def cf(date_dir, atmfile, filters):
"""
Call above functions to calculate cf and plot them
"""
# Read atmfile
molecules, p, T, abundances = mat.readatm(atmfile)
nlayers = len(p)
# Read tau.dat
file = date_dir + 'tau.dat'
tau, wns = readTauDat(file, nlayers)
# Calculate BB, reverse the order of p and T
p = p[::-1]
T = T[::-1]
BB = Planck(T, wns)
# Call cf_eq() to calculate cf
cf = cf_eq(BB, p, tau, nlayers, wns)
# Call filter_cf() to calculate cf
filt_cf, filt_cf_norm = filter_cf(filters, nlayers, wns, cf)
############ Plotting ################
print(" Plotting contribution functions.\n")
# Not normalized cf
plt.figure(4)
plt.clf()
gs = gridspec.GridSpec(1, 2, width_ratios=[5, 1])
ax0 = plt.subplot(gs[0])
for i in np.arange(len(filt_cf)):
(head, tail) = os.path.split(filters[i])
lbl = tail[:-4]
ax0.semilogy(filt_cf[i], p, '-', linewidth = 1, label=lbl)
ax0.legend(loc='center left', bbox_to_anchor=(1.0, 0.5), prop={'size':8})
ax0.set_ylim(max(p), min(p))
ax0.ticklabel_format(style='sci', axis='x', scilimits=(0,0))
ax0.set_xlabel('Contribution functions', fontsize=14)
ax0.set_ylabel('Pressure [bar]' , fontsize=14)
plt.savefig(date_dir + 'ContrFuncs.png')
# Normalized cf
plt.figure(5)
plt.clf()
gs = gridspec.GridSpec(1, 2, width_ratios=[5, 1])
ax0 = plt.subplot(gs[0])
for i in np.arange(len(filt_cf_norm)):
(head, tail) = os.path.split(filters[i])
lbl = tail[:-4]
ax0.semilogy(filt_cf_norm[i], p, '--', linewidth = 1, label=lbl)
ax0.legend(loc='center left', bbox_to_anchor=(1,0.5), prop={'size':8})
ax0.set_ylim(max(p), min(p))
ax0.set_xlim(0, 1.0)
ax0.ticklabel_format(style='sci', axis='x', scilimits=(0,0))
ax0.set_xlabel('Normalized contribution functions', fontsize=14)
ax0.set_ylabel('Pressure [bar]' , fontsize=14)
plt.savefig(date_dir + 'NormContrFuncs.png')
def cf(date_dir, atmfile, filters):
"""
Call above functions to calculate cf and plot them
"""
# Read atmfile
molecules, p, T, abundances = mat.readatm(atmfile)
nlayers = len(p)
# Read tau.dat
file = date_dir + 'tau.dat'
tau, wns = readTauDat(file, nlayers)
# Calculate BB, reverse the order of p and T
p = p[::-1]
T = T[::-1]
BB = Planck(T, wns)
# Call cf_eq() to calculate cf
cf = cf_eq(BB, p, tau, nlayers, wns)
# Call filter_cf() to calculate cf
filt_cf, filt_cf_norm = filter_cf(filters, nlayers, wns, cf)
############ Plotting ################
print(" Plotting contribution functions.\n")
# Not normalized cf
plt.figure(4)
plt.clf()
gs = gridspec.GridSpec(1, 2, width_ratios=[5, 1])
ax0 = plt.subplot(gs[0])
for i in np.arange(len(filt_cf)):
(head, tail) = os.path.split(filters[i])
lbl = tail[:-4]
ax0.semilogy(filt_cf[i], p, '-', linewidth=1, label=lbl)
ax0.legend(loc='center left', bbox_to_anchor=(1.0, 0.5), prop={'size': 8})
ax0.set_ylim(max(p), min(p))
ax0.ticklabel_format(style='sci', axis='x', scilimits=(0, 0))
ax0.set_xlabel('Contribution functions', fontsize=14)
ax0.set_ylabel('Pressure [bar]', fontsize=14)
plt.savefig(date_dir + 'ContrFuncs.png')
# Normalized cf
plt.figure(5)
plt.clf()
gs = gridspec.GridSpec(1, 2, width_ratios=[5, 1])
ax0 = plt.subplot(gs[0])
for i in np.arange(len(filt_cf_norm)):
(head, tail) = os.path.split(filters[i])
lbl = tail[:-4]
ax0.semilogy(filt_cf_norm[i], p, '--', linewidth=1, label=lbl)
ax0.legend(loc='center left', bbox_to_anchor=(1, 0.5), prop={'size': 8})
ax0.set_ylim(max(p), min(p))
ax0.set_xlim(0, 1.0)
ax0.ticklabel_format(style='sci', axis='x', scilimits=(0, 0))
ax0.set_xlabel('Normalized contribution functions', fontsize=14)
ax0.set_ylabel('Pressure [bar]', fontsize=14)
plt.savefig(date_dir + 'NormContrFuncs.png')
def transmittance(date_dir, atmfile, filters, fext='.png', plot=True):
"""
"""
# Read atmfile
molecules, p, T, abundances = mat.readatm(date_dir + atmfile)
nlayers = len(p)
p = p[::-1] # top to bottom of the atmosphere
# Read tau.dat
foo = date_dir + 'tau.dat'
tau, wns = readTauDat(foo, nlayers)
# Transmittance:
transmit = np.exp(-tau)
