def __init__(self, network, dt=0.001, seed=None, model=None):
"""Initialize the simulator with a network and (optionally) a model.
Most of the time, you will pass in a network and sometimes a dt::
sim1 = nengo.Simulator(my_network) # Uses default 0.001s dt
sim2 = nengo.Simulator(my_network, dt=0.01) # Uses 0.01s dt
For more advanced use cases, you can initialize the model yourself,
and also pass in a network that will be built into the same model
that you pass in::
sim = nengo.Simulator(my_network, model=my_model)
If you want full control over the build process, then you can build
your network into the model manually. If you do this, then you must
explicitly pass in ``None`` for the network::
sim = nengo.Simulator(None, model=my_model)
Parameters
----------
network : nengo.Network instance or None
A network object to the built and then simulated.
If a fully built ``model`` is passed in, then you can skip
building the network by passing in network=None.
dt : float
The length of a simulator timestep, in seconds.
seed : int
A seed for all stochastic operators used in this simulator.
Note that there are not stochastic operators implemented
currently, so this parameters does nothing.
model : nengo.builder.Model instance or None
A model object that contains build artifacts to be simulated.
Usually the simulator will build this model for you; however,
if you want to build the network manually, or to inject some
build artifacts in the Model before building the network,
then you can pass in a ``nengo.builder.Model`` instance.
"""
if model is None:
caching = runcom.getboolean('decoder_cache', 'enabled')
network_seed_set = network is not None and network.seed is not None
if caching:
decoder_cache = nengo.cache.DecoderCache(
runcom.getboolean('decoder_cache', 'readonly'))
elif caching is None and network_seed_set:
decoder_cache = nengo.cache.DecoderCache()
else:
decoder_cache = nengo.cache.NoDecoderCache()
self.model = Model(dt=dt,
label="%s, dt=%f" % (network.label, dt),
seed=network.seed, decoder_cache=decoder_cache)
else:
self.model = model
if network is not None:
# Build the network into the model
Builder.build(
network, model=self.model)
self.model.decoder_cache.shrink()
# Note: seed is not used right now, but one day...
assert seed is None, "Simulator seed not yet implemented"
self.seed = np.random.randint(npext.maxint) if seed is None else seed
# -- map from Signal.base -> ndarray
self.signals = SignalDict(__time__=np.asarray(0.0, dtype=np.float64))
for op in self.model.operators:
op.init_signals(self.signals, dt)
self.dg = operator_depencency_graph(self.model.operators)
self._step_order = [node for node in toposort(self.dg)
if hasattr(node, 'make_step')]
self._steps = [node.make_step(self.signals, dt)
for node in self._step_order]
self.n_steps = 0
# 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)
def __init__(self, network, dt=0.001, seed=None, model=None):
"""Initialize the simulator with a network and (optionally) a model.
Most of the time, you will pass in a network and sometimes a dt::
sim1 = nengo.Simulator(my_network) # Uses default 0.001s dt
sim2 = nengo.Simulator(my_network, dt=0.01) # Uses 0.01s dt
For more advanced use cases, you can initialize the model yourself,
and also pass in a network that will be built into the same model
that you pass in::
sim = nengo.Simulator(my_network, model=my_model)
If you want full control over the build process, then you can build
your network into the model manually. If you do this, then you must
explicitly pass in ``None`` for the network::
sim = nengo.Simulator(None, model=my_model)
Parameters
----------
network : nengo.Network instance or None
A network object to the built and then simulated.
If a fully built ``model`` is passed in, then you can skip
building the network by passing in network=None.
dt : float
The length of a simulator timestep, in seconds.
seed : int
A seed for all stochastic operators used in this simulator.
Note that there are not stochastic operators implemented
currently, so this parameters does nothing.
model : nengo.builder.Model instance or None
A model object that contains build artifacts to be simulated.
Usually the simulator will build this model for you; however,
if you want to build the network manually, or to inject some
build artifacts in the Model before building the network,
then you can pass in a ``nengo.builder.Model`` instance.
"""
if model is None:
caching = runcom.getboolean('decoder_cache', 'enabled')
network_seed_set = network is not None and network.seed is not None
if caching:
decoder_cache = nengo.cache.DecoderCache(
runcom.getboolean('decoder_cache', 'readonly'))
elif caching is None and network_seed_set:
decoder_cache = nengo.cache.DecoderCache()
else:
decoder_cache = nengo.cache.NoDecoderCache()
self.model = Model(dt=dt,
label="%s, dt=%f" % (network.label, dt),
seed=network.seed,
decoder_cache=decoder_cache)
else:
self.model = model
if network is not None:
# Build the network into the model
Builder.build(network, model=self.model)
self.model.decoder_cache.shrink()
# Note: seed is not used right now, but one day...
assert seed is None, "Simulator seed not yet implemented"
self.seed = np.random.randint(npext.maxint) if seed is None else seed
# -- map from Signal.base -> ndarray
self.signals = SignalDict(__time__=np.asarray(0.0, dtype=np.float64))
for op in self.model.operators:
op.init_signals(self.signals, dt)
self.dg = operator_depencency_graph(self.model.operators)
self._step_order = [
node for node in toposort(self.dg) if hasattr(node, 'make_step')
]
self._steps = [
node.make_step(self.signals, dt) for node in self._step_order
]
self.n_steps = 0
# 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)
class Simulator(object):
"""Reference simulator for Nengo models."""
