def plot_bestFit_Spectrum(spec, clr, specs, atmfile, filters, kurucz, tepfile,
outflux, data, uncert, direct):
'''
Plot Transit spectrum
'''
# get star data
R_star, T_star, sma, gstar = bf.get_starData(tepfile)
# get surface gravity
grav, Rp = mat.get_g(tepfile)
# convert Rp to m
Rp = Rp * 1000
# ratio planet to star
rprs = Rp / R_star
# read kurucz file
starfl, starwn, tmodel, gmodel = w.readkurucz(kurucz, T_star, gstar)
# read best-fit spectrum output file, take wn and spectra values
(head, tail) = os.path.split(outflux)
specwn, bestspectrum = rt.readspectrum(direct + '/' + tail, wn=True)
# convert wn to wl
specwl = 1e4 / specwn
# number of filters
nfilters = len(filters)
# read and resample the filters:
nifilter = [] # Normalized interpolated filter
istarfl = [] # interpolated stellar flux
wnindices = [] # wavenumber indices used in interpolation
meanwn = [] # Filter mean wavenumber
for i in np.arange(nfilters):
# read filter:
filtwaven, filttransm = w.readfilter(filters[i])
meanwn.append(np.sum(filtwaven * filttransm) / sum(filttransm))
# resample filter and stellar spectrum:
nifilt, strfl, wnind = w.resample(specwn, filtwaven, filttransm,
starwn, starfl)
nifilter.append(nifilt)
istarfl.append(strfl)
wnindices.append(wnind)
# convert mean wn to mean wl
meanwl = 1e4 / np.asarray(meanwn)
# band-integrate the flux-ratio or modulation:
bandflux = np.zeros(nfilters, dtype='d')
bandmod = np.zeros(nfilters, dtype='d')
for i in np.arange(nfilters):
fluxrat = (bestspectrum[wnindices[i]] / istarfl[i]) * rprs * rprs
bandflux[i] = w.bandintegrate(fluxrat, specwn, nifilter[i],
wnindices[i])
bandmod[i] = w.bandintegrate(bestspectrum[wnindices[i]], specwn,
nifilter[i], wnindices[i])
# stellar spectrum on specwn:
sinterp = si.interp1d(starwn, starfl)
sflux = sinterp(specwn)
frat = bestspectrum / sflux * rprs * rprs
###################### plot figure #############################
plt.rcParams["mathtext.default"] = 'rm'
matplotlib.rcParams.update({'mathtext.default': 'rm'})
#matplotlib.rcParams.update({'fontsize': 10,})
matplotlib.rcParams.update({
'axes.labelsize': 16,
#'text.fontsize': 10,
'legend.fontsize': 14,
'xtick.labelsize': 20,
'ytick.labelsize': 20,
})
plt.figure(2, (8.5, 5))
plt.clf()
#plt.xlim(0.60, 5.5)
#plt.xlim(min(specwl),max(specwl))
# plot eclipse spectrum
#gfrat = gaussf(frat, 0)
plt.semilogx(specwl,
frat * 1e3,
clr,
lw=1.5,
label="Spectrum",
linewidth=4)
#cornflowerblue, lightskyblue
#plt.errorbar(meanwl, data*1e3, uncert*1e3, fmt="ko", label="Data", alpha=0.7)
plt.errorbar(meanwl,
data * 1e3,
uncert * 1e3,
fmt=".",
color='k',
zorder=100,
capsize=2,
capthick=1,
label="Data",
alpha=0.7)
#plt.plot(meanwl, bandflux*1e3, "ko", label="model")
plt.ylabel(r"$F_p/F_s$ (10$^{-3}$)", fontsize=24)
leg = plt.legend(loc="upper left")
#leg.draw_frame(False)
leg.get_frame().set_alpha(0.5)
ax = plt.subplot(111)
ax.set_xscale('log')
ax.get_xaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())
ax.set_xticks([0.7, 0.8, 0.9, 1.0, 2.0, 3.0, 4.0, 5.0])
ax.set_xticklabels(["0.7", "", "", "1.0", "2.0", "3.0", "4.0", "5.0"])
