def _get_spike_send_buffer(self, vertex_slice):
"""
spikeArray is a list with one entry per 'neuron'. The entry for
one neuron is a list of times (in ms) when the neuron fires.
We need to transpose this 'matrix' and get a list of firing neuron
indices for each time tick:
List can come in two formats (both now supported):
1) Official PyNN format - single list that is used for all neurons
2) SpiNNaker format - list of lists, one per neuron
"""
send_buffer = None
key = (vertex_slice.lo_atom, vertex_slice.hi_atom)
if key not in self._send_buffers:
send_buffer = BufferedSendingRegion()
if isinstance(self._spike_times[0], list):
# This is in SpiNNaker 'list of lists' format:
for neuron in range(vertex_slice.lo_atom,
vertex_slice.hi_atom + 1):
for timeStamp in sorted(self._spike_times[neuron]):
time_stamp_in_ticks = int(
math.ceil((timeStamp * 1000.0) /
self._machine_time_step))
send_buffer.add_key(time_stamp_in_ticks,
neuron - vertex_slice.lo_atom)
else:
# This is in official PyNN format, all neurons use the
# same list:
neuron_list = range(vertex_slice.n_atoms)
for timeStamp in sorted(self._spike_times):
time_stamp_in_ticks = int(
math.ceil((timeStamp * 1000.0) /
self._machine_time_step))
# add to send_buffer collection
send_buffer.add_keys(time_stamp_in_ticks, neuron_list)
# Update the size
total_size = 0
for timestamp in send_buffer.timestamps:
n_keys = send_buffer.get_n_keys(timestamp)
total_size += BufferManager.get_n_bytes(n_keys)
total_size += EventStopRequest.get_min_packet_length()
if total_size > self._max_on_chip_memory_usage_for_spikes:
total_size = self._max_on_chip_memory_usage_for_spikes
send_buffer.buffer_size = total_size
self._send_buffers[key] = send_buffer
else:
send_buffer = self._send_buffers[key]
return send_buffer
def _get_spike_send_buffer(self, vertex_slice):
"""
spikeArray is a list with one entry per 'neuron'. The entry for
one neuron is a list of times (in ms) when the neuron fires.
We need to transpose this 'matrix' and get a list of firing neuron
indices for each time tick:
List can come in two formats (both now supported):
1) Official PyNN format - single list that is used for all neurons
2) SpiNNaker format - list of lists, one per neuron
"""
send_buffer = None
key = (vertex_slice.lo_atom, vertex_slice.hi_atom)
if key not in self._send_buffers:
send_buffer = BufferedSendingRegion()
if isinstance(self._spike_times[0], list):
# This is in SpiNNaker 'list of lists' format:
for neuron in range(vertex_slice.lo_atom,
vertex_slice.hi_atom + 1):
for timeStamp in sorted(self._spike_times[neuron]):
time_stamp_in_ticks = int(
math.ceil((timeStamp * 1000.0) /
self._machine_time_step))
send_buffer.add_key(time_stamp_in_ticks,
neuron - vertex_slice.lo_atom)
else:
# This is in official PyNN format, all neurons use the
# same list:
neuron_list = range(vertex_slice.n_atoms)
for timeStamp in sorted(self._spike_times):
time_stamp_in_ticks = int(
math.ceil(
(timeStamp * 1000.0) / self._machine_time_step))
# add to send_buffer collection
send_buffer.add_keys(time_stamp_in_ticks, neuron_list)
# Update the size
total_size = 0
for timestamp in send_buffer.timestamps:
n_keys = send_buffer.get_n_keys(timestamp)
total_size += BufferManager.get_n_bytes(n_keys)
total_size += EventStopRequest.get_min_packet_length()
if total_size > self._max_on_chip_memory_usage_for_spikes:
total_size = self._max_on_chip_memory_usage_for_spikes
send_buffer.buffer_size = total_size
self._send_buffers[key] = send_buffer
else:
send_buffer = self._send_buffers[key]
return send_buffer
