retina.py 文件源码

def stationary_nonlinearity(self, stim):
        """Stationary nonlinearity

        Nonlinearly rescale a temporal signal `stim` across space and time,
        based on a sigmoidal function dependent on the maximum value of `stim`.
        This is Box 4 in Nanduri et al. (2012).
        The parameter values of the asymptote, slope, and shift of the logistic
        function are given by self.asymptote, self.slope, and self.shift,
        respectively.

        Parameters
        ----------
        stim: array
           Temporal signal to process, stim(r, t) in Nanduri et al. (2012).

        Returns
        -------
        Rescaled signal, b4(r, t) in Nanduri et al. (2012).

        Notes
        -----
        Conversion to TimeSeries is avoided for the sake of speedup.
        """
        # use expit (logistic) function for speedup
        sigmoid = ss.expit((stim.max() - self.shift) / self.slope)
        return stim * sigmoid