code: def aamp_ostinatoed(client, Ts, m, p=2.0):
      """
      Find the non-normalized (i.e., without z-normalization) consensus motif of multiple
      time series with a distributed dask cluster
  
      This is a wrapper around the vanilla version of the ostinato algorithm
      which finds the best radius and a helper function that finds the most
      central conserved motif.
  
      Parameters
      ----------
      client : client
          A Dask or Ray Distributed client. Setting up a distributed cluster is beyond
          the scope of this library. Please refer to the Dask or Ray Distributed
          documentation.
  
      Ts : list
          A list of time series for which to find the most central consensus motif
  
      m : int
          Window size
  
      p : float, default 2.0
          The p-norm to apply for computing the Minkowski distance. Minkowski distance is
          typically used with `p` being 1 or 2, which correspond to the Manhattan distance
          and the Euclidean distance, respectively.
  
      Returns
      -------
      central_radius : float
          Radius of the most central consensus motif
  
      central_Ts_idx : int
          The time series index in `Ts` which contains the most central consensus motif
  
      central_subseq_idx : int
          The subsequence index within time series `Ts[central_motif_Ts_idx]` the contains
          most central consensus motif
  
      Notes
      -----
      `DOI: 10.1109/ICDM.2019.00140 \
      <https://www.cs.ucr.edu/~eamonn/consensus_Motif_ICDM_Long_version.pdf>`__
  
      See Table 2
  
      The ostinato algorithm proposed in the paper finds the best radius
      in `Ts`. Intuitively, the radius is the minimum distance of a
      subsequence to encompass at least one nearest neighbor subsequence
      from all other time series. The best radius in `Ts` is the minimum
      radius amongst all radii. Some data sets might contain multiple
      subsequences which have the same optimal radius.
      The greedy Ostinato algorithm only finds one of them, which might
      not be the most central motif. The most central motif amongst the
      subsequences with the best radius is the one with the smallest mean
      distance to nearest neighbors in all other time series. To find this
      central motif it is necessary to search the subsequences with the
      best radius via `stumpy.ostinato._get_central_motif`
      """
      if not isinstance(Ts, list):  # pragma: no cover
          raise ValueError(
              f"`Ts` is of type `{type(Ts)}` but a `list` is expected"
          )
  
      Ts_subseq_isfinite = [None] * len(Ts)
      for i, T in enumerate(Ts):
          (
              Ts[i],
              Ts_subseq_isfinite[i],
          ) = core.preprocess_non_normalized(T, m)
  
      bsf_radius, bsf_Ts_idx, bsf_subseq_idx = _aamp_ostinato(
          Ts,
          m,
          Ts_subseq_isfinite,
          p=p,
          client=client,
          mp_func=aamped,
      )
  
      (
          central_radius,
          central_Ts_idx,
          central_subseq_idx,
      ) = _get_aamp_central_motif(
          Ts,
          bsf_radius,
          bsf_Ts_idx,
          bsf_subseq_idx,
          m,
          Ts_subseq_isfinite,
          p=p,
      )
  
      return central_radius, central_Ts_idx, central_subseq_idx
  
file_path: stumpy/stumpy/aamp_ostinato.py
imports: import numpy
  from .None import core
  from .aamp import aamp
  from .aamped import aamped
label_desc: Use image node_id 5 for calling a global function with example usage: aamp_ostinatoed(client, Ts, m, p) and returns: central_radius,  central_Ts_idx,  central_subseq_idx