for i in range(len(snaps)):
#
# Read snapshot specific protostellar properties/SED
#
fname = run_dir + "/" + snaps[i] + "/dat/stars.inp"
f = open(fname, 'r')
trash = f.readline()
trash = f.readline()
r_star.append(float(f.readline().split(' ')[0]) * cs.rsol_per_cm)
t_star.append(-float(f.readlines()[-1]))
f.close()
#
fname = run_dir + "/" + snaps[i] + "/dat/spectrum" + str(int(
t_star[i])) + ".out"
wavs_s[i], flam_s[i], lam_flam, nu, fnu, nu_fnu = fs.SED_read(fname)
if sed_norm:
fnorm = ( (r_star[i] * cs.cm_per_rsol)**2. * cs.sigma_sb_cgs * \
(t_star[i])**4. ) / (cs.cm_per_pc)**2.
for j in range(len(flam_s[i])):
flam_s[i][j] /= fnorm
else:
fnorm = dist**2.0
for j in range(len(flam_s[i])):
flam_s[i][j] /= fnorm
#
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
#
# Loop over inclinations, and read in snapshot specific SEDs
#
print "\nNow processing cavity-only models\n"
for s in range(len(lums)):
#
for i in range(len(inclins)):
#
for c in range(len(cavity_theta)):
#
f_sed_o = dat_dir_o+"/"+r_cores[cut]+"/POST_"+ \
str(lums[s])+"L_cavity"+ str(cavity_theta[c])+ \
"/dat/spectrum"+str(int(temps[s]))+ \
"_ISRF_"+str(inclins[i])+"i.out"
wavs_o, flam_o, lam_flam_o, \
nu_o, fnu_o, nu_fnu_o = fs.SED_read(f_sed_o)
#
f_sed_q = dat_dir_q+"/"+r_cores[cut]+ \
"/POST_cavity"+str(cavity_theta[c])+ \
"/dat/spectrum3974_ISRF_"+str(inclins[i])+"i.out"
#
wavs_q, flam_q, lam_flam_q, \
nu_q, fnu_q, nu_fnu_q = fs.SED_read(f_sed_q)
#
flam_int_o = interp1d(wavs_o,flam_o, kind="linear")
flam_int_q = interp1d(wavs_q,flam_q, kind="linear")
for w in range(len(wavs_compare)):
flam_ratio[cut][s][c][0][i][w] = \
flam_int_o(wavs_compare[w]) / flam_int_q(wavs_compare[w])
def sed(arch_dir, dat_dir, sdat_ext, plt_dir, plt_ext, inclin, \
t_star, r_star, t_planet, r_planet, beam_r):
# Read in SED, as per sdat_ext, usinf SED_read() function
file_read = dat_dir + "/spectrum" + sdat_ext
wav, flam, lam_flam, nu, fnu, nu_fnu = fs.SED_read(file_read)
# Normalise plotted lam_flam list to user defined distance to source
lam_flam /= dist**(2.0)
# Do the same, for protostellar SED, as comparison to RT output
if incl_star and (mod_inp != "protostar"):
shutil.copy2(
arch_dir + "/protostar/spectrum" + str(int(t_star)) + ".out",
dat_dir + "/")
file_read = dat_dir + "/spectrum" + str(int(t_star)) + ".out"
wav_star, flam_star, lam_flam_star, \
nu_star, fnu_star, nu_fnu_star = fs.SED_read(file_read)
lam_flam_star /= dist**(2.0)
# If a planet is present in the SED calculation, apply the same read
# and normalisation to the relevant data
if incl_planet:
shutil.copy2(arch_dir+"/protostar/spectrum"+str(int(t_planet))+ \
".out", dat_dir+"/")
file_read = dat_dir + "/spectrum" + str(int(t_planet)) + ".out"
wav_planet, flam_planet, lam_flam_planet, \
nu_planet, fnu_planet, nu_fnu_planet = fs.SED_read(file_read)
lam_flam_planet /= dist**(2.0)
# Dependent on sed_norm, either set limits, then compute total flux
# from central source to normalise SED, or set flam limits based on
# absolute values
if sed_norm:
norm = ( (r_star * cs.cm_per_rsol)**2. * cs.sigma_sb_cgs * \
(t_star)**4. ) / (cs.cm_per_pc)**2.
lam_flam /= norm
if incl_star and (mod_inp != "protostar"):
lam_flam_star /= norm
ymax = max(lam_flam) * 10.0
elif not sed_norm:
ymax = max(lam_flam) * 10.0
ymin = ymax / 1.0e9
fig = plt.figure(1)
ax1 = plt.subplot(111)
plt.plot(wav, lam_flam, color="k", linewidth=1, label="Full Model")
if incl_star and (mod_inp != "protostar"):
plt.plot(wav_star, lam_flam_star, color = "g", linewidth = 1, \
label="Protostar - "+str(int(t_star))+"K")
if incl_planet:
plt.plot(wav_planet, lam_flam_planet, color = "r", linewidth = 1, \
label = "Planet - "+str(int(t_planet))+"K")
plt.legend(loc="upper left", fontsize=10)
plt.xlabel("Wavelength (" + (r"$\mu$m") + ")", fontsize=18, labelpad=0.5)
plt.xticks(fontsize=15)
ax1.set_xscale("log")
ax1.set_xlim(5.e-3, 10000.)
