code: def _calc_uniform_order_statistic_medians(n):
      """
      Approximations of uniform order statistic medians.
  
      Parameters
      ----------
      n : int
          Sample size.
  
      Returns
      -------
      v : 1d float array
          Approximations of the order statistic medians.
  
      References
      ----------
      .. [1] James J. Filliben, "The Probability Plot Correlation Coefficient
             Test for Normality", Technometrics, Vol. 17, pp. 111-117, 1975.
  
      Examples
      --------
      Order statistics of the uniform distribution on the unit interval
      are marginally distributed according to beta distributions.
      The expectations of these order statistic are evenly spaced across
      the interval, but the distributions are skewed in a way that
      pushes the medians slightly towards the endpoints of the unit interval:
  
      >>> n = 4
      >>> k = np.arange(1, n+1)
      >>> from scipy.stats import beta
      >>> a = k
      >>> b = n-k+1
      >>> beta.mean(a, b)
      array([ 0.2,  0.4,  0.6,  0.8])
      >>> beta.median(a, b)
      array([ 0.15910358,  0.38572757,  0.61427243,  0.84089642])
  
      The Filliben approximation uses the exact medians of the smallest
      and greatest order statistics, and the remaining medians are approximated
      by points spread evenly across a sub-interval of the unit interval:
  
      >>> from scipy.morestats import _calc_uniform_order_statistic_medians
      >>> _calc_uniform_order_statistic_medians(n)
      array([ 0.15910358,  0.38545246,  0.61454754,  0.84089642])
  
      This plot shows the skewed distributions of the order statistics
      of a sample of size four from a uniform distribution on the unit interval:
  
      >>> import matplotlib.pyplot as plt
      >>> x = np.linspace(0.0, 1.0, num=50, endpoint=True)
      >>> pdfs = [beta.pdf(x, a[i], b[i]) for i in range(n)]
      >>> plt.figure()
      >>> plt.plot(x, pdfs[0], x, pdfs[1], x, pdfs[2], x, pdfs[3])
  
      """
      v = np.zeros(n, dtype=np.float64)
      v[-1] = 0.5 ** (1.0 / n)
      v[0] = 1 - v[-1]
      i = np.arange(2, n)
      v[1:-1] = (i - 0.3175) / (n + 0.365)
      return v
  
imports: from __future__ import division, print_function, absolute_import
  import math
  import warnings
  from collections import namedtuple
  import numpy
  from numpy import isscalar, r_, log, around, unique, asarray, zeros, arange, sort, amin, amax, any, atleast_1d, sqrt, ceil, floor, array, compress, pi, exp, ravel, count_nonzero, sin, cos, arctan2, hypot
  from scipy._lib.six import string_types
  from scipy import optimize
  from scipy import special
  from .None import statlib
  from .None import stats
  from .stats import find_repeats, _contains_nan
  from .contingency import chi2_contingency
  from .None import distributions
  from ._distn_infrastructure import rv_generic
label_desc: Use image node_id 5 for calling a global function with example usage: _calc_uniform_order_statistic_medians(n) and returns: v