#%matplotlib inline

#import matplotlib.pyplot as plt
import numpy as np
#from astroML.time_series import lomb_scargle
#from astroML.time_series import multiterm_periodogram
#from astroML.time_series import generate_power_law
#from astroML.time_series import ACF_scargle , ACF_EK


from sim_poisson_seq import poisson_seq

#n_lc = 1000
n_start = 0
n_stop = 10
#total = 10000
total = 3000
mean_rate = 0.15
Efake = 0.1

#for i in range(0,n_lc):
for i in range(n_start,n_stop):
	t_seq = poisson_seq(total,mean_rate)
	
	#fig = plt.figure(figsize=(10, 4))
	#ax = fig.add_subplot(111)
	
	nbins = 200
	weight_seq=np.zeros(total)
	print "Duration comparison: ",t_seq[-1]-t_seq[0],total/mean_rate
	weight_seq[:]=float((nbins)/(t_seq[-1]-t_seq[0]))
	
	#ax.hist(t_seq, bins=nbins, weights=weight_seq,histtype='stepfilled', alpha=0.2, normed=False,label="Standard histogram")
	#ax.plot(t_seq,1./beta+2.*Asine*np.sin(2*np.pi/Tsine*t_seq))
	
	fname = 'simPoissonTTE_'+str(total)+'sample_rate'+str(mean_rate)+'_withE'+str(Efake)+'trial'+str(i)+'.txt'
	f = open(fname, 'w')
	for x in t_seq:
	  f.write(str(x)+'\t'+str(Efake)+'\n')
	
	f.close()

#plt.show()