plt.xlabel(r"${\rm Wavelength\ \ (um)}$", fontsize=24)
nfilters = len(filters)
# plot filter bandpasses
for i in np.arange(nfilters - 15):
(head, tail) = os.path.split(filters[i])
lbl = tail[:-4]
# read filter:
wn, respons = w.readfilter(filters[i])
respons = respons / 3 - 0.4
wl = 10000.0 / wn
#plt.plot(wl, respons, color='crimson', linewidth =1)
if lbl == 'spitzer_irac1_sa' or lbl == 'spitzer_irac2_sa':
respons = respons * 2 + 0.4
plt.plot(wl, respons, color='grey', linewidth=1, alpha=0.5)
#plt.plot(wl, respons*2, color='orangered', linewidth =1)
elif lbl == 'Wang-Hband' or lbl == 'Wang-Kband':
plt.plot(wl, respons, 'grey', linewidth=1, alpha=0.5)
elif lbl == 'VLT_1190' or lbl == 'VLT_2090':
plt.plot(wl, respons, color='grey', linewidth=2, alpha=0.5)
#plt.plot(wl, respons, color='firebrick', linewidth =2)
elif lbl == 'GROND_K_JB' or lbl == 'GROND_i_JB':
plt.plot(wl, respons, 'grey', linewidth=1, alpha=0.5)
elif lbl == 'Zhou_Ks':
plt.plot(wl, respons, 'grey', linewidth=1, alpha=0.5)
plt.ylim(-0.4, 7)
plt.text(1.9, 3, specs, color=clr, fontsize=26)
###################### INSET PT and ABUN FIGURE ####################
b = plt.axes([.21, .45, .14, .24])
# read atmfile
molecules, pres, temp, abundances = mat.readatm(atmfile)
plt.semilogy(temp, pres, color='r', linewidth=3)
plt.xlim(1000, 2200)
plt.ylim(max(pres), min(pres))
b.minorticks_off()
yticks = [1e2, 1e1, 1, 1e-1, 1e-2, 1e-3, 1e-4, 1e-5]
ylabels = [
"10$^{2}$", "", "10$^{0}$", "", "10$^{-2}$", "", "10$^{-4}$", ""
]
plt.yticks(yticks, ylabels, fontsize=8)
xticks = [1000, 1200, 1400, 1600, 1800, 2000, 2200]
xlabels = ["", "1200", "", "", "1800", ""]
plt.xticks(xticks, xlabels, fontsize=12)
plt.xlabel('T (K)', fontsize=12)
plt.ylabel('P (bar)', fontsize=12)
# ############################## SECOND INSET ABUN
c = plt.axes([.35, .45, .14, .24])
# Sets the second argument given as the species names
species = spec
# Open the atmospheric file and read
f = open(atmfile, 'r')
lines = np.asarray(f.readlines())
f.close()
# Get molecules names
imol = np.where(lines == "#SPECIES\n")[0][0] + 1
molecules = lines[imol].split()
nmol = len(molecules)
for m in np.arange(nmol):
molecules[m] = molecules[m].partition('_')[0]
nspec = 1
# Populate column numbers for requested species and
# update list of species if order is not appropriate
columns = []
spec = []
for i in np.arange(nmol):
if molecules[i] == species:
columns.append(i + 3) # defines p, T +2 or rad, p, T +3
spec.append(species)
# Convert spec to tuple
spec = tuple(spec)
# Concatenate spec with pressure for data and columns
data = tuple(np.concatenate((['p'], spec)))
usecols = tuple(np.concatenate(
([1], columns))) # defines p as 0 columns, or p as 1 columns
# Load all data for all interested species
data = np.loadtxt(atmfile, dtype=float, comments='#', delimiter=None, \
converters=None, skiprows=13, usecols=usecols, unpack=True)
plt.loglog(data[1], data[0], '-', color=clr, \
linewidth=3)
plt.ylim(max(pres), min(pres))
c.minorticks_off()
yticks = [1e2, 1e1, 1, 1e-1, 1e-2, 1e-3, 1e-4, 1e-5]
