9b254aaf76efaa0a3789a67137c9817684862cf9,snntoolbox/simulation/target_simulators/loihi_target_sim.py,SNN,get_spiketrains,#SNN#,250

Before Change

        i = len(self.layers) - 1 if kwargs[str("monitor_index")] == -1 else \
            kwargs[str("monitor_index")] + 1
        idx = self.probe_idx_map["spikes"]
        spiketrains_flat = self.probes[i][idx].data[:, -self._num_timesteps:]
        spiketrains_b_l_t = self.reshape_flattened_spiketrains(
            spiketrains_flat, shape, False)
        return spiketrains_b_l_t

    def get_spiketrains_input(self):
        shape = list(self.parsed_model.input_shape) + [self._num_timesteps]
        idx = self.probe_idx_map["spikes"]

After Change

        i = len(self.snn.layers) - 1 if kwargs[str("monitor_index")] == -1 \
            else kwargs[str("monitor_index")] + 1
        layer = self.snn.layers[i]
        probes = self.stack_layer_probes(self.spike_probes[layer.name])
        shape = self.spiketrains_n_b_l_t[j][0].shape
        spiketrains_b_l_t = self.reshape_flattened_spiketrains(probes, shape)
        return spiketrains_b_l_t / layer.vTh / 2 ** 6

    def get_spiketrains_input(self):
        if self.spike_probes is None:
            return

Instances