def __init__(self, n_neurons, machine_time_step, timescale_factor,
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)
AbstractDataSpecableVertex.__init__(
self, machine_time_step=machine_time_step,
timescale_factor=timescale_factor)
AbstractSpikeRecordable.__init__(self)
# Store the parameters
self._rate = rate
self._start = start
self._duration = duration
self._rng = numpy.random.RandomState(seed)
# Prepare for recording, and to get spikes
self._spike_recorder = SpikeRecorder(machine_time_step)
self._outgoing_edge_key_restrictor = \
OutgoingEdgeSameContiguousKeysRestrictor()
def __init__(
self, n_neurons, machine_time_step, timescale_factor,
constraints=None, label="SpikeSourcePoisson", rate=1.0, start=0.0,
duration=None, seed=None):
AbstractPartitionableVertex.__init__(
self, n_atoms=n_neurons, label=label, constraints=constraints,
max_atoms_per_core=self._model_based_max_atoms_per_core)
AbstractDataSpecableVertex.__init__(
self, machine_time_step=machine_time_step,
timescale_factor=timescale_factor)
AbstractSpikeRecordable.__init__(self)
ReceiveBuffersToHostBasicImpl.__init__(self)
AbstractProvidesOutgoingEdgeConstraints.__init__(self)
PopulationSettableChangeRequiresMapping.__init__(self)
# Store the parameters
self._rate = rate
self._start = start
self._duration = duration
self._rng = numpy.random.RandomState(seed)
# Prepare for recording, and to get spikes
self._spike_recorder = SpikeRecorder(machine_time_step)
self._spike_buffer_max_size = config.getint(
"Buffers", "spike_buffer_size")
self._buffer_size_before_receive = config.getint(
"Buffers", "buffer_size_before_receive")
self._time_between_requests = config.getint(
"Buffers", "time_between_requests")
def __init__(self,
n_neurons,
constraints=none_pynn_default_parameters['constraints'],
label=none_pynn_default_parameters['label'],
rate=default_parameters['rate'],
start=default_parameters['start'],
duration=default_parameters['duration'],
seed=none_pynn_default_parameters['seed']):
ApplicationVertex.__init__(self, label, constraints,
self._model_based_max_atoms_per_core)
AbstractSpikeRecordable.__init__(self)
AbstractProvidesOutgoingPartitionConstraints.__init__(self)
AbstractChangableAfterRun.__init__(self)
SimplePopulationSettable.__init__(self)
ProvidesKeyToAtomMappingImpl.__init__(self)
config = globals_variables.get_simulator().config
# atoms params
self._n_atoms = n_neurons
self._seed = None
# check for changes parameters
self._change_requires_mapping = True
self._change_requires_neuron_parameters_reload = False
# Store the parameters
self._rate = utility_calls.convert_param_to_numpy(rate, n_neurons)
self._start = utility_calls.convert_param_to_numpy(start, n_neurons)
self._duration = utility_calls.convert_param_to_numpy(
duration, n_neurons)
self._time_to_spike = utility_calls.convert_param_to_numpy(
0, n_neurons)
self._rng = numpy.random.RandomState(seed)
self._machine_time_step = None
# Prepare for recording, and to get spikes
self._spike_recorder = MultiSpikeRecorder()
self._time_between_requests = config.getint("Buffers",
"time_between_requests")
self._receive_buffer_host = config.get("Buffers",
"receive_buffer_host")
self._receive_buffer_port = helpful_functions.read_config_int(
config, "Buffers", "receive_buffer_port")
self._minimum_buffer_sdram = config.getint("Buffers",
"minimum_buffer_sdram")
self._using_auto_pause_and_resume = config.getboolean(
"Buffers", "use_auto_pause_and_resume")
spike_buffer_max_size = 0
self._buffer_size_before_receive = None
if config.getboolean("Buffers", "enable_buffered_recording"):
spike_buffer_max_size = config.getint("Buffers",
"spike_buffer_size")
self._buffer_size_before_receive = config.getint(
"Buffers", "buffer_size_before_receive")
self._maximum_sdram_for_buffering = [spike_buffer_max_size]
def __init__(
self, n_neurons, spike_times, machine_time_step, 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)
AbstractSpikeRecordable.__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")
