def visualise_connectivity(S):
Ns = len(S.source)
Nt = len(S.target)
b.figure(figsize=(10, 4))
b.subplot(121)
b.plot(b.zeros(Ns), b.arange(Ns), 'ok', ms=10)
b.plot(b.ones(Nt), b.arange(Nt), 'ok', ms=10)
for i, j in zip(S.i, S.j):
b.plot([0, 1], [i, j], '-k')
b.xticks([0, 1], ['Source', 'Target'])
b.ylabel('Neuron index')
b.xlim(-0.1, 1.1)
b.ylim(-1, max(Ns, Nt))
b.subplot(122)
b.plot(S.i, S.j, 'ok')
b.xlim(-1, Ns)
b.ylim(-1, Nt)
b.xlabel('Source neuron index')
b.ylabel('Target neuron index')
def Plot(monitor, number):
#pudb.set_trace()
br.plot(211)
if type(monitor) == list or type(monitor) == tuple:
br.plot(monitor[0].t/br.ms,monitor[0].v[0]/br.mV, label='v')
#br.plot(monitor[1].t/br.ms,monitor[1].u[0]/br.mV, label='u')
#br.plot(monitor[2].t/br.ms,monitor[0].ge[0]/br.mV, label='v')
else:
br.plot(monitor.t/br.ms,monitor.v[0]/br.mV, label='v')
#br.plot(monitor[0].t/br.ms,monitor[0].u[0]/br.mV, label='u')
#br.plot((monitor[2].t)/br.ms,(monitor[2][0]/br.mV), label='ge')
br.legend()
br.show()
def Plot(monitor, number):
#pudb.set_trace()
br.plot(211)
if type(monitor) == list or type(monitor) == tuple:
br.plot(monitor[0].t / br.ms, monitor[0].v[0] / br.mV, label='v')
#br.plot(monitor[1].t/br.ms,monitor[1].u[0]/br.mV, label='u')
#br.plot(monitor[2].t/br.ms,monitor[0].ge[0]/br.mV, label='v')
else:
br.plot(monitor.t / br.ms, monitor.v[0] / br.mV, label='v')
#br.plot(monitor[0].t/br.ms,monitor[0].u[0]/br.mV, label='u')
#br.plot((monitor[2].t)/br.ms,(monitor[2][0]/br.mV), label='ge')
br.legend()
br.show()
N_traces = 5
conv_width = 10 * b2.ms
leaveout_steps = int(conv_width / b2.defaultclock.dt)
# leaveout_steps = 10
b2.figure()
for trace in range(N_traces):
net = run_model(net=net)
r1 = net['r1']
r2 = net['r2']
r0 = net['r0']
rI = net['rI']
# ri1 = net['ri1']
# ri2 = net['ri2']
b2.plot(
r1.smooth_rate(width=conv_width)[:-leaveout_steps] / b2.Hz,
r2.smooth_rate(width=conv_width)[:-leaveout_steps] / b2.Hz)
ymin, ymax = b2.ylim()
xmin, xmax = b2.xlim()
b2.ylim([min(xmin, ymin), max(xmax, ymax)])
b2.xlim([min(xmin, ymin), max(xmax, ymax)])
b2.figure()
b2.plot(r1.t[:-leaveout_steps] / b2.ms,
r1.smooth_rate(width=conv_width)[:-leaveout_steps] / b2.Hz,
label='1')
b2.plot(r2.t[:-leaveout_steps] / b2.ms,
r2.smooth_rate(width=conv_width)[:-leaveout_steps] / b2.Hz,
label='2')
b2.plot(r0.t[:-leaveout_steps] / b2.ms,
r0.smooth_rate(width=conv_width)[:-leaveout_steps] / b2.Hz,
if not test_mode:
save_connections()
else:
np.save(data_path + 'activity/resultPopVecs' + str(num_examples), result_monitor)
np.save(data_path + 'activity/inputNumbers' + str(num_examples), input_numbers)
#------------------------------------------------------------------------------
# plot results
#------------------------------------------------------------------------------
if rate_monitors:
b2.figure(fig_num)
fig_num += 1
for i, name in enumerate(rate_monitors):
b2.subplot(len(rate_monitors), 1, 1+i)
b2.plot(rate_monitors[name].t/b2.second, rate_monitors[name].rate, '.')
b2.title('Rates of population ' + name)
if spike_monitors:
b2.figure(fig_num)
fig_num += 1
for i, name in enumerate(spike_monitors):
b2.subplot(len(spike_monitors), 1, 1+i)
b2.plot(spike_monitors[name].t/b2.ms, spike_monitors[name].i, '.')