def __init__(self, network, dt=0.001, seed=None, model=None):
"""Initialize the simulator with a network and (optionally) a model.
Most of the time, you will pass in a network and sometimes a dt::
sim1 = nengo.Simulator(my_network) # Uses default 0.001s dt
sim2 = nengo.Simulator(my_network, dt=0.01) # Uses 0.01s dt
For more advanced use cases, you can initialize the model yourself,
and also pass in a network that will be built into the same model
that you pass in::
sim = nengo.Simulator(my_network, model=my_model)
If you want full control over the build process, then you can build
your network into the model manually. If you do this, then you must
explicitly pass in ``None`` for the network::
sim = nengo.Simulator(None, model=my_model)
Parameters
----------
network : nengo.Network instance or None
A network object to the built and then simulated.
If a fully built ``model`` is passed in, then you can skip
building the network by passing in network=None.
dt : float
The length of a simulator timestep, in seconds.
seed : int
A seed for all stochastic operators used in this simulator.
Note that there are not stochastic operators implemented
currently, so this parameters does nothing.
model : nengo.builder.Model instance or None
A model object that contains build artifacts to be simulated.
Usually the simulator will build this model for you; however,
if you want to build the network manually, or to inject some
build artifacts in the Model before building the network,
then you can pass in a ``nengo.builder.Model`` instance.
"""
if model is None:
caching = runcom.getboolean('decoder_cache', 'enabled')
network_seed_set = network is not None and network.seed is not None
if caching:
decoder_cache = nengo.cache.DecoderCache(
runcom.getboolean('decoder_cache', 'readonly'))
elif caching is None and network_seed_set:
decoder_cache = nengo.cache.DecoderCache()
else:
decoder_cache = nengo.cache.NoDecoderCache()
self.model = Model(dt=dt,
label="%s, dt=%f" % (network.label, dt),
seed=network.seed, decoder_cache=decoder_cache)
else:
self.model = model
if network is not None:
# Build the network into the model
Builder.build(
network, model=self.model)
self.model.decoder_cache.shrink()
# Note: seed is not used right now, but one day...
assert seed is None, "Simulator seed not yet implemented"
self.seed = np.random.randint(npext.maxint) if seed is None else seed
# -- map from Signal.base -> ndarray
self.signals = SignalDict(__time__=np.asarray(0.0, dtype=np.float64))
for op in self.model.operators:
op.init_signals(self.signals, dt)
self.dg = operator_depencency_graph(self.model.operators)
self._step_order = [node for node in toposort(self.dg)
if hasattr(node, 'make_step')]
self._steps = [node.make_step(self.signals, dt)
for node in self._step_order]
self.n_steps = 0
# 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)
@property
def dt(self):
return self.model.dt
@dt.setter
def dt(self, dummy):
raise AttributeError("Cannot change simulator 'dt'. Please file "
"an issue at http://github.com/ctn-waterloo/nengo"
"/issues and describe your use case.")
def step(self):
"""Advance the simulator by `self.dt` seconds.
"""
old_err = np.seterr(invalid='raise', divide='ignore')
try:
for step_fn in self._steps:
step_fn()
finally:
np.seterr(**old_err)
# -- probes signals -> probe buffers
for probe in self.model.probes:
period = (1 if probe.sample_every is None
else int(probe.sample_every / self.dt))
if self.n_steps % period == 0:
tmp = self.signals[self.model.sig[probe]['in']].copy()
self._probe_outputs[probe].append(tmp)
self.n_steps += 1
self.signals['__time__'] = self.n_steps * self.dt
def run(self, time_in_seconds):
"""Simulate for the given length of time."""
steps = int(np.round(float(time_in_seconds) / self.dt))
logger.debug("Running %s for %f seconds, or %d steps",
self.model.label, time_in_seconds, steps)
self.run_steps(steps)
def run_steps(self, steps):
"""Simulate for the given number of `dt` steps."""
for i in range(steps):
if i % 1000 == 0:
logger.debug("Step %d", i)
self.step()
def reset(self):
"""Reset the simulator state."""
self.signals['__time__'][...] = 0.0
self.n_steps = 0
for key in self.signals:
if key != '__time__':
self.signals.reset(key)
for probe in self.model.probes:
self._probe_outputs[probe] = []
def trange(self, dt=None):
dt = self.dt if dt is None else dt
n_steps = int(np.ceil(self.n_steps * self.dt / dt))
return dt * np.arange(0, n_steps)
class Simulator(object):
"""Reference simulator for Nengo models."""
def __init__(self, network, dt=0.001, seed=None, model=None):
"""Initialize the simulator with a network and (optionally) a model.
