'Inverse inplace Transformation.'

def _inv_transform(self, a):

    masked = (np.abs(a) > (self.linthresh * self._linscale_adj))
    sign = np.sign(a[masked])
    exp = np.exp((((sign * a[masked]) / self.linthresh) - self._linscale_adj))
    exp *= (sign * self.linthresh)
    a[masked] = exp
    a[(~ masked)] /= self._linscale_adj
    return a
