plt.ion()
press = np.logspace(-8, 2, 100)
kappa = -3.0
gamma1 = 0.3
gamma2 = 0.1
alpha = 0.6
beta = 0.8
R_star = 0.756 * ac.R_sun.value
T_star = 5000
T_int = 100
sma = 0.03099 * ac.au.value
grav = 2182.73
T_int_type = 'const'
temp = PT.PT_line(press, kappa, gamma1, gamma2, alpha, beta, R_star,
T_star, T_int, sma, grav, T_int_type)
plt.plot(temp, press)
plt.yscale('log')
plt.gca().invert_yaxis()
plt.xlabel('Temperature (K)')
plt.ylabel('Pressure (bar)')
plt.savefig('inv.png', bbox_inches='tight')
plt.close()
out = np.zeros((100, 9))
out[:,0] = press
out[:,1] = temp
out[:,2] = 0.1499751
out[:,3] = 0.8498589
out[:,4] = 1e-4
# Read the pressure file to an array: press = PT.read_press_file(pfile) # System parameters: Rplanet = 1.0 * 6.995e8 # m Tstar = 5700.0 # K Tint = 100.0 # K gplanet = 2200.0 # cm s-2 smaxis = 0.050 * sc.au # m # Fitting parameters: [log10(kappa), log10(g1), log10(g2), alpha, beta] params = np.asarray([-2.0, -0.55, -0.8, 0.5, 1.0]) # Calculate the temperature profile: temp = PT.PT_line(press, params, Rplanet, Tstar, Tint, smaxis, gplanet) # :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: # ( 2) Make an atmospheric file with uniform abundances: import makeatm as ma # Output filename: atmfile = "uniform.atm" # Elemental abundances file: elemabun = "../inputs/abundances_Asplund2009.txt" # Transiting extrasolar planet filename: tep = "../inputs/tep/HD209458b.tep" # Atmospheric species: species = "He H2 CO CO2 CH4 H2O NH3 C2H2 C2H4" # Abundances (mole mixing ratio):
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, 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)
R_star, T_star, sma, gstar = bf.get_starData(tep_name)
# get best parameters
bestP, uncer = bf.read_MCMC_out(logfile)
# get all params
allParams = bf.get_params(bestP, stepsize, params)
# get PTparams and abundances factors
nparams = len(allParams)
nmol = len(molfit)
nPTparams = nparams - nmol
PTparams = allParams[:nPTparams]
kappa, gamma1, gamma2, alpha, beta = PTparams
# HARDCODED !
T_int = 100 # K
T_int_type = 'const'
# call PT line profile to calculate temperature
best_T = pt.PT_line(pressure, kappa, gamma1, gamma2, alpha, beta, R_star,
T_star, T_int, sma, grav, T_int_type)
# get MCMC data:
data = np.load(MCMCdata)
nchains, npars, niter = np.shape(data)
# this is data fro first chain zero
data_stack = data[0, :, burnin:]
# pick a chain that you do not like
bad_chain = 0
bad_chain2 = 0
# now he stack all other chains from 1, you can start from 5 here
for c in np.arange(1, nchains):
if c != bad_chain and c != bad_chain2:
data_stack = np.hstack((data_stack, data[c, :, burnin:]))
print c
