def callTransit(atmfile, tepfile, MCfile, stepsize, molfit, tconfig, date_dir):
# read atmfile
molecules, pressure, temp, abundances = mat.readatm(atmfile)
# get surface gravity
grav, Rp = mat.get_g(tepfile)
# get star data
R_star, T_star, sma = get_starData(tepfile)
# get best parameters
bestP, uncer, SN, mean = read_MCMC_out(MCfile)
# get all params
params = get_params(bestP, stepsize)
# get PTparams and abundances factors
nparams = len(params)
nmol = len(molfit)
nPTparams = nparams - nmol
PTparams = params[:nPTparams]
AbunFact = params[nPTparams:]
# HARDCODED !!!!!!!!!!!!
T_int = 100 # K
# call PT line profile to calculate temperature
T_line = pt.PT_line(pressure, PTparams, R_star, T_star, T_int, sma, grav)
# write best-fit atmospheric file
write_atmfile(atmfile, molfit, T_line, params, date_dir)
# bestFit atm file
bestFit_atm = date_dir + 'bestFit.atm'
# write new bestFit Transit config
bestFit_tconfig(tconfig, date_dir)
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(filters,
kurucz,
tepfile,
solution,
output,
data,
uncert,
date_dir,
fs=15):
'''
Plot BART best-model spectrum
Parameters
----------
filters : list, strings. Paths to filter files corresponding to data.
kurucz : string. Path to Kurucz stellar model file.
tepfile : string. Path to Transiting ExoPlanet (TEP) file.
solution: string. Observing geometry. 'eclipse' or 'transit'.
output : string. Best-fit spectrum output file name.
data : 1D array. Eclipse or transit depths.
uncert : 1D array. Uncertainties for data values.
date_dir: string. Path to directory where the plot will be saved.
fs : int. Font size for plots.
'''
# get star data
R_star, T_star, sma, gstar = 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
if solution == 'eclipse':
specwn, bestspectrum = rt.readspectrum(date_dir + output, wn=True)
# print on screen
print(" Plotting BART best-fit eclipse spectrum figure.")
elif solution == 'transit':
specwn, bestspectrum = rt.readspectrum(date_dir + output, wn=True)
# print on screen
print(" Plotting BART best-fit modulation spectrum figure.")
# 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({'font.size': fs - 2})
plt.figure(3, (8.5, 6))
plt.clf()
# depending on solution plot eclipse or modulation spectrum
if solution == 'eclipse':
gfrat = gaussf(frat, 2)
plt.semilogx(specwl, gfrat * 1e3, "b", lw=1.5, label="Best-fit")
plt.errorbar(meanwl, data * 1e3, uncert * 1e3, fmt="or", label="data")
plt.plot(meanwl, bandflux * 1e3, "ok", label="model", alpha=1.0)
plt.ylabel(r"$F_p/F_s$ (10$^{-3}$)", fontsize=fs)
elif solution == 'transit':
gmodel = gaussf(bestspectrum, 2)
plt.semilogx(specwl, gmodel, "b", lw=1.5, label="Best-fit")
plt.errorbar(meanwl, data, uncert, fmt="or", label="data")
plt.plot(meanwl, bandmod, "ok", label="model", alpha=0.5)
plt.ylabel(r"$(R_p/R_s)^2$", fontsize=fs)
leg = plt.legend(loc="best")
leg.get_frame().set_alpha(0.5)
ax = plt.subplot(111)
ax.set_xscale('log')
plt.xlabel("${\\rm Wavelength\ \ (\u03bcm)}$", fontsize=fs)
#plt.xticks(size=fs)
#plt.yticks(size=fs)
formatter = matplotlib.ticker.FuncFormatter(
lambda y, _: '{:.8g}'.format(y))
ax.get_xaxis().set_major_formatter(formatter)
ax.get_xaxis().set_minor_formatter(formatter)
plt.xlim(min(specwl), max(specwl))
plt.savefig(date_dir + "BART-bestFit-Spectrum.png")
plt.close()
def callTransit(atmfile,
tepfile,
MCfile,
stepsize,
molfit,
solution,
p0,
tconfig,
date_dir,
burnin,
abun_file,
PTtype,
PTfunc,
T_int,
T_int_type,
filters,
ctf=None,
fs=15):
"""
Call Transit to produce best-fit outputs.
Plot MCMC posterior PT plot.
