def __init__(self,
n_neurons,
machine_time_step,
timescale_factor,
spikes_per_second,
ring_buffer_sigma,
constraints=None,
label="SpikeSourcePoisson",
rate=1.0,
start=0.0,
duration=None,
seed=None):
"""
Creates a new SpikeSourcePoisson Object.
"""
AbstractPartitionableVertex.__init__(
self,
n_atoms=n_neurons,
label=label,
constraints=constraints,
max_atoms_per_core=self._model_based_max_atoms_per_core)
AbstractPopulationRecordableVertex.__init__(self, machine_time_step,
label)
AbstractDataSpecableVertex.__init__(
self,
machine_time_step=machine_time_step,
timescale_factor=timescale_factor)
AbstractOutgoingEdgeSameContiguousKeysRestrictor.__init__(self)
self._rate = rate
self._start = start
self._duration = duration
self._seed = seed
def __init__(self,
n_neurons,
max_delay_per_neuron,
source_vertex,
machine_time_step,
timescale_factor,
constraints=None,
label="DelayExtension"):
"""
Creates a new DelayExtension Object.
"""
AbstractPartitionableVertex.__init__(self,
n_atoms=n_neurons,
constraints=constraints,
label=label,
max_atoms_per_core=256)
AbstractDataSpecableVertex.__init__(
self,
machine_time_step=machine_time_step,
timescale_factor=timescale_factor)
AbstractProvidesIncomingEdgeConstraints.__init__(self)
AbstractOutgoingEdgeSameContiguousKeysRestrictor.__init__(self)
self._max_delay_per_neuron = max_delay_per_neuron
self._max_stages = 0
self._source_vertex = source_vertex
joint_constrant = PartitionerSameSizeAsVertexConstraint(source_vertex)
self.add_constraint(joint_constrant)
def __init__(self,
n_neurons,
spike_times,
machine_time_step,
spikes_per_second,
ring_buffer_sigma,
timescale_factor,
port=None,
tag=None,
ip_address=None,
board_address=None,
max_on_chip_memory_usage_for_spikes_in_bytes=None,
space_before_notification=640,
constraints=None,
label="SpikeSourceArray"):
if ip_address is None:
ip_address = config.get("Buffers", "receive_buffer_host")
if port is None:
port = config.getint("Buffers", "receive_buffer_port")
AbstractDataSpecableVertex.__init__(
self,
machine_time_step=machine_time_step,
timescale_factor=timescale_factor)
AbstractPartitionableVertex.__init__(
self,
n_atoms=n_neurons,
label=label,
max_atoms_per_core=self._model_based_max_atoms_per_core,
constraints=constraints)
AbstractOutgoingEdgeSameContiguousKeysRestrictor.__init__(self)
self._spike_times = spike_times
self._max_on_chip_memory_usage_for_spikes = \
max_on_chip_memory_usage_for_spikes_in_bytes
self._space_before_notification = space_before_notification
self.add_constraint(
TagAllocatorRequireIptagConstraint(ip_address,
port,
strip_sdp=True,
board_address=board_address,
tag_id=tag))
if self._max_on_chip_memory_usage_for_spikes is None:
self._max_on_chip_memory_usage_for_spikes = \
front_end_common_constants.MAX_SIZE_OF_BUFFERED_REGION_ON_CHIP
# check the values do not conflict with chip memory limit
if self._max_on_chip_memory_usage_for_spikes < 0:
raise ConfigurationException(
"The memory usage on chip is either beyond what is supportable"
" on the spinnaker board being supported or you have requested"
" a negative value for a memory usage. Please correct and"
" try again")
# Keep track of any previously generated buffers
self._send_buffers = dict()
def __init__(self, machine_time_step, timescale_factor, spikes_per_second,
ring_buffer_sigma, n_neurons, label, spinnaker_link_id):
if n_neurons != 20:
print "Warning, this device has 20 neurons"
AbstractVirtualVertex.__init__(self, n_neurons, spinnaker_link_id,
label, n_neurons)
AbstractOutgoingEdgeSameContiguousKeysRestrictor.__init__(self)
def __init__(self, machine_time_step, timescale_factor, spikes_per_second,
ring_buffer_sigma, n_neurons, label,
spinnaker_link_id):
if n_neurons != 20:
print "Warning, this device has 20 neurons"
AbstractVirtualVertex.__init__(self, n_neurons, spinnaker_link_id,
label, n_neurons)
AbstractOutgoingEdgeSameContiguousKeysRestrictor.__init__(self)
def __init__(
self, n_neurons, spike_times, machine_time_step, spikes_per_second,
ring_buffer_sigma, timescale_factor, port=None, tag=None,
ip_address=None, board_address=None,
max_on_chip_memory_usage_for_spikes_in_bytes=None,
space_before_notification=640,
constraints=None, label="SpikeSourceArray"):
if ip_address is None:
ip_address = config.get("Buffers", "receive_buffer_host")
if port is None:
port = config.getint("Buffers", "receive_buffer_port")
