def fitCon(confile,
confchain,
names=None,
threads=1,
set_plot=False,
nwalkers=100,
nburn=100,
nchain=100,
mode="test"):
""" fit the continuum model.
"""
if mode == "run":
confile = ".".join([confile, "myrun"])
zydata = get_data(confile, names=names)
cont = Cont_Model(zydata, "drw")
if mode == "test":
print(cont([np.log(sigma), np.log(tau)], set_retq=True))
return (None)
elif mode == "show":
cont.load_chain(confchain)
elif mode == "run":
confchain = ".".join([confchain, "myrun"])
cont.do_mcmc(nwalkers=nwalkers,
nburn=nburn,
nchain=nchain,
fburn=None,
fchain=confchain,
threads=1)
if set_plot:
cont.show_hist(bins=100, figout="mcmc0", figext=figext)
return (cont.hpd)
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 javelin_fit(ID):
# 对ugriz五个band分别跑javelin连续谱模型拟合
# 得到五个拟合结果
band_pho = ['u', 'g', 'r', 'i', 'z']
tau_pho = [] # in ugriz order
sigma_pho = [] # in ugriz order
for band in band_pho:
filename = "./data/light_curve/" + band + \
"_LC_" + ID + ".dat"
# JAVELIN
c = get_data([filename])
cmod = Cont_Model(c)
cmod.do_mcmc(threads=4)
# 这里只记录了med的结果:
# tau_mc_low = exp(cmod.hpd[0,1])
tau_mc_med = exp(cmod.hpd[1, 1])
# tau_mc_hig = exp(cmod.hpd[2,1])
# sigma_mc_low = exp(cmod.hpd[0,0])
sigma_mc_med = exp(cmod.hpd[1, 0])
# sigma_mc_hig = exp(cmod.hpd[2,0])
tau_pho.append(tau_mc_med)
sigma_pho.append(sigma_mc_med)
# 返回五个tau,sigma
# 顺序是urgriz
return tau_pho, sigma_pho
def do_javelin(source_name, source_ID, zspec_driving, jd_driving, flux_driving,
errflux_driving, jd_responding, flux_responding,
errflux_responding):
#Calculate lag with python Javelin program
print "########## computing Javelin for source %d ##########" % (
source_ID)
#converting lcs into a format accepted by the javelin method
aa = str(np.random.randint(100))
lcd_name = 'temp/driving_lc_' + aa + '_' + str(source_ID) + '.txt'
np.savetxt(
lcd_name,
np.transpose([
jd_driving / (1.0 + zspec_driving), flux_driving, errflux_driving
]))
lcr_name = 'temp/responding_lc_' + aa + '_' + str(source_ID) + '.txt'
np.savetxt(
lcr_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=50,
nchain=100,
fchain="javelin_stat/chain_cont_" + source_name + ".txt")
bothdata = get_data([lcd_name, lcr_name],
names=[driving_filter, responding_filter])
mod_2band = Pmap_Model(bothdata) #Rmap_Model(bothdata)
mod_2band.do_mcmc(nwalkers=100,
nburn=50,
nchain=100,
conthpd=cmod.hpd,
laglimit=[[cent_lowlimit, cent_uplimit]],
fchain="javelin_stat/jav_chain_all_" + source_name +
".txt")
sigma, tau, lag, width, scale = np.loadtxt("javelin_stat/jav_chain_all_" +
source_name + ".txt",
unpack=True,
usecols=[0, 1, 2, 3, 4])
centau_median = np.median(lag)
centau_uperr = (stats.scoreatpercentile(lag, perclim)) - centau_median
centau_loerr = centau_median - (stats.scoreatpercentile(
lag, (100. - perclim)))
len_chain = len(lag[np.where(lag > -2000000000000)])
return (source_ID, zspec_driving, len_chain, centau_median, centau_loerr,
centau_uperr)
# JAVELINE
#
import matplotlib.pyplot as plt
from javelin.zylc import get_data
from javelin.lcmodel import Cont_Model, Rmap_Model
javdata_con = get_data([
"../data/con_test.txt",
], names=[
"con",
])
javdata_rm = get_data(["../data/con_test.txt", "../data/line_test.txt"],
names=["con", "line"])
cont = Cont_Model(javdata_con)
#cont.do_mcmc(fchain="mychain0.dat",nwalkers=100, nburn=200, nchain=500)
cont.load_chain("mychain0.dat")
cont.show_hist(bins=100)
cont.get_hpd()
conthpd = cont.hpd
print(conthpd[1, :])
cont_best = cont.do_pred(conthpd[1, :])
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')
