code: def define_spotsignal(self):
          
          client = kplr.API()
          star = client.star(self.kic)
  
          lcs = star.get_light_curves(short_cadence=False)
          time, flux, ferr, qual = [], [], [], []
          for lc in lcs:
              with lc.open() as f:
                  hdu_data = f[1].data
                  time.append(hdu_data["time"])
                  flux.append(hdu_data["pdcsap_flux"])
                  ferr.append(hdu_data["pdcsap_flux_err"])
                  qual.append(hdu_data["sap_quality"])
              tout = np.array([])
              fout = np.array([])
              eout = np.array([])
              for i in range(len(flux)):
                  t = time[i][qual[i] == 0]
                  f = flux[i][qual[i] == 0]
                  e = ferr[i][qual[i] == 0]
  
                  t = t[np.isfinite(f)]
                  e = e[np.isfinite(f)]
                  f = f[np.isfinite(f)]
  
                  e /= np.median(f)
                  f /= np.median(f)
                  tout = np.append(tout, t[50:]+54833)
                  fout = np.append(fout, f[50:])
                  eout = np.append(eout, e[50:])
  
              self.spot_signal = np.zeros(52)
  
              for i in range(len(self.times)):
                  if self.times[i] < 55000:
                      self.spot_signal[i] = 1.0
                  else:
                      self.spot_signal[i] = fout[np.abs(self.times[i] - tout) == np.min(np.abs(self.times[i] - tout))]
docs: Identify the "expected" flux value at the time of each observation based on the 
          Kepler long-cadence data, to ensure variations observed are not the effects of a single
          large starspot. Only works if the target star was targeted for long or short cadence
          observations during the primary mission.