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
# handle recording
self._spike_recorder = EIEIOSpikeRecorder(machine_time_step)
#handle outgoing constraints
self._outgoing_edge_key_restrictor = \
OutgoingEdgeSameContiguousKeysRestrictor()
def __init__(
self, n_neurons, machine_time_step, timescale_factor,
constraints=None, label="SpikeSourcePoisson", rate=1.0, start=0.0,
duration=None, seed=None):
AbstractPartitionableVertex.__init__(
self, n_neurons, label, self._model_based_max_atoms_per_core,
constraints)
AbstractDataSpecableVertex.__init__(
self, machine_time_step=machine_time_step,
timescale_factor=timescale_factor)
AbstractSpikeRecordable.__init__(self)
AbstractProvidesOutgoingPartitionConstraints.__init__(self)
PopulationSettableChangeRequiresMapping.__init__(self)
# Store the parameters
self._rate = utility_calls.convert_param_to_numpy(rate, n_neurons)
self._start = utility_calls.convert_param_to_numpy(start, n_neurons)
self._duration = utility_calls.convert_param_to_numpy(
duration, n_neurons)
self._rng = numpy.random.RandomState(seed)
# Prepare for recording, and to get spikes
self._spike_recorder = MultiSpikeRecorder(machine_time_step)
self._spike_buffer_max_size = config.getint(
"Buffers", "spike_buffer_size")
self._buffer_size_before_receive = config.getint(
"Buffers", "buffer_size_before_receive")
self._time_between_requests = config.getint(
"Buffers", "time_between_requests")
self._enable_buffered_recording = config.getboolean(
"Buffers", "enable_buffered_recording")
self._receive_buffer_host = config.get(
"Buffers", "receive_buffer_host")
self._receive_buffer_port = config.getint(
"Buffers", "receive_buffer_port")
self._minimum_buffer_sdram = config.getint(
"Buffers", "minimum_buffer_sdram")
self._using_auto_pause_and_resume = config.getboolean(
"Buffers", "use_auto_pause_and_resume")
def __init__(
self, n_neurons, spike_times, machine_time_step, timescale_factor,
port=None, tag=None, ip_address=None, board_address=None,
max_on_chip_memory_usage_for_spikes_in_bytes=(
constants.SPIKE_BUFFER_SIZE_BUFFERING_IN),
space_before_notification=640,
constraints=None, label="SpikeSourceArray",
spike_recorder_buffer_size=(
constants.EIEIO_SPIKE_BUFFER_SIZE_BUFFERING_OUT),
buffer_size_before_receive=(
constants.EIEIO_BUFFER_SIZE_BEFORE_RECEIVE)):
self._ip_address = ip_address
if ip_address is None:
self._ip_address = config.get("Buffers", "receive_buffer_host")
self._port = port
if port is None:
self._port = config.getint("Buffers", "receive_buffer_port")
ReverseIpTagMultiCastSource.__init__(
self, n_keys=n_neurons, machine_time_step=machine_time_step,
timescale_factor=timescale_factor, label=label,
constraints=constraints,
max_atoms_per_core=(SpikeSourceArray.
_model_based_max_atoms_per_core),
board_address=board_address,
receive_port=None, receive_sdp_port=None, receive_tag=None,
virtual_key=None, prefix=None, prefix_type=None, check_keys=False,
send_buffer_times=spike_times,
send_buffer_max_space=max_on_chip_memory_usage_for_spikes_in_bytes,
send_buffer_space_before_notify=space_before_notification,
send_buffer_notification_ip_address=self._ip_address,
send_buffer_notification_port=self._port,
send_buffer_notification_tag=tag)
AbstractSpikeRecordable.__init__(self)
# handle recording
self._spike_recorder = EIEIOSpikeRecorder(machine_time_step)
self._spike_recorder_buffer_size = spike_recorder_buffer_size
self._buffer_size_before_receive = buffer_size_before_receive
def __init__(
self, n_neurons, machine_time_step, timescale_factor,
constraints=None, label="SpikeSourcePoisson", rate=1.0, start=0.0,
duration=None, seed=None):
AbstractPartitionableVertex.__init__(
self, n_neurons, label, self._model_based_max_atoms_per_core,
constraints)
AbstractDataSpecableVertex.__init__(
self, machine_time_step=machine_time_step,
timescale_factor=timescale_factor)
AbstractSpikeRecordable.__init__(self)
AbstractProvidesOutgoingPartitionConstraints.__init__(self)
PopulationSettableChangeRequiresMapping.__init__(self)