if sed_norm:
plt.ylabel(r"$\lambda$ F$_{\lambda}$ / F", fontsize=18, labelpad=0.5)
elif not sed_norm:
plt.ylabel(r"$\lambda$ F$_{\lambda}$", fontsize=18, labelpad=0.5)
ax1.set_yscale("log")
ax1.set_ylim(ymin, ymax)
plt.tight_layout()
plt.savefig(plt_dir+"/SED_"+plt_ext+"_"+str(inclin)+"i."+plt_form, \
format = plt_form)
plt.clf()
# Compute flux values for [350, 450, 850, 1300] microns
flux_values(wav, fnu, beam_r)
# Compute diagnostics for YSO classification from SED data
yso_class(nu_star, fnu_star, nu, fnu, nu_fnu, t_star, r_star)
return
#
leg_names = [r"$\theta_{c} = 10^\circ$", \
r"$\theta_{c} = 20^\circ$", \
r"$\theta_{c} = 30^\circ$"]
color = ["r", "g", "b"]
linestyle = ["-", "-", "-"]
linewidth = [1, 1, 1]
for i in range(3):
# Read SED data
fsed = dat_dir + theta[i] + "/spectrum6L_ISRF_" + str(
best_inclin) + "i.out"
wav[i], flam[i], lam_flam[i], nu, fnu, nu_fnu = fs.SED_read(fsed)
for j in range(len(lam_flam[i])):
lam_flam[i][j] /= dist**(2.0)
if bolometrics:
# Model bolometric luminosity
L_bol = fs.Lbol_calc(nu, fnu)
# Model bolometric temperature
T_bol = fs.Tbol_calc(nu, fnu, nu_fnu)
# Model sub-mm to bolometric luminosity ratio
plt_dir = run_dir + "/plots_analysis/POST"
elif (restricted[i] == "POST_1000"):
dat_dir = run_dir + "/POST_1000/dat"
plt_dir = run_dir + "/plots_analysis/POST_1000"
if not os.path.isdir(plt_dir):
os.system("mkdir {0}".format(plt_dir))
#
### ------------------------------------------------------------------------ ###
# Read SED data
for t in range(len(temps)):
fsed = dat_dir + "/spectrum" + names[t] + "_0i.out"
wav[t][i][j], flam[t][i][j], \
lam_flam, nu, fnu, nu_fnu = fs.SED_read(fsed)
flam[t][i][j] /= dist**(2.0)
### ------------------------------------------------------------------------ ###
# Singular cut method analysis:
#
# Now plot SED. Zoom into SED in region of interest, using second panel
#
# SED
#
color = ["b", "r", "g", "k", "c"]
#
fig = plt.figure(1)
ax1 = plt.subplot(111)
import constants as cs
import functions as fs
# ------------------------------------------------------------------------------
arch_dir = os.getcwd() + "/../../runs"
run_dir = arch_dir + "/OUTBURST/1686/POST"
file_read = run_dir + "/dat/spectrum19248_ISRF_0i.out"
# ------------------------------------------------------------------------------
# Read SED, using relevant function
wav, flam, lam_flam, nu, fnu, nu_fnu = fs.SED_read(file_read)
# From SED data, call relevant function to derive bolometric properties
L_bol = fs.Lbol_calc(nu, fnu)
print("\nBolometric Luminosity is {0} L_sol\n".format(L_bol))
T_bol = fs.Tbol_calc(nu, fnu, nu_fnu)
print("\nBolometric Temperature is {0} K\n".format(T_bol))
os.remove("./../constants.pyc")
os.remove("./../functions.pyc")
t_star.append(-float(f_star.readlines()[-1]))
#
l_star.append(round((r_star[s])**2.0 * (t_star[s] / 5780.)**4.0, 3))
round_ind = 2 - int(math.floor(math.log10(l_star[s])))
l_star[s] = round(l_star[s], round_ind)
#
f_star.close()
#
# Read protostellar SED data
#
f_psed = arch_dir + "/protostar/spectrum" + str(int(t_star[s])) + ".out"
if not os.path.isfile(f_psed):
print("Protostellar SED template not found, exiting...")
exit()
wavs_s[s], flam_s[s], lam_flam, nu, fnu, nu_fnu = fs.SED_read(f_psed)
# Normalise if called for..
if sed_norm:
fnorm = ( (r_star[s] * cs.cm_per_rsol)**2. * cs.sigma_sb_cgs * \
(t_star[s])**4. ) / (cs.cm_per_pc)**2.
for l in range(len(flam_s[s])):
flam_s[s][l] /= fnorm
elif not sed_norm:
fnorm = dist**2.0
for l in range(len(flam_s[s])):
flam_s[s][l] /= fnorm
# Now loop over cut, isrf and inclination regimes, reading in and appending
# SED data to wavs and flam arrays, and normalising as necessary
# = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = =
#
# Generate SEDs, comparing unscaled 10 L_sol, i = 30 deg. model with
# models where disc mass has been scaled to x0.1, or x2 initial mass
runs_dir = cwd + "/../../runs/OUTBURST/1686/1e4AU/POST_10L_cavity10"
pstar_dir = cwd + "/../../protostar"
plt_dir = cwd + "/../../runs/OUTBURST/plots_analysis"
plt_form = "png"
file = pstar_dir + "/spectrum5934.out"
f = open(file, "r")
wav_star, flam_star, lam_flam_star, \
nu_star, fnu_star, nu_fnu_star = fs.SED_read(file)
lam_flam_star /= dist**(2.0)
labels = ["_disc01", "", "_disc2"]
plt_labels = ["Disc x0.1", "Unscaled", "Disc x2"]
linestyle = ["--", "-", ":"]
wav = [[] for i in range(3)]
lam_flam = [[] for i in range(3)]
for i in range(3):
file = runs_dir + labels[i] + "/dat/spectrum5934_ISRF_30i.out"
wav[i], flam_tmp, lam_flam[i], \