Parameters:
-----------
atmfile: String
Atmospheric file.
tepfile: String
Transiting extra-solar planet file.
MCfile: String
File with MCMC log and best-fitting results.
stepsize: 1D float ndarray
Specified step sizes for each parameter.
molfit: 1D String ndarray
List of molecule names to modify their abundances.
solution: String
Flag to indicate transit or eclipse geometry
p0: Float
Atmosphere's 'surface' pressure level.
tconfig: String
Transit configuration file.
date_dir: String
Directory where to store results.
burnin: Integer
abun_file: String
Elemental abundances file.
PTtype: String
Pressure-temperature profile type ('line' for Line et al. 2013,
'madhu_noinv' or 'madhu_inv' for Madhusudhan & Seager 2009,
'iso' for isothermal)
PTfunc: pointer to function
Determines the method of evaluating the PT profile's temperature
T_int: float.
Internal temperature of the planet.
T_int_type: string.
Method to evaluate `T_int`.
'const' for constant value of `T_int`,
'thorngren' for Thorngren et al. (2019)
filters: list, strings.
Filter files associated with the eclipse/transit depths
ctf: 2D array.
Contribution or transmittance functions corresponding to `filters`
fs: int
Font size for plots.
"""
# make sure burnin is an integer
burnin = int(burnin)
# read atmfile
molecules, pressure, temp, abundances = mat.readatm(atmfile)
# get surface gravity
grav, Rp = mat.get_g(tepfile)
# get star data if needed
if PTtype == 'line':
R_star, T_star, sma, gstar = get_starData(tepfile)
PTargs = [R_star, T_star, T_int, sma, grav * 1e2, T_int_type]
else:
PTargs = None # For non-Line profiles
# Get best parameters
bestP, uncer = read_MCMC_out(MCfile)
allParams = bestP
# get PTparams and abundances factors
nparams = len(allParams)
nmol = len(molfit)
nradfit = int(solution == 'transit')
nPTparams = nparams - nmol - nradfit
PTparams = allParams[:nPTparams]
# call PT profile generator to calculate temperature
best_T = pt.PT_generator(pressure, PTparams, PTfunc, PTargs)
# Plot best PT profile
plt.figure(1)
plt.clf()
plt.semilogy(best_T, pressure, '-', color='r')
plt.xlim(0.9 * min(best_T), 1.1 * max(best_T))
plt.ylim(max(pressure), min(pressure))
plt.title('Best PT', fontsize=fs)
plt.xlabel('T (K)', fontsize=fs)
plt.ylabel('logP (bar)', fontsize=fs)
# Save plot to current directory
plt.savefig(date_dir + 'Best_PT.png')
# Update R0, if needed:
if nradfit:
Rp = allParams[nPTparams]
# write best-fit atmospheric file
write_atmfile(atmfile, abun_file, molfit, best_T,
allParams[nPTparams + nradfit:], date_dir, p0, Rp, grav)
# write new bestFit Transit config
if solution == 'transit':
bestFit_tconfig(tconfig, date_dir, allParams[nPTparams])
else:
bestFit_tconfig(tconfig, date_dir)
# Call Transit with the best-fit tconfig
Transitdir = os.path.join(os.path.dirname(os.path.realpath(__file__)),
"..", "modules", "transit", "")
bf_tconfig = os.path.join(date_dir, 'bestFit_tconfig.cfg')
Tcall = os.path.join(Transitdir, "transit", "transit")
subprocess.call(["{:s} -c {:s}".format(Tcall, bf_tconfig)],
shell=True,
cwd=date_dir)
# ========== plot MCMC PT profiles ==========
# get MCMC data:
MCMCdata = os.path.join(date_dir, "output.npy")
data = np.load(MCMCdata)
nchains, npars, niter = np.shape(data)
# stuck chains:
data_stack = data[0, :, burnin:]
for c in np.arange(1, nchains):
data_stack = np.hstack((data_stack, data[c, :, burnin:]))
# create array of PT profiles
PTprofiles = np.zeros((np.shape(data_stack)[1], len(pressure)))
# current PT parameters for each chain, iteration
curr_PTparams = PTparams
# fill-in PT profiles array
if ctf is None:
print(" Plotting MCMC PT profile figure.")
