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)
AbstractReceiveBuffersToHost.__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, buffering_output, resources_required, label,
constraints=None):
"""
:param buffering_output: True if the vertex is set to buffer output,\
False otherwise
:param resources_required: The approximate resources needed for\
the vertex
:type resources_required:\
:py:class:`pacman.models.resources.resource_container.ResourceContainer`
:param label: The name of the subvertex
:type label: str
:param constraints: The constraints of the subvertex
:type constraints: iterable of\
:py:class:`pacman.model.constraints.abstract_constraint\
.AbstractConstraint`
:raise pacman.exceptions.PacmanInvalidParameterException:
* If one of the constraints is not valid
"""
AbstractReceiveBuffersToHost.__init__(self)
PartitionedVertex.__init__(
self, resources_required=resources_required, label=label,
constraints=constraints)
self._buffering_output = buffering_output
def __init__(self, resample_factor, seed, n_fibres, ear_index, profile, fs,
n_lsr, n_msr, n_hsr, n_buffers_in_sdram_total, seq_size,
ihcan_neuron_recorder, ihcan_atom_slice, timer_period):
""" constructor
:param resample_factor: resample factor
:param seed: the seed used for its random number generator in SpiNNaker
:param n_fibres: how many fibres to simulate
:param ear_index: which ear its based on
:param profile: bool flag for profiling
:param fs: sample freq
:param n_lsr: number of low freq hair cells
:param n_msr: number of med freq hair cells
:param n_hsr: number of high freq hair cells
:param n_buffers_in_sdram_total: the total number of sdram buffers in \
the sdram edge
:param seg_size: the seq size
:param ihcan_neuron_recorder: recorder for the ihcan recordings
:param ihcan_atom_slice: the slice of atoms for the ihcan vertex from \
the global.
"""
MachineVertex.__init__(self, label="IHCAN Node", constraints=None)
AbstractProvidesNKeysForPartition.__init__(self)
AbstractEarProfiled.__init__(self, profile, self.REGIONS.PROFILE.value)
ProvidesProvenanceDataFromMachineImpl.__init__(self)
AbstractHasAssociatedBinary.__init__(self)
AbstractGeneratesDataSpecification.__init__(self)
AbstractReceiveBuffersToHost.__init__(self)
self._ihcan_neuron_recorder = ihcan_neuron_recorder
self._ihcan_recording_atom_slice = ihcan_atom_slice
self._ear_index = ear_index
self._re_sample_factor = resample_factor
self._fs = fs
self._dt = 1.0 / self._fs
self._n_atoms = n_fibres
self._n_buffers_in_sdram_total = n_buffers_in_sdram_total
self._seq_size = seq_size
self._timer_period = timer_period
if n_lsr + n_msr + n_hsr > n_fibres:
raise Exception(
self.N_FIBRES_ERROR.format(n_fibres, n_lsr, n_msr, n_hsr))
self._n_lsr = n_lsr
self._n_msr = n_msr
self._n_hsr = n_hsr
self._seed = seed
