title: Get pixel coordinates from ra, dec after oversampling FITS image
body: <p>I'm looking for a way to locate the pixel coordinates on my FITS image that correspond to ra and dec positions of an object in degrees, after oversampling. This would be simple if I wasn't oversampling, but I need to. Given an unaltered FITS image, I can do:</p>
  
  <pre><code>from astropy.wcs import WCS
  
  ra, dec = (43.603, 31.029)
  w = WCS('myimage.fits')
  x, y = w.all_world2pix(ra, dec, 1) #this gives me the pixel coordinates of the object at (ra, dec) position
  </code></pre>
  
  <p>However, when I oversample it and THEN try to find the pixel coordinates, it obviously isn't accurate since the (ra, dec) is no longer accurate for the oversampled image. Since I'm oversampling 5x5, I tried simply multiplying my <code>x, y</code> above by 5. But when I zoom in on this point in ds9, it shows the object off-center, so I don't think this is working. Below is my oversampling part of the code, since it may help to see that. Here, <code>data</code> is just the 2D numpy array of the data contained in my original FITS image.</p>
  
  <pre><code>from astropy.nddata import Cutout2D
  import numpy as np
  from scipy import interpolate
  
  def oversample(data_set, N):
      size = 120 #pixel size of my box cutout
      geom_ctr = (np.shape(data_set)[0]//2, np.shape(data_set)[1]//2)
      cutout = Cutout2D(data_set, geom_ctr, size).data
      Y, X = np.shape(cutout)
      x = np.linspace(0, 0.5, X)
      y = np.linspace(0, 0.5, Y)
  
      f = interpolate.interp2d(x, y, cutout, kind='cubic')
      Xnew = np.linspace(0, 0.5, X*N)
      Ynew = np.linspace(0, 0.5, Y*N)
      new_data = f(Xnew, Ynew)
  
      return new_data
  
  resampled_data = oversample(data, 5)
  </code></pre>
  
  <p>If anyone has any ideas on how I could recover the accurate pixel coordinates after oversampling, that would be great. Thank you!</p>