# Band intgrate it:
filt_tr = filter_cf(filters, nlayers, wns, transmit)
nfilters = len(filters)
meanwl = np.zeros(nfilters)
for i in np.arange(nfilters):
filtwaven, filttransm = w.readfilter(filters[i])
meanwn = np.sum(filtwaven * filttransm) / np.sum(filttransm)
meanwl[i] = 1e4 / meanwn
maxmeanwl = np.max(meanwl)
minmeanwl = np.min(meanwl)
colors = (meanwl - minmeanwl) / (maxmeanwl - minmeanwl)
if plot:
print(" Plotting contribution functions.\n")
# Not normalized cf
plt.figure(4, figsize=(8, 6.5))
plt.clf()
gs = gridspec.GridSpec(1, 2, width_ratios=[20, 1], wspace=0.05)
ax0 = plt.subplot(gs[0])
ax1 = plt.subplot(gs[1])
for i in np.arange(nfilters):
(head, tail) = os.path.split(filters[i])
lbl = tail[:-4]
ax0.semilogy(filt_tr[i],
p,
'-',
color=plt.cm.rainbow(colors[i]),
linewidth=1,
label=lbl)
norm = matplotlib.colors.Normalize(vmin=minmeanwl, vmax=maxmeanwl)
cbar = matplotlib.colorbar.ColorbarBase(ax1,
cmap=plt.cm.rainbow,
norm=norm,
orientation='vertical')
cbar.set_label("Mean Wavelength (\u00b5m)", fontsize=14)
ax0.set_ylim(max(p), min(p))
ax0.ticklabel_format(style='sci', axis='x', scilimits=(0, 0))
ax0.set_xlabel('Transmittance', fontsize=14)
ax0.set_ylabel('Pressure (bar)', fontsize=14)
plt.savefig(date_dir + 'Transmittance' + fext, bbox_inches='tight')
plt.close()
return filt_tr[:, ::-1]
def plotabun(date_dir, atmfile, molfit, fext='.png', fs=15):
'''
Plot abundance profiles from best fit run.
Input
-----
date_dir: string
Path to BART output directory
atmfile: string
Name of best fit atmospheric file
molfit: 1D string array
Molecules to plot
fext: string
File extension for the plots to be saved.
Options: .png, .pdf
Default: .png
fs: int
Font size for plots.
'''
# Import best fit atmosphere results
species, pressure, temp, abundances = mat.readatm(date_dir + atmfile)
# Create array of indices for species to plot
molfitindex = np.zeros(len(molfit), dtype='int')
k = 0
# Find the index of each species within the atmosphere file
for i in range(len(species)):
for j in range(len(molfit)):
if species[i] == molfit[j]:
molfitindex[k] = i
k += 1
plt.clf()
# Plot the abundance profile of each species
for i in range(len(molfit)):
plt.loglog(abundances[:,molfitindex[i]], pressure,
label=species[molfitindex[i]], linewidth=4)
plt.legend(loc='upper left')
plt.xlabel('Molar Mixing Fraction', fontsize=fs)
plt.ylabel('Pressure (bar)', fontsize=fs)
plt.ylim(np.amin(pressure), np.amax(pressure))
plt.title('Best Fit Abundance Profiles')
plt.gca().invert_yaxis()
plt.savefig(date_dir + 'abun_profiles' + fext, bbox_inches='tight')
def transmittance(date_dir, atmfile, filters, plot=True):
"""
"""
# Read atmfile
molecules, p, T, abundances = mat.readatm(date_dir + atmfile)
nlayers = len(p)
p = p[::-1] # top to bottom of the atmosphere
# Read tau.dat
foo = date_dir + 'tau.dat'
tau, wns = readTauDat(foo, nlayers)
# Transmittance:
transmit = np.exp(-tau)
# Band intgrate it:
filt_tr = filter_cf(filters, nlayers, wns, transmit)
nfilters = len(filters)
colors = plt.cm.rainbow(np.asarray(np.linspace(0, 255, nfilters), np.int))
if plot:
print(" Plotting contribution functions.\n")
# Not normalized cf
plt.figure(4)
plt.clf()
gs = gridspec.GridSpec(1, 2, width_ratios=[5, 1])
ax0 = plt.subplot(gs[0])
colormap = plt.cm.rainbow(np.linspace(0, 1, len(filters)))
ax0.set_prop_cycle(plt.cycler('color', colormap))
for i in np.arange(len(filt_tr)):
(head, tail) = os.path.split(filters[i])
lbl = tail[:-4]
ax0.semilogy(filt_tr[i],
p,
'-',
linewidth=1.5,
label=lbl,
color=colors[i])
lgd = ax0.legend(loc='center left',
bbox_to_anchor=(1.0, 0.5),
ncol=nfilters // 30 + 1,
prop={'size': 8})
ax0.set_ylim(max(p), min(p))
ax0.ticklabel_format(style='sci', axis='x', scilimits=(0, 0))
ax0.set_xlabel('Transmittance', fontsize=14)
ax0.set_ylabel('Pressure (bar)', fontsize=14)
plt.savefig(date_dir + 'Transmittance.png')
return filt_tr[:, ::-1]
def plotabun(date_dir, atmfile, molfit):
'''
Plot abundance profiles from best fit run.