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 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 data
if responding_filter != 'Q':
agn_responding = lc_path + responding_filter + '/agn_' + str(
ras) + '_' + str(decs) + '_' + responding_filter + '.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_filter + '/bin3_onechip_' + str(
ras) + '_' + field + '.fits'
arch_responding = pf.open(agn_responding)
except:
agn_driving = lc_path + responding_filter + '/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
lcr_name = 'temp/responding_lc_' + responding_filter + '_' + str(
ras) + '_' + str(decs) + '.txt'
np.savetxt(
lcr_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=50,
nchain=100,
fchain=jav_stat_path + "chain_cont_" +
driving_filter + "_vs_" + responding_filter + "_" +
str(ras) + "_" + str(decs) + ".txt")
bothdata = get_data([lcd_name, lcr_name],
names=[driving_filter, responding_filter])
mod_2band = Pmap_Model(bothdata) #Rmap_Model(bothdata)
mod_2band.do_mcmc(nwalkers=100,
nburn=50,
nchain=100,
conthpd=cmod.hpd,
laglimit=[[cent_lowlimit, cent_uplimit]],
widlimit=widlimit,
fchain=jav_stat_path + "jav_chain_all_" +
driving_filter + "_vs_" + responding_filter +
"_" + str(ras) + "_" + str(decs) + ".txt")
sigma, tau, lag, width, scale = np.loadtxt(
jav_stat_path + "jav_chain_all_" + driving_filter + "_vs_" +
responding_filter + "_" + str(ras) + "_" + str(decs) + ".txt",
unpack=True,
usecols=[0, 1, 2, 3, 4])
centau_median = np.median(lag)
centau_uperr = (stats.scoreatpercentile(lag,
perclim)) - centau_median
centau_loerr = centau_median - (stats.scoreatpercentile(
lag, (100. - perclim)))
len_chain = len(lag[np.where(lag > -2000000000000)])
return (ra, dec, zspec_driving, len_chain, centau_median,
centau_loerr, centau_uperr)
except:
print "########## computing iccf FAILS for source located at ra=%f and dec=%f, NO RESPONDING LC available ##########" % (
ra, dec)
cmd = 'rm ' + lcd_name
os.system(cmd)
return (ra, dec, -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)
# 22 Nov 2017 17:14:12
from javelin.lcmodel import Cont_Model, Disk_Model
from javelin.zylc import get_data
import os.path
data0 = get_data(["driver.dat",], names=["Driver",])
data0.plot()
cont = Cont_Model(data0, "drw")
if os.path.isfile("driver_chain.dat"):
print ("cont model chain exists")
cont.load_chain('driver_chain.dat')
cont.show_hist(bins=100)
else:
cont.do_mcmc(nwalkers=100, nburn=100, nchain=200, fburn=None, fchain="driver_chain.dat", threads=1)
conthpd = cont.hpd
print conthpd
data1 = get_data(["driver.dat", "wave2.dat", "wave3.dat", "wave4.dat"], names=["Driver", "Wave 2", "Wave 3", "Wave 4"])
data1.plot()
disk1 = Disk_Model(data1, effwave=[2000., 4000., 5000., 8000.])
if os.path.isfile("thin_disk_chain.dat"):
print ("disk model chain exists")
disk1.load_chain('thin_disk_chain.dat')
disk1.show_hist()
else:
disk1.do_mcmc(conthpd=conthpd, nwalkers=100, nburn=100, nchain=500, threads=1, fchain="thin_disk_chain.dat", flogp="thin_disk_flogp.dat", fburn="thin_disk_burn.dat")
#running the javelin code in easy mode from javelin.zylc import get_data from javelin.lcmodel import Cont_Model, Pmap_Model, Rmap_Model c = get_data(["continuum.txt"]) cm = Cont_Model(c) cm.do_mcmc(nwalkers=1000, nburn=200, nchain=100) cy = get_data(["continuum.txt", "hb.txt"]) cym = Pmap_Model(cy) cym.do_mcmc(conthpd=cm.hpd, nwalkers=1000, nburn=200, nchain=100) cym.show_hist() cym.get_hpd() cymhpd = cym.hpd par_best = cymhpd[1, :] cym_best = cym.do_pred(par_best) cym_best.plot(set_pred=True, obs=cy)
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