ylabels = []
plt.yticks(yticks, ylabels)
plt.xlim(1e-12, 1e-2)
xticks = [1e-11, 1e-9, 1e-7, 1e-5, 1e-3]
xlabels = ["10$^{-11}$", "", "10$^{-5}$", "", "10$^{-3}$"]
plt.xticks(xticks, xlabels, fontsize=12)
plt.xlabel('Mix. fraction', fontsize=12)
plt.subplots_adjust(bottom=0.16)
spec = spec[0]
print(spec)
plt.savefig(spec + "_transSpec_new.png")
plt.savefig(spec + "_transSpec_new.ps")
def plot_bestFit_Spectrum(low, high, xtic, xlab, spec, clr, specs, atmfile,
filters, kurucz, tepfile, outflux, data, uncert,
direct):
'''
Plot Transit spectrum
'''
# get star data
R_star, T_star, sma, gstar = bf.get_starData(tepfile)
# get surface gravity
grav, Rp = mat.get_g(tepfile)
# convert Rp to m
Rp = Rp * 1000
# ratio planet to star
rprs = Rp / R_star
# read kurucz file
starfl, starwn, tmodel, gmodel = w.readkurucz(kurucz, T_star, gstar)
# read best-fit spectrum output file, take wn and spectra values
(head, tail) = os.path.split(outflux)
specwn, bestspectrum = rt.readspectrum(direct + '/' + tail, wn=True)
# convert wn to wl
specwl = 1e4 / specwn
# number of filters
nfilters = len(filters)
# read and resample the filters:
nifilter = [] # Normalized interpolated filter
istarfl = [] # interpolated stellar flux
wnindices = [] # wavenumber indices used in interpolation
meanwn = [] # Filter mean wavenumber
for i in np.arange(nfilters):
# read filter:
filtwaven, filttransm = w.readfilter(filters[i])
meanwn.append(np.sum(filtwaven * filttransm) / sum(filttransm))
# resample filter and stellar spectrum:
nifilt, strfl, wnind = w.resample(specwn, filtwaven, filttransm,
starwn, starfl)
nifilter.append(nifilt)
istarfl.append(strfl)
wnindices.append(wnind)
# convert mean wn to mean wl
meanwl = 1e4 / np.asarray(meanwn)
# band-integrate the flux-ratio or modulation:
bandflux = np.zeros(nfilters, dtype='d')
bandmod = np.zeros(nfilters, dtype='d')
for i in np.arange(nfilters):
fluxrat = (bestspectrum[wnindices[i]] / istarfl[i]) * rprs * rprs
bandflux[i] = w.bandintegrate(fluxrat, specwn, nifilter[i],
wnindices[i])
bandmod[i] = w.bandintegrate(bestspectrum[wnindices[i]], specwn,
nifilter[i], wnindices[i])
# stellar spectrum on specwn:
sinterp = si.interp1d(starwn, starfl)
sflux = sinterp(specwn)
frat = bestspectrum / sflux * rprs * rprs
###################### plot figure #############################
plt.rcParams["mathtext.default"] = 'rm'
matplotlib.rcParams.update({'mathtext.default': 'rm'})
matplotlib.rcParams.update({
'axes.labelsize': 16,
'xtick.labelsize': 20,
'ytick.labelsize': 20,
})
plt.figure(2, (8.5, 5))
plt.clf()
# smooth the spectrum a fit
gfrat = gaussf(frat, 2)
# plot eclipse spectrum
plt.semilogx(specwl,
gfrat * 1e3,
clr,
lw=1.5,
label="Spectrum",
linewidth=4)
plt.errorbar(meanwl,
data * 1e3,
uncert * 1e3,
fmt="ko",
label="Data",
alpha=0.7)
plt.ylabel(r"$F_p/F_s$ (10$^{-3}$)", fontsize=24)
leg = plt.legend(loc="upper left")
leg.get_frame().set_alpha(0.5)
ax = plt.subplot(111)
ax.set_xscale('log')
plt.xlabel(r"${\rm Wavelength\ \ (um)}$", fontsize=24)
ax.get_xaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())
