docs: def orbit_fit_residuals(discoveries, blockID='O13AE'):
      """Brett: What are relevant and stunning are the actual orbit fit residuals
      for orbits with d(a)/a<1%. I would be happy with a simple histogram of all those numbers,
      although the 'tail' (probably blended and not yet completely flagged) should
      perhaps be excluded.  There will be a pretty clear gaussian I would think, peaking near 0.07".
      """
      ra_residuals = []
      dec_residuals = []
      ressum = []
      for i, orbit in enumerate(discoveries):
          if (orbit.da / orbit.a) < 0.01:
              print i, len(discoveries), orbit.observations[0].provisional_name, orbit.da / orbit.a
              res = orbit.residuals.split('\n')
              for r in res:
                  rr = r.split(' ')
                  if (len(rr) > 1) and not (rr[0].startswith('!')):
                      ra_residuals.append(float(r.split(' ')[4]))
                      dec_residuals.append(float(r.split(' ')[5]))
                      ressum.append(1000 * (float(r.split(' ')[4]) ** 2 + float(r.split(' ')[5]) ** 2))
  
      plt.figure(figsize=(6, 6))
      bins = [r for r in range(-25, 150, 25)]
      n, bins, patches = plt.hist(ressum, histtype='step', color='b', bins=bins, label='$dra^{2}+ddec^{2}$')
      # midpoints = [r/1000. for r in range(-350,350,50)]
      # popt, pcov = curve_fit(gaussian, midpoints, n)
      # print 'a', popt[0], 'mu', popt[1], 'sigma', popt[2]
      # xm = np.linspace(-.375, .375, 100)  # 100 evenly spaced points
      # plt.plot(xm, gaussian(xm, popt[0], popt[1], popt[2]), ls='--', color='#E47833', linewidth=2)
      # sigma = r'$\sigma = %.2f \pm %.2f$' % (popt[2], np.sqrt(pcov[2, 2]))
      # plt.annotate(sigma, (0.12, 50), color='#E47833')
      plt.xlim((-25, 150))  # sufficient for 13AE data
      plt.ylabel('observations of orbits with d(a)/a < 1% (i.e 3-5 month arcs)')
      plt.xlabel('orbit fit residuals (milliarcsec)')
      plt.legend()
      # clean_legend()
      plt.draw()
      outfile = 'orbit_fit_residuals_13AE_corrected'
      plt.savefig('/ossos_sequence/'+ outfile + '.pdf', transparent=True)
  
      return