for k in np.arange(0, np.shape(data_stack)[1]):
j = 0
for i in np.arange(len(PTparams)):
if stepsize[i] != 0.0:
curr_PTparams[i] = data_stack[j, k]
j += 1
else:
pass
PTprofiles[k] = pt.PT_generator(pressure, curr_PTparams, PTfunc,
PTargs)
# get percentiles (for 1,2-sigma boundaries):
low1 = np.percentile(PTprofiles, 15.87, axis=0)
hi1 = np.percentile(PTprofiles, 84.13, axis=0)
low2 = np.percentile(PTprofiles, 2.28, axis=0)
hi2 = np.percentile(PTprofiles, 97.72, axis=0)
median = np.median(PTprofiles, axis=0)
# plot figure
plt.figure(2, dpi=300)
plt.clf()
if ctf is not None:
plt.subplots_adjust(wspace=0.15)
ax1 = plt.subplot(121)
else:
ax1 = plt.subplot(111)
ax1.fill_betweenx(pressure,
low2,
hi2,
facecolor="#62B1FF",
edgecolor="0.5")
ax1.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.ylim(pressure[0], pressure[-1])
plt.legend(loc="best")
plt.xlabel("Temperature (K)", size=fs)
plt.ylabel("Pressure (bar)", size=fs)
if ctf is not None:
nfilters = len(filters)
# Add contribution or transmittance functions
ax2 = plt.subplot(122, sharey=ax1)
colormap = plt.cm.rainbow(np.linspace(0, 1, len(filters)))
ax2.set_prop_cycle(plt.cycler('color', colormap))
# Plot with filter labels
for i in np.arange(len(filters)):
(head, tail) = os.path.split(filters[i])
lbl = tail[:-4]
ax2.semilogy(ctf[i], pressure, '--', linewidth=1, label=lbl)
# Hide y axis tick labels
plt.setp(ax2.get_yticklabels(), visible=False)
# Place legend off figure in case there are many filters
lgd = ax2.legend(loc='center left',
bbox_to_anchor=(1.05, 0.5),
ncol=nfilters // 25 + int(nfilters % 25 != 0),
prop={'size': 8})
if solution == 'eclipse':
ax2.set_xlabel('Normalized Contribution\nFunctions', fontsize=fs)
else:
ax2.set_xlabel('Transmittance', fontsize=fs)
# save figure
if ctf is not None:
savefile = date_dir + "MCMC_PTprofiles_cf.png"
plt.savefig(savefile, bbox_extra_artists=(lgd, ), bbox_inches='tight')
else:
savefile = date_dir + "MCMC_PTprofiles.png"
plt.savefig(savefile)
plt.close()
def plot_bestFit_Spectrum(filters, kurucz, tepfile, solution, output, data,
uncert, date_dir):
'''
Plot BART best-model spectrum
'''
# get star data
R_star, T_star, sma, gstar = 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
if solution == 'eclipse':
specwn, bestspectrum = rt.readspectrum(date_dir + output, wn=True)
# print on screen
print(" Plotting BART best-fit eclipse spectrum figure.")
elif solution == 'transit':
specwn, bestspectrum = rt.readspectrum(date_dir + output, wn=True)
# print on screen
print(" Plotting BART best-fit modulation spectrum figure.")
# 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({'font.size':10})
plt.figure(3, (8.5, 5))
plt.clf()
# depending on solution plot eclipse or modulation spectrum
if solution == 'eclipse':
gfrat = gaussf(frat, 2)
plt.semilogx(specwl, gfrat*1e3, "b", lw=1.5, label="Best-fit")
plt.errorbar(meanwl, data*1e3, uncert*1e3, fmt="or", label="data")
plt.plot(meanwl, bandflux*1e3, "ok", label="model", alpha=1.0)
plt.ylabel(r"$F_p/F_s$ (10$^{3}$)", fontsize=12)
elif solution == 'transit':
gmodel = gaussf(bestspectrum, 2)
plt.semilogx(specwl, gmodel, "b", lw=1.5, label="Best-fit")
# Check units!
plt.errorbar(meanwl, data, uncert, fmt="or", label="data")
plt.plot(meanwl, bandmod, "ok", label="model", alpha=0.5)
plt.ylabel(r"$(R_p/R_s)^2$", fontsize=12)
leg = plt.legend(loc="lower right")
leg.get_frame().set_alpha(0.5)
ax = plt.subplot(111)
ax.set_xscale('log')
plt.xlabel(r"${\rm Wavelength\ \ (um)}$", fontsize=12)
ax.get_xaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())
ax.set_xticks(np.arange(min(specwl),max(specwl),1))
plt.xlim(min(specwl),max(specwl))
plt.savefig(date_dir + "BART-bestFit-Spectrum.png")
def callTransit(atmfile, tepfile, MCfile, stepsize, molfit, solution,
p0, tconfig, date_dir, params, burnin, abun_file):
"""
Call Transit to produce best-fit outputs.
