tuning_curves.py 文件源码

def tuning_curve_2d(position, spikes, xedges, yedges, occupied_thresh=0, gaussian_sigma=None):
    """Creates 2D tuning curves based on spikes and 2D position.

    Parameters
    ----------
    position : nept.Position
        Must be a 2D position.
    spikes : list
        Containing nept.SpikeTrain for each neuron.
    xedges : np.array
    yedges : np.array
    sampling_rate : float
    occupied_thresh: float
    gaussian_sigma : float
        Sigma used in gaussian filter if filtering.

    Returns
    -------
    tuning_curves : np.array
        Where each inner array is the tuning curve for an individual neuron.

    """
    sampling_rate = np.median(np.diff(position.time))

    position_2d, pos_xedges, pos_yedges = np.histogram2d(position.y, position.x, bins=[yedges, xedges])
    position_2d *= sampling_rate
    shape = position_2d.shape
    occupied_idx = position_2d > occupied_thresh

    tc = []
    for spiketrain in spikes:
        spikes_x = []
        spikes_y = []
        for spike_time in spiketrain.time:
            spike_idx = find_nearest_idx(position.time, spike_time)
            if np.abs(position.time[spike_idx] - spike_time) < sampling_rate:
                spikes_x.append(position.x[spike_idx])
                spikes_y.append(position.y[spike_idx])
        spikes_2d, spikes_xedges, spikes_yedges = np.histogram2d(spikes_y, spikes_x, bins=[yedges, xedges])

        firing_rate = np.zeros(shape)
        firing_rate[occupied_idx] = spikes_2d[occupied_idx] / position_2d[occupied_idx]

        tc.append(firing_rate)

    if gaussian_sigma is not None:
        tuning_curves = []
        for firing_rate in tc:
            tuning_curves.append(gaussian_filter(firing_rate, gaussian_sigma))
    else:
        print('Tuning curves with no filter.')
        tuning_curves = tc

    return np.array(tuning_curves)