code: def mjci(data, prob=[0.25, 0.5, 0.75], axis=None):
      """
      Returns the Maritz-Jarrett estimators of the standard error of selected
      experimental quantiles of the data.
  
      Parameters
      ----------
      data : ndarray
          Data array.
      prob : sequence, optional
          Sequence of quantiles to compute.
      axis : int or None, optional
          Axis along which to compute the quantiles. If None, use a flattened
          array.
  
      """
  
      def _mjci_1D(data, p):
          data = np.sort(data.compressed())
          n = data.size
          prob = (np.array(p) * n + 0.5).astype(int_)
          betacdf = beta.cdf
  
          mj = np.empty(len(prob), float_)
          x = np.arange(1, n + 1, dtype=float_) / n
          y = x - 1.0 / n
          for i, m in enumerate(prob):
              W = betacdf(x, m - 1, n - m) - betacdf(y, m - 1, n - m)
              C1 = np.dot(W, data)
              C2 = np.dot(W, data**2)
              mj[i] = np.sqrt(C2 - C1**2)
          return mj
  
      data = ma.array(data, copy=False)
      if data.ndim > 2:
          raise ValueError(
              "Array 'data' must be at most two dimensional, "
              "but got data.ndim = %d" % data.ndim
          )
  
      p = np.array(prob, copy=False, ndmin=1)
      # Computes quantiles along axis (or globally)
      if axis is None:
          return _mjci_1D(data, p)
      else:
          return ma.apply_along_axis(_mjci_1D, axis, data, p)
  
file_path: catboost/contrib/python/scipy/py2/scipy/stats/mstats_extras.py
imports: from __future__ import division, print_function, absolute_import
  import numpy
  from numpy import float_, int_, ndarray
  import numpy.ma
  from numpy.ma import MaskedArray
  from .None import mstats_basic
  from scipy.stats.distributions import norm, beta, t, binom
label_desc: Use image node_id 5 for calling a global function with example usage: mjci(data, prob, axis) and returns: mj, _mjci_1D, ma