def lnprob(params, incl, lbd, logF, dlogF, minfo, listpar, logF_grid, vsin_obs,
sig_vsin_obs, dist_pc, sig_dist_pc, isig, ranges, dims, include_rv,
model, stellar_prior, npy_star, pdf_mas, pdf_obl, pdf_age, grid_mas,
grid_obl, grid_age):
count = 0
inside_ranges = True
# print(params)
while inside_ranges * (count < len(params)):
inside_ranges = (params[count] >= ranges[count, 0]) *\
(params[count] <= ranges[count, 1])
count += 1
if inside_ranges:
if include_rv is True:
lim = 3
else:
lim = 2
if model == 'beatlas':
logF_mod = griddataBAtlas(minfo, logF_grid, params[:-lim], listpar,
dims, isig)
else:
if model == 'befavor_new':
logF_mod = bat.griddataBA(minfo,
logF_grid,
params[:-lim],
isig=isig,
silent=True)
elif model == 'bcmi_pol' or model == 'aara_pol':
# print(params)
try:
logF_mod = griddataBA(minfo, logF_grid, params, listpar,
dims)
except:
logF_mod = np.nan
else:
logF_mod = griddataBA(minfo, logF_grid, params[:-lim], listpar,
dims)
lp = lnprior(params, incl, vsin_obs, sig_vsin_obs, dist_pc,
sig_dist_pc, model, stellar_prior, npy_star, pdf_mas,
pdf_obl, pdf_age, grid_mas, grid_obl, grid_age)
lk = lnlike(params, lbd, logF, dlogF, logF_mod, include_rv, model)
lpost = lp + lk
# print('{0:.2f} , {1:.2f}, {2:.2f}'.format(lp, lk, lpost))
if not np.isfinite(lpost):
return -np.inf
else:
return lpost
else:
return -np.inf
def lnprob(params, lbd, logF, dlogF, minfo, listpar, logF_grid, vsin_obs,
sig_vsin_obs, dist_pc, sig_dist_pc, isig, ranges, dims, include_rv,
model, stellar_prior, npy_star, pdf_mas, pdf_obl, pdf_age, pdf_dis,
pdf_ebv, grid_mas, grid_obl, grid_age, grid_dis, grid_ebv):
count = 0
inside_ranges = True
while inside_ranges * (count < len(params)):
inside_ranges = (params[count] >= ranges[count, 0]) *\
(params[count] <= ranges[count, 1])
count += 1
if inside_ranges:
if include_rv is True:
lim = 3
else:
lim = 2
# print(params[:-lim])
# print(logF_grid)
if model == 'beatlas':
# [9.90066097 1.34002053 0.01568269 3.74540388 0.19852969]
logF_mod = griddataBAtlas(minfo, logF_grid, params[:-lim], listpar,
dims, isig)
else:
logF_mod = griddataBA(minfo, logF_grid, params[:-lim], listpar,
dims)
lp = lnprior(params, vsin_obs, sig_vsin_obs, dist_pc, sig_dist_pc,
ranges, model, stellar_prior, npy_star, pdf_mas, pdf_obl,
pdf_age, pdf_dis, pdf_ebv, grid_mas, grid_obl, grid_age,
grid_dis, grid_ebv)
lk = lnlike(params, lbd, logF, dlogF, logF_mod, ranges, include_rv,
model)
lpost = lp + lk
# print('{0:.2f} , {1:.2f}, {2:.2f}'.format(lp, lk, lpost))
if not np.isfinite(lpost):
return -np.inf
else:
return lpost
else:
return -np.inf
def plot_fit(par, lbd, logF, dlogF, minfo, listpar, lbdarr, logF_grid, isig,
dims, Nwalk, Nmcmc, par_list, ranges, include_rv, npy, log_scale):
'''
Plots best model fit over data
Usage:
plot_fit(par, lbd, logF, dlogF, minfo, logF_grid, isig, Nwalk, Nmcmc)
where
par = np.array([Mstar, oblat, Sig0, Rd, n, cosi, dist])
'''
# model parameters
if include_rv is True:
Mstar, oblat, Hfrac, cosi, dist, ebv, rv = par
lim = 3
lim2 = 2
else:
Mstar, oblat, Hfrac, cosi, dist, ebv = par
lim = 2
lim2 = 1
rv = 3.1
# Rpole, Lstar, Teff = vkg.geneve_par(Mstar, oblat, Hfrac, folder_tables)
# t = np.max(np.array([hfrac2tms(hfrac), 0.]))
t = np.max(np.array([hfrac2tms(Hfrac), 0.]))
