def fitLag(linfile,
linfchain,
conthpd,
names=None,
lagrange=[50, 300],
lagbinsize=1,
threads=1,
set_plot=False,
nwalkers=100,
nburn=100,
nchain=100,
mode="test"):
""" fit the Rmap model.
"""
if mode == "run":
linfile = ".".join([linfile, "myrun"])
zydata = get_data(linfile, names=names)
rmap = Rmap_Model(zydata)
if mode == "test":
if zydata.nlc == 2:
print(rmap([np.log(sigma), np.log(tau), lagy, widy, scaley]))
elif zydata.nlc == 3:
print(
rmap([
np.log(sigma),
np.log(tau), lagy, widy, scaley, lagz, widz, scalez
]))
return (None)
elif mode == "show":
rmap.load_chain(linfchain, set_verbose=False)
elif mode == "run":
laglimit = [
[0.0, tau],
] * (zydata.nlc - 1)
print(laglimit)
# laglimit = "baseline"
linfchain = ".".join([linfchain, "myrun"])
rmap.do_mcmc(conthpd=conthpd,
lagtobaseline=0.5,
laglimit=laglimit,
nwalkers=nwalkers,
nburn=nburn,
nchain=nchain,
fburn=None,
fchain=linfchain,
threads=threads)
if set_plot:
rmap.break_chain([
lagrange,
] * (zydata.nlc - 1))
rmap.get_hpd()
if zydata.nlc == 2:
figout = "mcmc1"
else:
figout = "mcmc2"
rmap.show_hist(bins=100,
lagbinsize=lagbinsize,
figout=figout,
figext=figext)
return (rmap.hpd)
def fitLag(linfile, linfchain, conthpd, names=None, lagrange=[50, 300], lagbinsize=1, threads=1, set_plot=False, nwalkers=100, nburn=50, nchain=50, mode="test") :
""" fit the Rmap model.
"""
if mode == "run" :
linfile = ".".join([linfile, "myrun"])
zydata = get_data(linfile, names=names)
rmap = Rmap_Model(zydata)
if mode == "test" :
if zydata.nlc == 2 :
print(rmap([np.log(2.), np.log(100), lagy, widy, scaley ]))
elif zydata.nlc == 3 :
print(rmap([np.log(2.), np.log(100), lagy, widy, scaley, lagz, widz, scalez]))
return(None)
elif mode == "show" :
rmap.load_chain(linfchain, set_verbose=False)
elif mode == "run" :
laglimit = [[0.0, 400.0],]*(zydata.nlc-1)
print(laglimit)
# laglimit = "baseline"
linfchain = ".".join([linfchain, "myrun"])
rmap.do_mcmc(conthpd=conthpd, lagtobaseline=0.5, laglimit=laglimit,
nwalkers=nwalkers, nburn=nburn, nchain=nchain,
fburn=None, fchain=linfchain, threads=threads)
if set_plot :
rmap.break_chain([lagrange,]*(zydata.nlc-1))
rmap.get_hpd()
if zydata.nlc == 2 :
figout = "mcmc1"
else :
figout = "mcmc2"
rmap.show_hist(bins=100, lagbinsize=lagbinsize, figout=figout, figext=figext)
return(rmap.hpd)
def computeLag(self, string, dirName, fileName, results, i):
# Creating light curve using the LightCurve class
LC = LightCurve.LightCurve(self.length) # Initialising light curve
LC.LCdir = dirName
LC.genContMag(1.0, self.mag, self.tau, self.sf, i) # Preparing necessary qualities
LC.genCont() # Creating continuum light curve
LC.genLineTH(self.tLag) # Creating emission line light curve
LC.printFunction(LC.length, LC.t, LC.cont, LC.line, string, i) # Printing light curves to file
# Converting generated light curves into files accepted by Javelin
self.convertToJavelin(string, dirName, i)
print 'Finished creating LC, over to the Javelin part of the code \n'
# Finding the continuum and emission line files for Javelin to use
contFile = dirName + string + "Cont" + str(i) + ".dat"
emissionFile = dirName + string + "Emission" + str(i) + ".dat"
chainFile = dirName + "Chains/MyChain" + string + str(i) + ".dat"
logPFile = dirName + "Chains/LogP" + string + str(i) + ".dat"
# Fitting continuum data according to Javelin description on their websites
c = get_data([contFile], names=["Continuum"])
cmod = Cont_Model(c)
cmod.do_mcmc(nwalkers=100)
# Fitting the continuum and emission line data according to Javelin description
# Rmap_Model is the spectroscopic reverberation mapping model
if not os.path.exists(dirName+"Chains"): # Check whether directory exists
os.makedirs(dirName+"Chains")
# End if-statement
cy = get_data([contFile, emissionFile], names=["Continuum","EmissionLine"])
cymod = Rmap_Model(cy)
cymod.do_mcmc(conthpd=cmod.hpd, nwalkers=100, nburn=500, nchain=1000, laglimit=[[self.lLag,self.uLag]],
lagtobaseline=1.0, fchain=chainFile, flogp=logPFile)
# Extracting results from Javelin
cymod.get_hpd() # Obtain Highest Posterior Density (HPD) intervals
savedHPD = cymod.hpd # 3x2 array w/18%, 50% and 84% values fro log sigma and log tau
lagVals = savedHPD[:,2] # Extracting the estimated value of the lag
if (results != '0'):
# Printing results from run to file
results[i,:] = np.array([self.tLag, lagVals[1], lagVals[0], lagVals[2]])[np.newaxis]
np.savetxt(fileName, results[results[:, 0] > 0])
x = cont_best.jlist[:]
y = cont_best.mlist[:] + cont_best.blist[:]
ey = cont_best.elist[:]
plt.plot(x[0], y[0] + javdata_con.cont_mean)
plt.fill_between(x[0],
y[0] - ey[0] + javdata_con.cont_mean,
y[0] + ey[0] + javdata_con.cont_mean,
where=None,
color='grey')
x = javdata_con.jlist[:]
y = javdata_con.mlist[:] + javdata_con.blist[:]
ey = javdata_con.elist[:]
plt.errorbar(x[0],
y[0] + javdata_con.cont_mean,
yerr=ey[0],
ls='none',
marker='o')
plt.show()
rmap1 = Rmap_Model(javdata_rm)
#rmap1.do_mcmc(conthpd=conthpd, fchain="mychain1.dat", laglimit=[[0, 10],], nwalkers=100, nburn=200, nchain=1000)
rmap1.load_chain("mychain1.dat")
rmap1.show_hist()
rmap1.get_hpd()
rmap1hpd = rmap1.hpd
par_best = rmap1hpd[1, :]
print(par_best)
javdata_best = rmap1.do_pred(par_best)
javdata_best.plot(set_pred=True, obs=javdata_rm)
