print((spk[0].spike_times))
# fig = plt.figure()
# ax = fig.add_subplot(111)
# ax.plot(ww[:N_INPUTS[0], N_INPUTS[0], 1], 'k.')
# Plot the results
fig = plt.figure(figsize=(15, 15))
ax = fig.add_subplot(4, 2, 1)
ax.plot(states_core0[:-1, 0, 0], 'b', lw=3, label='$x_m$')
for i in spk[0].spike_times:
plt.axvline(i, color='k', lw=1)
ax.set_ylabel('$V_m$')
# ax = fig.add_subplot(4, 2, 2)
# ax.plot(states_core0[:-1, 1, 0], 'b', lw=3, label='$x_m$')
# ax.set_ylabel('$V_m$')
ax = fig.add_subplot(4, 2, 3)
ax.plot(states_core0[:-1, 0, 1], 'b', lw=3, label='$I_{syn}$')
ax.set_ylabel('$I_{syn}$')
# ax = fig.add_subplot(4, 2, 4)
# ax.plot(states_core0[:-1, 1, 1], 'b', lw=3, label='$I_{syn}$')
# ax.set_ylabel('$I_{syn}$')
ax = fig.add_subplot(4, 2, 5)
ax.plot(states_core0[:-1, 0, 2], 'b', lw=3, label='$x_m$')
ax.set_ylabel('$x_m$')
# ax = fig.add_subplot(4, 2, 6)
# ax.plot(states_core0[:-1, 1, 1], 'b', lw=3, label='$x_m$')
# ax.set_ylabel('$x_m$')
print(wt.shape)
ax = fig.add_subplot(4, 2, 7)
# ax.plot(wt[wt[:, 0, :] == 0, 0], wt[wt[:, 1, :] == 0, 4], 'r', lw=3)
ax.plot(wt[:, 0, 0], wt[:, 1, 0], 'r', lw=3)
for i in spk[0].spike_times:
plt.axvline(i, color='k', lw=1)
for i in spk0[1].spike_times:
plt.axvline(i, color='r', lw=1)
ax.set_ylabel('$w$')
ax = fig.add_subplot(4, 2, 8)
# ax.plot(wt[wt[:, 1, :] == 1, 0], wt[wt[:, 1, :] == 1, 4], 'r', lw=3)
for i in spk[0].spike_times:
plt.axvline(i, color='k', lw=1)
for i in spk0[1].spike_times:
plt.axvline(i, color='r', lw=1)
ax.set_ylabel('$w$')
# ax = fig.add_subplot(4, 2, 8)
# ax.plot(wt[:, 1, 1], 'r', lw=3)
# ax.set_ylabel('$w$')
# fig = plt.figure(figsize=(10, 5))
# for i in spk[0].spike_times:
# plt.plot(wt[:, 1, 1], 'r', lw=3)
# plt.axvline(i, color='k', lw=1)
plt.savefig('/tmp/%s.png' %
(os.path.splitext(os.path.basename(__file__))[0]))
plt.close()
print('End %s:run()' % (os.path.splitext(os.path.basename(__file__))[0]))
def run(fnames):
# Call the C NSAT
print('Begin %s:run()' % (os.path.splitext(os.path.basename(__file__))[0]))
cfg = nsat.ConfigurationNSAT.readfileb(fnames.pickled)
nsat.run_c_nsat(fnames)
# Load the results (read binary files)
c_nsat_reader = nsat.C_NSATReader(cfg, fnames)
states = c_nsat_reader.read_c_nsat_states()
time_core0, states_core0 = states[0][0], states[0][1]
# w0 = c_nsat_reader.read_synaptic_weights_history(post=0)[0][:, 0, 1]
w0, pids0 = c_nsat_reader.read_synaptic_weights_history(post=[5])
w0 = w0[0][:, 4, 1]
w1, pids1 = c_nsat_reader.read_synaptic_weights_history(post=[6])
w1 = w1[0][:, 4, 1]
w2, pids2 = c_nsat_reader.read_synaptic_weights_history(post=[7])
w2 = w2[0][:, 4, 1]
np.save('w0', w0)
np.save('w1', w1)
np.save('w2', w2)
# w = c_nsat_reader.read_synaptic_weights()
out_spikelist = nsat.importAER(c_nsat_reader.read_events(0),
sim_ticks=sim_ticks)
np.save('spk0', out_spikelist[0].spike_times)
np.save('spk1', out_spikelist[1].spike_times)
np.save('spk2', out_spikelist[2].spike_times)
np.save('spk3', out_spikelist[3].spike_times)
