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 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 main(comm):
"""
This is a hacked version of MC3's func.py.
This function directly call's the modeling function for the BART project.
"""
# 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")
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("--filters",
action="store",
help="Waveband filter name [default: %(default)s]",
dest="filters",
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
# Dictionary of functions to calculate temperature for PTtype
PTfunc = {
'iso': pt.PT_iso,
'line': pt.PT_line,
'madhu_noinv': pt.PT_NoInversion,
'madhu_inv': pt.PT_Inversion
}
# 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") & \
(species != "H-") & (species != 'e-'))[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:
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]
else:
PTargs = None
# 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
# 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.filters # Filter files
kurucz = args2.kurucz # Kurucz file
# Log10(stellar gravity)
gstar = float(tep.getvalue('loggstar')[0])
# Planet-to-star radius ratio:
rprs = rplanet / rstar
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)
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)
# Check for the MCMC-end flag:
if params[0] == np.inf:
break
# Input converter calculate the profiles:
try:
tprofile[:] = pt.PT_generator(pressure, params[0:nPT],
PTfunc[PTtype], 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):
mu.comm_gather(comm, -np.ones(nfilters), MPI.DOUBLE)
continue
# 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)
if np.any(q < 0.0):
mu.comm_gather(comm, -np.ones(nfilters), MPI.DOUBLE)
continue
aprofiles[iH2] = ratio * q / (1.0 + ratio)
aprofiles[iHe] = q / (1.0 + ratio)
# Set the 'surface' level:
if solution == "transit":
trm.set_radius(params[nPT])
# Let transit calculate the model spectrum:
spectrum = trm.run_transit(profiles.flatten(), nwave)
# 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.comm_gather(comm, bandflux, MPI.DOUBLE)
# :::::: End main Loop :::::::::::::::::::::::::::::::::::::::::::
# ::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
# Close communications and disconnect:
mu.comm_disconnect(comm)
trm.free_memory()
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 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")
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,})
fig= plt.figure(figsize=(8.5, 5))
plt.ylabel(r"$F_p/F_s$ (10$^{-3}$)", fontsize=14)
plt.xlabel(r"${\rm Wavelength\ \ (um)}$", fontsize=14)
######################################### for each spectrum separately
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 noiseup(snr,
planetfile,
filters,
geometry,
outdir,
outpre,
kuruczfile,
mass=0.806,
radius=0.756,
temp=5000.,
planetrad=1.138,
seed=0):
"""
This function takes an input star and planet spectrum and computes
transit/eclipse depths with photon noise, as observed by a
JWST-like telescope.
Inputs
------
snr : float. Desired SNR value.
planetfile: string. Path/to/file of the planet's spectrum.
filters : list, strings. Paths/to/filter files.
geometry : string. Viewing geometry. 'eclipse' or 'transit'
outdir : string. Path/to/directory/ where the outputs will be saved.
outpre : string. Prefix to use for all saved out files.
kuruczfile: string. Path/to/file of the Kurucz stellar model.
mass : float. Mass of the host star [M_sun]
radius : float. Radius of the host star [R_sun]
temp : float. Temperature of the host star [K]
planetrad : float. Radius of the planet [R_jup]
seed : int. Seed for random number generation.
Outputs
-------
Four text files are produced:
- list of filers
- noiseless spectrum
- noised spectrum
- uncertainties on noised spectrum
Notes
-----
The default values are based on the HD 189733 system.
The default `ecltime` value comes from
https://academic.oup.com/mnras/article/395/1/335/1746726/Secondary-radio-eclipse-of-the-transiting-planet
Revisions
---------
2017-11-15 Ryan Original implementation
2018-04-18 Michael Added transit calculations
2019-06-06 Michael Reworked into function, incorporated into BARTTest
2020-08-08 Michael Simplified computation
"""
# Set the random seed for reproducibility
np.random.seed(seed=seed)
# Make sure `outdir` has trailing slash:
if outdir[-1] != '/':
outdir = outdir + '/'
# Constants in CGS units
G = const.G.cgs.value
h = const.h.cgs.value
c = const.c.cgs.value
Msun = const.M_sun.cgs.value
Rsun = const.R_sun.cgs.value
pc2cm = const.pc.cgs.value
Rjup = const.R_jup.cgs.value
# Convert system parameters
mass *= Msun
radius *= Rsun
planetrad *= Rjup
rprs = planetrad / radius #ratio of planet/star radii
# Temperature and log(g) of star
logg = np.log10(G * mass / radius**2) #log g (cm/s2)
# Read and interpolate Kurucz grid, planet spectrum
starfl, starwn, tmodel, gmodel = wine.readkurucz(kuruczfile, temp, logg)
planetwn, planetfl = rt.readspectrum(planetfile)
# Initialize arrays for band-integrated spectrum and mean wavelength
bandflux = np.zeros(len(filters))
meanwn = np.zeros(len(filters))
nifilter = []
wnindices = []
istarfl = [] # interpolated stellar flux
# Read filters. Resample to planetwn
for i in np.arange(len(filters)):
filtwn, filttransm = wine.readfilter(filters[i])
meanwn[i] = np.mean(filtwn)
nifilt, strfl, wnind = wine.resample(planetwn, filtwn, filttransm,
starwn, starfl)
nifilter.append(nifilt)
wnindices.append(wnind)
istarfl.append(strfl)
