class_docstr: Extreme value copula constructed from Pickand's dependence function.
  
  Currently only bivariate copulas are available.
  
  Parameters
  ----------
  transform: instance of transformation class
      Pickand's dependence function with required methods including first
      and second derivatives
  args : tuple
      Optional copula parameters. Copula parameters can be either provided
      when creating the instance or as arguments when calling methods.
  k_dim : int
      Currently only bivariate extreme value copulas are supported.
  
  Notes
  -----
  currently the following dependence function and copulas are available
  
  - AsymLogistic
  - AsymNegLogistic
  - AsymMixed
  - HR
  
  TEV and AsymBiLogistic currently do not have required derivatives for pdf.
  
  See Also
  --------
  dep_func_ev
docstr: Evaluate cdf of bivariate extreme value copula.
  
  Parameters
  ----------
  u : array_like
      Values of random bivariate random variable, each defined on [0, 1],
      for which cdf is computed.
      Can be two dimensional with multivariate components in columns and
      observation in rows.
  args : tuple
      Required parameters for the copula. The meaning and number of
      parameters in the tuple depends on the specific copula.
  
  Returns
  -------
  CDF values at evaluation points.
code: def cdf(self, u, args=()):
      """Evaluate cdf of bivariate extreme value copula.
  
      Parameters
      ----------
      u : array_like
          Values of random bivariate random variable, each defined on [0, 1],
          for which cdf is computed.
          Can be two dimensional with multivariate components in columns and
          observation in rows.
      args : tuple
          Required parameters for the copula. The meaning and number of
          parameters in the tuple depends on the specific copula.
  
      Returns
      -------
      CDF values at evaluation points.
      """
      # currently only Bivariate
      u, v = np.asarray(u).T
      args = self._handle_args(args)
      cdfv = np.exp(
          np.log(u * v)
          * self.transform(np.log(u) / np.log(u * v), *args)
      )
      return cdfv
  
file_path: statsmodels/statsmodels/distributions/copula/extreme_value.py
imports: import numpy
  from .copulas import Copula
label_desc: Use image node_id 3 for calling the ExtremeValueCopula obj's underlying member method code with example usage: obj.cdf(u, args) and returns: cdfv