plt.gca().xaxis.set_minor_formatter(matplotlib.ticker.NullFormatter())
ax.set_xticks([0.7, 0.8, 0.9, 1.0, 2.0, 3.0, 4.0, 5.0])
ax.set_xticklabels(["0.7", "", "", "1.0", "2.0", "3.0", "4.0", "5.0"])
plt.xlim(min(specwl), max(specwl))
nfilters = len(filters)
# plot filter bandpasses
for i in np.arange(nfilters - 15):
(head, tail) = os.path.split(filters[i])
lbl = tail[:-4]
# read filter:
wn, respons = w.readfilter(filters[i])
respons = respons / 3 - 0.4
wl = 10000.0 / wn
#plt.plot(wl, respons, color='crimson', linewidth =1)
if lbl == 'spitzer_irac1_sa' or lbl == 'spitzer_irac2_sa':
respons = respons * 2 + 0.4
plt.plot(wl, respons, color='grey', linewidth=1, alpha=0.5)
#plt.plot(wl, respons*2, color='orangered', linewidth =1)
elif lbl == 'Wang-Hband' or lbl == 'Wang-Kband':
plt.plot(wl, respons, 'grey', linewidth=1, alpha=0.5)
elif lbl == 'VLT_1190' or lbl == 'VLT_2090':
plt.plot(wl, respons, color='grey', linewidth=2, alpha=0.5)
#plt.plot(wl, respons, color='firebrick', linewidth =2)
elif lbl == 'GROND_K_JB' or lbl == 'GROND_i_JB':
plt.plot(wl, respons, 'grey', linewidth=1, alpha=0.5)
elif lbl == 'Zhou_Ks':
plt.plot(wl, respons, 'grey', linewidth=1, alpha=0.5)
plt.ylim(-0.4, 7)
plt.text(1.9, 3, specs, color=clr, fontsize=26)
plt.subplots_adjust(bottom=0.20)
plt.savefig(spec + "_BestFit-transSpec.png")
plt.savefig(spec + "_BestFit-transSpec.ps")
0.000431, 0.000414, 0.000482, 0.00046 , 0.000473, 0.000353,
0.000313, 0.00032 , 0.000394, 0.000439, 0.000458, 0.000595,
0.000614, 0.000732])
uncert = np.array([ 1.30000000e-04, 1.50000000e-04, 8.90000000e-05,
8.40000000e-05, 1.70000000e-04, 2.90000000e-04,
3.20000000e-04, 1.40000000e-04, 4.20000000e-04,
2.20000000e-04, 2.70000000e-04, 4.50000000e-05,
3.90000000e-05, 3.80000000e-05, 3.60000000e-05,
3.70000000e-05, 3.30000000e-05, 3.40000000e-05,
3.00000000e-05, 3.60000000e-05, 3.60000000e-05,
3.30000000e-05, 3.50000000e-05, 3.60000000e-05,
3.70000000e-05, 4.20000000e-05])
# get star data
R_star, T_star, sma, gstar = bf.get_starData(tep_name)
# get surface gravity
grav, Rp = mat.get_g(tep_name)
# convert Rp to m
Rp = Rp * 1000
# ratio planet to star
rprs = Rp/R_star
# read kurucz file
starfl, starwn, tmodel, gmodel = w.readkurucz(kurucz, T_star, gstar)
###################### plot figure #############################
plt.rcParams["mathtext.default"] = 'rm'
matplotlib.rcParams.update({'mathtext.default':'rm'})
matplotlib.rcParams.update({'axes.labelsize': 16,
'xtick.labelsize': 14,
def retrievedPT(datadir,
atmfile,
tepfile,
nmol,
solution,
outname,
outdir=None,
T_int=100.):
"""
Inputs
------
datadir: string. Path/to/directory containing BART-formatted output.
atmfile: string. Path/to/atmospheric model file.
tepfile: string. Path/to/Transiting ExoPlanet file.
nmol: int. Number of molecules being fit by MCMC.
solution: string. Geometry of the system. 'eclipse' or 'transit'.
outname: string. File name of resulting plot.
outdir: string. Path/to/dir to save `outname`. If None, defaults
to the results directory of BARTTest if `datadir` is
within BARTTest.
