code: def aampdist(T_A, T_B, m, percentage=0.05, k=None, p=2.0):
      """
      Compute the non-normalized (i.e., without z-normalization) matrix profile distance
      (MPdist) measure between any two time series with `stumpy.aamp`.
  
      The MPdist distance measure considers two time series to be similar if they share
      many subsequences, regardless of the order of matching subsequences. MPdist
      concatenates the output of an AB-join and a BA-join and returns the `k`th smallest
      value as the reported distance. Note that MPdist is a measure and not a metric.
      Therefore, it does not obey the triangular inequality but the method is highly
      scalable.
  
      Parameters
      ----------
      T_A : numpy.ndarray
          The first time series or sequence for which to compute the matrix profile
  
      T_B : numpy.ndarray
          The second time series or sequence for which to compute the matrix profile
  
      m : int
          Window size
  
      percentage : float, default 0.05
          The percentage of distances that will be used to report `mpdist`. The value
          is between 0.0 and 1.0.
  
      k : int
          Specify the `k`th value in the concatenated matrix profiles to return. When `k`
          is not `None`, then the `percentage` parameter is ignored.
  
      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
      -------
      MPdist : float
          The matrix profile distance
  
      Notes
      -----
      `DOI: 10.1109/ICDM.2018.00119 \
      <https://www.cs.ucr.edu/~eamonn/MPdist_Expanded.pdf>`__
  
      See Section III
      """
      partial_mp_func = functools.partial(aamp, p=p)
      return core._mpdist(T_A, T_B, m, partial_mp_func, percentage, k)
  
file_path: stumpy/stumpy/aampdist.py
imports: import functools
  import math
  import numpy
  from .None import core
  from .aamp import aamp
  from .aamped import aamped
label_desc: Use image node_id 2 for calling a global function with example usage: aampdist(T_A, T_B, m, percentage, k, p) and returns: core