sim.py 文件源码

def _check_branching(X,Xsamples,restart,threshold=0.25):
    """ Check whether time series branches.

        Args:
            X (np.array): current time series data.
            Xsamples (np.array): list of previous branching samples.
            restart (int): counts number of restart trials.
            threshold (float, optional): sets threshold for attractor
                identification.

        Returns:
            check = true if branching realization, Xsamples = updated list
    """
    check = True
    if restart == 0:
        Xsamples.append(X)
    else:
        for Xcompare in Xsamples:
            Xtmax_diff = np.absolute(X[-1,:] - Xcompare[-1,:])
            # If the second largest element is smaller than threshold
            # set check to False, i.e. at least two elements
            # need to change in order to have a branching.
            # If we observe all parameters of the system,
            # a new attractor state must involve changes in two
            # variables.
            if np.partition(Xtmax_diff,-2)[-2] < threshold:
                check = False
        if check:
            Xsamples.append(X)
    if not check:
        logg.m('realization {}:'.format(restart), 'no new branch', v=4)
    else:
        logg.m('realization {}:'.format(restart), 'new branch', v=4)
    return check, Xsamples