Plot MCMC posterior PT plot.
Parameters:
-----------
atmfile: String
Atmospheric file.
tepfile: String
Transiting extra-solar planet file.
MCfile: String
File with MCMC log and best-fitting results.
stepsize: 1D float ndarray
FINDME
molfit: 1D String ndarray
List of molecule names to modify their abundances.
solution: String
Flag to indicate transit or eclipse geometry
p0: Float
Atmosphere's 'surface' pressure level.
tconfig: String
Transit configuration file.
date_dir: String
Directory where to store results.
params: 1D float ndarray
burnin: Integer
abun_file: String
Elemental abundances file.
"""
# read atmfile
molecules, pressure, temp, abundances = mat.readatm(atmfile)
# get surface gravity
grav, Rp = mat.get_g(tepfile)
# get star data
R_star, T_star, sma, gstar = get_starData(tepfile)
# get best parameters
bestP, uncer, SN, mean = read_MCMC_out(MCfile)
# get all params
allParams = get_params(bestP, stepsize, params)
# get PTparams and abundances factors
nparams = len(allParams)
nmol = len(molfit)
nradfit = int(solution == 'transit')
nPTparams = nparams - nmol - nradfit
PTparams = allParams[:nPTparams]
# FINDME: Hardcoded value:
T_int = 100 # K
# call PT line profile to calculate temperature
best_T = pt.PT_line(pressure, PTparams, R_star, T_star, T_int,
sma, grav*1e2)
# Plot best PT profile
plt.figure(1)
plt.clf()
plt.semilogy(best_T, pressure, '-', color = 'r')
plt.xlim(0.9*min(best_T), 1.1*max(best_T))
plt.ylim(max(pressure), min(pressure))
plt.title('Best PT', fontsize=14)
plt.xlabel('T [K]' , fontsize=14)
plt.ylabel('logP [bar]', fontsize=14)
# Save plot to current directory
plt.savefig(date_dir + 'Best_PT.png')
# Update R0, if needed:
if nradfit:
Rp = allParams[nPTparams]
# Mean molecular mass:
mu = mat.mean_molar_mass(abun_file, atmfile)
# Re-calculate the layers' radii using the Hydrostatic-equilibrium calc:
# (Has to be in reversed order since the interpolation requires the
# pressure array in increasing order)
rad = mat.radpress(pressure[::-1], best_T[::-1], mu[::-1], p0, Rp, grav)
rad = rad[::-1]
# write best-fit atmospheric file
write_atmfile(atmfile, molfit, rad, best_T, allParams[nPTparams+nradfit:],
date_dir)
# bestFit atm file
bestFit_atm = date_dir + 'bestFit.atm'
# write new bestFit Transit config
bestFit_tconfig(tconfig, date_dir)
# ========== plot MCMC PT profiles ==========
# get MCMC data:
MCMCdata = date_dir + "/output.npy"
data = np.load(MCMCdata)
nchains, npars, niter = np.shape(data)
# stuck chains:
data_stack = data[0,:,burnin:]
for c in np.arange(1, nchains):
data_stack = np.hstack((data_stack, data[c, :, burnin:]))
# create array of PT profiles
PTprofiles = np.zeros((np.shape(data_stack)[1], len(pressure)))
# current PT parameters for each chain, iteration
curr_PTparams = PTparams
# fill-in PT profiles array
print(" Plotting MCMC PT profile figure.")