# Loop over each filter file, read the file, interpolate to the
# wavelength array, and integrate over the bandpass, weighted by the
# filter.
for i in np.arange(len(filters)):
# integrate over the bandpass
if geometry == "eclipse":
fluxrat = (planetfl[wnindices[i]] / istarfl[i]) * rprs * rprs
bandflux[i] = wine.bandintegrate(fluxrat, planetwn, nifilter[i],
wnindices[i])
elif geometry == "transit":
bandflux[i] = wine.bandintegrate(planetfl[wnindices[i]], planetwn,
nifilter[i], wnindices[i])
# Uncertainty
unc = bandflux / snr
# Save out files
# Save filter file paths for BART
with open(outdir + outpre + 'filters.txt', 'w') as f:
for i in range(len(filters)):
f.write('../00inputs/filters/' + \
os.path.basename(filters[i]) + '\n')
if geometry == 'eclipse':
strname = 'ecl'
elif geometry == 'transit':
strname = 'tra'
# Save noised depths to a file
with open(outdir + outpre + strname + 'depths.txt', 'w') as f:
for i in range(len(depths)):
f.write(str(bandflux[i]) + '\n')
# Save uncertainties to a file
with open(outdir + outpre + strname + 'uncs.txt', 'w') as f:
for i in range(len(unc)):
f.write(str(unc[i]) + '\n')
def noiseup(kuruczfile, planetfile, filters, geometry, outdir, outpre,
mass=0.806, radius=0.756, temp=5000., planetrad=1.138,
distance=19.3, diameter=650., ecltime=1.827, seed=0):
"""
This function takes an input star and planet spectrum and computes
transit/eclipse depths with photon noise, as observed by a
JWST-like telescope.
Inputs
------
kuruczfile: string. Path/to/file of the Kurucz stellar model.
planetfile: string. Path/to/file of the planet's spectrum.
filters : list, strings. Paths/to/filter files.
geometry : string. Viewing geometry. 'eclipse' or 'transit'
outdir : string. Path/to/directory/ where the outputs will be saved.
outpre : string. Prefix to use for all saved out files.
mass : float. Mass of the host star [M_sun]
radius : float. Radius of the host star [R_sun]
temp : float. Temperature of the host star [K]
planetrad : float. Radius of the planet [R_jup]
distance : float. Distance to planetary system [pc]
diameter : float. Telescope diameter [cm]
ecltime : float. Duration of the secondary eclipse [hours]
seed : int. Seed for random number generation.
Outputs
-------
Four files are produced:
-
-
-
-
Notes
-----
The default values are based on the HD 189733 system.
The default `ecltime` value comes from
https://academic.oup.com/mnras/article/395/1/335/1746726/Secondary-radio-eclipse-of-the-transiting-planet
Revisions
---------
2017-11-15 Ryan Original implementation
2018-04-18 Michael Added transit calculations
2019-06-06 Michael Reworked into function, incorporated into BARTTest
"""
# Set the random seed for reproducibility
np.random.seed(seed=seed)
# Make sure `outdir` has trailing slash:
if outdir[-1] != '/':
outdir = outdir + '/'
# Constants in CGS units
G = const.G.cgs.value
h = const.h.cgs.value
c = const.c.cgs.value
Msun = const.M_sun.cgs.value
Rsun = const.R_sun.cgs.value
pc2cm = const.pc.cgs.value
Rjup = const.R_jup.cgs.value
# Convert system parameters
mass *= Msun
radius *= Rsun
planetrad *= Rjup
distance *= pc2cm
ecltime *= 3600 #hours --> seconds
# Temperature and log(g) of star
logg = np.log10(G * mass / radius**2) #log g (cm/s2)
# Read and interpolate Kurucz grid, planet spectrum
starfl, starwn, tmodel, gmodel = wine.readkurucz(kuruczfile, temp, logg)
planetwn, planetfl = rt.readspectrum(planetfile)
nifilter = []
wnindices = []
# Multiply by 4pi steradians, surface area
# Planetary spectrum is already integrated over steradians
sPower = starfl * (4 * np.pi * radius**2)
pPower = planetfl * (4 * np.pi * planetrad**2)
# Multiply by eclipse duration
sEnergy = sPower * ecltime
pEnergy = pPower * ecltime
# Spread out over distance
sEdensity = sEnergy / (4 * np.pi * distance**2)
pEdensity = pEnergy / (4 * np.pi * distance**2)
# Multiply by telescope area (total energy received)
sSED = sEdensity * np.pi * (diameter/2.)**2.
pSED = pEdensity * np.pi * (diameter/2.)**2.
