start = 1
infile.close()
data = np.array(data)
# ========== Run xcor function on data ==========================================
# Create array of cross-correlation functions for the data sets
xca = [[] for i in range(len(data))]
for x in range(len(data)):
xc, tl, nb = xcor(data[x][1], data[x][0], data[xcord][1], data[xcord][0], n_bins, True, trange)
xca[x].append(tl)
xca[x].append(xc)
# ======= Plots =================================================================
# plot any figures requested by input params
xp = 1
yp = len(data)
fig = plt.figure()
for x in range(len(data)):
start = 0 xrayin= open(xraylocation, 'r') for y in xrayin: currtime, currflux,currfluxerr = y.split() if start == 1: xtime.append(float(currtime)) xflux.append(float(currflux)) start = 1 xrayin.close() # run xcor function on data xc, xce, tl = xcor(rflux, rtime, xflux, xtime, tstart, tend, n_bins) simtime = [[] for q in range(2)] # create array of positions of discrete times # 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
start = 0 for y in xrayin: currtime, currflux,currfluxerr = y.split() if start == 1: xtime.append(float(currtime)) xflux.append(float(currflux)) start = 1 xrayin.close() # ========== Run xcor function on data ========================================== # Create cross-correlation function for teh real data sets xc, tl = xcor(rflux, rtime, xflux, xtime, n_bins) # ========= Simulate Light Curves =============================================== # create artificial lighturves, interpolate curve with same sampling pattern # as data, cross-correlate with one real data set # --- simulated curve time arrays --- # create array of integer times from X-ray timing values from the start of the # observations, to correspond to the integer time sampling in the simulated # lightcurve, and the difference in time between the integer value and the actual # value, in order to interpolate a value for a simulated curve follwing the same # sampling pattern as the data simtime = [[] for q in range(2)] for n in xtime:
start = 0 for y in xrayin: currtime, currflux,currfluxerr = y.split() if start == 1: xtime = np.append(xtime, float(currtime)) xflux = np.append(xflux, float(currflux)) start = 1 xrayin.close() # ========== Run xcor function on data ========================================== # Create cross-correlation function for teh real data sets xc, tl, bav = xcor(rflux, rtime, xflux, xtime, n_bins,t_lim,t_range) print "average bin count in cross-correlation:",bav # ========= Simulate Light Curves =============================================== # create artificial lighturves, interpolate curve with same sampling pattern # as data, cross-correlate with one real data set # --- simulated curve time arrays --- # create array of integer times from X-ray timing values from the start of the # observations, to correspond to the integer time sampling in the simulated # lightcurve, and the difference in time between the integer value and the actual # 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)])