T_int: float. Internal planetary temperature. Default is 100 K.
"""
# Set outdir if not specified
if outdir is None:
try:
if datadir[-1] != '/':
datadir = datadir + '/'
outdir = 'results'.join(datadir.rsplit('code-output', \
1)).rsplit('/', 2)[0] + '/'
if not os.path.isdir(outdir):
os.makedirs(outdir)
except:
print("Data directory not located within BARTTest.")
print("Please specify an output directory `outdir` and try again.")
sys.exit(1)
# Read g_surf and R_planet from TEP file
grav, Rp = ma.get_g(tepfile)
# Read star data from TEP file, and semi-major axis
R_star, T_star, sma, gstar = bf.get_starData(tepfile)
# Read atmfile
mols, atminfo = readatm(atmfile)
pressure = atminfo[:, 1]
# Read MCMC output file
MCfile = datadir + 'MCMC.log'
bestP, uncer = bf.read_MCMC_out(MCfile)
allParams = bestP
# Get number of burned iterations
foo = open(MCfile, 'r')
lines = foo.readlines()
foo.close()
line = [
foop for foop in lines if " Burned in iterations per chain:" in foop
]
burnin = int(line[0].split()[-1])
# Figure out number of parameters
nparams = len(allParams)
nradfit = int(solution == 'transit')
nPTparams = nparams - nmol - nradfit
PTparams = allParams[:nPTparams]
# Plot the best PT profile
kappa, gamma1, gamma2, alpha, beta = PTparams
best_T = pt.PT_line(pressure, kappa, gamma1, gamma2, alpha, beta, R_star,
T_star, T_int, sma, grav * 1e2, 'const')
# Load MCMC data
MCMCdata = datadir + 'output.npy'
data = np.load(MCMCdata)
nchains, npars, niter = np.shape(data)
# Make datacube from MCMC data
data_stack = data[0, :, burnin:]
for c in np.arange(1, nchains):
data_stack = np.hstack((data_stack, data[c, :, burnin:]))
# Datacube of PT profiles
PTprofiles = np.zeros((np.shape(data_stack)[1], len(pressure)))
curr_PTparams = PTparams
for k in np.arange(0, np.shape(data_stack)[1]):
j = 0
for i in np.arange(len(PTparams)):
curr_PTparams[i] = data_stack[j, k]
j += 1
kappa, gamma1, gamma2, alpha, beta = curr_PTparams
PTprofiles[k] = pt.PT_line(pressure, kappa, gamma1, gamma2, alpha,
beta, R_star, T_star, T_int, sma,
grav * 1e2, 'const')
# Get percentiles (for 1, 2-sigma boundaries):
low1 = np.percentile(PTprofiles, 16.0, axis=0)
hi1 = np.percentile(PTprofiles, 84.0, axis=0)
low2 = np.percentile(PTprofiles, 2.5, axis=0)
hi2 = np.percentile(PTprofiles, 97.5, axis=0)
median = np.median(PTprofiles, axis=0)
# Plot and save figure
plt.figure(2)
plt.clf()
ax = plt.subplot(111)
ax.fill_betweenx(pressure, low2, hi2, facecolor="#62B1FF", edgecolor="0.5")
ax.fill_betweenx(pressure,
low1,
hi1,
facecolor="#1873CC",
edgecolor="#1873CC")
plt.semilogy(median, pressure, "-", lw=2, label='Median', color="k")
plt.semilogy(best_T, pressure, "-", lw=2, label="Best fit", color="r")
plt.semilogy(atminfo[:, 2], pressure, "--", lw=2, label='Input', color='r')
plt.ylim(pressure[0], pressure[-1])
plt.legend(loc="best")
plt.xlabel("Temperature (K)", size=15)
plt.ylabel("Pressure (bar)", size=15)
plt.savefig(outdir + outname)
plt.close()