def run(config_file):
conf = config.from_json(config_file)
io.setup_output_dir(conf)
nrn.load_neuron_modules(conf)
graph = BioGraph.from_config(conf, property_schema=AIPropertySchema)
net = BioNetwork.from_config(conf, graph)
sim = Simulation.from_config(conf, network=net)
sim.run()
nrn.quit_execution()
def run(config_file):
conf = config.from_json(config_file) # build configuration
io.setup_output_dir(conf) # set up output directories
nrn.load_neuron_modules(conf) # load NEURON modules and mechanisms
graph = BioGraph.from_config(conf,
network_format=TabularNetwork_AI,
property_schema=AIPropertySchema)
net = BioNetwork.from_config(conf, graph) # create network of in NEURON
sim = Simulation(conf, network=net) # initialize a simulation
sim.attach_current_clamp()
sim.set_recordings(
) # set recordings of relevant variables to be saved as an ouput
sim.run() # run simulation
nrn.quit_execution() # exit
def run(config_file):
conf = config.from_json(config_file) # build configuration
io.setup_output_dir(conf) # set up output directories
nrn.load_neuron_modules(conf) # load NEURON modules and mechanisms
nrn.load_py_modules(
cell_models=set_cell_params, # load custom Python modules
syn_models=set_syn_params,
syn_weights=set_weights)
graph = BioGraph.from_config(
conf, # create network graph containing parameters of the model
network_format=TabularNetwork_AI,
property_schema=AIPropertySchema)
net = BioNetwork.from_config(
conf, graph
) # create netwosim = Simulation.from_config(conf, network=net) rk of in NEURON
sim = Simulation.from_config(conf, network=net) # initialize a simulation
sim.run() # run simulation
if MPI_RANK == 0:
try:
# Check spikes
print2log0('Checking spike times')
assert (os.path.exists(conf['output']['spikes_ascii_file']))
assert (spike_files_equal(conf['output']['spikes_ascii_file'],
'expected/spikes.txt'))
print2log0('Spikes passed!')
# Check extracellular recordings
print2log0('Checking ECP output')
check_ecp()
print2log0('ECP passed!')
# Check saved variables
print2log0('Checking NEURON saved variables')
for saved_gids in conf['node_id_selections']['save_cell_vars']:
check_cellvars(saved_gids, conf)
print2log0('variables passed!')
except AssertionError:
_, _, tb = sys.exc_info()
traceback.print_tb(tb)
pc.barrier()
nrn.quit_execution() # exit
def run(config_file):
conf = config.from_json(config_file) # build configuration
io.setup_output_dir(conf) # set up output directories
nrn.load_neuron_modules(conf) # load NEURON modules and mechanisms
nrn.load_py_modules(
cell_models=set_cell_params, # load custom Python modules
syn_models=set_syn_params,
syn_weights=set_weights)
graph = BioGraph.from_config(
conf) # create network graph containing parameters of the model
net = BioNetwork.from_config(conf, graph) # create network of in NEURON
sim = Simulation.from_config(conf, net) # initialize a simulation
# sim.set_recordings() # set recordings of relevant variables to be saved as an ouput
sim.run() # run simulation
assert (os.path.exists(conf['output']['spikes_ascii_file']))
assert (spike_files_equal(conf['output']['spikes_ascii_file'],
'expected/spikes.txt'))
nrn.quit_execution() # exit
def run(config_file):
conf = config.from_json(config_file) # build configuration
io.setup_output_dir(conf) # set up output directories
nrn.load_neuron_modules(conf) # load NEURON modules and mechanisms
nrn.load_py_modules(
cell_models=set_cell_params, # load custom Python modules
syn_models=set_syn_params,
syn_weights=set_weights)
graph = BioGraph.from_config(
conf, # create network graph containing parameters of the model
network_format=TabularNetwork_AI,
property_schema=AIPropertySchema)
net = BioNetwork.from_config(
conf, graph
) # create netwosim = Simulation.from_config(conf, network=net) rk of in NEURON
sim = Simulation.from_config(conf, network=net) # initialize a simulation
# sim.set_recordings() # set recordings of relevant variables to be saved as an ouput
sim.run() # run simulation
assert (os.path.exists(conf['output']['spikes_ascii_file']))
assert (spike_files_equal(conf['output']['spikes_ascii_file'],
'expected/spikes.txt'))
# Test the results of the ecp
SAMPLE_SIZE = 100
expected_h5 = h5py.File('expected/ecp.h5', 'r')
nrows, ncols = expected_h5['ecp'].shape
expected_mat = np.matrix(expected_h5['ecp'])
results_h5 = h5py.File('output/ecp.h5', 'r')
assert ('ecp' in results_h5.keys())
results_mat = np.matrix(results_h5['ecp'][:])
assert (results_h5['ecp'].shape == (nrows, ncols))
for i, j in zip(randint(0, nrows, size=SAMPLE_SIZE),
randint(0, ncols, size=SAMPLE_SIZE)):
assert (results_mat[i, j] == expected_mat[i, j])
nrn.quit_execution() # exit
def run(config_file):
conf = config.from_json(config_file) # build configuration
io.setup_output_dir(conf) # set up output directories
nrn.load_neuron_modules(conf) # load NEURON modules and mechanisms
nrn.load_py_modules(
cell_models=set_cell_params, # load custom Python modules
syn_models=set_syn_params,
syn_weights=set_weights)
graph = BioGraph.from_config(
conf, # create network graph containing parameters of the model
network_format=TabularNetwork_AI,
property_schema=AIPropertySchema)
net = BioNetwork.from_config(conf, graph)
sim = Simulation.from_config(conf, network=net)
sim.run()
assert (os.path.exists(conf['output']['spikes_ascii_file']))
assert (spike_files_equal(conf['output']['spikes_ascii_file'],
'expected/spikes.txt'))
nrn.quit_execution() # exit
def run(config_file):
conf = config.from_json(config_file) # build configuration
io.setup_output_dir(conf) # set up output directories
nrn.load_neuron_modules(conf) # load NEURON modules and mechanisms
nrn.load_py_modules(
cell_models=set_cell_params, # load custom Python modules
syn_models=set_syn_params,
syn_weights=set_weights)
graph = BioGraph.from_config(
conf, # create network graph containing parameters of the model
#network_format=TabularNetwork_AI,
property_schema=AIPropertySchema)
'''
graph = BioGraph.from_config(conf)
'''
net = BioNetwork.from_config(conf, graph) # create network of in NEURON
sim = Simulation(conf, network=net) # initialize a simulation
sim.set_recordings(
) # set recordings of relevant variables to be saved as an ouput
sim.run() # run simulation
nrn.quit_execution() # exit