def showfit(linhpd,
linfile,
names=None,
set_plot=False,
mode="test",
model='Rmap'):
if mode == "run":
linfile = ".".join([linfile, "myrun"])
print linfile
zydata = get_data(linfile, names=names)
if model == 'Rmap' or model == 'Rmap2':
rmap = Rmap_Model(zydata)
elif model == 'Pmap':
rmap = Pmap_Model(zydata)
elif model == 'DPmap':
rmap = DPmap_Model(zydata)
if mode == "test":
return (None)
else:
zypred = rmap.do_pred(linhpd[1, :])
zypred.names = names
print names
if set_plot:
zypred.plot(set_pred=True,
obs=zydata,
figout="prediction_" + model,
figext=figext)
def showfit(linhpd, linfile, names=None, set_plot=False, mode="test") :
if mode == "run" :
linfile = ".".join([linfile, "myrun"])
print (linfile)
zydata = get_data(linfile, names=names)
rmap = Rmap_Model(zydata)
if mode == "test" :
return(None)
else :
zypred = rmap.do_pred(linhpd[1,:])
zypred.names = names
if set_plot :
zypred.plot(set_pred=True, obs=zydata, figout="prediction", figext=figext)
def obtainlag(file_con, file_line):
# Load and fit continuum data
c = get_data([file_con])
cmod = Cont_Model(c)
cmod.do_mcmc(threads=4)
# Load and fit continuum and line data
cy = get_data([file_con, file_line])
cymod = Rmap_Model(cy)
# Get time lag
data_out = "cont-" + file_line.split(".")[0] + ".txt"
cymod.do_mcmc(conthpd=cmod.hpd, threads=4, fchain=data_out)
def showfit(linhpd, linfile, names=None, set_plot=False, mode="test") :
if mode == "run" :
linfile = ".".join([linfile, "myrun"])
print linfile
zydata = get_data(linfile, names=names)
rmap = Rmap_Model(zydata)
if mode == "test" :
return(None)
else :
zypred = rmap.do_pred(linhpd[1,:])
zypred.names = names
if set_plot :
zypred.plot(set_pred=True, obs=zydata, figout="prediction", figext=figext)
def rm_single(rmid, nwalker, nchain, nburn, min_lag, max_lag, fig_out):
# Input and output data position and name
file_con = Location.project_loca + "result/light_curve/" + str(rmid) + \
"/cont.txt"
file_hbeta = Location.project_loca + "result/light_curve/" + str(rmid) + \
"/Hbetab.txt"
lc_plot = Location.project_loca + "result/light_curve/" + str(rmid) + \
"/lightcurve"
data_out = Location.project_loca + "result/light_curve/" + str(rmid) + \
"/cont-hbeta.txt"
last_mcmc = Location.project_loca + "result/light_curve/" + str(rmid) + \
"/last_mcmc"
# Fit continuum
c = get_data([file_con])
cmod = Cont_Model(c)
cmod.do_mcmc(threads=100, nwalkers=nwalker, nchain=nchain, nburn=nburn)
# Do mcmc
cy = get_data([file_con, file_hbeta], names=["Continuum", "Hbeta"])
cy.plot(figout=lc_plot, figext="png")
cymod = Rmap_Model(cy)
cymod.do_mcmc(conthpd=cmod.hpd,
threads=100,
fchain=data_out,
nwalkers=nwalker,
nchain=2.0 * nchain,
nburn=2.0 * nburn,
laglimit=[[min_lag, max_lag]])
# Output mcmc result
cymod.show_hist(figout=fig_out, figext="png")
cypred = cymod.do_pred()
cypred.plot(set_pred=True, obs=cy, figout=last_mcmc, figext="png")
return [cymod.hpd[0][2], cymod.hpd[1][2], cymod.hpd[2][2]]
