0408da50b93ea31e3ae92e65be746b8ebe066825,spynnaker/pyNN/models/common/spike_recorder.py,SpikeRecorder,get_spikes,#SpikeRecorder#Any#Any#Any#Any#Any#Any#Any#,43

Before Change

        spike_times = list()
        spike_ids = list()
        ms_per_tick = machine_time_step / 1000.0

        vertices = graph_mapper.get_machine_vertices(application_vertex)
        missing_str = ""
        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
            lo_atom = vertex_slice.lo_atom

            // Read the spikes
            n_words = int(math.ceil(vertex_slice.n_atoms / 32.0))
            n_bytes = n_words * 4
            n_words_with_timestamp = n_words + 1

            // for buffering output info is taken form the buffer manager
            neuron_param_region_data_pointer, data_missing = \
                buffer_manager.get_data_for_vertex(
                    placement, region)
            if data_missing:
                missing_str += "({}, {}, {}); ".format(x, y, p)
            record_raw = neuron_param_region_data_pointer.read_all()
            raw_data = (numpy.asarray(record_raw, dtype="uint8").
                        view(dtype="<i4")).reshape(
                [-1, n_words_with_timestamp])
            if len(raw_data) > 0:
                split_record = numpy.array_split(raw_data, [1, 1], 1)
                record_time = split_record[0] * float(ms_per_tick)
                spikes = split_record[2].byteswap().view("uint8")
                bits = numpy.fliplr(numpy.unpackbits(spikes).reshape(
                    (-1, 32))).reshape((-1, n_bytes * 8))
                time_indices, indices = numpy.where(bits == 1)
                times = record_time[time_indices].reshape((-1))
                indices = indices + lo_atom
                spike_ids.append(indices)
                spike_times.append(times)

After Change

        spike_times = list()
        spike_ids = list()
        sampling_interval = self.get_spikes_sampling_interval()

        vertices = graph_mapper.get_machine_vertices(application_vertex)
        missing_str = ""
        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
            n_words = int(math.ceil(vertex_slice.n_atoms / 32.0))
            n_bytes = n_words * 4
            n_words_with_timestamp = n_words + 1

            // for buffering output info is taken form the buffer manager
            neuron_param_region_data_pointer, data_missing = \
                buffer_manager.get_data_for_vertex(
                    placement, region)
            if data_missing:
                missing_str += "({}, {}, {}); ".format(
                    placement.x, placement.y, placement.p)
            record_raw = neuron_param_region_data_pointer.read_all()
            raw_data = (numpy.asarray(record_raw, dtype="uint8").
                        view(dtype="<i4")).reshape(
                [-1, n_words_with_timestamp])
            if len(raw_data) > 0:
                split_record = numpy.array_split(raw_data, [1, 1], 1)

                record_time = raw_data[0][0] * float(sampling_interval)
                spikes = raw_data[0][1:].byteswap().view("uint8")
                bits = numpy.fliplr(numpy.unpackbits(spikes).reshape(
                    (-1, 32))).reshape((-1, n_bytes * 8))
                indices = numpy.where(bits == 1)[1]
                times = numpy.repeat(record_time, len(indices))
                indices = indices + vertex_slice.lo_atom
                spike_ids.extend(indices)
                spike_times.extend(times)

Instances