def get_q_s_prob(q,s,eventname,eventinfo,data,parms_names,isspitz,use_color,I_minus_L,ntheta,outputlc=False,finmod_opt=False,date_mod=[]):
alpha,delta,oblats,oblongs,obgammas = eventinfo
parmfit,namefit,parmflx,nameflx,parmref = parms_names
tbegin,talert,t0par,t0base,mjd = parmref
thetas = np.linspace(0,360,ntheta+1)
binlcs,binfull = [],[]
bincau = []
use_fsfb = True
start = time.clock()
############################
### fake data generation ###
############################
print '----- Generating binary lcs... ------'
for i in range(ntheta):
itheta = thetas[i]
parmbfit = np.hstack([parmfit,[itheta,q,s]])
parmbflx = parmflx
iobbinlc,iobfull = [],[]
for iob in range(len(data)):
date,flux,ferr,mag,merr = data[iob]
ibinlc,xcau,ycau = genlc.getbinlc(alpha,delta,oblats[iob],oblongs[iob],date,parmbfit,parmbflx[iob],obgammas[iob],t0par,isspitz[iob],use_fsfb,caustic=bincau)
bincau = [xcau,ycau]
iobbinlc.append([date,ibinlc[:,2],ferr])
iobfull.append([date,ibinlc[:,0],ibinlc[:,1],ibinlc[:,2],ferr])
### find the fine input model ###
if finmod_opt == True:
ibinlc_mod,xcau,ycau = genlc.getbinlc(alpha,delta,oblats[iob],oblongs[iob],date_mod,parmbfit,parmbflx[iob],obgammas[iob],t0par,isspitz[iob],use_fsfb,caustic=bincau)
np.savetxt('output/models/finmod-%s-%.5f-%.2f-%03d-%d.dat'%(eventname,q,s,itheta,iob+1),np.vstack([date_mod,ibinlc_mod.T]).T,fmt='%f')
binlcs.append(iobbinlc)
binfull.append(iobfull)
np.savetxt('output/caustics/caustic-%s-%.5f-%.2f.dat'%(eventname,q,s),np.array(bincau).T,fmt='%f')
print '------ Binary lcs generated: ',time.clock()-start,' s ------'
##################################
### employ sfitpy to fake data ###
##################################
if talert > max(data[0][0]): ## obj event
print '--- This is an obj event ---'
trials = sfitpy.massfit_obj(q,s,thetas,binlcs,binfull,eventinfo,parms_names,isspitz,use_color,I_minus_L,outputlc)
else: ## subj event
print '--- This is a subj event ---'
trials = sfitpy.massfit_sub(q,s,thetas,binlcs,binfull,eventinfo,parms_names,isspitz,use_color,I_minus_L,outputlc)
return trials
def main():
eventname = 'ob140939'
### find event information, input data, program setup ###
alpha,delta,datfiles,oblat,oblong,obgamma = getparm.getEventInfo(eventname)
parmref,parmfit,parmflx,efacs = getparm.getFitSetting(eventname)
t0par = parmref[0]
t0base= parmref[1]
HMJD = parmref[2]
nobs = len(datfiles)
parmfit[0] += t0base
print 'Total number of obs: ',nobs
print 'Parameters (ref): ',parmref
print 'Parameters (sfit): ',parmfit
print 'Parameters (flux): ',parmflx
print 'Errfacs: ',efacs
# isspit = np.zeros(nobs).astype(bool)
isspit = np.zeros(nobs-1).astype(bool)
isspit = np.hstack([isspit,True])
linestrs = ['-','--']
date,flux,ferr,magr,merr = getdat.getdat1(nobs,datfiles,efacs,isspit,HMJD)
#############################################################
### use more intensive time sequence ###
date_mod = np.linspace(6800,6900,1000)