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])
def rm_single(rmid, nwalker, nchain, nburn, min_lag, max_lag, fig_out):
# Input and output data position and name
file_con = Location.project_loca + "result/light_curve/" + str(rmid) + \
"/cont.txt"
file_hbeta = Location.project_loca + "result/light_curve/" + str(rmid) + \
"/Hbetab.txt"
lc_plot = Location.project_loca + "result/light_curve/" + str(rmid) + \
"/lightcurve"
data_out = Location.project_loca + "result/light_curve/" + str(rmid) + \
"/cont-hbeta.txt"
last_mcmc = Location.project_loca + "result/light_curve/" + str(rmid) + \
"/last_mcmc"
# Fit continuum
c = get_data([file_con])
cmod = Cont_Model(c)
cmod.do_mcmc(threads=100, nwalkers=nwalker, nchain=nchain, nburn=nburn)
# Do mcmc
cy = get_data([file_con, file_hbeta], names=["Continuum", "Hbeta"])
cy.plot(figout=lc_plot, figext="png")
cymod = Rmap_Model(cy)
cymod.do_mcmc(conthpd=cmod.hpd, threads=100, fchain=data_out,
nwalkers=nwalker, nchain=2.0 * nchain, nburn=2.0 * nburn,
laglimit=[[min_lag, max_lag]])
# Output mcmc result
cymod.show_hist(figout=fig_out, figext="png")
cypred = cymod.do_pred()
cypred.plot(set_pred=True, obs=cy, figout=last_mcmc, figext="png")
return [cymod.hpd[0][2], cymod.hpd[1][2], cymod.hpd[2][2]]
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)
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)
from javelin.zylc import get_data
from javelin.lcmodel import Cont_Model
from javelin.lcmodel import Rmap_Model
# for i in xrange(50):
for i in xrange(1):
c = get_data(["dat/continuum.dat"])
cmod = Cont_Model(c)
cmod.do_mcmc(nwalkers=200, nburn=100, nchain=200, threads=1)
a = cmod.get_hpd()
javdata4 = get_data(["dat/continuum.dat", "dat/yelm.dat", "dat/zing.dat"],
names=["Continuum", "Yelm", "Zing"])
rmap2 = Rmap_Model(javdata4)
rmap2.do_mcmc(conthpd=a, nwalkers=200, nburn=100, nchain=200, threads=1)
del rmap2
del c
print i
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)
from javelin.zylc import get_data
from javelin.lcmodel import Cont_Model
from javelin.lcmodel import Rmap_Model
import numpy as np
c = get_data(["dat/continuum.dat"])
cmod = Cont_Model(c)
cmod.do_mcmc(nwalkers=100, nburn=100, nchain=100, threads=1)
cmod.show_hist()
print cmod.hpd
javdata4 = get_data(["dat/continuum.dat", "dat/yelm.dat"],
names=["Continuum", "Yelm"])
rmap2 = Rmap_Model(javdata4)
rmap2.do_mcmc(conthpd=cmod.hpd,
nwalkers=100,
nburn=100,
nchain=100,
threads=1,
laglimit=[
[10, 200],
],
fixed=[1, 0, 1, 0, 1],
p_fix=[np.log(0.1), np.log(400), 10, 2.0, 5.0])
rmap2.show_hist()
print rmap2.hpd
def do_javelin(ra, dec):
#ra and dec are converted to 6 decimals format
ras = "%.6f" % ra
decs = "%.6f" % dec
print "########## computing Javelin for source located at ra=%f and dec=%f ##########" % (
ra, dec)
try:
#the diver lc is loaded, we take into account the different formats used for opt and NIR data.
