How to use the matrixprofile.order function in matrixprofile

Calculate the Matrix Profile using the more efficient MASS calculation. Distance profiles are computed in a random order.

    Parameters
    ----------
    tsA: Time series containing the queries for which to calculate the Matrix Profile.
    m: Length of subsequence to compare.
    tsB: Time series to compare the query against. Note that, if no value is provided, tsB = tsA by default.
    sampling: The percentage of all possible distance profiles to sample for the final Matrix Profile. 0 to 1
    n_threads: Number of threads to use in parallel mode. Defaults to single threaded mode. Set to -1 to use all threads.
    random_state: Set the random seed generator for reproducible results.
    """
    if sampling > 1 or sampling < 0:
        raise ValueError('Sampling value must be a percentage in decimal format from 0 to 1.')
    
    if n_threads is None:
        return _matrixProfile_sampling(tsA,m,order.randomOrder,distanceProfile.massDistanceProfile,tsB,sampling=sampling,random_state=random_state)
    
    return _stamp_parallel(tsA, m, tsB=tsB, sampling=sampling, n_threads=n_threads, random_state=random_state)

def naiveMP(tsA,m,tsB=None):
    """
    Calculate the Matrix Profile using the naive all-pairs calculation.

    Parameters
    ----------
    tsA: Time series containing the queries for which to calculate the Matrix Profile.
    m: Length of subsequence to compare.
    tsB: Time series to compare the query against. Note that, if no value is provided, tsB = tsA by default.
    """
    return _matrixProfile(tsA,m,order.linearOrder,distanceProfile.naiveDistanceProfile,tsB)