# Plot the results
fig = plt.figure(figsize=(15, 15))
ax = fig.add_subplot(5, 2, 1)
ax.plot(states_core0[:-1, 0, 0], 'b', lw=3, label='$x_m$')
ax.set_ylabel('$V_m$')
ax = fig.add_subplot(5, 2, 2)
ax.plot(states_core0[:-1, 1, 0], 'b', lw=3, label='$x_m$')
ax.set_ylabel('$V_m$')
ax = fig.add_subplot(5, 2, 3)
ax.plot(states_core0[:-1, 0, 1], 'b', lw=3, label='$I_{syn}$')
ax.set_ylabel('$I_{syn}$')
ax = fig.add_subplot(5, 2, 4)
ax.plot(states_core0[:-1, 1, 1], 'b', lw=3, label='$I_{syn}$')
ax.set_ylabel('$I_{syn}$')
ax = fig.add_subplot(5, 2, 5)
ax.plot(states_core0[:-1, 0, 2], 'b', lw=3, label='$x_m$')
ax.set_ylabel('$x_m$')
ax = fig.add_subplot(5, 2, 6)
ax.plot(states_core0[:-1, 1, 2], 'b', lw=3, label='$x_m$')
ax.set_ylabel('$x_m$')
fig = plt.figure()
ax = fig.add_subplot(4, 1, 1)
for i in range(4):
for i in out_spikelist[0].spike_times:
ax.axvline(i, color='k', lw=1, zorder=0, label='lal')
ax.set_ylabel('Spikes')
ax.set_xlim([0, sim_ticks])
# ax = fig.add_subplot(4, 1, 2)
ax.step(w0, 'r', lw=2, zorder=10, where='post', label='lala')
for i in out_spikelist[1].spike_times:
ax.axvline(i, color='k', lw=1, zorder=0, label='dsds')
# ax.set_ylabel('Spikes')
# ax.set_xlim([0, sim_ticks])
# ax = fig.add_subplot(4, 1, 3)
ax.step(w1, 'r', lw=2, zorder=10, where='post', label='ta')
for i in out_spikelist[2].spike_times:
ax.axvline(i, color='k', lw=1, zorder=0, label='dss')
# ax.set_xlim([0, sim_ticks])
# ax = fig.add_subplot(4, 1, 4)
ax.step(w2, 'r', lw=2, zorder=10, where='post', label='ar')
for i in out_spikelist[3].spike_times:
ax.axvline(i, color='k', lw=1, zorder=0, label='dadsa')
# ax.set_xlim([0, sim_ticks])
ax.set_xlim([0, sim_ticks])
plt.savefig('/tmp/%s.png' %
(os.path.splitext(os.path.basename(__file__))[0]))
plt.close()
print('End %s:run()' % (os.path.splitext(os.path.basename(__file__))[0]))
c_nsat_reader = nsat.C_NSATReader(cfg, c_nsat_writer.fname)
states = c_nsat_reader.read_c_nsat_states()
states_core0 = states[0][1]
np.save('states_vmem_', states_core0)
fW = c_nsat_reader.read_c_nsat_synaptic_weights()
# Load results
# Output spikes
# test_spikelist = nsat.importAER(nsat.read_from_file(c_nsat_writer.fname.events + '_core_0.dat'),
# sim_ticks=sim_ticks,
# id_list=range(N_NEURONS))
test_spikelist = nsat.importAER(c_nsat_reader.read_events(0),
sim_ticks=sim_ticks,
id_list=range(N_NEURONS))
test_spikelist.save('nsat_spikes')
# # Plot data
figure(figsize=(8, 4))
gs = gridspec.GridSpec(4 + 5, 1)
ax1 = plt.subplot(gs[0:3, 0])
np.save('times_vmem_', tp)
for t in tp:
ax1.fill_between([t - 25, t + pdict['tp'] - 25], [0, 0],
[N_INPUTS, N_INPUTS],
linewidth=0,
alpha=0.5,
facecolor='b')
# Write the C NSAT parameters binary files
c_nsat_writer = nsat.C_NSATWriterMultithread(cfg,
path='/tmp',
prefix='test_stdp')
c_nsat_writer.write()
# Write Intel FPGA hex parameters files
# intel_fpga_writer = nsat.IntelFPGAWriter(cfg, path='.',
# prefix='test_stdp')
# intel_fpga_writer.write()
# intel_fpga_writer.write_globals()
# Call the C NSAT
print("Running C NSAT!")
