code: def uniform_sphere(RAlim, DEClim, size=1):
      """Draw a uniform sample on a sphere
  
      Parameters
      ----------
      RAlim : tuple
          select Right Ascension between RAlim[0] and RAlim[1]
          units are degrees
      DEClim : tuple
          select Declination between DEClim[0] and DEClim[1]
      size : int (optional)
          the size of the random arrays to return (default = 1)
  
      Returns
      -------
      RA, DEC : ndarray
          the random sample on the sphere within the given limits.
          arrays have shape equal to size.
      """
      zlim = np.sin(np.pi * np.asarray(DEClim) / 180.0)
  
      z = zlim[0] + (zlim[1] - zlim[0]) * np.random.random(size)
      DEC = (180.0 / np.pi) * np.arcsin(z)
      RA = RAlim[0] + (RAlim[1] - RAlim[0]) * np.random.random(size)
  
      return RA, DEC
  
file_path: astroML/astroML/correlation.py
imports: import numpy
  from sklearn.neighbors import KDTree
  from sklearn.utils import check_random_state
label_desc: Use image node_id 1 for calling a global function with example usage: uniform_sphere(RAlim, DEClim, size) and returns: RA,  DEC