def __init__(self,
network,
dt=0.001,
seed=None,
model=None,
progress_bar=True,
optimize=True):
self.closed = True # Start closed in case constructor raises exception
self.progress_bar = progress_bar
self.optimize = optimize
if model is None:
self.model = Model(
dt=float(dt),
label="%s, dt=%f" % (network, dt),
decoder_cache=get_default_decoder_cache(),
)
else:
self.model = model
pt = ProgressTracker(progress_bar, Progress("Building", "Build"))
with pt:
if network is not None:
# Build the network into the model
self.model.build(network,
progress=pt.next_stage("Building", "Build"))
# Order the steps (they are made in `Simulator.reset`)
self.dg = operator_dependency_graph(self.model.operators)
if optimize:
with pt.next_stage("Building (running optimizer)",
"Optimization"):
opmerge_optimize(self.model, self.dg)
self._step_order = [
op for op in toposort(self.dg) if hasattr(op, "make_step")
]
# -- map from Signal.base -> ndarray
self.signals = SignalDict()
for op in self.model.operators:
op.init_signals(self.signals)
# Add built states to the raw simulation data dictionary
self._sim_data = self.model.params
# Provide a nicer interface to simulation data
self.data = SimulationData(self._sim_data)
if seed is None:
if network is not None and network.seed is not None:
seed = network.seed + 1
else:
seed = np.random.randint(npext.maxint)
self.closed = False
self.reset(seed=seed)
def __init__(self,
network,
dt=0.001,
seed=None,
model=None,
progress_bar=True,
optimize=True):
self.closed = True # Start closed in case constructor raises exception
self.progress_bar = progress_bar
if model is None:
self.model = Model(dt=float(dt),
label="%s, dt=%f" % (network, dt),
decoder_cache=get_default_decoder_cache())
else:
self.model = model
if network is not None:
# Build the network into the model
self.model.build(network, progress_bar=self.progress_bar)
# Order the steps (they are made in `Simulator.reset`)
self.dg = operator_dependency_graph(self.model.operators)
if optimize:
opmerge_optimize(self.model, self.dg)
self._step_order = [
op for op in toposort(self.dg) if hasattr(op, 'make_step')
]
# -- map from Signal.base -> ndarray
self.signals = SignalDict()
for op in self.model.operators:
op.init_signals(self.signals)
# Add built states to the probe dictionary
self._probe_outputs = self.model.params
# Provide a nicer interface to probe outputs
self.data = ProbeDict(self._probe_outputs)
if seed is None:
if network is not None and network.seed is not None:
seed = network.seed + 1
else:
seed = np.random.randint(npext.maxint)
self.closed = False
self.reset(seed=seed)