nsat.run_c_nsat(c_nsat_writer.fname)
# Load the results (read binary files)
c_nsat_reader = nsat.C_NSATReader(cfg, c_nsat_writer.fname)
ww = np.array(c_nsat_reader.read_c_nsat_synaptic_weights()[0])
# spk = nsat.importAER(nsat.read_from_file(c_nsat_writer.fname.events+'_core_0.dat'), sim_ticks=sim_ticks)
spk = nsat.importAER(c_nsat_reader.read_c_nsat_raw_events()[0],
sim_ticks=sim_ticks)
spk.raster_plot()
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(ww[:N_INPUTS[0], N_INPUTS[0], 1], 'k.')
plt.show()
# intel_fpga_writer.write_globals()
# Call the C NSAT
print("Running C NSAT!")
nsat.run_c_nsat(c_nsat_writer.fname)
# Load the results (read binary files)
c_nsat_reader = nsat.C_NSATReader(cfg, c_nsat_writer.fname)
#ww = np.array(c_nsat_reader.read_c_nsat_synaptic_weights()[0])
states = c_nsat_reader.read_c_nsat_states()
time_core0, states_core0 = states[0][0], states[0][1]
# wt = c_nsat_reader.read_c_nsat_weights_evo(2)[0]
wt = c_nsat_reader.read_c_nsat_syn_evo()[0][0]
spk = nsat.importAER(nsat.read_from_file(c_nsat_writer.fname.events +
'_core_0.dat'),
sim_ticks=sim_ticks)
spk.raster_plot()
print((SL[0].spike_times))
print((SL[1].spike_times))
print((spk[0].spike_times))
#fig = plt.figure()
#ax = fig.add_subplot(111)
#ax.plot(ww[:N_INPUTS[0], N_INPUTS[0], 1], 'k.')
# Plot the results
fig = plt.figure(figsize=(15, 15))
ax = fig.add_subplot(4, 2, 1)
def run():
# Call the C NSAT
print('Begin %s:run()' % (os.path.splitext(os.path.basename(__file__))[0]))
cfg = nsat.ConfigurationNSAT.readfileb(nsat.fnames.pickled)
nsat.run_c_nsat()
# Load the results (read binary files)
c_nsat_reader = nsat.C_NSATReader(cfg, nsat.fnames)
states = c_nsat_reader.read_c_nsat_states()
time_core0, states_core0 = states[core][0], states[core][1]
wt, pids = c_nsat_reader.read_synaptic_weights_history(
post=[130, 150, 120])
wt, pids = wt[0], pids[0]
in_spikelist = SL
out_spikelist = nsat.importAER(c_nsat_reader.read_events(0),
sim_ticks=sim_ticks,
id_list=[0])
plt.matplotlib.rcParams['figure.subplot.bottom'] = .1
plt.matplotlib.rcParams['figure.subplot.left'] = .2
plt.matplotlib.rcParams['figure.subplot.right'] = .98
plt.matplotlib.rcParams['figure.subplot.hspace'] = .1
plt.matplotlib.rcParams['figure.subplot.top'] = 1.0
# Plot the results
fig = plt.figure(figsize=(14, 10))
i = 4
ax1 = fig.add_subplot(5, 1, i)
for t in SL[100].spike_times:
plt.axvline(t, color='g', alpha=.4)
