class_docstr: Joint Recurrence Plot.
  
  A recurrence plot is an image representing the distances between
  trajectories extracted from the original time series.
  
  A joint recurrence plot is an extension of recurrence plots for
  multivariate time series: it is the Hadamard of the recurrence
  plots obtained for each feature of the multivariate time series.
  
  Parameters
  ----------
  dimension : int or float (default = 1)
      Dimension of the trajectory. If float, it represents a percentage of
      the size of each time series and must be between 0 and 1.
  
  time_delay : int or float (default = 1)
      Time gap between two back-to-back points of the trajectory. If
      float, it represents a percentage of the size of each time series and
      must be between 0 and 1.
  
  threshold : float, 'point', 'distance' or None or list thereof (default = None)
      Threshold for the minimum distance. If None, the recurrence plots
      are not binarized. If ``threshold='point'``, the threshold is computed
      such as ``percentage`` percents of the points are smaller than the
      threshold. If ``threshold='distance'``, the threshold is computed as
      the ``percentage`` of the maximum distance.
  
  percentage : int, float or list thereof (default = 10)
      Percentage of black points if ``threshold='point'`` or percentage of
      maximum distance for threshold if ``threshold='distance'``.
      Ignored if ``threshold`` is a float or None. Note that the percentage
      is calculated for each recurrence plot independently, which implies
      that there will probably be less than `percentage` percents of black
      points in the joint recurrence plot.
  
  References
  ----------
  .. [1] M. Romano, M. Thiel, J. Kurths and W. con Bloh, "Multivariate
         Recurrence Plots". Physics Letters A (2004)
  
  Examples
  --------
  >>> from pyts.datasets import load_basic_motions
  >>> from pyts.multivariate.image import JointRecurrencePlot
  >>> X, _, _, _ = load_basic_motions(return_X_y=True)
  >>> transformer = JointRecurrencePlot()
  >>> X_new = transformer.transform(X)
  >>> X_new.shape
  (40, 100, 100)
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docstr: Pass.
  
  Parameters
  ----------
  X
      Ignored
  y
      Ignored
  
  Returns
  -------
  self : object
docstr_tok: ["Pass",".","Parameters","--","--","--","--","--","X","Ignored","y","Ignored","Returns","--","--","--","-","self",":","object"]
code: def fit(self, X=None, y=None):
      """Pass.
  
      Parameters
      ----------
      X
          Ignored
      y
          Ignored
  
      Returns
      -------
      self : object
  
      """
      return self
  
file_path: pyts/pyts/multivariate/image/joint_rp.py
imports: import numpy
  from sklearn.base import BaseEstimator
  from ...base import MultivariateTransformerMixin
  from ...image import RecurrencePlot
  from ..utils import check_3d_array
label_desc: Use image node_id 2 for calling the JointRecurrencePlot obj's underlying member method code with example usage: obj.fit(X, y) and returns: self