#### choose s and q for sensitivity mapping ###
# plot_opt = False
# finmod_opt = False
# ntheta = 300
# qList = np.logspace(np.log10(1e-4),np.log10(1e-2),50)
# sList = np.logspace(np.log10(0.1),np.log10(10),50)
# ### five parallel programs ###
# ncore = 6
# each = 50/ncore
## qList = qList[:each]
## qList = qList[each:2*each]
## qList = qList[2*each:3*each]
## qList = qList[3*each:4*each]
## qList = qList[4*each:5*each]
# qList = qList[5*each:]
#################################################
### for test purpose ###
plot_opt = True
finmod_opt = True
ntheta = 6
qList = [1e-2]
sList = [1.1]
########################
for q in qList:
trials = []
for s in sList:
theta = np.linspace(0,360,ntheta+1)
xtrajs,ytrajs,binlcs = [],[],[]
binlcs_mod = []
use_fsfb = True
start = time.clock()
############################
### fake data generation ###
############################
for i in range(ntheta):
itheta = theta[i]
parmbfit = np.hstack([parmfit,[itheta,q,s]])
parmbflx = parmflx
iobxtraj,iobytraj,iobbinlc = [],[],[]
iobbinlc_mod = []
for iob in range(nobs):
ibinlc,xcau,ycau = genlc.getbinlc(alpha,delta,oblat[iob],oblong[iob],date[iob],parmbfit,parmbflx[iob],obgamma[iob],t0par,isspit[iob],use_fsfb)
iobxtraj.append(ibinlc[:,0])
iobytraj.append(ibinlc[:,1])
iobbinlc.append(ibinlc[:,2])
### find the fine input model ###
if finmod_opt == True:
ibinlc_mod,xcau,ycau = genlc.getbinlc(alpha,delta,oblat[iob],oblong[iob],date_mod,parmbfit,parmbflx[iob],obgamma[iob],t0par,isspit[iob],use_fsfb)
iobbinlc_mod.append(ibinlc_mod[:,2])
xtrajs.append(iobxtraj)
ytrajs.append(iobytraj)
binlcs.append(iobbinlc)
binlcs_mod.append(iobbinlc_mod)
xtrajs = np.array(xtrajs)
ytrajs = np.array(ytrajs)
binlcs = np.array(binlcs)
print 'binary lcs generated: ',time.clock()-start
########################################################
################################
### employ sfit to fake data ###
################################
########################################################
sinlcs,chi2all = [],[]
parmfit0 = parmfit
parmflx0 = parmflx
for i in range(ntheta):
itheta = theta[i]
########################################
############# SFITPY MODULE ############
########################################
### if sfit.getchi2 is used, the plotted
### light curve will not be right, beca-
### -use fs, fb are still the input vals
########################################
######## Downhill minimization only ###
iparmfit,iparmflx,chi2 = sfit.downhill(sfit.lnprob_func,parmfit,parmflx,[date,binlcs[i],ferr],nobs,[alpha,delta,oblat,oblong,obgamma],t0par,isspit)
# iparmfit,iparmflx,chi2 = sfit.downhill(sfit.getchi2,parmfit,parmflx,[date,binlcs[i],ferr],nobs,[alpha,delta,oblat,oblong,obgamma],t0par,isspit)
######## MCMC minimization only ###
# iparmfit,iparmflx,chi2 = sfit.mcmcfit(sfit.getchi2,parmfit,parmflx,[date,binlcs[i],ferr],nobs,[alpha,delta,oblat,oblong,obgamma],t0par,isspit)
######## Downhill + MCMC ####
