# value, in order to interpolate a value for a simulated curve follwing the same # sampling pattern as the data simtime = np.array([(xtime - xtime[0]).astype(int),xtime - xtime.astype(int)]) simarray = [[] for h in range(n_bins)] # binned, simulted CCF array first = 1 shows = 0 # for n curves.... for curve in range(ncurves): # -- simulate curve -- lc = lcsim(xflux,xtime,psdindexlow, psdindexhigh, psdbrkfreq, np.mean(xflux),np.std(xflux),curve) # simulate curve # -- interpolate values with data sampling pattern -- lcsamp = [] for val in range(len(simtime[0])): index = simtime[0][val] # index of nearest value in time (before) # interpolate amplitude from nearest two values & gradient between them intval= lc[index] + ( lc[index+1]-lc[index] ) * ( simtime[1][val] ) lcsamp.append(intval) # sampled simulated lightcurve # -- cross correlate with 2nd data set -- sxc, stl, sba = xcor(rflux,rfluxerr, rtime, lcsamp,np.zeros(len(lcsamp)), xtime, n_bins,t_lim,t_range)
# below each xray time and the time difference for n in xtime: simtime[0].append( int(n) - xtime[0] ) simtime[1].append( n - int(n) ) # --- simulate and create array of xcor fns of simulated light curves ---------- simarray = [[] for h in range(n_bins)] first = 1 for curve in range(ncurves): # -- simulate curves, applying the sampling pattern of the data curve -- lc = lcsim(psdindexlow, psdindexhigh, psdbrkfreq,curve)# simulate curve # interpolate values with data sampling pattern lcsamp = [] for val in range(len(simtime[0])): intval= lc[int(simtime[0][val])] + ( lc[int(simtime[0][val]+1)]\ -lc[int(simtime[0][val])] ) * ( simtime[1][val] ) lcsamp.append(intval) # -- cross correlate with second data curve and add to sorted data array sxc, sxce, stl = xcor(rflux, rtime, lcsamp, xtime, tstart, tend, n_bins)
