def sample_indexes_by_state(indexes, nsample, subset=None, replace=True):
    """Samples trajectory/time indexes according to the given sequence of states

    Parameters
    ----------
    indexes : list of ndarray( (N_i, 2) )
        For each state, all trajectory and time indexes where this state occurs.
        Each matrix has a number of rows equal to the number of occurrences of the corresponding state,
        with rows consisting of a tuple (i, t), where i is the index of the trajectory and t is the time index
        within the trajectory.
    nsample : int
        Number of samples per state. If replace = False, the number of returned samples per state could be smaller
        if less than nsample indexes are available for a state.
    subset : ndarray((n)), optional, default = None
        array of states to be indexed. By default all states in dtrajs will be used
    replace : boolean, optional
        Whether the sample is with or without replacement

    Returns
    -------
    indexes : list of ndarray( (N, 2) )
        List of the sampled indices by state.
        Each element is an index array with a number of rows equal to N=len(sequence), with rows consisting of a
        tuple (i, t), where i is the index of the trajectory and t is the time index within the trajectory.

    """
    # how many states in total?
    n = len(indexes)
    # define set of states to work on
    if subset is None:
        subset = list(range(n))

    # list of states
    res = np.ndarray((len(subset)), dtype=object)
    for i in range(len(subset)):
        # sample the following state
        s = subset[i]
        # how many indexes are available?
        m_available = indexes[s].shape[0]
        # do we have no indexes for this state? Then insert empty array.
        if (m_available == 0):
            res[i] = np.zeros((0,2), dtype=int)
        elif replace:
            I = np.random.choice(m_available, nsample, replace=True)
            res[i] = indexes[s][I,:]
        else:
            I = np.random.choice(m_available, min(m_available,nsample), replace=False)
            res[i] = indexes[s][I,:]

    return res
def sample_indexes_by_state ( indexes , nsample , subset = None , replace = True ) : n = len ( indexes ) if subset is None : subset = list ( range ( n ) ) res = np . ndarray ( ( len ( subset ) ) , dtype = object ) for i in range ( len ( subset ) ) : s = subset [ i ] m_available = indexes [ s ] . shape [ 0 ] if ( m_available == 0 ) : res [ i ] = np . zeros ( ( 0 , 2 ) , dtype = int ) elif replace : I = np . random . choice ( m_available , nsample , replace = True ) res [ i ] = indexes [ s ] [ I , : ] else : I = np . random . choice ( m_available , min ( m_available , nsample ) , replace = False ) res [ i ] = indexes [ s ] [ I , : ] return res
Samples trajectory/time indexes according to the given sequence of states

    Parameters
    ----------
    indexes : list of ndarray( (N_i, 2) )
        For each state, all trajectory and time indexes where this state occurs.
        Each matrix has a number of rows equal to the number of occurrences of the corresponding state,
        with rows consisting of a tuple (i, t), where i is the index of the trajectory and t is the time index
        within the trajectory.
    nsample : int
        Number of samples per state. If replace = False, the number of returned samples per state could be smaller
        if less than nsample indexes are available for a state.
    subset : ndarray((n)), optional, default = None
        array of states to be indexed. By default all states in dtrajs will be used
    replace : boolean, optional
        Whether the sample is with or without replacement

    Returns
    -------
    indexes : list of ndarray( (N, 2) )
        List of the sampled indices by state.
        Each element is an index array with a number of rows equal to N=len(sequence), with rows consisting of a
        tuple (i, t), where i is the index of the trajectory and t is the time index within the trajectory.