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plot_PMN_emission_wfc3_cc.py
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plot_PMN_emission_wfc3_cc.py
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import matplotlib as mpl
mpl.use('TkAgg')
from matplotlib.pyplot import *
from matplotlib import pyplot as plt
import pickle
import pdb
from fm import *
from scipy import interp
rc('font',family='serif')
import pickle
import corner
'''
#for dynesty
module purge
module load anaconda3/4.4.0
#for multinest
module purge
module load anaconda3/4.2.0
module load multinest/3.10
'''
#PLOTTING UP CORNER PLOT----------------------------------------------------------------
#import run
pic=pickle.load(open('./OUTPUT/pmn_emission_wfc3_cc.pic','rb'), encoding='latin1')
samples=pic[:,:-1]
lnprob=pic[:,-1]
outname='pmn_emission_wfc3_cc'
# corner plot
titles=np.array(['T$_{irr}$', 'log($\kappa_{IR}$)','log($\gamma_{v}$)', '[M/H]', 'log(C/O)', 'log(K$_{zz}$)', 'f$_{sed}$' ,'log(P$_{b}$)','log(VMR$_{cld,b}$)'])
priorlow=np.array([400, -3.0, -2.0, -2, -2.0, 0.5, 0.5, -6.0, -10.0])
priorhigh=np.array([1800, 0, 2, 3.0, 0.3, 11, 6.0, 1.5, -2.0])
Npars=len(titles)
ext=np.zeros([2,Npars])
ext=ext.T
ext[:,0]=priorlow
ext[:,1]=priorhigh
#'''
corner.corner(samples,labels=titles, bins=25,plot_datapoints='False',quantiles=[.16,0.5,.84],show_titles='True',plot_contours='True',extents=ext,levels=(1.-np.exp(-(1)**2/2.),1.-np.exp(-(2)**2/2.),1.-np.exp(-(3)**2/2.)))
savefig('./plots/'+outname+"_stair_pairs.pdf",format='pdf')
show()
#'''
#GENERATING RANDOMLY SAMPLES SPECTRA & TP PROFILES-----------------------------------------------------
import numpy as np
xsecs=xsects_HST(2000, 30000)
Nspectra=200
#loading in data again just to be safe
wlgrid, y_meas, err=np.loadtxt('w43b_emis.txt').T
#setting up default parameter values--SET THESE TO SAME VALUES AS IN LOG-LIKE FUNCTION
#planet/star system params--xRp is the "Rp" free parameter, M right now is fixed, but could be free param
Rp= 1.036#0.930#*x[4]# Planet radius in Jupiter Radii--this will be forced to be 10 bar radius--arbitrary (scaling to this is free par)
Rstar=0.667#0.598 #Stellar Radius in Solar Radii
M =2.034#1.78 #Mass in Jupiter Masses
D=0.01562 #semimajor axis in AU
#TP profile params (3--Guillot 2010, Parmentier & Guillot 2013--see Line et al. 2013a for implementation)
Tirr=1600#1500#x[0]#544.54 #terminator **isothermal** temperature--if full redistribution this is equilibrium temp
logKir=-0.7 #TP profile IR opacity controlls the "vertical" location of the gradient
logg1=-0.6 #single channel Vis/IR opacity. Controls the delta T between deep T and TOA T
Tint=200
#Composition parameters---assumes "chemically consistnat model" described in Kreidberg et al. 2015
logMet=0.0#x[1]#1.5742E-2 #. #Metallicity relative to solar log--solar is 0, 10x=1, 0.1x = -1 used -1.01*log10(M)+0.6
logCtoO=-0.26#x[2]#-1.97 #log C-to-O ratio: log solar is -0.26
logPQCarbon=-5.5 #CH4, CO, H2O Qunech pressure--forces CH4, CO, and H2O to constant value at quench pressure value
logPQNitrogen=-5.5 #N2, NH3 Quench pressure--forces N2 and NH3 to "" --ad hoc for chemical kinetics--reasonable assumption
#A&M Cloud parameters--includes full multiple scattering (for realzz) in both reflected and emitted light
logKzz=7 #log Rayleigh Haze Amplitude (relative to H2)
fsed=3.0 #haze slope--4 is Rayeigh, 0 is "gray" or flat.