for k in np.arange(0, np.shape(data_stack)[1]):
j = 0
for i in np.arange(len(PTparams)):
if stepsize[i] != 0.0:
curr_PTparams[i] = data_stack[j,k]
j +=1
else:
pass
PTprofiles[k] = pt.PT_line(pressure, curr_PTparams, R_star, T_star,
T_int, sma, grav*1e2)
# 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 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.ylim(pressure[0], pressure[-1])
plt.legend(loc="best")
plt.xlabel("Temperature (K)", size=15)
plt.ylabel("Pressure (bar)", size=15)
# save figure
savefile = date_dir + "MCMC_PTprofiles.png"
plt.savefig(savefile)
def plot_bestFit_Spectrum(filters, kurucz, tepfile, solution, output, data,
uncert, date_dir):
'''
Plot BART best-model spectrum
'''
# get star data
R_star, T_star, sma, gstar = 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
if solution == 'eclipse':
specwn, bestspectrum = rt.readspectrum(date_dir + output, wn=True)
# print on screen
print(" Plotting BART best-fit eclipse spectrum figure.")
elif solution == 'transit':
specwn, bestspectrum = rt.readspectrum(date_dir + output, wn=True)
# print on screen
print(" Plotting BART best-fit modulation spectrum figure.")
# 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({'font.size':10})
plt.figure(3, (8.5, 5))
plt.clf()
# depending on solution plot eclipse or modulation spectrum
if solution == 'eclipse':
gfrat = gaussf(frat, 2)
plt.semilogx(specwl, gfrat*1e3, "b", lw=1.5, label="Best-fit")
plt.errorbar(meanwl, data*1e3, uncert*1e3, fmt="or", label="data")
plt.plot(meanwl, bandflux*1e3, "ok", label="model", alpha=1.0)
plt.ylabel(r"$F_p/F_s$ (10$^{3}$)", fontsize=12)
elif solution == 'transit':
gmodel = gaussf(bestspectrum, 2)
plt.semilogx(specwl, gmodel, "b", lw=1.5, label="Best-fit")
# Check units!
plt.errorbar(meanwl, data, uncert, fmt="or", label="data")
plt.plot(meanwl, bandmod, "ok", label="model", alpha=0.5)
plt.ylabel(r"$(R_p/R_s)^2$", fontsize=12)
leg = plt.legend(loc="lower right")
leg.get_frame().set_alpha(0.5)
ax = plt.subplot(111)
ax.set_xscale('log')
plt.xlabel(r"${\rm Wavelength\ \ (um)}$", fontsize=12)
ax.get_xaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())
ax.set_xticks(np.arange(round(min(specwl)),max(specwl),1))
plt.xlim(min(specwl),max(specwl))
plt.savefig(date_dir + "BART-bestFit-Spectrum.png")
def callTransit(atmfile, tepfile, MCfile, stepsize, molfit, solution,
p0, tconfig, date_dir, params, burnin, abun_file):
"""
Call Transit to produce best-fit outputs.
Plot MCMC posterior PT plot.
Parameters:
-----------
atmfile: String
Atmospheric file.
tepfile: String
Transiting extra-solar planet file.
MCfile: String
File with MCMC log and best-fitting results.
stepsize: 1D float ndarray
FINDME
molfit: 1D String ndarray
List of molecule names to modify their abundances.
solution: String
Flag to indicate transit or eclipse geometry
p0: Float
Atmosphere's 'surface' pressure level.
tconfig: String
Transit configuration file.
date_dir: String
Directory where to store results.
params: 1D float ndarray
burnin: Integer
abun_file: String
Elemental abundances file.
