code: def fetch_rrlyrae_combined(data_home=None, download_if_missing=True):
      """Loader for RR-Lyrae combined data
  
      This returns the combined RR-Lyrae colors and SDSS standards colors.
      The RR-Lyrae sample is confirmed through time-domain observations;
      this result in a nice dataset for testing classification routines.
  
      Parameters
      ----------
      data_home : optional, default=None
          Specify another download and cache folder for the datasets. By default
          all astroML data is stored in '~/astroML_data'.
  
      download_if_missing : optional, default=True
          If False, raise a IOError if the data is not locally available
          instead of trying to download the data from the source site.
  
      Returns
      -------
      X : ndarray
          a shape (n_samples, 4) array.  Columns are u-g, g-r, r-i, i-z
  
      y : ndarray
          a shape (n_samples,) array of labels.  1 indicates an RR Lyrae,
          0 indicates a background star.
      """
      # ----------------------------------------------------------------------
      # Load data
      kwds = dict(
          data_home=data_home, download_if_missing=download_if_missing
      )
      rrlyrae = fetch_rrlyrae_mags(**kwds)
      standards = fetch_sdss_S82standards(**kwds)
  
      # ------------------------------------------------------------
      # perform color cuts on standard stars
      # these come from eqns 1-4 of Sesar et al 2010, ApJ 708:717
  
      u_g = standards["mmu_u"] - standards["mmu_g"]
      g_r = standards["mmu_g"] - standards["mmu_r"]
      r_i = standards["mmu_r"] - standards["mmu_i"]
      i_z = standards["mmu_i"] - standards["mmu_z"]
  
      standards = standards[
          (u_g > 0.7)
          & (u_g < 1.35)
          & (g_r > -0.15)
          & (g_r < 0.4)
          & (r_i > -0.15)
          & (r_i < 0.22)
          & (i_z > -0.21)
          & (i_z < 0.25)
      ]
  
      # ----------------------------------------------------------------------
      # get magnitudes and colors; split into train and test sets
  
      mags_rr = np.vstack([rrlyrae[f + "mag"] for f in "ugriz"])
      colors_rr = mags_rr[:-1] - mags_rr[1:]
  
      mags_st = np.vstack([standards["mmu_" + f] for f in "ugriz"])
      colors_st = mags_st[:-1] - mags_st[1:]
  
      # stack the two sets of colors together
      X = np.vstack((colors_st.T, colors_rr.T))
      y = np.zeros(X.shape[0])
      y[-colors_rr.shape[1] :] = 1
  
      return X, y
  
imports: import os
  import numpy
  from .None import get_data_home
  from .None import fetch_sdss_S82standards
  from .tools import download_with_progress_bar
label_desc: Use image node_id 2 for calling a global function with example usage: fetch_rrlyrae_combined(data_home, download_if_missing) and returns: X,  y