class_docstr: Recurrence Plot.
  
  A recurrence plot is an image representing the distances between
  trajectories extracted from the original time series.
  
  Parameters
  ----------
  dimension : int or float (default = 1)
      Dimension of the trajectory. If float, 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, 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 (default = None)
      Threshold for the minimum distance. If None, the recurrence plots
      are not binarized. If 'point', the threshold is computed such as
      `percentage` percents of the points are smaller than the threshold.
      If 'distance', the threshold is computed as the `percentage` of the
      maximum distance.
  
  percentage : int or float (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.
  
  flatten : bool (default = False)
      If True, images are flattened to be one-dimensional.
  
  Notes
  -----
  Given a time series :math:`(x_1, \ldots, x_n)`, the extracted
  trajectories are
  
  .. math::
  
      \vec{x}_i = (x_i, x_{i + \tau}, \ldots, x_{i + (m - 1)\tau}), \quad
      \forall i \in \{1, \ldots, n - (m - 1)\tau \}
  
  where :math:`m` is the ``dimension`` of the trajectories and :math:`\tau`
  is the ``time_delay``. The recurrence plot, denoted :math:`R`, is the
  pairwise distance between the trajectories
  
  .. math::
  
      R_{i, j} = \Theta(\varepsilon - \| \vec{x}_i - \vec{x}_j \|), \quad
      \forall i,j \in \{1, \ldots, n - (m - 1)\tau \}
  
  where :math:`\Theta` is the Heaviside function and :math:`\varepsilon`
  is the ``threshold``.
  
  References
  ----------
  .. [1] J.-P Eckmann, S. Oliffson Kamphorst and D Ruelle, "Recurrence
         Plots of Dynamical Systems". Europhysics Letters (1987).
  
  Examples
  --------
  >>> from pyts.datasets import load_gunpoint
  >>> from pyts.image import RecurrencePlot
  >>> X, _, _, _ = load_gunpoint(return_X_y=True)
  >>> transformer = RecurrencePlot()
  >>> X_new = transformer.transform(X)
  >>> X_new.shape
  (50, 150, 150)
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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/image/recurrence.py
imports: import numpy
  from math import ceil
  from numpy.lib.stride_tricks import as_strided
  from sklearn.base import BaseEstimator
  from sklearn.utils.validation import check_array
  from ..base import UnivariateTransformerMixin
label_desc: Use image node_id 2 for calling the RecurrencePlot obj's underlying member method code with example usage: obj.fit(X, y) and returns: self