class_docstr: Estimation of cospectral covariance matrix.
  
  Co-spectral matrices are the real part of complex cross-spectral matrices
  (see :func:`pyriemann.utils.covariance.cross_spectrum`), estimated as the
  spectrum covariance in the frequency domain. This method returns a 4-d
  array with a cospectral covariance matrix for each input and in each
  frequency bin of the FFT.
  
  Parameters
  ----------
  window : int, default=128
      The length of the FFT window used for spectral estimation.
  overlap : float, default=0.75
      The percentage of overlap between window.
  fmin : float | None, default=None
      The minimal frequency to be returned.
  fmax : float | None, default=None
      The maximal frequency to be returned.
  fs : float | None, default=None
      The sampling frequency of the signal.
  
  Attributes
  ----------
  freqs_ : ndarray, shape (n_freqs,)
      If transformed, the frequencies associated to cospectra.
      None if ``fs`` is None.
  
  See Also
  --------
  Covariances
  HankelCovariances
  Coherences
docstr: Init.
docstr_tok: ["Init","."]
code: def __init__(
      self, window=128, overlap=0.75, fmin=None, fmax=None, fs=None
  ):
      """Init."""
      self.window = _nextpow2(window)
      self.overlap = overlap
      self.fmin = fmin
      self.fmax = fmax
      self.fs = fs
  
imports: import numpy
  from sklearn.base import BaseEstimator, TransformerMixin
  from sklearn.covariance import shrunk_covariance
  from sklearn.metrics.pairwise import pairwise_kernels
  from .spatialfilters import Xdawn
  from .utils.covariance import covariances, covariances_EP, cospectrum, coherence, block_covariances
label_desc: Use image node_id 1 to create a new CospCovariances object from inherited base classes: BaseEstimator, TransformerMixin with example: obj = CospCovariances(window, overlap, fmin, fmax, fs)