b2.title('Spikes of population ' + name)
if spike_counters:
b2.figure(fig_num)
fig_num += 1
b2.plot(spike_monitors['Ae'].count[:])
b2.title('Spike count of population Ae')
#Ni = br.NeuronGroup(3, '''dv/dt = (vt - vr)/period : volt (unless refractory)
# period: second
# fire_once: boolean''', \
# threshold='v>vt', reset='v=vr',
# refractory='fire_once')
#Ni.period = [1, 1, 7] * br.ms
#Ni.fire_once[:] = [True] * 3
indices = np.asarray([0])
times = np.asarray([6]) * br.ms
Ni = br.SpikeGeneratorGroup(3, indices=indices, times=times)
#Nh = br.NeuronGroup(1, model="""dv/dt=(gtot-v)/(10*ms) : 1
# gtot : 1""")
Nh = br.NeuronGroup(1, model="""v = I :1
I : 1""")
S = br.Synapses(Ni, Nh,
model='''tl : second
alpha=exp((tl - t)/tau1) - exp((tl - t)/tau2) : 1
w : 1
I_post = w*alpha : 1 (summed)
''',
pre='tl+=t - tl')
S.connect('True')
S.w[:, :] = '(80+rand())'
S.tl[:, :] = '0*second'
M = br.StateMonitor(Nh, 'v', record=True)
br.run(20*br.ms)
br.plot(M[0].t, M[0].v)
br.show()
r[l_idx] = la
r[r_idx] = ra
return r * b.Hz
b.defaultclock.dt = 1 * b.ms
b.prefs.codegen.target = 'numpy'
num_parts = 3
theta_max = 25.0
e = env.WormFoodEnv((2, 3), num_parts=num_parts, theta_max=theta_max)
N_i = num_parts * 4
N_h = 200
N_o = num_parts * 4
inp_eq = 'rates: Hz '
inp_th = 'rand() < rates*dt'
inp_group = b.NeuronGroup(N_i, inp_eq, threshold=inp_th)
inp_group.run_regularly('rates=inp_rates(i)', dt=b.defaultclock.dt)
M = b.StateMonitor(inp_group, 'rates', record=True)
b.run(100 * b.ms)
for ri in M.rates:
b.plot(M.t / b.ms, ri / b.Hz)
b.show()
# for i in range(N_out):
synapse_mu.mu = f'(j / {float(N_out)})'
mon = b2.StateMonitor(synapse_mu, 'w', record=[0, 1])
mu_vals = np.array(synapse_mu.mu)
# print(mu_vals.shape)
# b2.plot(mu_vals[synapse_mu.i == 0])
# b2.show()
net = b2.Network(b2.collect())
net.store('initialised') # not used...
net.run(100 * b2.second, report='stdout')
b2.plot(mon.t / b2.second, mon.w.T / w_max)
b2.show()
weights_rates = b2.array(synapse_rates.w)
rates_weight_histograms = b2.empty((N_bins, N_out))
for i in range(N_out):
# brian2 uses i->j synapse syntax, while I use j->i
mask = synapse_rates.j == i
rates_weight_histograms[:,
i] = np.histogram(weights_rates[mask] / w_max,
bins=N_bins,
density=True,
range=(0., 1.))[0]
weights_mu = b2.array(synapse_mu.w)
mu_weight_histograms = b2.empty((N_bins, N_out))
if not test_mode:
save_connections()
else:
np.save(data_path + 'activity/resultPopVecs' + str(num_examples), result_monitor)
np.save(data_path + 'activity/inputNumbers' + str(num_examples), input_numbers)
#------------------------------------------------------------------------------
# plot results
#------------------------------------------------------------------------------
if rate_monitors:
b2.figure(fig_num)
fig_num += 1
for i, name in enumerate(rate_monitors):
b2.subplot(len(rate_monitors), 1, 1+i)
b2.plot(rate_monitors[name].t/b2.second, rate_monitors[name].rate, '.')
b2.title('Rates of population ' + name)
if spike_monitors:
b2.figure(fig_num)
fig_num += 1
for i, name in enumerate(spike_monitors):
b2.subplot(len(spike_monitors), 1, 1+i)
b2.plot(spike_monitors[name].t/b2.ms, spike_monitors[name].i, '.')
b2.title('Spikes of population ' + name)
if spike_counters:
b2.figure(fig_num)
fig_num += 1
b2.plot(spike_monitors['Ae'].count[:])
b2.title('Spike count of population Ae')
oa_group = b.Synapses(op_group, act_group, model=syn_oa.model, on_pre=syn_oa.on_pre)
oa_group.connect(j='i')
# spikemon = b.SpikeMonitor(inp_group)
# spikemon1 = b.SpikeMonitor(neuron_group)
# M = b.StateMonitor(hh_group, ['z', 'zeta'], record=True)
# M1 = b.StateMonitor(ih_group, 'w', record=True)
# M2 = b.StateMonitor(hh_group, 'w', record=True)
# Ma = b.StateMonitor(oa_group, 'a', record=True)
print e.disToFood
b.run(1 * b.second)
print e.disToFood
e.plot()
b.figure()
b.plot(e.d_history)
# b.figure()
# b.plot(spikemon.t / b.ms, spikemon.i, '.k')
# b.xlabel('Time (in ms)')
# b.ylabel('Neuron index')
#
# b.figure()
# b.plot(spikemon1.t / b.ms, spikemon1.i, '.k')
# b.xlabel('Time (in ms)')
# b.ylabel('Neuron index')
#
# b.figure()
# for ai in Ma.a[:5]:
# b.plot(Ma.t / b.ms, ai)
#
def _plot_slice(time,
var,
sind,
eind,
var_name,
ind_names,
title,
unit,
folder=None,
miny=None,
maxy=None):
"""Plot a time slice for a variable recording.
