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 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 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 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 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)
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)
