def test_DumpNodes2(self):
"""Test dumping nodes, two layers."""
ldict = {'elements': 'iaf_neuron', 'rows': 3, 'columns':3,
'extent': [2., 2.], 'edge_wrap': True}
nest.ResetKernel()
l = topo.CreateLayer(ldict)
topo.DumpLayerNodes(l*2, os.path.join(self.nest_tmpdir(), 'test_DumpNodes2.out.lyr') )
self.assertTrue(True)
save_gabors(gabors_to_nest, images_to_simulate)
if get_output_images_bool:
quit()
############################################################ Connectivity ######################################################################
t = time.time()
poiss_layers_dict, lgn_layers_dict, v1_layers_dict = main_get_and_connect_layers(
)
conn_time = time.time() - t
######################################################### Dump layers
t = time.time()
for layer, j in zip(v1_layers_dict, range(0, len(v1_layers_dict))):
tp.DumpLayerNodes(v1_layers_dict[layer], 'positions-' + str(layer))
for layer, j in zip(lgn_layers_dict, range(0, len(lgn_layers_dict))):
tp.DumpLayerNodes(lgn_layers_dict[layer], 'positions-' + str(layer))
dump_time = time.time() - t
print("Dump layers done!")
####################################################### Position files
t = time.time()
save_positions_to_file()
positions_time = time.time() - t
print("Positions to file done!")
################################################# Simulation
'edge_wrap': True
})
topo.ConnectLayers(
l1, l2, {
'connection_type': direction,
'mask': {
'circular': {
'radius': 0.4
}
},
'weights': {
'linear': {
'c': 1.,
'a': -5.
}
}
})
topo.DumpLayerNodes(l1 + l2, 'topo_mpi_test.lyr_tmp')
topo.DumpLayerConnections(l1, 'static_synapse', 'topo_mpi_test.cnn_tmp')
# combine all layer and connection files into one sorted file, respectively
nest.sli_run('SyncProcesses') # make sure all are done dumping
if nest.Rank() == 0:
for filetype in ['cnn', 'lyr']:
os.system('cat *.{0}_tmp | sort > all_sorted.{0}'.format(filetype))
os.system('rm *.{0}_tmp'.format(filetype))
# directories for any number of MPI processes should now be diff-able
inserted as a string and the parameter to be specified in a
dictionary. All instances of theses models created after this point
will have the properties specified in the dictionary by default.
'''
nest.SetDefaults("iaf_psc_alpha", neuron_params)
'''
Creation of the topology layers for excitatory and inhibitory neurons.
GIDs and neuron positions are written to file.
'''
layer_in = tp.CreateLayer(layerdict_in)
layer_ex = tp.CreateLayer(layerdict_ex)
layer_stim = tp.CreateLayer(layerdict_stim)
tp.DumpLayerNodes(
layer_ex, os.path.join(spike_output_path, label_positions + '-0.dat'))
tp.DumpLayerNodes(
layer_in, os.path.join(spike_output_path, label_positions + '-1.dat'))
tp.DumpLayerNodes(
layer_stim, os.path.join(spike_output_path,
label_positions + '-2.dat'))
nodes_ex = nest.GetChildren(layer_ex)[0] # nodes of ex/in neurons
nodes_in = nest.GetChildren(layer_in)[0]
nodes_stim = nest.GetChildren(layer_stim)[0]
'''
Distribute initial membrane voltages.
'''
for neurons in [nodes_ex, nodes_in]:
for neuron in neurons:
t = time.time()
steady_state_time = time.time() - t
for i in tqdm(range(num_images_to_simulate)):
set_poisson_values(gabors_to_nest['image_' + str(i)], poiss_layers,
num_orientations)
nest.Simulate(ms_per_stimuli)
sim_time = time.time() - t
######################################################### Data Treatment #################################################################
layers_to_record = {}
spike_detectors = {}
for i in layers:
layers_to_record.update(dict(list(layers[i].items())[:2]))
spike_detectors.update(dict(list(layers[i].items())[2:]))
save_dict(layers_to_record, 'to_record_layer')
save_dict(spike_detectors, 'to_record_sd')
for layer, j in zip(layers_to_record, range(0, len(layers_to_record))):
tp.DumpLayerNodes(layers_to_record[layer], 'positions-' + str(layer))
print("Times: \n\n Building architecture: " +
str(np.around(conn_time / 60, 2)) + "m")
print("\n Image processing: " + str(np.around(gabors_time, 2)) + "s")
print("\n Simulation: " + str(np.around(sim_time / 60, 2)) + "m")
print("\n")
print(image_selected, "\n")
