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:py:mod:`wildboar.annotate`
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.. py:module:: wildboar.annotate

.. autoapi-nested-parse::

   
   Annotation of time series.

   See the :ref:`annotation section in the User Guide
   <guide-annotate>` for more details and examples.















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Package Contents
----------------


Functions
---------

.. autoapisummary::

   wildboar.annotate.motifs
   wildboar.annotate.segment



.. py:function:: motifs(x, mp=None, *, window='auto', exclude=0.2, max_distance='auto', max_neighbours=10, min_neighbours=1, max_motif=1, return_distance=False)

   
   Find motifs.


   :Parameters:

       **x** : array-like of shape (n_samples, n_timestep)
           The time series

       **mp** : ndarray or shape (n_samples, profile_size), optional
           The matrix profile. The matrix profile is computed if None.

       **window** : "auto", int or float, optional
           The window size of the matrix profile.
           
           - if "auto" the window is math.ceil(0.1 * n_timesteps) if mp=None, and the
             window of the matrix profile if mp is not None.
           - if float, a fraction of n_timestep
           - if int, the exact window size

       **exclude** : float, optional
           The size of the exclusion zone.

       **max_distance** : str, optional
           The maximum distance between motifs.

       **max_neighbours** : int, optional
           The maximum number of neighbours

       **min_neighbours** : int, optional
           The minimum number of neighbours

       **max_motif** : int, optional
           The maximum number of motifs to return.

       **return_distance** : bool, optional
           Return the distance from main to neighbours

   :Returns:

       **motif_indicies** : list
           List of arrays of motif neighbour indicies

       **motif_distance** : list, optional
           List of arrays of distance from motif to neighbours







   .. seealso::

       
       :obj:`wildboar.distance.subsequence_match`
           find subsequence matches
       :obj:`wildboar.distance.matrix_profile`
           compute the matrix profile
       
       

   .. rubric:: References

   Yeh, C. C. M. et al. (2016).
       Matrix profile I: All pairs similarity joins for time series: a unifying view
       that includes motifs, discords and shapelets. In 2016 IEEE 16th international
       conference on data mining (ICDM)

   .. only:: latex

      




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.. py:function:: segment(x=None, *, mpi=None, n_segments=1, window=0.1, exclude=0.2, boundry=1.0, return_arc_curve=False)

   
   Find change regimes in a time series.


   :Parameters:

       **x** : array-like of shape (n_samples, n_timestep) or (n_timestep, ), optional
           The time series. If x is given, the matrix profile of x is computed.

       **mpi** : array-like of shape (n_samples, profile_size) or (profile_size), optional
           The matrix profile index. Must be given unless x is given.

       **n_segments** : int, optional
           The number of segmentations to identify

       **window** : int or float, optional
           The window size. The window parameter is ignored if mpi is not None.
           
           - if float, a fraction of n_timestep
           - if int, the exact window size

       **exclude** : float, optional
           The self-join exclusion for the matrix profile. Ignored if `mpi` is given.

       **boundry** : float, optional
           Ignore the region around each identified segmentation. Expressed as a
           fraction of `window`.

       **return_arc_curve** : bool, optional
           Return the arc curve.

   :Returns:

       **segments** : ndarray of shape (n_samples, n_segments), (n_segments) or int
           The start index of a segment

       **arc_curves** : ndarray of shape (n_samples, profile_size) or (profile_size, )
           The arc curves







   .. seealso::

       
       :obj:`wildboar.distance.matrix_profile`
           compute the matrix profile
       
       

   .. rubric:: References

   Gharghabi, Shaghayegh, et al. (2017)
       Matrix profile VIII: domain agnostic online semantic segmentation at superhuman
       performance levels. In proceedings of International Conference on Data Mining

   .. only:: latex

      




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