if driving_filter != 'Q':
agn_driving = lc_path + driving_filter + '/agn_' + str(
ras) + '_' + str(decs) + '_' + driving_filter + '.fits'
arch_driving = pf.open(agn_driving)
jd_0 = 55000
head_driving = arch_driving[0].header
datos_driving = arch_driving[1].data
jd_driving = datos_driving['JD'] - jd_0
flux_driving = datos_driving[
'FLUX_2'] * 1e27 #the flux value is multiplicated by 1e27 to avoid numerical errors produced by small numbers
errflux_driving = datos_driving['FLUXERR_2'] * 1e27
zspec_driving = head_driving['REDSHIFT']
else:
try:
agn_driving = lc_path + driving_filter + '/bin3_onechip_' + str(
ras) + '_' + str(decs) + '_' + field + '.fits'
arch_driving = pf.open(agn_driving)
except:
agn_driving = lc_path + driving_filter + '/bin3_morechip_' + str(
ras) + '_' + str(decs) + '_' + field + '.fits'
arch_driving = pf.open(agn_driving)
jd_0 = 2455000
head_driving = arch_driving[0].header
datos_driving = arch_driving[1].data
jd_driving = datos_driving['JD'] - jd_0
flux_driving = datos_driving[
'fluxQ'] * 1e27 #the flux value is multiplicated by 1e27 to avoid numerical errors produced by small numbers
errflux_driving = datos_driving['errfluxQ'] * 1e27
zspec_driving = head_driving['REDSHIFT']
lcd_name = 'temp/driving_lc_' + driving_filter + '_' + str(
ras) + '_' + str(decs) + '.txt'
np.savetxt(
lcd_name,
np.transpose([
jd_driving / (1.0 + zspec_driving), flux_driving,
errflux_driving
]))
try:
#reading the responding filter 1 data
if responding_filter1 != 'Q':
agn_responding = lc_path + responding_filter1 + '/agn_' + str(
ras) + '_' + str(decs) + '_' + responding_filter1 + '.fits'
arch_responding = pf.open(agn_responding)
jd_0 = 55000
head_responding = arch_responding[0].header
datos_responding = arch_responding[1].data
jd_responding = datos_responding['JD'] - jd_0
flux_responding = datos_responding[
'FLUX_2'] * 1e27 #the flux value is multiplicated by 1e27 to avoid numerical errors produced by small numbers
errflux_responding = datos_responding['FLUXERR_2'] * 1e27
zspec_responding = head_responding['REDSHIFT']
else:
try:
agn_driving = lc_path + responding_filter1 + '/bin3_onechip_' + str(
ras) + '_' + field + '.fits'
arch_responding = pf.open(agn_responding)
except:
agn_driving = lc_path + responding_filter1 + '/bin3_morechip_' + str(
ras) + '_' + field + '.fits'
arch_responding = pf.open(agn_responding)
jd_0 = 2455000
head_responding = arch_responding[0].header
datos_responding = arch_responding[1].data
jd_responding = datos_responding['JD'] - jd_0
flux_responding = datos_responding[
'fluxQ'] * 1e27 #the flux value is multiplicated by 1e27 to avoid numerical errors produced by small numbers
errflux_responding = datos_responding['errfluxQ'] * 1e27
zspec_responding = head_responding['REDSHIFT']
#converting lcs into a format accepted by the fortran method
lcr1_name = 'temp/responding_lc_' + responding_filter1 + '_' + str(
ras) + '_' + str(decs) + '.txt'
np.savetxt(
lcr1_name,
np.transpose([
jd_responding / (1.0 + zspec_driving), flux_responding,
errflux_responding
]))
#reading the responding filter 2 data
if responding_filter2 != 'Q':
agn_responding = lc_path + responding_filter2 + '/agn_' + str(
ras) + '_' + str(decs) + '_' + responding_filter2 + '.fits'
arch_responding = pf.open(agn_responding)
jd_0 = 55000
head_responding = arch_responding[0].header
datos_responding = arch_responding[1].data
jd_responding = datos_responding['JD'] - jd_0
flux_responding = datos_responding[
'FLUX_2'] * 1e27 #the flux value is multiplicated by 1e27 to avoid numerical errors produced by small numbers
errflux_responding = datos_responding['FLUXERR_2'] * 1e27
zspec_responding = head_responding['REDSHIFT']
else:
try:
agn_driving = lc_path + responding_filter2 + '/bin3_onechip_' + str(
ras) + '_' + field + '.fits'
