# -*- coding: utf-8 -*-
from astropy.io import fits
import numpy as np
import matplotlib.pyplot as plt
import ast


kwdlist_lc=['kMJDAVG', 'kFLUXTOT', 'kCHNRMS']
tvar=[]
tvar_fff=[]
fvar=[]
tvar2=[]
tvar2_fff=[]
fvar=[]
z=0.651
images_b3 = ['/b3/1/J0635-7516.fits', '/b3/2/J0635-7516.fits', '/b3/3/J0635-7516.fits']

for i in images_b3:
 
	AKF(imName=i,kwdlist=kwdlist_lc, include=True)
        	hdul = fits.open(i)

	tvar_f= hdul[0].header['kMJDAVG']
	tvar_ff=float(tvar_f)
	tvar_min=min(tvar)
	tvar_fff.append(float((tvar_ff-tvar_min)/(1+z)))

	fvar_f= hdul[0].header['kFLUXTOT']
	fvar_s= ast.literal_eval(fvar_f)
	fvar_p= fvar_s['I']
	fvar_m= log(fvar_p, 10)
	chnrms= hdul[0].header['kCHNRMS']
	chnrms_s= ast.literal_eval(chnrms)
	chnrms_p= chnrms_s['I']
	efvarb3=5*fvar_p/100 + chnrms_p
	lefvarb3= efvarb3/(fvar_p*log(10))	
	fvar.append(float(fvar_m))
	
images_b6 = ['/b6/1/J0635-7516.fits', '/b6/2/J0635-7516.fits', '/b6/3/J0635-7516.fits']

for l in images_b6:
 
	AKF(imName=l,kwdlist=kwdlist_lc, include=True)
        	hdul = fits.open(i)

	tvar2_f= hdul[0].header['kMJDAVG']
	tvar2_ff=float(tvar2_f)
	tvar2_min=min(tvar2)
	tvar2_fff.append(float((tvar2_ff-tvar2_min)/(1+z)))

	fvar2_f= hdul[0].header['kFLUXTOT']
	fvar2_s= ast.literal_eval(fvar2_f)
	fvar2_p= fvar2_s['I']
	fvar2_m= log(fvar2_p, 10)
	chnrms2= hdul[0].header['kCHNRMS']
	chnrms2_s= ast.literal_eval(chnrms2)
	chnrms2_p= chnrms2_s['I']
	efvarb6=5*fvar2_p/100 + chnrms2_p
	lefvarb6= efvarb6/(fvar2_p*log(10))	
	fvar2.append(float(fvar2_m))
	
plt.figure()
plt.title('J0635-7516')
plt.xlabel('t[days]')
plt.ylabel('Log(S[Jy]')
plt.errorbar( tvar_fff,  fvar, yerr=lefvarb3, label="band 3", fmt="bs", linewidth=3) 	
plt.errorbar( tvar2_fff,  fvar2, yerr=lefvarb6,  label="band 6", fmt="rs", linewidth=3) 
plt.ylim(-0.5,0.5)	
plt.legend()
plt.show()