print('Resaving netpyne spike data to %s'%event_file)
import tables # pytables for HDF5 support
h5file=tables.open_file(event_file,mode='w')
spike_grp = h5file.create_group("/", 'spikes')
gids = netpyneSpkid
spiketimes = netpyneSpkt
# for nml_q in events:
# nml_pop, nml_index = _get_nml_pop_id(nml_q)
# (sonata_node, sonata_node_id) = sr.nml_ids_vs_gids[nml_pop][nml_index]
# for t in events[nml_q]:
# gids.append(sonata_node_id)
# spiketimes.append(t*1000.0)
h5file.create_array(spike_grp, 'gids', gids)
h5file.create_array(spike_grp, 'timestamps', spiketimes)
h5file.close()
if plotSpikesUsingBMTK:
from bmtk.analyzer.spike_trains import raster_plot
raster_plot(rootFolder + '/network/internal_nodes.h5', rootFolder + '/network/internal_node_types.csv', rootFolder + '/output/spikes.h5', group_key='node_type_id', title='Simulator: NEURON via BMTK', save_as=None, show=0)
ax = plt.gcf().get_axes()[0]
ax.get_legend().remove()
plt.savefig('bmtk_300_cells_raster.png', dpi=300)
raster_plot(rootFolder + '/network/internal_nodes.h5', rootFolder + '/network/internal_node_types.csv', 'netpyne_spikes.h5', group_key='node_type_id', title='Simulator: NEURON via NetPyNE', save_as=None, show=0)
ax = plt.gcf().get_axes()[0]
ax.get_legend().remove()
plt.savefig('netpyne_300_cells_raster.png', dpi=300)
from bmtk.analyzer.spike_trains import raster_plot
raster_plot('network/cortex_nodes.h5', 'network/cortex_node_types.csv', 'output/spikes.h5')
from bmtk.analyzer.spike_trains import raster_plot, rates_plot
import matplotlib.pyplot as plt
raster_plot('network/ff_ff_nodes.h5',
'network/ff_ff_node_types.csv',
'PN_IClamp/output/spikes.h5',
group_key='pop_name')
plt.show()