"""
# read atmfile
molecules, pressure, temp, abundances = mat.readatm(atmfile)
# get surface gravity
grav, Rp = mat.get_g(tepfile)
# get star data
R_star, T_star, sma, gstar = get_starData(tepfile)
# Get best parameters
bestP, uncer = read_MCMC_out(MCfile)
# get all params
#allParams = get_params(bestP, stepsize, params)
allParams = bestP
# get PTparams and abundances factors
nparams = len(allParams)
nmol = len(molfit)
nradfit = int(solution == 'transit')
nPTparams = nparams - nmol - nradfit
PTparams = allParams[:nPTparams]
# FINDME: Hardcoded value:
T_int = 100 # K
# call PT line profile to calculate temperature
best_T = pt.PT_line(pressure, PTparams, R_star, T_star, T_int,
sma, grav*1e2)
# Plot best PT profile
plt.figure(1)
plt.clf()
plt.semilogy(best_T, pressure, '-', color = 'r')
plt.xlim(0.9*min(best_T), 1.1*max(best_T))
plt.ylim(max(pressure), min(pressure))
plt.title('Best PT', fontsize=14)
plt.xlabel('T [K]' , fontsize=14)
plt.ylabel('logP [bar]', fontsize=14)
# Save plot to current directory
plt.savefig(date_dir + 'Best_PT.png')
# Update R0, if needed:
if nradfit:
Rp = allParams[nPTparams]
# Mean molecular mass:
mu = mat.mean_molar_mass(abun_file, atmfile)
# Re-calculate the layers' radii using the Hydrostatic-equilibrium calc:
# (Has to be in reversed order since the interpolation requires the
# pressure array in increasing order)
rad = mat.radpress(pressure[::-1], best_T[::-1], mu[::-1], p0, Rp, grav)
rad = rad[::-1]
# write best-fit atmospheric file
write_atmfile(atmfile, molfit, rad, best_T, allParams[nPTparams+nradfit:],
date_dir)
# bestFit atm file
bestFit_atm = date_dir + 'bestFit.atm'
# write new bestFit Transit config
bestFit_tconfig(tconfig, date_dir)
# ========== plot MCMC PT profiles ==========
# get MCMC data:
MCMCdata = date_dir + "/output.npy"
data = np.load(MCMCdata)
nchains, npars, niter = np.shape(data)
# stuck chains:
data_stack = data[0,:,burnin:]
for c in np.arange(1, nchains):
data_stack = np.hstack((data_stack, data[c, :, burnin:]))
# create array of PT profiles
PTprofiles = np.zeros((np.shape(data_stack)[1], len(pressure)))
# current PT parameters for each chain, iteration
curr_PTparams = PTparams
# fill-in PT profiles array
print(" Plotting MCMC PT profile figure.")
for k in np.arange(0, np.shape(data_stack)[1]):
j = 0
for i in np.arange(len(PTparams)):
if stepsize[i] != 0.0:
curr_PTparams[i] = data_stack[j,k]
j +=1
else:
pass
PTprofiles[k] = pt.PT_line(pressure, curr_PTparams, R_star, T_star,
T_int, sma, grav*1e2)
# 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 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.ylim(pressure[0], pressure[-1])
plt.legend(loc="best")
plt.xlabel("Temperature (K)", size=15)
plt.ylabel("Pressure (bar)", size=15)
# save figure
savefile = date_dir + "MCMC_PTprofiles.png"
plt.savefig(savefile)
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,
'ytick.labelsize': 14,})
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")
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()
def callTransit(atmfile, tepfile, MCfile, stepsize, molfit, solution, p0,
tconfig, date_dir, burnin, abun_file, PTtype, PTfunc,
filters, ctf=None):
"""
Call Transit to produce best-fit outputs.
Plot MCMC posterior PT plot.
Parameters:
-----------
atmfile: String
Atmospheric file.
tepfile: String
Transiting extra-solar planet file.
MCfile: String
File with MCMC log and best-fitting results.
stepsize: 1D float ndarray
Specified step sizes for each parameter.
molfit: 1D String ndarray
List of molecule names to modify their abundances.
solution: String
Flag to indicate transit or eclipse geometry
p0: Float
Atmosphere's 'surface' pressure level.
tconfig: String
Transit configuration file.
date_dir: String
Directory where to store results.
burnin: Integer
abun_file: String
Elemental abundances file.
PTtype: String
Pressure-temperature profile type ('line' for Line et al. 2013,
'madhu_noinv' or 'madhu_inv' for Madhusudhan & Seager 2009,
'iso' for isothermal)
PTfunc: pointer to function
Determines the method of evaluating the PT profile's temperature
filters: list, strings.
Filter files associated with the eclipse/transit depths
ctf: 2D array.