logPbase=-1.0 #gray "large particle" cloud opacity (-35 - -25)
logCldVMR=-25.0 #cloud fraction
#simple 'grey+rayleigh' parameters--non scattering--just pure extinction
logKcld = -40
logRayAmp = -30
RaySlope = 0
#plotting reconstructed TP
draws=np.random.randint(0, samples.shape[0], 500)
logP = np.arange(-6.8,1.5,0.1)+0.1
Tarr=np.zeros((len(draws),len(logP)))
P = 10.0**logP
g0=6.67384E-11*M*1.898E27/(Rp*71492.*1.E3)**2
for i in range(len(draws)):
xx=samples[draws[i],:]
logg1=xx[2]
Tiso=xx[0]
logKir=xx[1]
kv=10.**(logg1+logKir)
kth=10.**logKir
tp=TP(Tiso, Tint,g0 , kv, kv, kth, 0.5)
Tarr[i,:] = interp(logP,np.log10(tp[1]),tp[0])
Tmedian=np.zeros(P.shape[0])
Tlow_1sig=np.zeros(P.shape[0])
Thigh_1sig=np.zeros(P.shape[0])
Tlow_2sig=np.zeros(P.shape[0])
Thigh_2sig=np.zeros(P.shape[0])
for i in range(P.shape[0]):
percentiles=np.percentile(Tarr[:,i],[4.55, 15.9, 50, 84.1, 95.45])
Tlow_2sig[i]=percentiles[0]
Tlow_1sig[i]=percentiles[1]
Tmedian[i]=percentiles[2]
Thigh_1sig[i]=percentiles[3]
Thigh_2sig[i]=percentiles[4]
fig, ax=subplots()
fill_betweenx(P,Tlow_2sig,Thigh_2sig,facecolor='r',edgecolor='None',alpha=0.1,label='2-sigma')
fill_betweenx(P,Tlow_1sig,Thigh_1sig,facecolor='r',edgecolor='None',alpha=1.,label='1-sigma')
ax.axis([0.5*Tmedian.min(),1.5*Tmedian.max(),P.max(),P.min()])
ax.semilogy()
plot(Tmedian, P,'b',label='median')
xlabel('Temperature [K]',size='xx-large')
ylabel('Pressure [bar]',size='xx-large')
ax.minorticks_on()
ax.tick_params(length=10,width=1,labelsize='xx-large',which='major')
ax.set_xticks([500, 1500, 2500])
ax.legend(frameon=False,loc=0)
fig.subplots_adjust(left=0.3, right=0.6, top=0.9, bottom=0.1)
savefig('./plots/'+outname+"_TP.pdf",format='pdf')
show()
#'''
#choosing random indicies to draw from properly weighted posterior samples
draws=np.random.randint(0, samples.shape[0], Nspectra)
Nwno_bins=xsecs[2].shape[0]
y_mod_array=np.zeros((Nwno_bins, Nspectra))
Reflec_array=np.zeros((Nwno_bins, Nspectra))
Therm_array=np.zeros((Nwno_bins, Nspectra))
y_binned_array=np.zeros((len(wlgrid), Nspectra))
for i in range(Nspectra):
print(i)
#make sure this is the same as in log-Like
Tirr, logKir,logg1, logMet, logCtoO, logKzz, fsed ,logPbase,logCldVMR=samples[draws[i],:]
x=np.array([Tirr, logKir,logg1, Tint,logMet, logCtoO, logPQCarbon,logPQNitrogen, Rp, Rstar, M, D, logKzz, fsed,logPbase,logCldVMR, logKcld, logRayAmp, RaySlope])
gas_scale=np.array([1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1., 1., 1.])