Most of the time, you will pass in a network and sometimes a dt::
sim1 = nengo.Simulator(my_network) # Uses default 0.001s dt
sim2 = nengo.Simulator(my_network, dt=0.01) # Uses 0.01s dt
For more advanced use cases, you can initialize the model yourself,
and also pass in a network that will be built into the same model
that you pass in::
sim = nengo.Simulator(my_network, model=my_model)
If you want full control over the build process, then you can build
your network into the model manually. If you do this, then you must
explicitly pass in ``None`` for the network::
sim = nengo.Simulator(None, model=my_model)
Parameters
----------
network : nengo.Network instance or None
A network object to the built and then simulated.
If a fully built ``model`` is passed in, then you can skip
building the network by passing in network=None.
dt : float
The length of a simulator timestep, in seconds.
seed : int
A seed for all stochastic operators used in this simulator.
Note that there are not stochastic operators implemented
currently, so this parameters does nothing.
model : nengo.builder.Model instance or None
A model object that contains build artifacts to be simulated.
Usually the simulator will build this model for you; however,
if you want to build the network manually, or to inject some
build artifacts in the Model before building the network,
then you can pass in a ``nengo.builder.Model`` instance.
"""
if model is None:
caching = runcom.getboolean('decoder_cache', 'enabled')
network_seed_set = network is not None and network.seed is not None
if caching:
decoder_cache = nengo.cache.DecoderCache(
runcom.getboolean('decoder_cache', 'readonly'))
elif caching is None and network_seed_set:
decoder_cache = nengo.cache.DecoderCache()
else:
decoder_cache = nengo.cache.NoDecoderCache()
self.model = Model(dt=dt,
label="%s, dt=%f" % (network.label, dt),
seed=network.seed,
decoder_cache=decoder_cache)
else:
self.model = model
if network is not None:
# Build the network into the model
Builder.build(network, model=self.model)
self.model.decoder_cache.shrink()
# Note: seed is not used right now, but one day...
assert seed is None, "Simulator seed not yet implemented"
self.seed = np.random.randint(npext.maxint) if seed is None else seed
# -- map from Signal.base -> ndarray
self.signals = SignalDict(__time__=np.asarray(0.0, dtype=np.float64))
for op in self.model.operators:
op.init_signals(self.signals, dt)
self.dg = operator_depencency_graph(self.model.operators)
self._step_order = [
node for node in toposort(self.dg) if hasattr(node, 'make_step')
]
self._steps = [
node.make_step(self.signals, dt) for node in self._step_order
]
self.n_steps = 0
# 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)
@property
def dt(self):
return self.model.dt
@dt.setter
def dt(self, dummy):
raise AttributeError("Cannot change simulator 'dt'. Please file "
"an issue at http://github.com/ctn-waterloo/nengo"
"/issues and describe your use case.")
def step(self):
"""Advance the simulator by `self.dt` seconds.
"""
old_err = np.seterr(invalid='raise', divide='ignore')
try:
for step_fn in self._steps:
step_fn()
finally:
np.seterr(**old_err)
# -- probes signals -> probe buffers
for probe in self.model.probes:
period = (1 if probe.sample_every is None else int(
probe.sample_every / self.dt))
if self.n_steps % period == 0:
tmp = self.signals[self.model.sig[probe]['in']].copy()
self._probe_outputs[probe].append(tmp)
self.n_steps += 1
self.signals['__time__'] = self.n_steps * self.dt
def run(self, time_in_seconds):
"""Simulate for the given length of time."""
steps = int(np.round(float(time_in_seconds) / self.dt))
logger.debug("Running %s for %f seconds, or %d steps",
self.model.label, time_in_seconds, steps)
self.run_steps(steps)
def run_steps(self, steps):
"""Simulate for the given number of `dt` steps."""
for i in range(steps):
if i % 1000 == 0:
logger.debug("Step %d", i)
self.step()
def reset(self):
"""Reset the simulator state."""
self.signals['__time__'][...] = 0.0
self.n_steps = 0
for key in self.signals:
if key != '__time__':
self.signals.reset(key)
for probe in self.model.probes:
self._probe_outputs[probe] = []
def trange(self, dt=None):
dt = self.dt if dt is None else dt
n_steps = int(np.ceil(self.n_steps * self.dt / dt))
return dt * np.arange(0, n_steps)