def __init__(self):
self.model_interpreter = NetPyNEModelInterpreter()
# Geppetto model of a created network
self.geppetto_model = None
self.netParams = specs.NetParams()
self.simConfig = specs.SimConfig()
self.run_config = model.RunConfig()
self.experiments = experiments
model.register(metadata)
synchronization.startSynchronization(self.__dict__)
logging.debug("Initializing the original model")
jupyter_geppetto.context = {'netpyne_geppetto': self}
# Set running experiments without any subprocess to ERRROR
experiments.get_experiments()
running_exps = experiments.get_by_states([
model.ExperimentState.PENDING, model.ExperimentState.SIMULATING,
model.ExperimentState.INSTANTIATING
])
if not simulations.local.is_running():
[experiments.set_to_error(e) for e in running_exps]
def __init__(self):
self.model_interpreter = NetPyNEModelInterpreter()
self.netParams = specs.NetParams()
self.simConfig = specs.SimConfig()
synchronization.startSynchronization(self.__dict__)
logging.debug("Initializing the original model")
jupyter_geppetto.context = {'netpyne_geppetto': self}
def __init__(self):
"""
Initializes HNN Geppetto
Loads initial cfg
Loads initial .param file
Gets all the evoked information
Loads initial experimental data
Takes snapshot
Starts synchronization on cfg
"""
self.model_interpreter = NetPyNEModelInterpreter()
# loads the param file on top of the cfg contained in cfg.py
self.cfg = set_cfg_from_file('load_examples/ERPYes100Trials.param',
self.load_cfg())
self.evoked_dict = self.get_evoked_dict(self.cfg)
self.experimental_data = self.load_experimental_from_file()
# use to decide whether or not to update the canvas in the front end
self.last_cfg_snapshot = self.cfg.__dict__.copy()
synchronization.startSynchronization(self.__dict__)
logging.debug("Initializing the original model")
jupyter_geppetto.context = {'hnn_geppetto': self}