class_docstr: Artefact detection with the Riemannian Potato.
  
  The Riemannian Potato [1]_ is a clustering method used to detect artifact
  in EEG signals. The algorithm iteratively estimates the centroid of clean
  signal by rejecting every trial that is too far from it.
  
  Parameters
  ----------
  metric : string, default='riemann'
      The type of metric used for centroid and distance estimation.
  threshold : float, default=3
      Threshold on z-score of distance to reject artifacts. It is the number
      of standard deviations from the mean of distances to the centroid.
  n_iter_max : int, default=100
      The maximum number of iteration to reach convergence.
  pos_label : int, default=1
      The positive label corresponding to clean data.
  neg_label : int, default=0
      The negative label corresponding to artifact data.
  
  Notes
  -----
  .. versionadded:: 0.2.3
  
  Attributes
  ----------
  covmean_ : ndarray, shape (n_channels, n_channels)
      Centroid of potato.
  
  See Also
  --------
  Kmeans
  MDM
  
  References
  ----------
  .. [1] `The Riemannian Potato: an automatic and adaptive artifact detection
      method for online experiments using Riemannian geometry
      <https://hal.archives-ouvertes.fr/hal-00781701>`_
      A. Barachant, A Andreev, and M. Congedo. TOBI Workshop lV, Jan 2013,
      Sion, Switzerland. pp.19-20.
  .. [2] `The Riemannian Potato Field: A Tool for Online Signal Quality Index
      of EEG
      <https://hal.archives-ouvertes.fr/hal-02015909>`_
      Q. Barthélemy, L. Mayaud, D. Ojeda, and M. Congedo. IEEE Transactions
      on Neural Systems and Rehabilitation Engineering, IEEE Institute of
      Electrical and Electronics Engineers, 2019, 27 (2), pp.244-255
docstr: Get right-tailed proba from z-score.
docstr_tok: ["Get","right-tailed","proba","from","z-score","."]
code: def _get_proba(self, z):
      """Get right-tailed proba from z-score."""
      proba = 1 - norm.cdf(z)
      return proba
  
imports: import numpy
  from joblib import Parallel, delayed
  from scipy.stats import norm, chi2
  import sklearn
  from sklearn.base import BaseEstimator, ClassifierMixin, TransformerMixin, ClusterMixin, clone
  from sklearn.cluster import KMeans
  from .classification import MDM, _check_metric
  from .utils.mean import mean_covariance
  from .utils.geodesic import geodesic
label_desc: Use image node_id 9 for calling the Potato obj's underlying member method code with example usage: obj._get_proba(z) and returns: proba