def evaluate(self, raster_plot_interval, firing_rates_interval):
""" Displays simulation results.
Creates a spike raster plot.
Calculates the firing rate of each population and displays them as a
box plot.
Parameters
----------
raster_plot_interval
Times (in ms) to start and stop loading spike times for raster plot
(included).
firing_rates_interval
Times (in ms) to start and stop lading spike times for computing
firing rates (included).
Returns
-------
None
"""
if nest.Rank() == 0:
print(
'Interval to plot spikes: {} ms'.format(raster_plot_interval))
helpers.plot_raster(self.data_path, 'spike_recorder',
raster_plot_interval[0],
raster_plot_interval[1],
self.net_dict['N_scaling'])
print('Interval to compute firing rates: {} ms'.format(
firing_rates_interval))
helpers.firing_rates(self.data_path, 'spike_recorder',
firing_rates_interval[0],
firing_rates_interval[1])
helpers.boxplot(self.data_path, self.net_dict['populations'])
def evaluate(self, raster_plot_time_idx, fire_rate_time_idx):
""" Displays output of the simulation.
Calculates the firing rate of each population,
creates a spike raster plot and a box plot of the
firing rates.
"""
if nest.Rank() == 0:
print(
'Interval to compute firing rates: %s ms'
% np.array2string(fire_rate_time_idx)
)
fire_rate(
self.data_path, 'spike_detector',
fire_rate_time_idx[0], fire_rate_time_idx[1]
)
print(
'Interval to plot spikes: %s ms'
% np.array2string(raster_plot_time_idx)
)
plot_raster(
self.data_path, 'spike_detector',
raster_plot_time_idx[0], raster_plot_time_idx[1]
)
boxplot(self.net_dict, self.data_path)
def evaluate(self, raster_plot_interval, firing_rates_interval):
""" Displays simulation results.
Creates a spike raster plot.
Calculates the firing rate of each population and displays them as a
box plot.
Parameters
----------
raster_plot_interval
Times (in ms) to start and stop loading spike times for raster plot
(included).
firing_rates_interval
Times (in ms) to start and stop lading spike times for computing
firing rates (included).
Returns
-------
None
"""
for i_pop in range(len(self.pops)):
population = self.pops[i_pop]
data = []
for i_neur in range(len(population)):
spike_times = ngpu.GetRecSpikeTimes(population[i_neur])
if (len(spike_times) != 0):
# print("i_pop:", i_pop, " i_neur:", i_neur, " n_spikes:",
# len(spike_times))
for t in spike_times:
data.append([population[i_neur], t])
arr = np.array(data)
fn = os.path.join(self.data_path,
'spike_times_' + str(i_pop) + '.dat')
fmt = '%d\t%.3f'
np.savetxt(fn, arr, fmt=fmt, header="sender time_ms", comments='')
if self.Rank == 0:
print(
'Interval to plot spikes: {} ms'.format(raster_plot_interval))
helpers.plot_raster(self.data_path, 'spike_detector',
raster_plot_interval[0],
raster_plot_interval[1],
self.net_dict['N_scaling'])
print('Interval to compute firing rates: {} ms'.format(
firing_rates_interval))
helpers.firing_rates(self.data_path, 'spike_detector',
firing_rates_interval[0],
firing_rates_interval[1])
helpers.boxplot(self.data_path, self.net_dict['populations'])