Input
-----
date_dir: string
Path to BART output directory
atmfile: string
Name of best fit atmospheric file
molfit: 1D string array
Molecules to plot
'''
# Import best fit atmosphere results
species, pressure, temp, abundances = mat.readatm(date_dir + atmfile)
# Create array of indices for species to plot
molfitindex = np.zeros(len(molfit), dtype='int')
k = 0
# Find the index of each species within the atmosphere file
for i in range(len(species)):
for j in range(len(molfit)):
if species[i] == molfit[j]:
molfitindex[k] = i
k += 1
plt.clf()
# Plot the abundance profile of each species
for i in range(len(molfit)):
plt.loglog(abundances[:,molfitindex[i]], pressure, label=species[molfitindex[i]], linewidth=4)
plt.legend(loc='upper left')
plt.xlabel('Molar Mixing Fraction')
plt.ylabel('Pressure (bars)')
plt.title('Best Fit Abundance Profiles')
plt.gca().invert_yaxis()
plt.savefig(date_dir + 'abun_profiles.png')
def transmittance(date_dir, atmfile, filters, plot=True):
"""
"""
# Read atmfile
molecules, p, T, abundances = mat.readatm(date_dir + atmfile)
nlayers = len(p)
p = p[::-1] # top to bottom of the atmosphere
# Read tau.dat
foo = date_dir + 'tau.dat'
tau, wns = readTauDat(foo, nlayers)
# Transmittance:
transmit = np.exp(-tau)
# Band intgrate it:
filt_tr = filter_cf(filters, nlayers, wns, transmit)
nfilters = len(filters)
colors = plt.cm.rainbow(np.asarray(np.linspace(0, 255, nfilters), np.int))
if plot:
print(" Plotting contribution functions.\n")
# Not normalized cf
plt.figure(4)
plt.clf()
gs = gridspec.GridSpec(1, 2, width_ratios=[5, 1])
ax0 = plt.subplot(gs[0])
colormap = plt.cm.rainbow(np.linspace(0, 1, len(filters)))
ax0.set_prop_cycle(plt.cycler('color', colormap))
for i in np.arange(len(filt_tr)):
(head, tail) = os.path.split(filters[i])
lbl = tail[:-4]
ax0.semilogy(filt_tr[i], p, '-', linewidth = 1.5, label=lbl,
color=colors[i])
lgd = ax0.legend(loc='center left', bbox_to_anchor=(1.0, 0.5),
ncol=nfilters//30 + 1, prop={'size':8})
ax0.set_ylim(max(p), min(p))
ax0.ticklabel_format(style='sci', axis='x', scilimits=(0,0))
ax0.set_xlabel('Transmittance', fontsize=14)
ax0.set_ylabel('Pressure (bar)' , fontsize=14)
plt.savefig(date_dir + 'Transmittance.png')
return filt_tr[:,::-1]
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 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 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)
def cf(date_dir, atmfile, filters, plot=True, fs=15):
"""
Call above functions to calculate cf and plot them
"""
# Read atmfile
molecules, p, T, abundances = mat.readatm(date_dir + atmfile)
nlayers = len(p)
# Read tau.dat
foo = date_dir + 'tau.dat'
tau, wns = readTauDat(foo, nlayers)
# Calculate BB, reverse the order of p and T
p = p[::-1]
T = T[::-1]
BB = Planck(T, wns)
# Call cf_eq() to calculate cf
cf = cf_eq(BB, p, tau, nlayers, wns)
# Call filter_cf() to calculate cf
filt_cf, filt_cf_norm = filter_cf(filters,
nlayers,
wns,
cf,
normalize=True)
nfilters = len(filters)
meanwl = np.zeros(nfilters)
for i in np.arange(nfilters):
filtwaven, filttransm = w.readfilter(filters[i])
meanwn = np.sum(filtwaven * filttransm) / np.sum(filttransm)
meanwl[i] = 1e4 / meanwn
maxmeanwl = np.max(meanwl)
minmeanwl = np.min(meanwl)
colors = (meanwl - minmeanwl) / (maxmeanwl - minmeanwl)
if plot:
print(" Plotting contribution functions.\n")
# Not normalized cf
plt.figure(4)
plt.clf()
gs = gridspec.GridSpec(1, 2, width_ratios=[20, 1], wspace=0.05)
ax0 = plt.subplot(gs[0])
ax1 = plt.subplot(gs[1])