# iparmfit,iparmflx,chi2 = sfit.downhill(sfit.getchi2,parmfit,parmflx,[date,binlcs[i],ferr],nobs,[alpha,delta,oblat,oblong,obgamma],t0par,isspit)
# if chi2[0] > 200:
# iparmfit,iparmflx,chi2 = sfit.mcmcfit(sfit.getchi2,iparmfit,iparmflx,[date,binlcs[i],ferr],nobs,[alpha,delta,oblat,oblong,obgamma],t0par,isspit)
########################################
if chi2[0] < 100:
parmfit0 = iparmfit
parmflx0 = iparmflx
### find best-fit model ###
isinlc = []
for iob in range(nobs):
itraj,ifmod = genlc.getslc(alpha,delta,oblat[iob],oblong[iob],date_mod,iparmfit,iparmflx[iob],obgamma[iob],t0par,isspit[iob],False,True)
isinlc.append(ifmod)
###########################
temp = np.hstack([q,s,theta[i],chi2.reshape(-1),iparmfit,iparmflx.reshape(-1)])
trials.append(temp)
sinlcs.append(isinlc)
chi2all.append(chi2[0])
print '##########',q,s,theta[i],chi2[0],'############'
sinlcs = np.array(sinlcs)
############################################################
###############################
### fake data demonstration ###
###############################
if plot_opt == True:
color1 = ['#99d8c9','#66c2a4','#41ae76','#238b45','#005824'] #green
color2 = ['#fdbb84','#fc8d59','#ef6548','#d7301f','#990000'] #red
color3 = ['#a6bddb','#74a9cf','#3690c0','#0570b0','#034e7b'] #blue
color4 = ['#bdbdbd','#969696','#737373','#525252','#252525'] #black
colors = np.array([color1,color2,color3,color4]).reshape(-1)
fig1 = plt.figure(figsize=(8.5,11))
nax1 = ntheta/2
axs = []
fig2 = plt.figure(figsize=(8.6,8))
ax0 = fig2.add_subplot(111) # axis to plot the trajectories
ax0.plot(xcau,ycau,linestyle='none',color='k',marker='o',markersize=1)
ax0.axis('equal')
for i in range(ntheta):
itheta = theta[i]
axi = fig1.add_subplot(nax1,2,i+1)
axs.append(axi)
for iob in range(nobs):
### plot fake data ###
# iflux,iferr = binlcs[i][iob],ferr[iob] #do not convert to OGLE system
iflux,iferr = getdat.convert_to_ogle_dat(binlcs[i][iob],ferr[iob],parmflx[iob],parmflx[0]) #convert to OGLE system
axi.errorbar(date[iob],iflux,yerr=iferr,color=colors[i],linestyle='none',marker='o')
if finmod_opt == True:
# ifmod = binlcs_mod[i][iob] #do not convert to OGLE system
ifmod = getdat.convert_to_ogle_mod(binlcs_mod[i][iob],parmflx[iob],parmflx[0]) #convert to OGLE system
axi.plot(date_mod,ifmod,color=colors[i],linestyle='--')
### plot best-fit smod ###
# ifmod = sinlcs[i][iob] #do not convert to OGLE system
ifmod = getdat.convert_to_ogle_mod(sinlcs[i][iob],parmflx[iob],parmflx[0]) #convert to OGLE system
axi.plot(date_mod,ifmod,color=colors[i])
### plot trajectory ###
ax0.plot(xtrajs[i][iob],ytrajs[i][iob],color=colors[i],linestyle=linestrs[iob])
axi.set_xlim(6800,6900)
x1,x2,y1,y2 = axs[i].axis()
xpos = x1+(x2-x1)/10.
ypos = y2-(y2-y1)/10.
axi.text(xpos,ypos,r'$\theta=%3.1f^\circ, \chi^2=%3.1f$'%(itheta,chi2all[i]),va='center')
axi.invert_yaxis()
plt.show()
### save parameters and chi2 ###
np.savetxt('output/grid-%s-%e.dat'%(eventname,q),np.array(trials),fmt='%f')
return
def main():
## Event Information ##
eventname = 'ob151346'
alpha = (17. + 59/60. + 13.67/3600.)*15
delta =-(28. + 56/60. + 11.8/3600.)