# Store the parameters
self._rate = rate
self._start = start
self._duration = duration
self._rng = numpy.random.RandomState(seed)
# Prepare for recording, and to get spikes
self._spike_recorder = SpikeRecorder(machine_time_step)
self._spike_buffer_max_size = config.getint(
"Buffers", "spike_buffer_size")
self._buffer_size_before_receive = config.getint(
"Buffers", "buffer_size_before_receive")
self._time_between_requests = config.getint(
"Buffers", "time_between_requests")
self._enable_buffered_recording = config.getboolean(
"Buffers", "enable_buffered_recording")
self._receive_buffer_host = config.get(
"Buffers", "receive_buffer_host")
self._receive_buffer_port = config.getint(
"Buffers", "receive_buffer_port")
self._minimum_buffer_sdram = config.getint(
"Buffers", "minimum_buffer_sdram")
self._using_auto_pause_and_resume = config.getboolean(
"Buffers", "use_auto_pause_and_resume")
def __init__(
self,
n_neurons,
machine_time_step,
timescale_factor,
spike_times=None,
port=None,
tag=None,
ip_address=None,
board_address=None,
max_on_chip_memory_usage_for_spikes_in_bytes=(constants.SPIKE_BUFFER_SIZE_BUFFERING_IN),
space_before_notification=640,
constraints=None,
label="SpikeSourceArray",
spike_recorder_buffer_size=(constants.EIEIO_SPIKE_BUFFER_SIZE_BUFFERING_OUT),
buffer_size_before_receive=(constants.EIEIO_BUFFER_SIZE_BEFORE_RECEIVE),
):
self._ip_address = ip_address
if ip_address is None:
self._ip_address = config.get("Buffers", "receive_buffer_host")
self._port = port
if port is None:
self._port = config.getint("Buffers", "receive_buffer_port")
if spike_times is None:
spike_times = []
ReverseIpTagMultiCastSource.__init__(
self,
n_keys=n_neurons,
machine_time_step=machine_time_step,
timescale_factor=timescale_factor,
label=label,
constraints=constraints,
max_atoms_per_core=(SpikeSourceArray._model_based_max_atoms_per_core),
board_address=board_address,
receive_port=None,
receive_sdp_port=None,
receive_tag=None,
virtual_key=None,
prefix=None,
prefix_type=None,
check_keys=False,
send_buffer_times=spike_times,
send_buffer_max_space=max_on_chip_memory_usage_for_spikes_in_bytes,
send_buffer_space_before_notify=space_before_notification,
send_buffer_notification_ip_address=self._ip_address,
send_buffer_notification_port=self._port,
send_buffer_notification_tag=tag,
)
AbstractSpikeRecordable.__init__(self)
AbstractProvidesOutgoingEdgeConstraints.__init__(self)
SimplePopulationSettable.__init__(self)
AbstractMappable.__init__(self)
AbstractHasFirstMachineTimeStep.__init__(self)
# handle recording
self._spike_recorder = EIEIOSpikeRecorder(machine_time_step)
self._spike_recorder_buffer_size = spike_recorder_buffer_size
self._buffer_size_before_receive = buffer_size_before_receive
# Keep track of any previously generated buffers
self._send_buffers = dict()
self._spike_recording_region_size = None
self._partitioned_vertices = list()
self._partitioned_vertices_current_max_buffer_size = dict()
# used for reset and rerun
self._requires_mapping = True
self._last_runtime_position = 0
self._max_on_chip_memory_usage_for_spikes = max_on_chip_memory_usage_for_spikes_in_bytes
self._space_before_notification = space_before_notification
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 exceptions.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
def __init__(self,
n_neurons,
spike_times=default_parameters['spike_times'],
port=none_pynn_default_parameters['port'],
tag=none_pynn_default_parameters['tag'],
ip_address=none_pynn_default_parameters['ip_address'],
board_address=none_pynn_default_parameters['board_address'],
max_on_chip_memory_usage_for_spikes_in_bytes=DEFAULT1,
space_before_notification=none_pynn_default_parameters[
'space_before_notification'],
constraints=none_pynn_default_parameters['constraints'],
label=none_pynn_default_parameters['label'],
spike_recorder_buffer_size=none_pynn_default_parameters[
'spike_recorder_buffer_size'],
buffer_size_before_receive=none_pynn_default_parameters[
'buffer_size_before_receive']):