for i in np.arange(len(filt_cf)):
(head, tail) = os.path.split(filters[i])
lbl = tail[:-4]
ax0.semilogy(filt_cf[i],
p,
'-',
color=plt.cm.rainbow(colors[i]),
linewidth=1,
label=lbl)
norm = matplotlib.colors.Normalize(vmin=minmeanwl, vmax=maxmeanwl)
cbar = matplotlib.colorbar.ColorbarBase(ax1,
cmap=plt.cm.rainbow,
norm=norm,
orientation='vertical')
cbar.set_label("Mean Wavelength (\u00b5m)", fontsize=fs)
cbar.ax.tick_params(labelsize=fs - 4)
ax0.set_ylim(max(p), min(p))
ax0.ticklabel_format(style='sci', axis='x', scilimits=(0, 0))
ax0.set_xlabel('Contribution Functions', fontsize=fs)
ax0.set_ylabel('Pressure (bar)', fontsize=fs)
plt.xticks(size=fs - 4)
plt.yticks(size=fs - 4)
plt.savefig(date_dir + 'ContrFuncs.png', bbox_inches='tight')
# Normalized cf
plt.figure(5, figsize=(8, 6.5))
plt.clf()
gs = gridspec.GridSpec(1, 2, width_ratios=[20, 1], wspace=0.05)
ax0 = plt.subplot(gs[0])
ax1 = plt.subplot(gs[1])
for i in np.arange(len(filt_cf_norm)):
(head, tail) = os.path.split(filters[i])
lbl = tail[:-4]
ax0.semilogy(filt_cf_norm[i],
p,
'--',
color=plt.cm.rainbow(colors[i]),
linewidth=1,
label=lbl)
norm = matplotlib.colors.Normalize(vmin=minmeanwl, vmax=maxmeanwl)
cbar = matplotlib.colorbar.ColorbarBase(ax1,
cmap=plt.cm.rainbow,
norm=norm,
orientation='vertical')
cbar.set_label("Mean Wavelength (\u00b5m)", fontsize=fs)
cbar.ax.tick_params(labelsize=fs - 4)
ax0.set_ylim(max(p), min(p))
ax0.set_xlim(0, 1.0)
ax0.ticklabel_format(style='sci', axis='x', scilimits=(0, 0))
ax0.set_xlabel('Normalized Contribution Functions', fontsize=fs)
ax0.set_ylabel('Pressure (bar)', fontsize=fs)
plt.xticks(size=fs - 4)
plt.yticks(size=fs - 4)
plt.savefig(date_dir + 'NormContrFuncs.png', bbox_inches='tight')
return filt_cf[:, ::-1], filt_cf_norm[:, ::-1]
def cf(date_dir, atmfile, filters, plot=True):
"""
Call above functions to calculate cf and plot them
"""
# Read atmfile
molecules, p, T, abundances = mat.readatm(date_dir + atmfile)
nlayers = len(p)
# Read tau.dat
foo = date_dir + 'tau.dat'
tau, wns = readTauDat(foo, nlayers)
# Calculate BB, reverse the order of p and T
p = p[::-1]
T = T[::-1]
BB = Planck(T, wns)
# Call cf_eq() to calculate cf
cf = cf_eq(BB, p, tau, nlayers, wns)
# Call filter_cf() to calculate cf
filt_cf, filt_cf_norm = filter_cf(filters, nlayers, wns, cf, normalize=True)
if plot:
print(" Plotting contribution functions.\n")
# Not normalized cf
plt.figure(4)
plt.clf()
gs = gridspec.GridSpec(1, 2, width_ratios=[5, 1])
ax0 = plt.subplot(gs[0])
colormap = plt.cm.rainbow(np.linspace(0, 1, len(filters)))
ax0.set_prop_cycle(plt.cycler('color', colormap))
for i in np.arange(len(filt_cf)):
(head, tail) = os.path.split(filters[i])
lbl = tail[:-4]
ax0.semilogy(filt_cf[i], p, '-', linewidth = 1, label=lbl)
lgd = ax0.legend(loc='center left', bbox_to_anchor=(1.0, 0.5),
ncol=len(filt_cf)//30 + 1, prop={'size':8})
ax0.set_ylim(max(p), min(p))
ax0.ticklabel_format(style='sci', axis='x', scilimits=(0,0))
ax0.set_xlabel('Contribution Functions', fontsize=14)
ax0.set_ylabel('Pressure (bar)' , fontsize=14)
plt.savefig(date_dir + 'ContrFuncs.png', bbox_extra_artists=(lgd,),
bbox_inches='tight')
# Normalized cf
plt.figure(5)
plt.clf()
gs = gridspec.GridSpec(1, 2, width_ratios=[5, 1])
ax0 = plt.subplot(gs[0])
colormap = plt.cm.rainbow(np.linspace(0, 1, len(filters)))
ax0.set_prop_cycle(plt.cycler('color', colormap))
for i in np.arange(len(filt_cf_norm)):
(head, tail) = os.path.split(filters[i])
lbl = tail[:-4]
ax0.semilogy(filt_cf_norm[i], p, '--', linewidth = 1, label=lbl)