arch_responding = pf.open(agn_responding)
except:
agn_driving = lc_path + responding_filter2 + '/bin3_morechip_' + str(
ras) + '_' + field + '.fits'
arch_responding = pf.open(agn_responding)
jd_0 = 2455000
head_responding = arch_responding[0].header
datos_responding = arch_responding[1].data
jd_responding = datos_responding['JD'] - jd_0
flux_responding = datos_responding[
'fluxQ'] * 1e27 #the flux value is multiplicated by 1e27 to avoid numerical errors produced by small numbers
errflux_responding = datos_responding['errfluxQ'] * 1e27
zspec_responding = head_responding['REDSHIFT']
#converting lcs into a format accepted by the fortran method
lcr2_name = 'temp/responding_lc_' + responding_filter2 + '_' + str(
ras) + '_' + str(decs) + '.txt'
np.savetxt(
lcr2_name,
np.transpose([
jd_responding / (1.0 + zspec_driving), flux_responding,
errflux_responding
]))
#reading the responding filter 3 data
if responding_filter3 != 'Q':
agn_responding = lc_path + responding_filter3 + '/agn_' + str(
ras) + '_' + str(decs) + '_' + responding_filter3 + '.fits'
arch_responding = pf.open(agn_responding)
jd_0 = 55000
head_responding = arch_responding[0].header
datos_responding = arch_responding[1].data
jd_responding = datos_responding['JD'] - jd_0
flux_responding = datos_responding[
'FLUX_2'] * 1e27 #the flux value is multiplicated by 1e27 to avoid numerical errors produced by small numbers
errflux_responding = datos_responding['FLUXERR_2'] * 1e27
zspec_responding = head_responding['REDSHIFT']
else:
try:
agn_driving = lc_path + responding_filter3 + '/bin3_onechip_' + str(
ras) + '_' + field + '.fits'
arch_responding = pf.open(agn_responding)
except:
agn_driving = lc_path + responding_filter3 + '/bin3_morechip_' + str(
ras) + '_' + field + '.fits'
arch_responding = pf.open(agn_responding)
jd_0 = 2455000
head_responding = arch_responding[0].header
datos_responding = arch_responding[1].data
jd_responding = datos_responding['JD'] - jd_0
flux_responding = datos_responding[
'fluxQ'] * 1e27 #the flux value is multiplicated by 1e27 to avoid numerical errors produced by small numbers
errflux_responding = datos_responding['errfluxQ'] * 1e27
zspec_responding = head_responding['REDSHIFT']
#converting lcs into a format accepted by the fortran method
lcr3_name = 'temp/responding_lc_' + responding_filter3 + '_' + str(
ras) + '_' + str(decs) + '.txt'
np.savetxt(
lcr2_name,
np.transpose([
jd_responding / (1.0 + zspec_driving), flux_responding,
errflux_responding
]))
#running Javelin
cont = get_data([lcd_name], names=[driving_filter])
cmod = Cont_Model(cont)
cmod.do_mcmc(nwalkers=100,
nburn=100,
nchain=200,
fchain=jav_stat_path + "chain_cont_" +
driving_filter + "_vs_" + responding_filter1 + "_" +
responding_filter2 + "_and_" + responding_filter3 +
"_" + str(ras) + "_" + str(decs) + ".txt")
bothdata = get_data([lcd_name, lcr1_name, lcr2_name, lcr2_name],
names=[
driving_filter, responding_filter1,
responding_filter2, responding_filter3
])
laglimit = [[cent_lowlimit, cent_uplimit],
[cent_lowlimit, cent_uplimit],
[cent_lowlimit, cent_uplimit]]
mod_2band = Rmap_Model(bothdata) #Rmap_Model(bothdata)
mod_2band.do_mcmc(nwalkers=100,
nburn=100,
nchain=200,
conthpd=cmod.hpd,
laglimit=laglimit,
fchain=jav_stat_path + "jav_chain_all_" +
driving_filter + "_vs_" + responding_filter1 +
"_" + responding_filter2 + "_and_" +
responding_filter3 + "_" + str(ras) + "_" +
str(decs) + ".txt")
sigma, tau, lag1, width1, scale1, lag2, width2, scale2, lag3, width3, scale3 = np.loadtxt(
jav_stat_path + "jav_chain_all_" + driving_filter + "_vs_" +
responding_filter1 + "_" + responding_filter2 + "_and_" +
responding_filter3 + "_" + str(ras) + "_" + str(decs) + ".txt",
unpack=True,
usecols=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
centau_median1 = np.median(lag1)