Rpole, logL = geneva_interp_fast(Mstar,
oblat,
t,
neighbours_only=True,
isRpole=False)
norma = (10. / dist)**2 # (Lstar*Lsun) / (4. * pi * (dist*pc)**2)
uplim = dlogF == 0
keep = np.logical_not(uplim)
# chain = np.load(npy)
# interpolate models
logF_mod = griddataBA(minfo, logF_grid, par[:-lim], listpar, dims)
logF_list = np.zeros([len(par_list), len(logF_mod)])
chi2 = np.zeros(len(logF_list))
for i in range(len(par_list)):
logF_list[i] = griddataBA(minfo, logF_grid, par_list[i, :-lim],
listpar, dims)
# convert to physical units
logF_mod += np.log10(norma)
logF_list += np.log10(norma)
for j in range(len(logF_list)):
chi2[j] = np.sum(
(logF[keep] - logF_list[j][keep])**2 / (dlogF[keep])**2)
# chib = chi2[np.argsort(chi2)[-30:]] / max(chi2)
flux = 10.**logF
dflux = dlogF * flux
flux_mod = 10.**logF_mod
flux_mod = pyasl.unred(lbd * 1e4, flux_mod, ebv=-1 * ebv, R_V=rv)
# Plot definitions
bottom, left = 0.75, 0.48
width, height = 0.96 - left, 0.97 - bottom
plt.axes([left, bottom, width, height])
# plot fit
if log_scale is True:
for i in range(len(par_list)):
if i % 80 == 0:
ebv_temp = np.copy(par_list[i][-lim2])
F_temp = pyasl.unred(lbd * 1e4,
10**logF_list[i],
ebv=-1 * ebv_temp,
R_V=rv)
plt.plot(lbd, lbd * F_temp, color='gray', alpha=0.1)
plt.errorbar(lbd,
lbd * flux,
yerr=lbd * dflux,
ls='',
marker='o',
alpha=0.5,
ms=10,
color='blue',
linewidth=2)
plt.plot(lbd,
lbd * flux_mod,
color='red',
ls='-',
lw=3.5,
alpha=0.4,
label='$\mathrm{Best\, fit}$')
plt.xlabel('$\lambda\,\mathrm{[\mu m]}$', fontsize=20)
plt.ylabel(r'$\lambda F_{\lambda}\,\mathrm{[erg\, s^{-1}\, cm^{-2}]}$',
fontsize=20)
plt.yscale('log')
else:
for i in range(len(par_list)):
if i % 80 == 0:
ebv_temp = np.copy(par_list[i][-lim2])
F_temp = pyasl.unred(lbd * 1e4,
10**logF_list[i],
ebv=-1 * ebv_temp,
R_V=rv)
plt.plot(lbd, F_temp, color='gray', alpha=0.1)
plt.errorbar(lbd,
flux,
yerr=lbd * dflux,
ls='',
marker='o',
alpha=0.5,
ms=10,
color='blue',
linewidth=2)
plt.plot(lbd,
flux_mod,
color='red',
ls='-',
lw=3.5,
alpha=0.4,
label='$\mathrm{Best\, fit}$')
plt.xlabel('$\lambda\,\mathrm{[\mu m]}$', fontsize=20)
plt.ylabel(r'$F_{\lambda}\,\mathrm{[erg\, s^{-1}\, cm^{-2} \mu m]}$',
fontsize=20)
plt.xlim(min(lbd), max(lbd))
plt.tick_params(direction='in',
length=6,
width=2,
colors='gray',
which='both')
plt.legend(loc='upper right')
return
def plot_residuals(par, lbd, logF, dlogF, minfo, listpar, lbdarr, logF_grid,
isig, dims, Nwalk, Nmcmc, ranges, include_rv, npy,
log_scale, model):
'''
Plots best model fit over data
Usage:
plot_fit(par, lbd, logF, dlogF, minfo, logF_grid, isig, Nwalk, Nmcmc)
where
par = np.array([Mstar, oblat, Sig0, Rd, n, cosi, dist])
'''
# model parameters
if include_rv is True:
Mstar, oblat, Hfrac, cosi, dist, ebv, rv = par
lim = 3
lim2 = 2
else:
if model == 'befavor':
Mstar, oblat, Hfrac, cosi, dist, ebv = par
if model == 'aara' or model == 'acol' or model == 'bcmi':
Mstar, oblat, Hfrac, Sig0, Rd, n, cosi, dist, ebv = par
if model == 'beatlas':
Mstar, oblat, Sig0, n, cosi, dist, ebv = par
Hfrac = 0.3
lim = 2
lim2 = 1
rv = 3.1
# Rpole, Lstar, Teff = vkg.geneve_par(Mstar, oblat, Hfrac, folder_tables)
# t = np.max(np.array([hfrac2tms(hfrac), 0.]))
t = np.max(np.array([hfrac2tms(Hfrac), 0.]))