lgd = ax0.legend(loc='center left', bbox_to_anchor=(1,0.5),
ncol=len(filt_cf)//30 + 1, prop={'size':8})
ax0.set_ylim(max(p), min(p))
ax0.set_xlim(0, 1.0)
ax0.ticklabel_format(style='sci', axis='x', scilimits=(0,0))
ax0.set_xlabel('Normalized Contribution Functions', fontsize=14)
ax0.set_ylabel('Pressure (bar)' , fontsize=14)
plt.savefig(date_dir + 'NormContrFuncs.png', bbox_extra_artists=(lgd,),
bbox_inches='tight')
return filt_cf[:,::-1], filt_cf_norm[:,::-1]
'xtick.labelsize': 14,
'ytick.labelsize': 14,
})
# 4 mol uniform
atmfile = './7species_7opac_uniform/bestFit.atm'
params = np.array([
-0.6, -0.4, 0.0, 0.0, 1.09, -1.07, -1.168, 1.784, -1.749, -3.0, 3.0, -4.5
])
logfile = './7species_7opac_uniform/MCMC.log'
stepsize = np.array(
[0.01, 0.01, 0.0, 0.0, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01])
MCMCdata = './7species_7opac_uniform/output.npy'
# read atmfile
molecules, pressure, temp, abundances = mat.readatm(atmfile)
# get surface gravity
grav, Rp = mat.get_g(tep_name)
# convert gravity to cm/s^2
grav = grav * 1e2
# get star data
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]
def cf(date_dir, atmfile, filters, plot=True):
"""
Call above functions to calculate cf and plot them
"""
# Read atmfile
molecules, p, T, abundances = mat.readatm(date_dir + atmfile)
nlayers = len(p)
# Read tau.dat
foo = date_dir + 'tau.dat'
tau, wns = readTauDat(foo, nlayers)
# Calculate BB, reverse the order of p and T
p = p[::-1]
T = T[::-1]
BB = Planck(T, wns)
# Call cf_eq() to calculate cf
cf = cf_eq(BB, p, tau, nlayers, wns)
# Call filter_cf() to calculate cf
filt_cf, filt_cf_norm = filter_cf(filters,
nlayers,
wns,
cf,
normalize=True)
if plot:
print(" Plotting contribution functions.\n")
# Not normalized cf
plt.figure(4)
plt.clf()
gs = gridspec.GridSpec(1, 2, width_ratios=[5, 1])
ax0 = plt.subplot(gs[0])
colormap = plt.cm.rainbow(np.linspace(0, 1, len(filters)))
ax0.set_prop_cycle(plt.cycler('color', colormap))
for i in np.arange(len(filt_cf)):
(head, tail) = os.path.split(filters[i])
lbl = tail[:-4]
ax0.semilogy(filt_cf[i], p, '-', linewidth=1, label=lbl)
lgd = ax0.legend(loc='center left',
bbox_to_anchor=(1.0, 0.5),
ncol=len(filt_cf) // 30 + 1,
prop={'size': 8})
ax0.set_ylim(max(p), min(p))
ax0.ticklabel_format(style='sci', axis='x', scilimits=(0, 0))
ax0.set_xlabel('Contribution Functions', fontsize=14)
ax0.set_ylabel('Pressure (bar)', fontsize=14)
plt.savefig(date_dir + 'ContrFuncs.png',
bbox_extra_artists=(lgd, ),
bbox_inches='tight')
# Normalized cf
plt.figure(5)
plt.clf()
gs = gridspec.GridSpec(1, 2, width_ratios=[5, 1])
ax0 = plt.subplot(gs[0])
colormap = plt.cm.rainbow(np.linspace(0, 1, len(filters)))
ax0.set_prop_cycle(plt.cycler('color', colormap))
for i in np.arange(len(filt_cf_norm)):
(head, tail) = os.path.split(filters[i])
lbl = tail[:-4]
ax0.semilogy(filt_cf_norm[i], p, '--', linewidth=1, label=lbl)
lgd = ax0.legend(loc='center left',
bbox_to_anchor=(1, 0.5),
ncol=len(filt_cf) // 30 + 1,
prop={'size': 8})
ax0.set_ylim(max(p), min(p))
ax0.set_xlim(0, 1.0)
ax0.ticklabel_format(style='sci', axis='x', scilimits=(0, 0))
ax0.set_xlabel('Normalized Contribution Functions', fontsize=14)
ax0.set_ylabel('Pressure (bar)', fontsize=14)
plt.savefig(date_dir + 'NormContrFuncs.png',
bbox_extra_artists=(lgd, ),
bbox_inches='tight')
return filt_cf[:, ::-1], filt_cf_norm[:, ::-1]
def get_widths(cfile):
"""
Calculate the max and min Lorentz and Doppler broadening HWHM for
the given configuration file.