Args:
time: The time array.
var: The recorded values.
sind: Start index of slice.
eind: End index of slice.
var_name: Name of the recorded variable.
ind_names: Names of the recorded indices.
title: Plot titel.
unit: Variable unit.
folder: Where to store plot.
miny: Minimum y limit.
maxy: Maximum y limit.
Returns:
"""
if len(var.shape) == 2:
for i in range(var.shape[0]):
label = '%s_%d' % (var_name, ind_names[i])
values = var[i, sind:eind]
b2.plot(time[sind:eind], values, label=label)
else:
label = var_name
values = var[sind:eind]
b2.plot(time[sind:eind], values, label=label)
plt.legend()
plt.title(title)
plt.xlabel('time (seconds)')
unit = str(unit)
if unit == 'rad':
plt.ylabel('%s' % (var_name))
else:
plt.ylabel('%s (%s)' % (var_name, unit))
axes = plt.gca()
# Make sure one also can see min and max vals in plot.
if miny is not None and maxy is not None:
eps = 0.01 * (maxy - miny)
miny -= eps
maxy += eps
if miny is not None:
axes.set_ylim(bottom=miny)
if maxy is not None:
axes.set_ylim(top=maxy)
if folder is not None:
plot_name = 'plot_%s_%d_%d.png' % (var_name, sind, eind)
plot_name = os.path.join(folder, plot_name)
plt.savefig(plot_name)
plt.close()
# fire_once: boolean''', \
# threshold='v>vt', reset='v=vr',
# refractory='fire_once')
#Ni.period = [1, 1, 7] * br.ms
#Ni.fire_once[:] = [True] * 3
indices = np.asarray([0])
times = np.asarray([6]) * br.ms
Ni = br.SpikeGeneratorGroup(3, indices=indices, times=times)
#Nh = br.NeuronGroup(1, model="""dv/dt=(gtot-v)/(10*ms) : 1
# gtot : 1""")
Nh = br.NeuronGroup(1,
model="""v = I :1
I : 1""")
S = br.Synapses(Ni,
Nh,
model='''tl : second
alpha=exp((tl - t)/tau1) - exp((tl - t)/tau2) : 1
w : 1
I_post = w*alpha : 1 (summed)
''',
pre='tl+=t - tl')
S.connect('True')
S.w[:, :] = '(80+rand())'
S.tl[:, :] = '0*second'
M = br.StateMonitor(Nh, 'v', record=True)
br.run(20 * br.ms)
br.plot(M[0].t, M[0].v)
br.show()
def show_state_monitor(self,
monitor,
window='',
figure_title='',
name='Element {}',
name_values=None,
linestyle='-',
xlab='Time (ms)',
ylab='',
use_grid=True,
is_figure=True):
"""
Plots a line chart for a specific `StateMonitor`.
Parameters
----------
monitor : str
Name of the `StateMonitor`.
window : str, optional
Name of the window. Default value is ``monitor``.
figure_title : str, optional
Name of the figure.
Default value 'Evolution of ``variable`` over time'.
name : str, optional
Name of each set. `{}` is replaced by the set ID.
name_values list of str, optional
Values replacing the set IDs for ``name``.
linestyle : str, optional
Line style.
xlab : str, optional
X-axis label.
ylab : str, optional
Y-axis label. Default value is '``variable`` (`dimension`)'.
use_grid : bool, optional
Is the grid activated?
Warnings
--------
`dimension` of Volt is "m^2 kg s^-3 A^-1"!
"""
var = self._state_monitors[monitor]
values = getattr(self[monitor], var)
try:
dim = values.dim
except AttributeError:
dim = '1'
if is_figure:
figure(monitor if window == '' else window)
title('Evolution of {} over time'.format(var) if figure_title ==
'' else figure_title)
i = 0
for v in values:
plot(
self[monitor].t / ms,
v,
linestyle=linestyle,
label=(
name.format(i if name_values is None else name_values[i])))
i += 1
xlabel(xlab)
ylabel('{} ({})'.format(var, dim) if ylab == '' else ylab)
if use_grid:
grid()
if name != '':
legend()