Function mmotifs

stumpy/mmotifs.py:22–265

Discover the top motifs for the multi-dimensional time series ``T``. Parameters ---------- T : numpy.ndarray The multi-dimensional time series or sequence. P : numpy.ndarray Multi-dimensional Matrix Profile of ``T``. I : numpy.ndarray M

(
    T,
    P,
    I,
    min_neighbors=1,
    max_distance=None,
    cutoffs=None,
    max_matches=10,
    max_motifs=1,
    atol=1e-8,
    k=None,
    include=None,
    normalize=True,
    p=2.0,
    T_subseq_isconstant=None,
)

    ],
)
def mmotifs(
    T,
    P,
    I,
    min_neighbors=1,
    max_distance=None,
    cutoffs=None,
    max_matches=10,
    max_motifs=1,
    atol=1e-8,
    k=None,
    include=None,
    normalize=True,
    p=2.0,
    T_subseq_isconstant=None,
):
    """
    Discover the top motifs for the multi-dimensional time series ``T``.

    Parameters
    ----------
    T : numpy.ndarray
        The multi-dimensional time series or sequence.

    P : numpy.ndarray
        Multi-dimensional Matrix Profile of ``T``.

    I : numpy.ndarray
        Multi-dimensional Matrix Profile indices.

    min_neighbors : int, default 1
        The minimum number of similar matches a subsequence needs to have in order
        to be considered a motif. This defaults to ``1``, which means that a
        subsequence must have at least one similar match in order to be considered a
        motif.

    max_distance : float, default None
        Maximal distance that is allowed between a query subsequence
        (a candidate motif) and all subsequences in ``T`` to be considered as a
        match. If ``None``, this defaults to
        ``np.nanmax([np.nanmean(D) - 2 * np.nanstd(D), np.nanmin(D)])``
        (i.e. at least the closest match will be returned).

    cutoffs : numpy.ndarray or float, default None
        The largest matrix profile value (distance) for each dimension of the
        multidimensional matrix profile that a multidimenisonal candidate motif is
        allowed to have. If ``cutoffs`` is a scalar value, then this value will be
        applied to every dimension.

    max_matches : int, default 10
        The maximum number of similar matches (nearest neighbors) to return for each
        motif. The first match is always the self/trivial-match for each motif.

    max_motifs : int, default 1
        The maximum number of motifs to return. To consider returning all possible
        valid motifs, try setting `max_motifs` to the length of your input matrix
        profile (i.e., ``max_motifs=len(P)``)