config = globals_variables.get_simulator().config
self._ip_address = ip_address
if ip_address is None:
self._ip_address = config.get("Buffers", "receive_buffer_host")
self._port = port
if port is None:
self._port = helpful_functions.read_config_int(
config, "Buffers", "receive_buffer_port")
if spike_times is None:
spike_times = []
ReverseIpTagMultiCastSource.__init__(
self,
n_keys=n_neurons,
label=label,
constraints=constraints,
max_atoms_per_core=(
SpikeSourceArray._model_based_max_atoms_per_core),
board_address=board_address,
receive_port=None,
receive_tag=None,
virtual_key=None,
prefix=None,
prefix_type=None,
check_keys=False,
send_buffer_times=spike_times,
send_buffer_partition_id=constants.SPIKE_PARTITION_ID,
send_buffer_max_space=max_on_chip_memory_usage_for_spikes_in_bytes,
send_buffer_space_before_notify=space_before_notification,
buffer_notification_ip_address=self._ip_address,
buffer_notification_port=self._port,
buffer_notification_tag=tag)
AbstractSpikeRecordable.__init__(self)
AbstractProvidesOutgoingPartitionConstraints.__init__(self)
SimplePopulationSettable.__init__(self)
AbstractChangableAfterRun.__init__(self)
ProvidesKeyToAtomMappingImpl.__init__(self)
# handle recording
self._spike_recorder = EIEIOSpikeRecorder()
self._spike_recorder_buffer_size = spike_recorder_buffer_size
self._buffer_size_before_receive = buffer_size_before_receive
# Keep track of any previously generated buffers
self._send_buffers = dict()
self._spike_recording_region_size = None
self._machine_vertices = list()
# used for reset and rerun
self._requires_mapping = True
self._last_runtime_position = 0
self._max_on_chip_memory_usage_for_spikes = \
max_on_chip_memory_usage_for_spikes_in_bytes
self._space_before_notification = space_before_notification
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 exceptions.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
def __init__(
self, n_neurons, binary, label, max_atoms_per_core,
machine_time_step, timescale_factor, spikes_per_second,
ring_buffer_sigma, model_name, neuron_model, input_type,
synapse_type, threshold_type, additional_input=None,
constraints=None):
ReceiveBuffersToHostBasicImpl.__init__(self)
AbstractPartitionableVertex.__init__(
self, n_neurons, label, max_atoms_per_core, constraints)
AbstractDataSpecableVertex.__init__(
self, machine_time_step, timescale_factor)
AbstractSpikeRecordable.__init__(self)
AbstractVRecordable.__init__(self)
AbstractGSynRecordable.__init__(self)
AbstractProvidesOutgoingEdgeConstraints.__init__(self)
AbstractProvidesIncomingEdgeConstraints.__init__(self)
AbstractPopulationInitializable.__init__(self)
AbstractPopulationSettable.__init__(self)
AbstractMappable.__init__(self)
self._binary = binary
self._label = label
self._machine_time_step = machine_time_step
self._timescale_factor = timescale_factor
self._model_name = model_name
self._neuron_model = neuron_model
self._input_type = input_type
self._threshold_type = threshold_type
self._additional_input = additional_input
# Set up for recording
self._spike_recorder = SpikeRecorder(machine_time_step)
self._v_recorder = VRecorder(machine_time_step)
self._gsyn_recorder = GsynRecorder(machine_time_step)
self._spike_buffer_max_size = config.getint(
"Buffers", "spike_buffer_size")
self._v_buffer_max_size = config.getint(
"Buffers", "v_buffer_size")
self._gsyn_buffer_max_size = config.getint(
"Buffers", "gsyn_buffer_size")
self._buffer_size_before_receive = config.getint(
"Buffers", "buffer_size_before_receive")
self._time_between_requests = config.getint(
"Buffers", "time_between_requests")
# Set up synapse handling
self._synapse_manager = SynapticManager(
synapse_type, machine_time_step, ring_buffer_sigma,
spikes_per_second)
# Get buffering information for later use
self._receive_buffer_host = config.get(
"Buffers", "receive_buffer_host")
self._receive_buffer_port = config.getint(
"Buffers", "receive_buffer_port")
self._enable_buffered_recording = config.getboolean(
"Buffers", "enable_buffered_recording")
