def build_cells(self):
"""Instantiate cells based on parameters provided in the InternalCell table and Internal CellModel table"""
self._select_local_nodes()
for node in self._local_nodes:
gid = node.node_id
if node.cell_type == CellTypes.Biophysical:
self._cells[gid] = BioCell(node, self.spike_threshold, self.dL,
self.calc_ecp,
self._save_connections)
self._local_biophys_gids.append(gid)
elif node.cell_type == CellTypes.Point:
self._cells[gid] = LIFCell(node)
elif node.cell_type == CellTypes.Virtual:
# Just in case, should never see
continue
else:
io.print2log0('ERROR: not implemented class')
# raise NotImplementedError('not implemented cell class')
nrn.quit_execution()
# TODO: Add ability to easily extend the Cell-Types without hardcoding into this loop!!
pc.barrier() # wait for all hosts to get to this point
self.make_morphologies()
self.set_seg_props() # set segment properties by creating Morphologies
# self.set_tar_segs() # set target segments needed for computing the synaptic innervations
self.calc_seg_coords() # use for computing the ECP
self._cells_built = True
def setup_output_dir(conf):
start_from_state =False
if start_from_state: # starting from a previously saved state
try:
assert os.path.exists(conf["output"]["output_dir"])
print2log0('Will run simulation from a previously saved state...')
except:
print('ERROR: directory with the initial state does not exist')
nrn.quit_execution()
elif not start_from_state: # starting from a new (init) state
if int(pc.id()) == 0:
if os.path.exists(conf["output"]["output_dir"]):
if conf["run"]['overwrite_output_dir']:
shutil.rmtree(conf["output"]["output_dir"])
print('Overwriting the output directory %s:' %conf["output"]["output_dir"]) # must print to stdout because the log file is not created yet
else:
print('ERROR: Directory already exists')
print("To overwrite existing output_dir set 'overwrite_output_dir': True")
nrn.quit_execution()
os.makedirs(conf["output"]["output_dir"])
os.makedirs(conf["output"]["cell_vars_dir"])
create_log(conf)
config.copy(conf)
pc.barrier()
print2log0('Output directory: %s' % conf["output"]["output_dir"])
print2log0('Config file: %s' % conf["config_path"])
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 load_csv(fullpath):
"""Load a csv file
:param fullpath: path to the file name to be loaded
:return: pandas dataframe
"""
try:
data = pd.read_csv(fullpath, sep=' ')
return data
except IOError:
print2log0("ERROR: cannot open {}".format(fullpath))
nrn.quit_execution()
def load_h5(file_name):
"""load hdf5 file into memory
:param file_name: full path to h5 file
:return: file handle to hdf5 object
"""
assert (os.path.exists(file_name))
try:
data_handle = h5py.File(file_name, 'r')
return data_handle
except IOError:
print2log0("ERROR: cannot open {}".format(file_name))
nrn.quit_execution()
def load_json(fullpath):
"""Tries to load a json file
:param fullpath: path to file name to be loaded
:return: json dictionary object
"""
try:
with open(fullpath, 'r') as f:
data = json.load(f)
return data
except IOError:
print2log0("ERROR: cannot open {}".format(fullpath))
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