centau_uperr1 = (stats.scoreatpercentile(lag1,
perclim)) - centau_median1
centau_loerr1 = centau_median1 - (stats.scoreatpercentile(
lag1, (100. - perclim)))
len_chain1 = len(lag1[np.where(lag1 > -2000000000000)])
centau_median2 = np.median(lag2)
centau_uperr2 = (stats.scoreatpercentile(lag2,
perclim)) - centau_median2
centau_loerr2 = centau_median2 - (stats.scoreatpercentile(
lag2, (100. - perclim)))
len_chain2 = len(lag2[np.where(lag2 > -2000000000000)])
centau_median3 = np.median(lag3)
centau_uperr3 = (stats.scoreatpercentile(lag3,
perclim)) - centau_median3
centau_loerr3 = centau_median3 - (stats.scoreatpercentile(
lag3, (100. - perclim)))
len_chain3 = len(lag3[np.where(lag3 > -2000000000000)])
return (ra, dec, zspec_driving, len_chain1, centau_median1,
centau_loerr1, centau_uperr1, len_chain2, centau_median2,
centau_loerr2, centau_uperr2, len_chain3, centau_median3,
centau_loerr3, centau_uperr3)
except:
print "########## computing iccf FAILS for source located at ra=%f and dec=%f, NO RESPONDING LCs available ##########" % (
ra, dec)
cmd = 'rm ' + lcd_name
os.system(cmd)
return (ra, dec, -9999, -9999, -9999, -9999, -9999, -9999, -9999,
-9999, -9999, -9999, -9999, -9999, -9999)
except:
print "########## computing iccf FAILS for source located at ra=%f and dec=%f, NO DRIVING LC available ##########" % (
ra, dec)
return (ra, dec, -9999, -9999, -9999, -9999, -9999, -9999, -9999,
-9999, -9999, -9999, -9999, -9999, -9999)
def javelin_modeling(time, cflux, lflux, suff='', nburn=1000, nchain=500, mods=None):
"""Do lag calculations with Javelin
Parameters:
time: [n] array of time axis
cflux: [2,n] continuum flux and error
lflux: [2,n] line flux and error
suff: suffix for printing
mods: javelin models if already calculated [cont_mod, cont_line_mod].
In that case, this function just does the plotting.
"""
if mods is None:
# write light curves to file so javelin can read them #
irand = np.random.randint(100000)
text = '\n'.join(['{} {} {}'.format(*x) for x in zip(time, cflux[0], cflux[1])])
with open('tmp_c%d.dat'%irand, 'w') as fp: fp.write(text)
text = '\n'.join(['{} {} {}'.format(*x) for x in zip(time, lflux[0], lflux[1])])
with open('tmp_l%d.dat'%irand, 'w') as fp: fp.write(text)
# continuum first #
cont_lc = get_data(['tmp_c%d.dat'%irand])
cont_mod = Cont_Model(cont_lc)
cont_mod.do_mcmc(set_verbose=False)
#cont_mod.show_hist();return 0,cont_mod,0
# continuum and line #
cont_line_lc = get_data(['tmp_c%d.dat'%irand, 'tmp_l%d.dat'%irand])
cont_line_mod = Rmap_Model(cont_line_lc)
llimit = [-100, 100]
pmap,_ = cont_line_mod.do_map([-0.6, 1.5, 1.0, 0.1, .2],
fixed=[1,1,1,1,1], set_verbose=False)
cont_line_mod.do_mcmc(conthpd=cont_mod.hpd, laglimit=[llimit], nburn=nburn, nchain=nchain,
#fixed=[1,1,1,1,1], p_fix=pmap, threads=20,set_verbose=False)
threads=30,set_verbose=False)
os.system('rm tmp_c%d.dat tmp_l%d.dat'%(irand, irand))
else:
cont_mod, cont_line_mod = mods
# plot the result of javelin fit #
chains = cont_line_mod.flatchain
lag_javelin = plt.histogram(chains[:,2], 400, density=1)
bins_cent = (lag_javelin[1][1:] + lag_javelin[1][:-1])/2
bins_err = (lag_javelin[1][1:] - lag_javelin[1][:-1])/2
percentile = lambda l: '[{:.4}, {:.4}]'.format(
*np.percentile(chains[:,2], [(100-l)/2, l+(100-l)/2]))
text = '# percentiles: 68, 90, 99: {}, {}, {}'.format(*[percentile(x) for x in [68, 90, 99]])
text += '\n# mean lag: {:.4} +{:.4} {:.4}'.format(
np.percentile(chains[:,2],50),
np.percentile(chains[:,2],68+16)-np.percentile(chains[:,2],50),
np.percentile(chains[:,2],16)-np.percentile(chains[:,2],50))
print(text)
text += '\ndescriptor lag_javelin{0},+- lag_javelin_prob{0}\n'.format(suff)
text += '\n'.join(['{:.4} {:.4} {:.4}'.format(*x)
for x in zip(bins_cent, bins_err, lag_javelin[0])])
return text, cont_mod, cont_line_mod, lag_javelin