# bool for if state has changed.
self._change_requires_mapping = True
def __init__(self,
n_neurons,
binary,
label,
max_atoms_per_core,
machine_time_step,
timescale_factor,
spikes_per_second,
ring_buffer_sigma,
incoming_spike_buffer_size,
model_name,
neuron_model,
input_type,
synapse_type,
threshold_type,
additional_input=None,
constraints=None):
AbstractPartitionableVertex.__init__(self, n_neurons, label,
max_atoms_per_core, constraints)
AbstractDataSpecableVertex.__init__(self, machine_time_step,
timescale_factor)
AbstractSpikeRecordable.__init__(self)
AbstractVRecordable.__init__(self)
AbstractGSynRecordable.__init__(self)
AbstractProvidesOutgoingPartitionConstraints.__init__(self)
AbstractProvidesIncomingPartitionConstraints.__init__(self)
AbstractPopulationInitializable.__init__(self)
AbstractPopulationSettable.__init__(self)
AbstractChangableAfterRun.__init__(self)
self._binary = binary
self._label = label
self._machine_time_step = machine_time_step
self._timescale_factor = timescale_factor
self._incoming_spike_buffer_size = incoming_spike_buffer_size
if incoming_spike_buffer_size is None:
self._incoming_spike_buffer_size = config.getint(
"Simulation", "incoming_spike_buffer_size")
self._model_name = model_name
self._neuron_model = neuron_model
self._input_type = input_type
self._threshold_type = threshold_type
self._additional_input = additional_input
# Set up for recording
self._spike_recorder = SpikeRecorder(machine_time_step)
self._v_recorder = VRecorder(machine_time_step)
self._gsyn_recorder = GsynRecorder(machine_time_step)
self._spike_buffer_max_size = config.getint("Buffers",
"spike_buffer_size")
self._v_buffer_max_size = config.getint("Buffers", "v_buffer_size")
self._gsyn_buffer_max_size = config.getint("Buffers",
"gsyn_buffer_size")
self._buffer_size_before_receive = config.getint(
"Buffers", "buffer_size_before_receive")
self._time_between_requests = config.getint("Buffers",
"time_between_requests")
self._minimum_buffer_sdram = config.getint("Buffers",
"minimum_buffer_sdram")
self._using_auto_pause_and_resume = config.getboolean(
"Buffers", "use_auto_pause_and_resume")
self._receive_buffer_host = config.get("Buffers",
"receive_buffer_host")
self._receive_buffer_port = config.getint("Buffers",
"receive_buffer_port")
self._enable_buffered_recording = config.getboolean(
"Buffers", "enable_buffered_recording")
# Set up synapse handling
self._synapse_manager = SynapticManager(synapse_type,
machine_time_step,
ring_buffer_sigma,
spikes_per_second)
# bool for if state has changed.
self._change_requires_mapping = True
def __init__(
self, n_neurons, machine_time_step, timescale_factor,
spike_times=None, port=None, tag=None, ip_address=None,
board_address=None, max_on_chip_memory_usage_for_spikes_in_bytes=(
constants.SPIKE_BUFFER_SIZE_BUFFERING_IN),
space_before_notification=640,
constraints=None, label="SpikeSourceArray",
spike_recorder_buffer_size=(
constants.EIEIO_SPIKE_BUFFER_SIZE_BUFFERING_OUT),
buffer_size_before_receive=(
constants.EIEIO_BUFFER_SIZE_BEFORE_RECEIVE)):
self._ip_address = ip_address
if ip_address is None:
self._ip_address = config.get("Buffers", "receive_buffer_host")
self._port = port
if port is None:
self._port = config.getint("Buffers", "receive_buffer_port")
if spike_times is None:
spike_times = []
self._minimum_sdram_for_buffering = config.getint(
"Buffers", "minimum_buffer_sdram")
self._using_auto_pause_and_resume = config.getboolean(
"Buffers", "use_auto_pause_and_resume")
ReverseIpTagMultiCastSource.__init__(
self, n_keys=n_neurons, machine_time_step=machine_time_step,
timescale_factor=timescale_factor, label=label,
constraints=constraints,
max_atoms_per_core=(SpikeSourceArray.
_model_based_max_atoms_per_core),
board_address=board_address,
receive_port=None, receive_sdp_port=None, receive_tag=None,
virtual_key=None, prefix=None, prefix_type=None, check_keys=False,
send_buffer_times=spike_times,
send_buffer_max_space=max_on_chip_memory_usage_for_spikes_in_bytes,
send_buffer_space_before_notify=space_before_notification,
send_buffer_notification_ip_address=self._ip_address,
send_buffer_notification_port=self._port,
send_buffer_notification_tag=tag)
AbstractSpikeRecordable.__init__(self)
AbstractProvidesOutgoingPartitionConstraints.__init__(self)
SimplePopulationSettable.__init__(self)
AbstractChangableAfterRun.__init__(self)
AbstractHasFirstMachineTimeStep.__init__(self)
# handle recording
self._spike_recorder = EIEIOSpikeRecorder(machine_time_step)
self._spike_recorder_buffer_size = spike_recorder_buffer_size
self._buffer_size_before_receive = buffer_size_before_receive
# Keep track of any previously generated buffers
self._send_buffers = dict()
self._spike_recording_region_size = None
self._partitioned_vertices = list()
self._partitioned_vertices_current_max_buffer_size = dict()
# used for reset and rerun
self._requires_mapping = True
self._last_runtime_position = 0
self._max_on_chip_memory_usage_for_spikes = \
max_on_chip_memory_usage_for_spikes_in_bytes
self._space_before_notification = space_before_notification
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 exceptions.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
