time_range = None
else:
if t_min is None:
time_range = [0.0, t_max]
else:
time_range = [t_min, t_max]
if not populations:
populations = ['eachPop']
if graph_type is None:
graph_type = 'matrix'
plot.plot_spike_histogram_autocorr(spike_events_path,
spike_events_namespace,
include=populations,
time_range=time_range,
time_variable=t_variable,
lag=lag,
bin_size=spike_hist_bin,
maxCells=max_cells,
graph_type=graph_type,
fontSize=font_size,
saveFig=True)
if __name__ == '__main__':
main(args=sys.argv[(
utils.list_find(lambda x: os.path.basename(x) == script_name, sys.argv
) + 1):])
new_response_dict = {}
if gid is not None:
random_gid = random_gids[gid-population_start]
new_response_dict[random_gid] = {'rate': stimulus_dict['rate'],
'spiketrain': np.asarray(stimulus_dict['spiketrain'],
dtype=np.float32),
'modulation': stimulus_dict['modulation'],
'peak_index': stimulus_dict['peak_index'] }
print('Rank %i; source: %s; assigned spike trains for gid %i to gid %i in %.2f s' % \
(rank, population, gid, random_gid+population_start, time.time() - local_time))
count += 1
if not debug:
append_cell_attributes(comm, stimulus_path, population, new_response_dict,
namespace=output_stimulus_namespace,
io_size=io_size, chunk_size=chunk_size,
value_chunk_size=value_chunk_size)
sys.stdout.flush()
del new_response_dict
gc.collect()
global_count = comm.gather(count, root=0)
if rank == 0:
print('%i ranks randomized spike trains for %i cells in %.2f s' % (comm.size, np.sum(global_count),
time.time() - start_time))
if __name__ == '__main__':
main(args=sys.argv[(utils.list_find(lambda s: s.find(script_name) != -1,sys.argv)+1):])