Contribution or transmittance functions corresponding to `filters`
"""
# make sure burnin is an integer
burnin = int(burnin)
# read atmfile
molecules, pressure, temp, abundances = mat.readatm(atmfile)
# get surface gravity
grav, Rp = mat.get_g(tepfile)
# get star data if needed
if PTtype == 'line':
R_star, T_star, sma, gstar = get_starData(tepfile)
# FINDME: Hardcoded value:
T_int = 100 # K
PTargs = [R_star, T_star, T_int, sma, grav*1e2]
else:
PTargs = None # For non-Line profiles
# Get best parameters
bestP, uncer = read_MCMC_out(MCfile)
allParams = bestP
# get PTparams and abundances factors
nparams = len(allParams)
nmol = len(molfit)
nradfit = int(solution == 'transit')
nPTparams = nparams - nmol - nradfit
PTparams = allParams[:nPTparams]
# call PT profile generator to calculate temperature
best_T = pt.PT_generator(pressure, PTparams, PTfunc, PTargs)
# Plot best PT profile
plt.figure(1)
plt.clf()
plt.semilogy(best_T, pressure, '-', color = 'r')
plt.xlim(0.9*min(best_T), 1.1*max(best_T))
plt.ylim(max(pressure), min(pressure))
plt.title('Best PT', fontsize=14)
plt.xlabel('T (K)' , fontsize=14)
plt.ylabel('logP (bar)', fontsize=14)
# Save plot to current directory
plt.savefig(date_dir + 'Best_PT.png')
# Update R0, if needed:
if nradfit:
Rp = allParams[nPTparams]
# write best-fit atmospheric file
write_atmfile(atmfile, abun_file, molfit, best_T,
allParams[nPTparams+nradfit:], date_dir, p0, Rp, grav)
# write new bestFit Transit config
if solution == 'transit':
bestFit_tconfig(tconfig, date_dir, allParams[nPTparams])
else:
bestFit_tconfig(tconfig, date_dir)
# Call Transit with the best-fit tconfig
Transitdir = os.path.dirname(os.path.realpath(__file__)) + \
"/../modules/transit/"
bf_tconfig = date_dir + 'bestFit_tconfig.cfg'
Tcall = Transitdir + "/transit/transit"
subprocess.call(["{:s} -c {:s}".format(Tcall, bf_tconfig)],
shell=True, cwd=date_dir)
# ========== plot MCMC PT profiles ==========
# get MCMC data:
MCMCdata = date_dir + "/output.npy"
data = np.load(MCMCdata)
nchains, npars, niter = np.shape(data)
# stuck chains:
data_stack = data[0,:,burnin:]
for c in np.arange(1, nchains):
data_stack = np.hstack((data_stack, data[c, :, burnin:]))
# create array of PT profiles
PTprofiles = np.zeros((np.shape(data_stack)[1], len(pressure)))
# current PT parameters for each chain, iteration
curr_PTparams = PTparams
# fill-in PT profiles array
if ctf is None:
print(" Plotting MCMC PT profile figure.")
for k in np.arange(0, np.shape(data_stack)[1]):
j = 0
for i in np.arange(len(PTparams)):
if stepsize[i] != 0.0:
curr_PTparams[i] = data_stack[j,k]
j +=1
else:
pass
PTprofiles[k] = pt.PT_generator(pressure, curr_PTparams,
PTfunc, PTargs)
# 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 figure
plt.figure(2, dpi=300)
plt.clf()
if ctf is not None:
plt.subplots_adjust(wspace=0.15)
ax1=plt.subplot(121)
else:
ax1=plt.subplot(111)
ax1.fill_betweenx(pressure, low2, hi2, facecolor="#62B1FF",
edgecolor="0.5")
ax1.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.ylim(pressure[0], pressure[-1])
plt.legend(loc="best")
plt.xlabel("Temperature (K)", size=15)
plt.ylabel("Pressure (bar)", size=15)
if ctf is not None:
# Add contribution or transmittance functions
ax2=plt.subplot(122, sharey=ax1)
colormap = plt.cm.rainbow(np.linspace(0, 1, len(filters)))
ax2.set_prop_cycle(plt.cycler('color', colormap))
# Plot with filter labels
for i in np.arange(len(filters)):
(head, tail) = os.path.split(filters[i])
lbl = tail[:-4]
ax2.semilogy(ctf[i], pressure, '--', linewidth = 1, label=lbl)
# Hide y axis tick labels
plt.setp(ax2.get_yticklabels(), visible=False)
# Place legend off figure in case there are many filters
lgd = ax2.legend(loc='center left', bbox_to_anchor=(1,0.5),
ncol=len(filters)//30 + 1, prop={'size':8})
if solution == 'eclipse':
ax2.set_xlabel('Normalized Contribution\nFunctions', fontsize=15)
else:
ax2.set_xlabel('Transmittance', fontsize=15)
# save figure
if ctf is not None:
savefile = date_dir + "MCMC_PTprofiles_cf.png"
plt.savefig(savefile, bbox_extra_artists=(lgd,), bbox_inches='tight')
else:
savefile = date_dir + "MCMC_PTprofiles.png"
plt.savefig(savefile)
