def plot_2d_input_weights():
name = 'XeAe'
weights = get_2d_input_weights()
fig = b2.figure(fig_num, figsize = (18, 18))
im2 = b2.imshow(weights, interpolation = "nearest", vmin = 0, vmax = wmax_ee, cmap = cmap.get_cmap('hot_r'))
b2.colorbar(im2)
b2.title('weights of connection' + name)
fig.canvas.draw()
return im2, fig
def plot_2d_input_weights():
name = 'XeAe'
weights = get_2d_input_weights()
fig = b2.figure(fig_num, figsize = (18, 18))
im2 = b2.imshow(weights, interpolation = "nearest", vmin = 0, vmax = wmax_ee, cmap = cmap.get_cmap('hot_r'))
b2.colorbar(im2)
b2.title('weights of connection' + name)
fig.canvas.draw()
return im2, fig
def plot_performance(fig_num):
num_evaluations = int(num_examples/update_interval)
time_steps = range(0, num_evaluations)
performance = np.zeros(num_evaluations)
fig = b2.figure(fig_num, figsize = (5, 5))
fig_num += 1
ax = fig.add_subplot(111)
im2, = ax.plot(time_steps, performance) #my_cmap
b2.ylim(ymax = 100)
b2.title('Classification performance')
fig.canvas.draw()
return im2, performance, fig_num, fig
def plot_performance(fig_num):
num_evaluations = int(num_examples/update_interval)
time_steps = range(0, num_evaluations)
performance = np.zeros(num_evaluations)
fig = b2.figure(fig_num, figsize = (5, 5))
fig_num += 1
ax = fig.add_subplot(111)
im2, = ax.plot(time_steps, performance) #my_cmap
b2.ylim(ymax = 100)
b2.title('Classification performance')
fig.canvas.draw()
return im2, performance, fig_num, fig
def show_event_monitor(self,
monitor,
window='',
figure_title='',
xlab='Time (ms)',
ylab='Neuron Index',
marker='o',
use_grid=True,
is_figure=True):
"""
Plots a cloud chart for a specific `EventMonitor`.
Parameters
----------
monitor : str
Name of the `EventMonitor`.
window : str, optional
Name of the window. Defaults to ``monitor``.
figure_title : str, optional
Name of the figure.
Defaults to 'Firing of ``event`` events during simulation'.
ylab : str, optional
Y-axis label.
xlab : str, optional
X-axis label.
marker : str, optional
Marker style.
use_grid : bool, optional
Is the grid activated?
"""
if is_figure:
figure(monitor if window == '' else window)
title('Firing of {} events during simulation'.
format(self._event_monitors[monitor]) if figure_title ==
'' else figure_title)
scatter(self[monitor].t / ms, self[monitor].i, marker=marker)
xlabel(xlab)
ylabel(ylab)
if use_grid:
grid()
pause(0.001)
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')
density=True,
range=(0., 1.))[0]
b2.figure(figsize=(4, 4))
im = b2.imshow(
rates_weight_histograms,
origin='lower',
extent=[postsyn_rates[0] / b2.Hz, postsyn_rates[-1] / b2.Hz, 0, 1],
aspect='auto',
cmap=b2.plt.cm.gray_r)
# b2.colorbar(label='probability density')
b2.xlabel("Postsynaptic Firing Rate (Hz)")
# b2.ylabel(r"$\frac{w}{w_{max}}$", rotation=0)
b2.ylabel("$w/w_{max}$")
b2.title(
'Synaptic Strength Distributions\nas function of postsynaptic firing rate'
)
b2.savefig('images_and_animations/synaptic_strengths_vs_firing_rates.png')
# b2.show()
b2.figure(figsize=(4, 4))
im = b2.imshow(mu_weight_histograms,
origin='lower',
extent=[0, 1, 0, 1],
aspect='auto',
cmap=b2.plt.cm.gray_r)
# b2.colorbar(label='probability density')
# b2.xticks(np.linspace(0., 1., 5)**2)
b2.xlabel("$\mu$")
# b2.ylabel(r"$\frac{w}{w_{max}}$", rotation=0)
b2.ylabel("$w/w_{max}$")
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')
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()