Parameters:
-----------
cfile: String
A BART configuration file.
"""
# Read config:
config = ConfigParser.SafeConfigParser()
config.optionxform = str # This one enable Uppercase in arguments
config.read([cfile])
defaults = dict(config.items("MCMC"))
# Get min-max wavenumber:
try:
wnmin = 1.0/(float(defaults["wlhigh"])*float(defaults["wlfct"]))
except:
wnmin = float(defaults["wnlow"]) * float(defaults["wnfct"])
try:
wnmax = 1.0/(float(defaults["wllow"])*float(defaults["wlfct"]))
except:
wnmax = float(defaults["wnhigh"]) * float(defaults["wnfct"])
# Read atmospheric file:
molecs, pressure, temps, abun = ma.readatm(defaults["atmfile"])
# Get min-max temperatures:
try:
tmin = float(defaults["tlow"])
except:
tmin = np.amin(temps)
try:
tmax = float(defaults["thigh"])
except:
tmax = np.amax(temps)
# Get min-max pressures:
pmin = np.amin(pressure) * 1e6
pmax = np.amax(pressure) * 1e6
# Get masses:
molfile = scriptsdir + "/../modules/transit/inputs/molecules.dat"
ID, mol, mass, diam = readmol(molfile)
# Keep only molecules from the atmospheric file:
mask = np.zeros(len(mol), int)
for i in np.arange(len(mol)):
mask[i] = mol[i] in molecs
mol = mol [np.where(mask)]
mass = mass[np.where(mask)] * sc.u * 1e3 # grams
diam = diam[np.where(mask)] * sc.angstrom * 100 # cm
# Sort molecules according to the order in the atmospheric file:
isort = np.zeros(len(mol), int)
for i in np.arange(len(mol)):
isort[i] = np.where(np.asarray(mol) == molecs[i])[0][0]
mol = mol [isort]
mass = mass[isort]
diam = diam[isort]
# Calculate minimum and maximum Doppler widths:
dmin = Doppler(wnmin, tmin, np.amin(mass))
dmax = Doppler(wnmax, tmax, np.amax(mass))
iH2 = np.where(mol == 'H2')[0]
iHe = np.where(mol == 'He')[0]
# Calculate minimum and maximum Lorentz widths:
lmin = Lorentz(pmin, tmax, mass, iH2, iHe, abun[-1], diam, True)
lmax = Lorentz(pmax, tmin, mass, iH2, iHe, abun[ 0], diam, False)
print("Doppler minimum and maximum HWHM (cm-1): {:.3e}, {:.3e}\n"
"Lorentz minimum and maximum HWHM (cm-1): {:.3e}, {:.3e}".
format(dmin, dmax, lmin, lmax))
def main(comm): """ This is a hacked version of MC3's func.py. This function directly call's the modeling function for the BART project. Modification History: --------------------- 2014-04-19 patricio Initial implementation. [email protected] 2014-06-25 patricio Added support for inner-MPI loop. """ # Parse arguments: cparser = argparse.ArgumentParser(description=__doc__, add_help=False, formatter_class=argparse.RawDescriptionHelpFormatter) # Add config file option: cparser.add_argument("-c", "--config_file", help="Configuration file", metavar="FILE") # Remaining_argv contains all other command-line-arguments: args, remaining_argv = cparser.parse_known_args() # Get parameters from configuration file: cfile = args.config_file if cfile: config = ConfigParser.SafeConfigParser() config.optionxform = str config.read([cfile]) defaults = dict(config.items("MCMC")) else: defaults = {} parser = argparse.ArgumentParser(parents=[cparser]) parser.add_argument("--func", dest="func", type=mu.parray, action="store", default=None) parser.add_argument("--indparams", dest="indparams", type=mu.parray, action="store", default=[]) parser.add_argument("--params", dest="params", type=mu.parray, action="store", default=None, help="Model-fitting parameters [default: %(default)s]") parser.add_argument("--molfit", dest="molfit", type=mu.parray, action="store", default=None, help="Molecules fit [default: %(default)s]") parser.add_argument("--Tmin", dest="Tmin", type=float, action="store", default=400.0, help="Lower Temperature boundary [default: %(default)s]") parser.add_argument("--Tmax", dest="Tmax", type=float, action="store", default=3000.0, help="Higher Temperature boundary [default: %(default)s]") parser.add_argument("--quiet", action="store_true", help="Set verbosity level to minimum", dest="quiet") # Input-Converter Options: group = parser.add_argument_group("Input Converter Options") group.add_argument("--atmospheric_file", action="store", help="Atmospheric file [default: %(default)s]", dest="atmfile", type=str, default=None) group.add_argument("--PTtype", action="store", help="PT profile type.", dest="PTtype", type=str, default="none") #choices=('line', 