2f2cfbf4cccfa0f9b1aaf114baa3c1be3c777f5e,spynnaker/pyNN/models/common/eieio_spike_recorder.py,EIEIOSpikeRecorder,get_spikes,#EIEIOSpikeRecorder#Any#Any#Any#Any#Any#Any#Any#Any#,39

Before Change

                   placements, graph_mapper, application_vertex,
                   base_key_function, machine_time_step):
        results = list()
        missing_str = ""
        ms_per_tick = machine_time_step / 1000.0
        vertices = graph_mapper.get_machine_vertices(application_vertex)
        progress = ProgressBar(vertices,
                               "Getting spikes for {}".format(label))
        for vertex in progress.over(vertices):
            placement = placements.get_placement_of_vertex(vertex)
            vertex_slice = graph_mapper.get_slice(vertex)

            x = placement.x
            y = placement.y
            p = placement.p

            // Read the spikes
            raw_spike_data, data_missing = \
                buffer_manager.get_data_for_vertex(placement, region)
            if data_missing:
                missing_str += "({}, {}, {}); ".format(x, y, p)
            spike_data = str(raw_spike_data.read_all())
            number_of_bytes_written = len(spike_data)

            offset = 0
            while offset < number_of_bytes_written:
                length = _ONE_WORD.unpack_from(spike_data, offset)[0]
                time = _ONE_WORD.unpack_from(spike_data, offset + 4)[0]
                time *= ms_per_tick
                data_offset = offset + 8
                eieio_header = EIEIODataHeader.from_bytestring(
                    spike_data, data_offset)
                if eieio_header.eieio_type.payload_bytes > 0:
                    raise Exception("Can only read spikes as keys")
                data_offset += eieio_header.size
                timestamps = numpy.repeat([time], eieio_header.count)
                key_bytes = eieio_header.eieio_type.key_bytes
                keys = numpy.frombuffer(
                    spike_data, dtype="<u{}".format(key_bytes),
                    count=eieio_header.count, offset=data_offset)

                neuron_ids = ((keys - base_key_function(vertex)) +
                              vertex_slice.lo_atom)
                offset += length + 8
                results.append(numpy.dstack((neuron_ids, timestamps))[0])

        if missing_str != "":
            logger.warn(
                "Population %s is missing spike data in region %s from the"
                " following cores: %s", label, region, missing_str)
        if not results:

After Change

                   base_key_function, machine_time_step):
        // pylint: disable=too-many-arguments
        results = list()
        missing = []
        ms_per_tick = machine_time_step / 1000.0
        vertices = graph_mapper.get_machine_vertices(application_vertex)
        progress = ProgressBar(vertices,
                               "Getting spikes for {}".format(label))
        for vertex in progress.over(vertices):
            placement = placements.get_placement_of_vertex(vertex)
            vertex_slice = graph_mapper.get_slice(vertex)

            // Read the spikes
            raw_spike_data, data_missing = \
                buffer_manager.get_data_for_vertex(placement, region)
            if data_missing:
                missing.append(placement)
            spike_data = str(raw_spike_data.read_all())
            number_of_bytes_written = len(spike_data)

            offset = 0
            while offset < number_of_bytes_written:
                length = _ONE_WORD.unpack_from(spike_data, offset)[0]
                time = _ONE_WORD.unpack_from(spike_data, offset + 4)[0]
                time *= ms_per_tick
                data_offset = offset + 8
                eieio_header = EIEIODataHeader.from_bytestring(
                    spike_data, data_offset)
                if eieio_header.eieio_type.payload_bytes > 0:
                    raise Exception("Can only read spikes as keys")
                data_offset += eieio_header.size
                timestamps = numpy.repeat([time], eieio_header.count)
                key_bytes = eieio_header.eieio_type.key_bytes
                keys = numpy.frombuffer(
                    spike_data, dtype="<u{}".format(key_bytes),
                    count=eieio_header.count, offset=data_offset)

                neuron_ids = ((keys - base_key_function(vertex)) +
                              vertex_slice.lo_atom)
                offset += length + 8
                results.append(numpy.dstack((neuron_ids, timestamps))[0])

        if missing:
            missing_str = recording_utils.make_missing_string(missing)
            logger.warn(
                "Population %s is missing spike data in region %s from the"
                " following cores: %s", label, region, missing_str)
        if not results:

Instances