def main(eventname):
alpha,delta,datfiles,oblats,oblongs,obgammas,isspitz,parmref,parmfit,parmflx,errfacs,namefit,nameflx,use_color,I_minus_L = getparm.getEventInfo(eventname)
data = getdat.getalldat(datfiles,errfacs,isspitz,parmref)
tbegin,talert,t0par,t0base,mjd = parmref
### use more intensive time sequence ###
date_mod = np.linspace(6700,7000,2000)
##### choose s and q for sensitivity mapping ###
# ## for ob0124 ##
# ntheta = 300
# qList = np.logspace(np.log10(1e-5),np.log10(1e-2),20)
# sList = np.logspace(-0.6,0.6,30)
# nprocesses = 25
# ## for ob0939 ##
# ntheta = 300
# qList = np.logspace(np.log10(1e-4),np.log10(1e-2),20)
# sList = np.logspace(-0.4,0.4,100)
# nprocesses = 6
# ### pool.apply_async to parallelize ###
# pool = mp.Pool(nprocesses)
# for q in qList:
# for s in sList:
# pool.apply_async(get_q_s_prob,args=(q,s,eventname,[alpha,delta,oblats,oblongs,obgammas],data,[parmfit,namefit,parmflx,nameflx,parmref],isspitz,use_color,I_minus_L,ntheta),callback=mycallback)
# pool.close()
# pool.join()
#################################################
#### for test purpose ###
plot_opt = True
finmod_opt = True
ntheta = 5
qList = [7e-4]
sList = [0.94]
#######################
for q in qList:
for s in sList:
trials = get_q_s_prob(q,s,eventname,[alpha,delta,oblats,oblongs,obgammas],data,[parmfit,namefit,parmflx,nameflx,parmref],isspitz,use_color,I_minus_L,ntheta,outputlc=True,finmod_opt=True,date_mod=date_mod)
mycallback(trials)
return
def main():
eventname = 'ob150448'
# eventname = raw_input('Please give the event name (eg, ob140124): ')
alpha,delta,datfiles,oblats,oblongs,obgammas,isspitz,parmref,parmfit,parmflx,errfacs,namefit,nameflx,use_color,I_minus_L = getparm.getEventInfo(eventname)
data = getdat.getalldat(datfiles,errfacs,isspitz,parmref)
tbegin,talert,t0par,t0base,mjd = parmref
parms_names = [parmfit,namefit,parmflx,nameflx,parmref]
parmfree,namefree,parmfix,namefix = sfitpy.initialization(parms_names,isspitz)
## generating fake data ##
q,s,theta = 1e-6,10,0.
parmbfit = np.hstack([parmfit,[theta,q,s]])
parmbflx = parmflx
# data_fake = []
# 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],True)
# imag = 18-2.5*np.log10(ibinlc[:,2])
# ## save the fake data sets ##
# np.savetxt('fake-data-%d.dat'%(iob+1),np.vstack([date,imag,merr,np.ones_like(date),np.ones_like(date)]).T,fmt='%f')
# data_fake.append([date,ibinlc[:,2],ferr,imag,merr])
# data = data_fake
# ## change (pien,piee) ##
# parmfree[namefree=='pien'] = 0.2
# parmfree[namefree=='piee'] = 1.
# print parmfree
### Downhill simplex method ###
# parmfit,parmflx,chi2s,dof = sfitpy.downhill(sfitpy.lnprob_func,namefree,parmfree,namefix,parmfix,data,[alpha,delta,oblats,oblongs,obgammas],parmref,isspitz,use_color=use_color,I_minus_L=I_minus_L)
### MCMC to minimize the chisq ###
# parmfit,parmflx,chi2s,dof = sfitpy.mcmc(sfitpy.lnprob_func,eventname,namefree,parmfree,namefix,parmfix,data,[alpha,delta,oblats,oblongs,obgammas],parmref,isspitz,use_color=use_color,I_minus_L=I_minus_L)
#### use getchi2 instead of lnprob_func: TBD ###
### Downhill simplex method ###
parmfit,parmflx,chi2s,dof = sfitpy.downhill(sfitpy.getchi2,namefree,parmfree,namefix,parmfix,data,[alpha,delta,oblats,oblongs,obgammas],parmref,isspitz,use_color=use_color,I_minus_L=I_minus_L)
### MCMC to minimize the chisq ###
# parmfit,parmflx,chi2s,dof = sfitpy.mcmc(sfitpy.getchi2,eventname,namefree,parmfree,namefix,parmfix,data,[alpha,delta,oblats,oblongs,obgammas],parmref,isspitz,use_color=use_color,I_minus_L=I_minus_L)
#### Find the model according to the given parameters ###
fmod,mmod = [],[] ## model in flux and magnitude
trajmod = []
fmod_fin,mmod_fin = [],[]
trajmod_fin = []
# parmfit,parmflx = sfitpy.getparmfit(parmfree,parmfix,namefree,namefix)
te = parmfit[namefit=='te']
npts = (7300-6500)*10000./te
npts = min([npts,100000])
tmod = np.linspace(6500,7300,int(npts))
for iob in range(len(data)):
## find model ##
date,flux,ferr = data[iob][:3]
itraj,ifmod = genlc.getslc(alpha,delta,oblats[iob],oblongs[iob],date,parmfit,parmflx[iob],obgammas[iob],t0par,isspitz[iob],False,True)
immod = 18.-2.5*np.log10(ifmod)
fmod.append(ifmod)
mmod.append(immod)
trajmod.append(itraj)
## find a finer light curve ##
date = tmod
itraj,ifmod = genlc.getslc(alpha,delta,oblats[iob],oblongs[iob],date,parmfit,parmflx[iob],obgammas[iob],t0par,isspitz[iob],False,True)
immod = 18.-2.5*np.log10(ifmod)
fmod_fin.append(ifmod)
mmod_fin.append(immod)
trajmod_fin.append(itraj)
# fmod = np.array(fmod)
# mmod = np.array(mmod)
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))]