fs,fb = 0.07
Gamma = 0.60
## OGLE site ##
qlong = -70.702
qlat = -29.0083
## planet/binary parameters ##
t0 = 7190.74
u0 = 0.18
te = 25.12
rho = 0.001
theta = 80. # in degrees
q = 0.0014
s = 1.116
Ibase = 19.4027
fb_over_fs = -0.4134
# ## Binary Parameters for ob150914 ##
# t0 = 7172.14
# u0 = 0.0243
# te = 36.8418
# rho = 0.0047
# theta = 3.81532/np.pi*180
# theta = 180-theta ## Bozza system
# q = 0.369
# s = 1.4718
# ## Bozza gave Ibase and Fb/Fs ##
# Ibase = 14.6422
# fb_over_fs = 18.445
# ## binary event ob150060 ##
# t0 = 7148.109
# u0 = 0.019
# te = 71.8
# rho = 0.00479
# theta = 3.593/np.pi*180
# q = 1.10
# s = 3.40
# Ibase = 16.6885
# fb_over_fs = 0.3337
# ## Planetary Event OB150051 ##
# t0 = 7083.100
# u0 = 0.221
# te = 10.95
# rho = 0.045
# theta = 5.351*180/np.pi #Han
# q = 5.94e-3
# s = 0.939
# Ibase = 16.629
# fb_over_fs = -0.0216
###########
fb_plus_fs = 10**(0.4*(18-Ibase))
fs = fb_plus_fs/(1+fb_over_fs)
fb = fb_over_fs*fs
print fs,fb
###########
fig = plt.figure(figsize=(8.5,11))
ax1 = plt.subplot(311)
ax2 = plt.subplot(312)
ax3 = plt.subplot(313)
linestr = ['-','--','-.']
# colors = ['k','#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','0.5']
# pies = [-0.1,0,0.1]
pieNs = [0.0193]
pieEs = [-0.2949]
colors = cm.rainbow(np.linspace(0,1,len(pieEs)*len(pieNs)))
nmod = 0
# ogle = np.loadtxt('data/ob150966.dat')
# dates = ogle[:,0]-2450000
dates = np.linspace(7100,7250,15000)
for pieE in pieEs:
for pieN in pieNs:
piex = pieN
piey = pieE
parmfit = [t0,u0,te,rho,piex,piey,theta,q,s]
parmflx = [fs,fb]
t0par = t0
lcg,xcau,ycau = genlc.getbinlc(alpha,delta,qlat,qlong,dates,parmfit,parmflx,Gamma,t0par,False,True)
lcs,xcau,ycau = genlc.getbinlc(alpha,delta,qlat,qlong,dates,parmfit,parmflx,Gamma,t0par,True,True)
magg = 18-2.5*np.log10(lcg[:,2])
mags = 18-2.5*np.log10(lcs[:,2])
if nmod == 0:
ax1.plot(dates,magg,color='b',linestyle='--',label='OGLE',lw=2)
# np.savetxt('ob150966-Bozza.mod',np.vstack([dates,magg]).T,fmt='%f')
ax2.plot(dates,magg,color='b',linestyle='--',label='OGLE',lw=2)
ax3.plot(xcau,ycau,marker='.',markersize=2,linestyle='none',color='k')
ax3.plot(lcg[:,0],lcg[:,1],color='b')
ax1.plot(dates,mags,color=colors[nmod],label='Spitzer,(%.1f,%.1f)'%(pieE,pieN))
ax2.plot(dates,mags,color=colors[nmod],label='Spitzer,(%.1f,%.1f)'%(pieE,pieN))
ax3.plot(lcs[:,0],lcs[:,1],color=colors[nmod])
nmod += 1
ax3.axis('equal')
plt.suptitle(eventname,fontsize=14,fontweight='bold')
ax1.legend(loc=0,fontsize=10)
ax1.invert_yaxis()
ax2.invert_yaxis()
ax2.axis([7143,7156,15.3,11.7])
ax1.yaxis.set_minor_locator(AutoMinorLocator(5))
ax1.xaxis.set_minor_locator(AutoMinorLocator(5))
ax2.yaxis.set_minor_locator(AutoMinorLocator(5))
ax2.xaxis.set_minor_locator(AutoMinorLocator(5))
ax3.yaxis.set_minor_locator(AutoMinorLocator(5))
ax3.xaxis.set_minor_locator(AutoMinorLocator(5))
plt.show()