'madhu')) group.add_argument("--tint", action="store", help="Internal temperature of the planet [default: " "%(default)s].", dest="tint", type=float, default=100.0) # transit Options: group = parser.add_argument_group("transit Options") group.add_argument("--config", action="store", help="transit configuration file [default: %(default)s]", dest="config", type=str, default=None) # Output-Converter Options: group = parser.add_argument_group("Output Converter Options") group.add_argument("--filter", action="store", help="Waveband filter name [default: %(default)s]", dest="filter", type=mu.parray, default=None) group.add_argument("--tep_name", action="store", help="A TEP file [default: %(default)s]", dest="tep_name", type=str, default=None) group.add_argument("--kurucz_file", action="store", help="Stellar Kurucz file [default: %(default)s]", dest="kurucz", type=str, default=None) group.add_argument("--solution", action="store", help="Solution geometry [default: %(default)s]", dest="solution", type=str, default="None", choices=('transit', 'eclipse')) parser.set_defaults(**defaults) args2, unknown = parser.parse_known_args(remaining_argv) # Quiet all threads except rank 0: rank = comm.Get_rank() verb = rank == 0 # Get (Broadcast) the number of parameters and iterations from MPI: array1 = np.zeros(2, np.int) mu.comm_bcast(comm, array1) npars, niter = array1 # ::::::: Initialize the Input converter :::::::::::::::::::::::::: atmfile = args2.atmfile molfit = args2.molfit PTtype = args2.PTtype params = args2.params tepfile = args2.tep_name tint = args2.tint Tmin = args2.Tmin Tmax = args2.Tmax solution = args2.solution # Solution type # Extract necessary values from the TEP file: tep = rd.File(tepfile) # Stellar temperature in K: tstar = float(tep.getvalue('Ts')[0]) # Stellar radius (in meters): rstar = float(tep.getvalue('Rs')[0]) * c.Rsun # Semi-major axis (in meters): sma = float(tep.getvalue( 'a')[0]) * sc.au # Planetary radius (in meters): rplanet = float(tep.getvalue('Rp')[0]) * c.Rjup # Planetary mass (in kg): mplanet = float(tep.getvalue('Mp')[0]) * c.Mjup # Number of fitting parameters: nfree = len(params) # Total number of free parameters nmolfit = len(molfit) # Number of molecular free parameters nradfit = int(solution == 'transit') # 1 for transit, 0 for eclipse nPT = nfree - nmolfit - nradfit # Number of PT free parameters # Read atmospheric file to get data arrays: species, pressure, temp, abundances = mat.readatm(atmfile) # Reverse pressure order (for PT to work): pressure = pressure[::-1] nlayers = len(pressure) # Number of atmospheric layers nspecies = len(species) # Number of species in the atmosphere mu.msg(verb, "There are {:d} layers and {:d} species.".format(nlayers, nspecies)) # Find index for Hydrogen and Helium: species = np.asarray(species) iH2 = np.where(species=="H2")[0] iHe = np.where(species=="He")[0] # Get H2/He abundance ratio: ratio = (abundances[:,iH2] / abundances[:,iHe]).squeeze() # Find indices for the metals: imetals = np.where((species != "He") & (species != "H2"))[0] # Index of molecular abundances being modified: imol = np.zeros(nmolfit, dtype='i') for i in np.arange(nmolfit): imol[i] = np.where(np.asarray(species) == molfit[i])[0] # Pressure-Temperature profile: PTargs = [PTtype] if PTtype == "line": # Planetary surface gravity (in cm s-2): gplanet = 100.0 * sc.G * mplanet / rplanet**2 # Additional PT arguments: PTargs += [rstar, tstar, tint, sma, gplanet] # Allocate arrays for receiving and sending data to master: freepars = np.zeros(nfree, dtype='d') profiles = np.zeros((nspecies+1, nlayers), dtype='d') # This are sub-sections of profiles, containing just the temperature and # the abundance profiles, respectively: tprofile = profiles[0, :] aprofiles = profiles[1:,:] # Store abundance profiles: for i in np.arange(nspecies): aprofiles[i] = abundances[:, i] # ::::::: Spawn transit code ::::::::::::::::::::::::::::::::::::: # # transit configuration file: transitcfile = args2.tconfig # FINDME: Find a way to set verb to the transit subprocesses. # Silence all threads except rank 0: # if verb == 0: # rargs = ["--quiet"] # else: # rargs = [] # Initialize the transit python module: transit_args = ["transit", "-c", transitcfile] trm.transit_init(len(transit_args), transit_args) # Get