class_docstr: Alignment path based on lucky dynamic time warping distance.
  
  This aligner returns the alignment path produced by the lucky time warping
  distance [1]_.
  Uses Euclidean distance for multivariate data.
  
  Based on code by Krisztian A Buza's research group.
  
  Parameters
  ----------
  window: int, optional (default=None)
      Maximum distance between indices of aligned series, aka warping window.
      If None, defaults to max(len(ts1), len(ts2)), i.e., no warping window.
  
  References
  ----------
  ..[1] Stephan Spiegel, Brijnesh-Johannes Jain, and Sahin Albayrak.
      Fast time series classification under lucky time warping distance.
      Proceedings of the 29th Annual ACM Symposium on Applied Computing. 2014.
docstr: Return alignment for sequences/series passed in fit (iloc indices).
  
  Behaviour: returns an alignment for sequences in X passed to fit
      model should be in fitted state, fitted model parameters read from self
  
  Returns
  -------
  pd.DataFrame in alignment format, with columns 'ind'+str(i) for integer i
      cols contain iloc index of X[i] mapped to alignment coordinate for alignment
code: def _get_alignment(self):
      """Return alignment for sequences/series passed in fit (iloc indices).
  
      Behaviour: returns an alignment for sequences in X passed to fit
          model should be in fitted state, fitted model parameters read from self
  
      Returns
      -------
      pd.DataFrame in alignment format, with columns 'ind'+str(i) for integer i
          cols contain iloc index of X[i] mapped to alignment coordinate for alignment
      """
      align = pd.DataFrame(
          {"ind0": self.align_i_, "ind1": self.align_j_}
      )
      return align
  
file_path: sktime/sktime/alignment/lucky.py
imports: import numpy
  import pandas
  from sktime.alignment.base import BaseAligner
label_desc: Use image node_id 3 for calling the AlignerLuckyDtw obj's underlying member method code with example usage: obj._get_alignment() and returns: align