y_binned,y_mod,wno,atm,Ftoa,Fstar,Fstar_TOA,Fup_therm,Fup_ref=fx_emis(x,wlgrid,gas_scale,xsecs)
FpFstar_ref=Fup_ref/Fstar*(Rp/Rstar*0.10279)**2
FpFstar_therm=Fup_therm/Fstar*(Rp/Rstar*0.10279)**2
y_mod_array[:,i]=y_mod
y_binned_array[:,i]=y_binned
Reflec_array[:,i]=FpFstar_ref
Therm_array[:,i]=FpFstar_therm
#saving these arrays since it takes a few minutes to generate
pickle.dump([wlgrid, y_meas, err, y_binned_array, wno, y_mod_array, Reflec_array,Therm_array],open('./OUTPUT/spectral_samples_emission_pmn_wfc3_cc.pic','wb'))
#pdb.set_trace()
#'''
#PLOTTING SPECTRAL SPREAD-----------------------------------------------------
wlgrid, y_meas, err, y_binned_array, wno, y_mod_array, Reflec_array,Therm_array=pickle.load(open('./OUTPUT/spectral_samples_emission_pmn_wfc3_cc.pic','rb'))
from matplotlib.pyplot import *
from matplotlib.ticker import FormatStrFormatter
ymax=np.min(y_meas)*1E3*10
ymin=0#np.max(y_meas)*1E2*1.02
fig1, ax=subplots()
xlabel('$\lambda$ ($\mu$m)',fontsize=14)
ylabel('(R$_{p}$/R$_{*}$)$^{2} \%$',fontsize=14)
minorticks_on()
y_median=np.zeros(wno.shape[0])
y_high_1sig=np.zeros(wno.shape[0])
y_high_2sig=np.zeros(wno.shape[0])
y_low_1sig=np.zeros(wno.shape[0])
y_low_2sig=np.zeros(wno.shape[0])
for i in range(wno.shape[0]):
percentiles=np.percentile(y_mod_array[i,:],[4.55, 15.9, 50, 84.1, 95.45])
y_low_2sig[i]=percentiles[0]
y_low_1sig[i]=percentiles[1]
y_median[i]=percentiles[2]
y_high_1sig[i]=percentiles[3]
y_high_2sig[i]=percentiles[4]
fill_between(1E4/wno[::-1],y_low_2sig[::-1]*1000,y_high_2sig[::-1]*1000,facecolor='g',alpha=0.5,edgecolor='None')
fill_between(1E4/wno[::-1],y_low_1sig[::-1]*1000,y_high_1sig[::-1]*1000,facecolor='g',alpha=0.75,edgecolor='None')
plot(1E4/wno, y_median*1000,'g')
#reflected component
y_median=np.zeros(wno.shape[0])
y_high_1sig=np.zeros(wno.shape[0])
y_high_2sig=np.zeros(wno.shape[0])
y_low_1sig=np.zeros(wno.shape[0])
y_low_2sig=np.zeros(wno.shape[0])
for i in range(wno.shape[0]):
percentiles=np.percentile(Reflec_array[i,:],[4.55, 15.9, 50, 84.1, 95.45])
y_low_2sig[i]=percentiles[0]
y_low_1sig[i]=percentiles[1]
y_median[i]=percentiles[2]
y_high_1sig[i]=percentiles[3]
y_high_2sig[i]=percentiles[4]
fill_between(1E4/wno[::-1],y_low_2sig[::-1]*1000,y_high_2sig[::-1]*1000,facecolor='b',alpha=0.5,edgecolor='None')
fill_between(1E4/wno[::-1],y_low_1sig[::-1]*1000,y_high_1sig[::-1]*1000,facecolor='b',alpha=0.75,edgecolor='None')
plot(1E4/wno, y_median*1000,'b')
#thermal emission component
y_median=np.zeros(wno.shape[0])
y_high_1sig=np.zeros(wno.shape[0])
y_high_2sig=np.zeros(wno.shape[0])
y_low_1sig=np.zeros(wno.shape[0])
y_low_2sig=np.zeros(wno.shape[0])
for i in range(wno.shape[0]):
percentiles=np.percentile(Therm_array[i,:],[4.55, 15.9, 50, 84.1, 95.45])
y_low_2sig[i]=percentiles[0]
y_low_1sig[i]=percentiles[1]
y_median[i]=percentiles[2]
y_high_1sig[i]=percentiles[3]
y_high_2sig[i]=percentiles[4]
fill_between(1E4/wno[::-1],y_low_2sig[::-1]*1000,y_high_2sig[::-1]*1000,facecolor='r',alpha=0.5,edgecolor='None')
fill_between(1E4/wno[::-1],y_low_1sig[::-1]*1000,y_high_1sig[::-1]*1000,facecolor='r',alpha=0.75,edgecolor='None')
plot(1E4/wno, y_median*1000,'r')
#for i in range(20): plot(wlgrid, y_binned_array[:,i]*100.,alpha=0.5,color='red')
#for i in range(20): plot(1E4/wno, y_mod_array[:,i]*100.,alpha=0.5,color='red')
errorbar(wlgrid, y_meas*1000, yerr=err*1000, xerr=None, fmt='Dk')
ax.set_xscale('log')
ax.set_xticks([0.3, 0.5,0.8,1,1.4, 2, 3, 4, 5])
ax.axis([0.3,5.0,ymin,ymax])
ax.get_xaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())
ax.tick_params(length=5,width=1,labelsize='small',which='major')
savefig('./plots/'+outname+'spectrum_fits.pdf',fmt='pdf')
show()
close()