class_docstr: Generate a set of numbers on the real axis, concentrating on
  'interesting' regions and covering all orders of magnitude.
class_docstr_tok: ["Generate","a","set","of","numbers","on","the","real","axis",",","concentrating","on","'interesting","'","regions","and","covering","all","orders","of","magnitude","."]
docstr: Return an array containing n numbers.
docstr_tok: ["Return","an","array","containing","n","numbers","."]
code: def values(self, n):
      """Return an array containing n numbers."""
      a, b = self.a, self.b
      if a == b:
          return np.zeros(n)
  
      if not self.inclusive_a:
          n += 1
      if not self.inclusive_b:
          n += 1
  
      if n % 2 == 0:
          n1 = n // 2
          n2 = n1
      else:
          n1 = n // 2
          n2 = n1 + 1
  
      if a >= 0:
          pospts = self._positive_values(a, b, n)
          negpts = []
      elif b <= 0:
          pospts = []
          negpts = -self._positive_values(-b, -a, n)
      else:
          pospts = self._positive_values(0, b, n1)
          negpts = -self._positive_values(0, -a, n2 + 1)
          # Don't want to get zero twice
          negpts = negpts[1:]
      pts = np.hstack((negpts[::-1], pospts))
  
      if not self.inclusive_a:
          pts = pts[1:]
      if not self.inclusive_b:
          pts = pts[:-1]
      return pts
  
imports: import os
  import sys
  import time
  import numpy
  from numpy.testing import assert_
  import pytest
  from scipy.special._testutils import assert_func_equal
label_desc: Use image node_id 3 for calling the Arg obj's underlying member method code with example usage: obj.values(n) and returns: pts, np