code: def plotting_positions(data, alpha=0.4, beta=0.4):
      """
      Returns plotting positions (or empirical percentile points) for the data.
  
      Plotting positions are defined as ``(i-alpha)/(n+1-alpha-beta)``, where:
          - i is the rank order statistics
          - n is the number of unmasked values along the given axis
          - `alpha` and `beta` are two parameters.
  
      Typical values for `alpha` and `beta` are:
          - (0,1)    : ``p(k) = k/n``, linear interpolation of cdf (R, type 4)
          - (.5,.5)  : ``p(k) = (k-1/2.)/n``, piecewise linear function
            (R, type 5)
          - (0,0)    : ``p(k) = k/(n+1)``, Weibull (R type 6)
          - (1,1)    : ``p(k) = (k-1)/(n-1)``, in this case,
            ``p(k) = mode[F(x[k])]``. That's R default (R type 7)
          - (1/3,1/3): ``p(k) = (k-1/3)/(n+1/3)``, then
            ``p(k) ~ median[F(x[k])]``.
            The resulting quantile estimates are approximately median-unbiased
            regardless of the distribution of x. (R type 8)
          - (3/8,3/8): ``p(k) = (k-3/8)/(n+1/4)``, Blom.
            The resulting quantile estimates are approximately unbiased
            if x is normally distributed (R type 9)
          - (.4,.4)  : approximately quantile unbiased (Cunnane)
          - (.35,.35): APL, used with PWM
          - (.3175, .3175): used in scipy.stats.probplot
  
      Parameters
      ----------
      data : array_like
          Input data, as a sequence or array of dimension at most 2.
      alpha : float, optional
          Plotting positions parameter. Default is 0.4.
      beta : float, optional
          Plotting positions parameter. Default is 0.4.
  
      Returns
      -------
      positions : MaskedArray
          The calculated plotting positions.
  
      """
      data = ma.array(data, copy=False).reshape(1, -1)
      n = data.count()
      plpos = np.empty(data.size, dtype=float)
      plpos[n:] = 0
      plpos[data.argsort(axis=None)[:n]] = (
          np.arange(1, n + 1) - alpha
      ) / (n + 1.0 - alpha - beta)
      return ma.array(plpos, mask=data._mask)
  
imports: from __future__ import division, print_function, absolute_import
  import numpy
  from numpy import ndarray
  import numpy.ma
  from numpy.ma import masked, nomask
  from scipy._lib.six import iteritems
  import itertools
  import warnings
  from collections import namedtuple
  from .None import distributions
  import scipy.special
  from ._stats_mstats_common import _find_repeats, linregress, theilslopes, siegelslopes
label_desc: Use image node_id 53 for calling a global function with example usage: plotting_positions(data, alpha, beta) and returns: ma