ax1.plot(states_core0[:-1, 0, 2], 'b', lw=3, label='$x_m$')
ax1.set_ylabel('$x_m$')
ax1.get_yaxis().set_label_coords(-0.12, 0.5)
plt.setp(ax1.get_xticklabels(), visible=False)
plt.axhline(0, color='b', alpha=.5, linewidth=3)
plt.locator_params(axis='y', nbins=4)
i = 5
ax = fig.add_subplot(5, 1, i, sharex=ax1)
for t in SL[0].spike_times:
plt.axvline(t, color='k', alpha=.4)
ax.plot(wt[:, 19, 1], 'r', lw=3)
# ax.imshow(wt[:, :, 1], aspect='auto', interpolation='nearest')
ax.set_ylabel('$w$')
ax.set_xlabel('Time Step')
ax.get_yaxis().set_label_coords(-0.12, 0.5)
plt.locator_params(axis='y', nbins=4)
i = 2
ax = fig.add_subplot(5, 1, i, sharex=ax1)
ax.plot(states_core0[:-1, 0, 0], 'b', lw=3, label='$V_m$')
ax.set_ylabel('$V_m$')
ax.get_yaxis().set_label_coords(-0.12, 0.5)
plt.setp(ax.get_xticklabels(), visible=False)
plt.locator_params(axis='y', nbins=4)
i = 3
ax = fig.add_subplot(5, 1, i, sharex=ax1)
ax.plot(states_core0[:-1, 0, 1], 'b', lw=3, label='$I_{syn}$')
ax.set_ylabel('$I_{syn}$')
ax.get_yaxis().set_label_coords(-0.12, 0.5)
plt.setp(ax.get_xticklabels(), visible=False)
plt.locator_params(axis='y', nbins=4)
for t in np.ceil(SL[0].spike_times):
plt.axvline(t, color='k', alpha=.4)
ax1 = fig.add_subplot(5, 1, 1, sharex=ax1)
out_spikelist.id_slice([0]).raster_plot(display=ax1, kwargs={'color': 'b'})
out_spikelist.id_slice(list(range(1,
30))).raster_plot(display=ax1,
kwargs={'color': 'k'})
ax1.set_xlabel('')
plt.setp(ax1.get_xticklabels(), visible=False)
ax1.get_yaxis().set_label_coords(-0.12, 0.5)
plt.tight_layout()
plt.savefig('/tmp/%s.png' %
(os.path.splitext(os.path.basename(__file__))[0]))
plt.close()
print('End %s:run()' % (os.path.splitext(os.path.basename(__file__))[0]))
copyfile(
fname_train.shared_mem + '_core_0.dat',
fname_test.syn_wgt_table + '_core_0.dat',
)
# c_nsat_writer_test.write_L0connectivity()
# c_nsat_writer_train.write_L0connectivity()
s = c_nsat_reader_train.read_stats()[0]
stats_nsat.append(s)
#stats_ext.append(n_ext)
if test_every > 0:
if i % test_every == test_every - 1:
nsat.run_c_nsat(fname_test)
test_spikelist = nsat.importAER(
nsat.read_from_file(fname_test.events + '_core_0.dat'),
sim_ticks=sim_ticks_test,
id_list=np.arange(sP, sP + Np))
pip.append([
i,
float(
sum(
np.argmax(test_spikelist.id_slice(range(
sP, sP + Np)).firing_rate(t_sample_test).T,
axis=1) == targets_classify[:N_test])) /
N_test * 100
])
print exp_name
print pip
copyfile(
# Call the C NSAT
print("Running C NSAT!")
