def patch_adaptor(adaptor, node_group, network):
node_adaptor = NodeAdaptor.patch_adaptor(adaptor, node_group, network)
# If dynamics params is stored in the nodes.h5 then we have to build each node separate
if node_group.has_dynamics_params:
node_adaptor.batch_process = False
# If there is a non-null value in the model_processing column then it potentially means that every cell is
# uniquly built (currently model_processing is applied to each individ. cell) and nodes can't be batched
if 'model_processing' in node_group.columns:
node_adaptor.batch_process = False
elif 'model_processing' in node_group.all_columns and not all_null(node_group, 'model_processing'):
node_adaptor.batch_process = False
if node_adaptor.batch_process:
io.log_info('Batch processing nodes for {}/{}.'.format(node_group.parent.name, node_group.group_id))
return node_adaptor
def run(self, tstop=None):
if tstop is None:
tstop = self._tstop
for mod in self._mods:
mod.initialize(self)
io.barrier()
io.log_info('Starting Simulation')
n, res, data_res = self._get_block_trial(tstop)
if n > 0:
for r in moves.range(n):
nest.Simulate(data_res)
if res > 0:
nest.Simulate(res * self.dt)
if n < 0:
nest.Simulate(tstop)
io.barrier()
io.log_info('Simulation finished, finalizing results.')
for mod in self._mods:
mod.finalize(self)
io.barrier()
io.log_info('Done.')
def run(self, duration=None):
if duration is None:
duration = self.duration
for mod in self._mods:
mod.initialize(self)
io.barrier()
io.log_info('Starting Simulation')
n, res, data_res = self._get_block_trial(duration)
if n > 0:
for r in xrange(n):
nest.Simulate(data_res)
if res > 0:
nest.Simulate(res * self.dt)
if n < 0:
nest.Simulate(duration)
io.barrier()
io.log_info('Simulation finished, finalizing results.')
for mod in self._mods:
mod.finalize(self)
io.barrier()
io.log_info('Done.')
def from_config(cls, configure, graph):
# load the json file or object
if isinstance(configure, string_types):
config = Config.from_json(configure, validate=True)
elif isinstance(configure, dict):
config = configure
else:
raise Exception('Could not convert {} (type "{}") to json.'.format(
configure, type(configure)))
if 'run' not in config:
raise Exception(
'Json file is missing "run" entry. Unable to build Bionetwork.'
)
run_dict = config['run']
# Get network parameters
# step time (dt) is set in the kernel and should be passed
overwrite = run_dict[
'overwrite_output_dir'] if 'overwrite_output_dir' in run_dict else True
print_time = run_dict[
'print_time'] if 'print_time' in run_dict else False
dt = run_dict['dt'] # TODO: make sure dt exists
network = cls(graph, dt=dt, overwrite=overwrite)
if 'output_dir' in config['output']:
network.output_dir = config['output']['output_dir']
if 'block_run' in run_dict and run_dict['block_run']:
if 'block_size' not in run_dict:
raise Exception(
'"block_run" is set to True but "block_size" not found.')
network._block_size = run_dict['block_size']
if 'duration' in run_dict:
network.tstop = run_dict['duration']
elif 'tstop' in run_dict:
network.tstop = run_dict['tstop']
if 'precise_times' in run_dict:
network.set_spike_generator_params(
precise_times=run_dict['precise_times'])
if run_dict.get('allow_offgrid_spikes', False):
network.set_spike_generator_params(allow_offgrid_spikes=True)
# Create the output-directory, or delete existing files if it already exists
graph.io.log_info('Setting up output directory')
if not os.path.exists(config['output']['output_dir']):
os.mkdir(config['output']['output_dir'])
elif overwrite:
for gfile in glob.glob(
os.path.join(config['output']['output_dir'], '*.gdf')):
os.remove(gfile)
graph.io.log_info('Building cells.')