wavenumber array from transit: nwave = trm.get_no_samples() specwn = trm.get_waveno_arr(nwave) # ::::::: Output Converter ::::::::::::::::::::::::::::::::::::::: ffile = args2.filter # Filter files kurucz = args2.kurucz # Kurucz file # Log10(stellar gravity) gstar = float(tep.getvalue('loggstar')[0]) # Planet-to-star radius ratio: rprs = rplanet / rstar mu.msg(verb, "OCON FLAG 10: {}, {}, {}".format(tstar, gstar, rprs)) nfilters = len(ffile) # Number of filters: # FINDME: Separate filter/stellar interpolation? # Get stellar model: starfl, starwn, tmodel, gmodel = w.readkurucz(kurucz, tstar, gstar) # Read and resample the filters: nifilter = [] # Normalized interpolated filter istarfl = [] # interpolated stellar flux wnindices = [] # wavenumber indices used in interpolation for i in np.arange(nfilters): # Read filter: filtwaven, filttransm = w.readfilter(ffile[i]) # Check that filter boundaries lie within the spectrum wn range: if filtwaven[0] < specwn[0] or filtwaven[-1] > specwn[-1]: mu.exit(message="Wavenumber array ({:.2f} - {:.2f} cm-1) does not " "cover the filter[{:d}] wavenumber range ({:.2f} - {:.2f} " "cm-1).".format(specwn[0], specwn[-1], i, filtwaven[0], filtwaven[-1])) # Resample filter and stellar spectrum: nifilt, strfl, wnind = w.resample(specwn, filtwaven, filttransm, starwn, starfl) mu.msg(verb, "OCON FLAG 67: mean star flux: %.3e"%np.mean(strfl)) nifilter.append(nifilt) istarfl.append(strfl) wnindices.append(wnind) # Allocate arrays for receiving and sending data to master: spectrum = np.zeros(nwave, dtype='d') bandflux = np.zeros(nfilters, dtype='d') # Allocate array to receive parameters from MPI: params = np.zeros(npars, np.double) # :::::: Main MCMC Loop :::::::::::::::::::::::::::::::::::::::::: # :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: while niter >= 0: niter -= 1 # Receive parameters from MCMC: mu.comm_scatter(comm, params) #mu.msg(verb, "ICON FLAG 71: incon pars: {:s}". # format(str(params).replace("\n", ""))) # Input converter calculate the profiles: try: tprofile[:] = pt.PT_generator(pressure, params[0:nPT], PTargs)[::-1] except ValueError: mu.msg(verb, 'Input parameters give non-physical profile.') # FINDME: what to do here? # If the temperature goes out of bounds: if np.any(tprofile < Tmin) or np.any(tprofile > Tmax): print("Out of bounds") mu.comm_gather(comm, -np.ones(nfilters), MPI.DOUBLE) continue #mu.msg(verb, "T pars: \n{}\n".format(PTargs)) mu.msg(verb-20, "Temperature profile: {}".format(tprofile)) # Scale abundance profiles: for i in np.arange(nmolfit): m = imol[i] # Use variable as the log10: aprofiles[m] = abundances[:, m] * 10.0**params[nPT+nradfit+i] # Update H2, He abundances so sum(abundances) = 1.0 in each layer: q = 1.0 - np.sum(aprofiles[imetals], axis=0) aprofiles[iH2] = ratio * q / (1.0 + ratio) aprofiles[iHe] = q / (1.0 + ratio) # print("qH2O: {}, Qmetals: {}, QH2: {} p: {}".format(params[nPT], # q[50], profiles[iH2+1,50], profiles[:,50])) # Set the 'surface' level: if solution == "transit": trm.set_radius(params[nPT]) if rank == 1: print("Iteration: {:05}".format(niter)) # Let transit calculate the model spectrum: spectrum = trm.run_transit(profiles.flatten(), nwave) # Output converter band-integrate the spectrum: # Calculate the band-integrated intensity per filter: for i in np.arange(nfilters): if solution == "eclipse": fluxrat = (spectrum[wnindices[i]]/istarfl[i]) * rprs*rprs bandflux[i] = w.bandintegrate(fluxrat, specwn, nifilter[i], wnindices[i]) elif solution == "transit": bandflux[i] = w.bandintegrate(spectrum[wnindices[i]], specwn, nifilter[i], wnindices[i]) # Send resutls back to MCMC: #mu.msg(verb, "OCON FLAG 95: Flux band integrated ({})".format(bandflux)) #mu.msg(verb, "{}".format(params[nPT:])) mu.comm_gather(comm, bandflux, MPI.DOUBLE) #mu.msg(verb, "OCON FLAG 97: Sent results back to MCMC") # :::::: End main Loop ::::::::::::::::::::::::::::::::::::::::::: # :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: # Close communications and disconnect: mu.comm_disconnect(comm) mu.msg(verb, "FUNC FLAG 99: func out") # Close the transit communicators: trm.free_memory() mu.msg(verb, "FUNC FLAG OUT ~~ 100 ~~")
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)