nsat.run_c_nsat(c_nsat_writer.fname)
print("Plotting data")
# Load the results (read binary files)
c_nsat_reader = nsat.C_NSATReader(cfg, c_nsat_writer.fname)
states = c_nsat_reader.read_c_nsat_states()
time_core0, states_core0 = states[0][0], states[0][1]
# pip = nsat.importAER(nsat.read_from_file(c_nsat_writer.fname.events+'_core_0.dat'),
# sim_ticks=sim_ticks,
# id_list=range(N_NEURONS[0])).time_slice(1000, sim_ticks-1000).mean_rates()
pip = nsat.importAER(c_nsat_reader.read_c_nsat_raw_events()[0],
sim_ticks=sim_ticks,
id_list=list(range(N_NEURONS[0]))).time_slice(
1000, sim_ticks - 1000).mean_rates()
x = np.arange(pip.shape[0])
from scipy.optimize import curve_fit
# define "to-fit" function
def sigmoid(x, a, b, c):
return a / (1 + np.exp(-b * x / a) * (a - c) / a)
# Fit data to function sigmoid
popt, pcov = curve_fit(sigmoid, x, pip)
print(("Sigmoid's parameters: a = {}, b = {}, c = {}".format(
popt[0], popt[1], popt[2])))
fig = plt.figure()
def run(fnames):
# Call the C NSAT
print('Begin %s:run()' % (os.path.splitext(os.path.basename(__file__))[0]))
cfg = nsat.ConfigurationNSAT.readfileb(fnames.pickled)
nsat.run_c_nsat(fnames)
# Load the results (read binary files)
c_nsat_reader = nsat.C_NSATReader(cfg, fnames)
states = c_nsat_reader.read_c_nsat_states()
time_core0, states_core0 = states[0][0], states[0][1]
wt, _ = c_nsat_reader.read_c_nsat_weights_evo([0, 1])
wt = wt[0]
out_spikelist = nsat.importAER(nsat.read_from_file(fnames.events +
'_core_0.dat'),
sim_ticks=sim_ticks)
# Plot the results
fig = plt.figure(figsize=(15, 15))
ax = fig.add_subplot(4, 2, 1)
ax.plot(states_core0[:-1, 0, 0], 'b', lw=3, label='$x_m$')
ax.set_ylabel('$V_m$')
ax = fig.add_subplot(4, 2, 2)
ax.plot(states_core0[:-1, 1, 0], 'b', lw=3, label='$x_m$')
ax.set_ylabel('$V_m$')
ax = fig.add_subplot(4, 2, 3)
ax.plot(states_core0[:-1, 0, 1], 'b', lw=3, label='$I_{syn}$')
ax.set_ylabel('$I_{syn}$')
ax = fig.add_subplot(4, 2, 4)
ax.plot(states_core0[:-1, 1, 1], 'b', lw=3, label='$I_{syn}$')
ax.set_ylabel('$I_{syn}$')
ax = fig.add_subplot(4, 2, 5)
ax.plot(states_core0[:-1, 0, 2], 'b', lw=3, label='$x_m$')
ax.set_ylabel('$x_m$')
ax = fig.add_subplot(4, 2, 6)
ax.plot(states_core0[:-1, 1, 2], 'b', lw=3, label='$x_m$')
ax.set_ylabel('$x_m$')
ax = fig.add_subplot(4, 2, 7)
ax.plot(wt[:, 1, 1], 'r', lw=3)
ax.set_ylabel('$w$')
ax = fig.add_subplot(4, 2, 8)
ax.plot(wt[:, 0, 1], 'r', lw=3)
ax.set_ylabel('$w$')
fig = plt.figure(figsize=(10, 5))
plt.plot(wt[:, 0, 1], 'r', lw=2, zorder=10)
plt.plot(wt[:, 1, 1], 'y', lw=2, zorder=10)
# for i in out_spikelist[0].spike_times:
# plt.axvline(i, color='k', lw=1, zorder=0)
# for i in out_spikelist[1].spike_times:
# plt.axvline(i, color='b', lw=1, zorder=0)
plt.savefig('/tmp/%s.png' %
(os.path.splitext(os.path.basename(__file__))[0]))
plt.close()
print('End %s:run()' % (os.path.splitext(os.path.basename(__file__))[0]))
# Load the results (read binary files)
c_nsat_reader = nsat.C_NSATReader(cfg, c_nsat_writer.fname)
states = c_nsat_reader.read_c_nsat_states()
states_core0 = states[0][1]
plt.figure()
for i in range(1, 5):
plt.subplot(4, 1, i)
plt.plot(states_core0[:, :, i - 1])
plt.figure()
S = np.maximum(states_core0[:, :, 0], 0)
plt.imshow(S,
interpolation='spline36',
cmap=plt.cm.gray,
aspect='auto',
origin='lower')
spks = nsat.importAER(nsat.read_from_file(c_nsat_writer.fname.events +
'_core_0.dat'),
sim_ticks=sim_ticks,
id_list=list(range(N_NEURONS[0])))
spks.raster_plot()
# Plot the results
events = spks.convert()
mat = np.zeros((sim_ticks, N_NEURONS[0]))
mat[events[0].astype('i'), events[1].astype('i')] = 1
plt.show()