graph.build_nodes()
graph.io.log_info('Building recurrent connections')
graph.build_recurrent_edges()
for sim_input in inputs.from_config(config):
node_set = graph.get_node_set(sim_input.node_set)
if sim_input.input_type == 'spikes':
io.log_info('Build virtual cell stimulations for {}'.format(
sim_input.name))
path = sim_input.params['input_file']
spikes = SpikeTrains.load(path=path,
file_type=sim_input.module,
**sim_input.params)
#spikes = spike_trains.SpikesInput.load(name=sim_input.name, module=sim_input.module,
# input_type=sim_input.input_type, params=sim_input.params)
graph.add_spike_trains(spikes, node_set,
network.get_spike_generator_params())
elif sim_input.input_type == 'current_clamp':
# TODO: Need to make this more robust
amp_times = sim_input.params.get('amplitude_times', [])
amp_values = sim_input.params.get('amplitude_values', [])
if 'delay' in sim_input.params:
amp_times.append(sim_input.params['delay'])
amp_values.append(sim_input.params['amp'])
if 'duration' in sim_input.params:
amp_times.append(sim_input.params['delay'] +
sim_input.params['duration'])
amp_values.append(0.0)
network.add_step_currents(amp_times, amp_values, node_set,
sim_input.name)
else:
graph.io.log_warning('Unknown input type {}'.format(
sim_input.input_type))
sim_reports = reports.from_config(config)
for report in sim_reports:
if report.module == 'spikes_report':
mod = mods.SpikesMod(**report.params)
elif isinstance(report, reports.MembraneReport):
# For convience and for compliance with SONATA format. "membrane_report" and "multimeter_report is the
# same in pointnet.
mod = mods.MultimeterMod(**report.params)
else:
graph.io.log_exception('Unknown report type {}'.format(
report.module))
network.add_mod(mod)
io.log_info('Network created.')
return network
def from_config(cls, configure, graph):
# load the json file or object
if isinstance(configure, basestring):
config = Config.from_json(configure, validate=True)
elif isinstance(configure, dict):
config = configure
else:
raise Exception('Could not convert {} (type "{}") to json.'.format(
configure, type(configure)))
if 'run' not in config:
raise Exception(
'Json file is missing "run" entry. Unable to build Bionetwork.'
)
run_dict = config['run']
# Get network parameters
# step time (dt) is set in the kernel and should be passed
overwrite = run_dict[
'overwrite_output_dir'] if 'overwrite_output_dir' in run_dict else True
print_time = run_dict[
'print_time'] if 'print_time' in run_dict else False
dt = run_dict['dt'] # TODO: make sure dt exists
network = cls(graph, dt=dt, overwrite=overwrite)
if 'output_dir' in config['output']:
network.output_dir = config['output']['output_dir']
if 'block_run' in run_dict and run_dict['block_run']:
if 'block_size' not in run_dict:
raise Exception(
'"block_run" is set to True but "block_size" not found.')
network._block_size = run_dict['block_size']
if 'duration' in run_dict:
network.duration = run_dict['duration']
elif 'tstop' in run_dict:
network.duration = run_dict['tstop']
# Create the output-directory, or delete existing files if it already exists
graph.io.log_info('Setting up output directory')
if not os.path.exists(config['output']['output_dir']):
os.mkdir(config['output']['output_dir'])
elif overwrite:
for gfile in glob.glob(
os.path.join(config['output']['output_dir'], '*.gdf')):
os.remove(gfile)
graph.io.log_info('Building cells.')
graph.build_nodes()
graph.io.log_info('Building recurrent connections')
graph.build_recurrent_edges()
for sim_input in inputs.from_config(config):
node_set = graph.get_node_set(sim_input.node_set)
if sim_input.input_type == 'spikes':
spikes = spike_trains.SpikesInput.load(
name=sim_input.name,
module=sim_input.module,
input_type=sim_input.input_type,
params=sim_input.params)
io.log_info('Build virtual cell stimulations for {}'.format(
sim_input.name))
graph.add_spike_trains(spikes, node_set)
sim_reports = reports.from_config(config)
for report in sim_reports:
if report.module == 'spikes_report':
mod = mods.SpikesMod(**report.params)
else:
graph.io.log_exception('Unknown report type {}'.format(
report.module))
network.add_mod(mod)
io.log_info('Network created.')
return network
