code: def maamp_multi_distance_profile(
      query_idx, T, m, include=None, discords=False, p=2.0
  ):
      """
      Multi-dimensional wrapper to compute the multi-dimensional non-normalized (i.e.,
      without z-normalization) distance profile for a given query window within the
      times series or sequence that is denoted by the `query_idx` index.
  
      Parameters
      ----------
      query_idx : int
          The window index to calculate the multi-dimensional distance profile
  
      T : numpy.ndarray
          The time series or sequence for which the multi-dimensional distance profile
          will be returned
  
      m : int
          Window size
  
      include : numpy.ndarray, default None
          A list of (zero-based) indices corresponding to the dimensions in `T` that
          must be included in the constrained multidimensional motif search.
          For more information, see Section IV D in:
  
          `DOI: 10.1109/ICDM.2017.66 \
          <https://www.cs.ucr.edu/~eamonn/Motif_Discovery_ICDM.pdf>`__
  
      discords : bool, default False
          When set to `True`, this reverses the distance profile to favor discords rather
          than motifs. Note that indices in `include` are still maintained and respected.
  
      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
      -------
      D : numpy.ndarray
          Multi-dimensional distance profile for the window with index equal to
          `query_idx`
      """
      T, T_subseq_isfinite = core.preprocess_non_normalized(T, m)
  
      if T.ndim <= 1:  # pragma: no cover
          err = f"T is {T.ndim}-dimensional and must be at least 1-dimensional"
          raise ValueError(f"{err}")
  
      core.check_window_size(m, max_size=T.shape[1])
  
      if include is not None:  # pragma: no cover
          include = core._preprocess_include(include)
  
      excl_zone = int(
          np.ceil(m / config.STUMPY_EXCL_ZONE_DENOM)
      )  # See Definition 3 and Figure 3
  
      D = _maamp_multi_distance_profile(
          query_idx,
          T,
          T,
          m,
          T_subseq_isfinite,
          p,
          include,
          discords,
          excl_zone,
      )
  
      return D
  
file_path: stumpy/stumpy/maamp.py
imports: from functools import partial
  import numpy
  from numba import njit, prange
  from .None import config, core
label_desc: Use image node_id 6 for calling a global function with example usage: maamp_multi_distance_profile(query_idx, T, m, include, discords, p) and returns: D