# Interpolate stellar SED
sSEDinterp = si.interp1d(starwn, sSED)
isSED = sSEDinterp(planetwn)
if geometry == 'transit':
# Interpolate stellar flux
starflinterp = si.interp1d(starwn, starfl)
istarfl = starflinterp(planetwn)
# Initialize arrays for band-integrated energy and mean wavelength
bandintegratedstar = np.zeros(len(filters))
bandintegratedplanet = np.zeros(len(filters))
meanwn = np.zeros(len(filters))
# Read filters. Resample to planetwn
for i in np.arange(len(filters)):
filtwn, filttransm = wine.readfilter(filters[i])
meanwn[i] = np.mean(filtwn)
nifilt, rsSED, wnind = wine.resample(planetwn, filtwn, filttransm,
starwn, sSED)
nifilter.append(nifilt)
wnindices.append(wnind)
# Loop over each filter file, read the file, interpolate to the
# wavelength array, and integrate over the bandpass, weighted by the
# filter.
for i in np.arange(len(filters)):
# integrate over the bandpass
bandintegratedstar[i] = wine.bandintegrate(
isSED[wnindices[i][0]], planetwn,
nifilter[i], wnindices[i])
if geometry == 'eclipse':
bandintegratedplanet[i] = wine.bandintegrate(
pSED[wnindices[i][0]],
planetwn, nifilter[i], wnindices[i])
elif geometry == 'transit':
bandintegratedplanet[i] = wine.bandintegrate(
planetfl[wnindices[i][0]],
planetwn, nifilter[i], wnindices[i])
else:
print("Invalid `geometry` specification.\n")
sys.exit()
# Divide by photon energy to get number of photons (counts)
# Find total photon signal
sphotons = bandintegratedstar / (h * meanwn * c)
if geometry == 'eclipse':
pphotons = bandintegratedplanet / (h * meanwn * c)
phot_tot = sphotons + pphotons
else:
phot_tot = sphotons #planet flux is negligible in transit geometry
# Multiply by 1 minus the transit depth = signal during transit
phot_tot_rat = phot_tot * (1. - bandintegratedplanet)
# Noise it up
poisson_tot = phot_tot**(.5)
poisson_s = sphotons**(.5)
if geometry == 'eclipse':
poisson_p = pphotons**(.5)
noise = np.random.normal(0, poisson_tot)
# Add noise to the band-integrated photon counts
noisedpts = phot_tot + noise
noisedplanet = pphotons + noise
noisedstar = sphotons + noise
# Calculate eclipse depths
depths = noisedplanet / phot_tot
# Calculate eclipse depth uncertainty
unc = phot_tot/sphotons * ((poisson_tot/phot_tot)**2 + \
(poisson_s /sphotons)**2)**(.5)
else:
poisson_tot_rat = (phot_tot_rat)**(.5)
# Propagate error
unc = phot_tot_rat / phot_tot * \
((poisson_tot_rat / phot_tot_rat)**2 + \
(poisson_tot / phot_tot )**2)**(.5)
noise = np.random.normal(0, poisson_tot_rat) / phot_tot_rat
depths = bandintegratedplanet + noise
# Save out files
# Save filter file paths for BART
with open(outdir+outpre+'filters.txt', 'w') as f:
for i in range(len(filters)):
f.write('../00inputs/filters/' + \
os.path.basename(filters[i]) + '\n')
if geometry == 'eclipse':
# Save depths to a file
with open(outdir+outpre+'ecldepths.txt', 'w') as f:
for i in range(len(depths)):
f.write(str(depths[i]) + '\n')
# Save uncertainties to a file
with open(outdir+outpre+'ecluncs.txt', 'w') as f:
for i in range(len(unc)):
f.write(str(unc[i]) + '\n')
# Save noiseless depths
with open(outdir+outpre+'ecl_noiseless.txt', 'w') as f:
for i in range(len(filters)):
f.write(str(pphotons[i]/phot_tot[i]) + '\n')
else:
# Save depths to a file
with open(outdir+outpre+'tradepths.txt', 'w') as f:
for i in range(len(depths)):
f.write(str(depths[i]) + '\n')
# Save uncertainties to a file
with open(outdir+outpre+'trauncs.txt', 'w') as f:
for i in range(len(unc)):
f.write(str(unc[i]) + '\n')
# Save noiseless depths
with open(outdir+outpre+'tra_noiseless.txt', 'w') as f:
for i in range(len(filters)):
f.write(str(bandintegratedplanet[i]) + '\n')