d59159739f78bacf661fc0a8b47c9b960fe600f6,snntoolbox/target_simulators/MegaSim_target_sim.py,SNN_compiled,poisson_spike_generator_megasim_flatten,#SNN_compiled#Any#,850

Before Change

            // Create poisson input.
            spike_snapshot = np.random.random_sample(mnist_digit.shape) * rescale_fac
            inp = (spike_snapshot <= mnist_digit).astype("float32")
            neuron_id = np.nonzero(inp)[0]
            spike_for_t = np.zeros((len(neuron_id),6),dtype="int")
            spike_for_t[:,0] = t
            spike_for_t[:,1] = -1
            spike_for_t[:,2] = -1

After Change

            spike_snapshot = np.random.random_sample(mnist_digit.shape) * rescale_fac
            inp = (spike_snapshot <= mnist_digit).astype("float32")
            // find the indexes of the non-zero
            neuron_id = np.nonzero(inp)
            // check if input is flattened or 2d in order to extract the X,Y addresses correctly
            if len(inp.shape)==1:
                megasim_x = neuron_id[0]
                megasim_y = 0
            else:
                megasim_x = neuron_id[2]
                megasim_y = neuron_id[1]

            spike_for_t = np.zeros((len(megasim_x), 6), dtype="int")
            spike_for_t[:,0] = t            // time-stamps
            spike_for_t[:,1] = -1           // REQ

Instances