AbstractDataSpecableVertex.__init__(
self, machine_time_step=machine_time_step,
timescale_factor=timescale_factor)
AbstractPartitionableVertex.__init__(
self, n_atoms=n_neurons, label=label,
max_atoms_per_core=self._model_based_max_atoms_per_core,
constraints=constraints)
AbstractOutgoingEdgeSameContiguousKeysRestrictor.__init__(self)
AbstractPopulationRecordableVertex.__init__(
self, machine_time_step, label)
self._spike_times = spike_times
self._max_on_chip_memory_usage_for_spikes = \
max_on_chip_memory_usage_for_spikes_in_bytes
self._space_before_notification = space_before_notification
self.add_constraint(TagAllocatorRequireIptagConstraint(
ip_address, port, strip_sdp=True, board_address=board_address,
tag_id=tag))
if self._max_on_chip_memory_usage_for_spikes is None:
self._max_on_chip_memory_usage_for_spikes = \
front_end_common_constants.MAX_SIZE_OF_BUFFERED_REGION_ON_CHIP
# check the values do not conflict with chip memory limit
if self._max_on_chip_memory_usage_for_spikes < 0:
raise ConfigurationException(
"The memory usage on chip is either beyond what is supportable"
" on the spinnaker board being supported or you have requested"
" a negative value for a memory usage. Please correct and"
" try again")
if (self._max_on_chip_memory_usage_for_spikes <
self._space_before_notification):
self._space_before_notification =\
self._max_on_chip_memory_usage_for_spikes
# Keep track of any previously generated buffers
self._send_buffers = dict()
self._spike_recording_region_size = None
def __init__(self, binary, n_neurons, label, constraints,
max_atoms_per_core, machine_time_step, timescale_factor,
spikes_per_second, ring_buffer_sigma,
master_pop_algorithm=None):
AbstractSynapticManager.__init__(self, master_pop_algorithm)
AbstractPartitionablePopulationVertex.__init__(
self, n_atoms=n_neurons, label=label,
machine_time_step=machine_time_step,
timescale_factor=timescale_factor, constraints=constraints,
max_atoms_per_core=max_atoms_per_core)
AbstractOutgoingEdgeSameContiguousKeysRestrictor.__init__(self)
self._binary = binary
self._spikes_per_second = spikes_per_second
self._ring_buffer_sigma = ring_buffer_sigma
def __init__(self, n_neurons, machine_time_step, timescale_factor,
spikes_per_second, ring_buffer_sigma,
constraints=None, label="SpikeSourcePoisson",
rate=1.0, start=0.0, duration=None, seed=None):
"""
Creates a new SpikeSourcePoisson Object.
"""
AbstractPartitionableVertex.__init__(
self, n_atoms=n_neurons, label=label, constraints=constraints,
max_atoms_per_core=self._model_based_max_atoms_per_core)
AbstractPopulationRecordableVertex.__init__(
self, machine_time_step, label)
AbstractDataSpecableVertex.__init__(
self, machine_time_step=machine_time_step,
timescale_factor=timescale_factor)
AbstractOutgoingEdgeSameContiguousKeysRestrictor.__init__(self)
self._rate = rate
self._start = start
self._duration = duration
self._seed = seed
def __init__(self, n_neurons, max_delay_per_neuron, source_vertex,
machine_time_step, timescale_factor, constraints=None,
label="DelayExtension"):
"""
Creates a new DelayExtension Object.
"""
AbstractPartitionableVertex.__init__(self, n_atoms=n_neurons,
constraints=constraints,
label=label,
max_atoms_per_core=256)
AbstractDataSpecableVertex.__init__(
self, machine_time_step=machine_time_step,
timescale_factor=timescale_factor)
AbstractProvidesIncomingEdgeConstraints.__init__(self)
AbstractOutgoingEdgeSameContiguousKeysRestrictor.__init__(self)
self._max_delay_per_neuron = max_delay_per_neuron
self._max_stages = 0
self._source_vertex = source_vertex
joint_constrant = PartitionerSameSizeAsVertexConstraint(source_vertex)
self.add_constraint(joint_constrant)
def __init__(self,
binary,
n_neurons,
label,
constraints,
max_atoms_per_core,
machine_time_step,
timescale_factor,
spikes_per_second,
ring_buffer_sigma,
master_pop_algorithm=None):
AbstractSynapticManager.__init__(self, master_pop_algorithm)
AbstractPartitionablePopulationVertex.__init__(
self,
n_atoms=n_neurons,
label=label,
machine_time_step=machine_time_step,
timescale_factor=timescale_factor,
constraints=constraints,
max_atoms_per_core=max_atoms_per_core)
AbstractOutgoingEdgeSameContiguousKeysRestrictor.__init__(self)
self._binary = binary
self._spikes_per_second = spikes_per_second
self._ring_buffer_sigma = ring_buffer_sigma
