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
trajmod = np.array(trajmod)
trajmod_fin = np.array(trajmod_fin)
print 'Total chisq = ',chi2s[0]
print ['%.2f/%d'%(chi2s[i+1],dof[i]) for i in range(len(dof))]
### start plotting ###
fig = plt.figure(figsize=(8.5,11))
ax = plt.subplot(211)
ax_traj = plt.subplot(212)
ax.set_title(eventname)
# colorstr = ['k','r','g','m']
colorstr = cm.rainbow(np.linspace(0,1,len(data)))
ground_mod = True
for iob in range(len(data)):
## plot data ##
date,flux,ferr = data[iob][:3]
imag,imerr = getdat.convert_to_ogle_dat(flux,ferr,parmflx[iob],parmflx[0],use_mag=True)
ax.errorbar(date,imag,yerr=imerr,marker='o',linestyle='none',color=colorstr[iob],markeredgecolor=colorstr[iob],markerfacecolor='none')
## plot model ##
imod = getdat.convert_to_ogle_mod(fmod[iob],parmflx[iob],parmflx[0],use_mag=True)
imod_fin = getdat.convert_to_ogle_mod(fmod_fin[iob],parmflx[iob],parmflx[0],use_mag=True)
np.savetxt('output/%s-%d.dat'%(eventname,iob+1),np.vstack([date,trajmod[iob].T,imag,imerr,imod]).T,fmt='%f')
np.savetxt('output/%s-%d-fin.dat'%(eventname,iob+1),np.vstack([tmod,trajmod_fin[iob].T,imod_fin]).T,fmt='%f')
if isspitz[iob]==False and ground_mod==False:
continue
ax.plot(tmod,imod_fin,color=colorstr[iob])
ax_traj.plot(trajmod[iob][:,0],trajmod[iob][:,1],marker='o',color=colorstr[iob])
ax_traj.plot(trajmod_fin[iob][:,0],trajmod_fin[iob][:,1],color=colorstr[iob])
ground_mod = False
ax.invert_yaxis()
ax.set_xlabel('HJD')
ax.set_ylabel('I (OGLE)')