Rpole, logL = geneva_interp_fast(Mstar,
oblat,
t,
neighbours_only=True,
isRpole=False)
norma = (10. / dist)**2 # (Lstar*Lsun) / (4. * pi * (dist*pc)**2)
uplim = dlogF == 0
keep = np.logical_not(uplim)
# ***
chain = np.load(npy)
par_list = chain[:, -1, :]
# interpolate models
if model == 'beatlas':
logF_mod = griddataBAtlas(minfo, logF_grid, par[:-lim], listpar, dims,
isig)
else:
logF_mod = griddataBA(minfo, logF_grid, par[:-lim], listpar, dims)
logF_list = np.zeros([len(par_list), len(logF_mod)])
chi2 = np.zeros(len(logF_list))
for i in range(len(par_list)):
if model == 'beatlas':
logF_list[i] = griddataBAtlas(minfo, logF_grid, par_list[i, :-lim],
listpar, dims, isig)
else:
logF_list[i] = griddataBA(minfo, logF_grid, par_list[i, :-lim],
listpar, dims)
# convert to physical units
logF_mod += np.log10(norma)
logF_list += np.log10(norma)
for j in range(len(logF_list)):
chi2[j] = np.sum(
(logF[keep] - logF_list[j][keep])**2 / (dlogF[keep])**2)
# chib = chi2[np.argsort(chi2)[-30:]] / max(chi2)
flux = 10.**logF
dflux = dlogF * flux
flux_mod = 10.**logF_mod
flux_mod = pyasl.unred(lbd * 1e4, flux_mod, ebv=-1 * ebv, R_V=rv)
# alphas = (1. - chi2 / max(chi2)) / 50.
# Plot definitions
bottom, left = 0.71, 0.48
width, height = 0.96 - left, 0.785 - bottom
plt.axes([left, bottom, width, height])
# plot fit
for i in range(len(par_list)):
ebv_temp = np.copy(par_list[i][-lim2])
F_temp = pyasl.unred(lbd * 1e4,
10**logF_list[i],
ebv=-1 * ebv_temp,
R_V=rv)
plt.plot(lbd, (flux - F_temp) / dflux, 'bs', alpha=0.2)
# plt.plot(lbd, (flux - flux_mod) / dflux, 'bs', markersize=5, alpha=0.1)
# plt.ylabel('$(\mathrm{F}-F_\mathrm{model})/\sigma$', fontsize=20)
plt.ylabel('$(F-F_\mathrm{m})/\sigma$', fontsize=20)
plt.xlabel('$\lambda\,\mathrm{[\mu m]}$', fontsize=20)
# plt.ylim(-100, 100)
plt.hlines(y=0,
xmin=min(lbd),
xmax=max(lbd),
linestyles='--',
color='black')
plt.xlim(min(lbd), max(lbd))
if model == 'aara' or model == 'beatlas' or\
model == 'acol' or model == 'bcmi':
plt.xscale('log')
plt.tick_params(direction='in',
length=6,
width=2,
colors='gray',
which='both')
plt.legend(loc='upper right')
return
def plot_fit_last(par, lbd, logF, dlogF, minfo, listpar, lbdarr, logF_grid,
isig, dims, Nwalk, Nmcmc, ranges, include_rv, npy, log_scale,
model):
'''
Plots best model fit over data
Usage:
plot_fit(par, lbd, logF, dlogF, minfo, logF_grid, isig, Nwalk, Nmcmc)
where
par = np.array([Mstar, oblat, Sig0, Rd, n, cosi, dist])
'''
# model parameters
if include_rv is True:
if model == 'befavor':
Mstar, oblat, Hfrac, cosi, dist, ebv, rv = par
if model == 'befavor_new':
Mstar, oblat, t, cosi, dist, ebv, rv = par
if model == 'aara' or model == 'acol' or\
model == 'bcmi':
Mstar, oblat, Hfrac, Sig0, Rd, n, cosi, dist, ebv, rv = par
if model == 'bcmi_pol' or model == 'aara_pol':
Mstar, oblat, Hfrac, Sig0, Rd, n, cosi = par
if model == 'beatlas':
Mstar, oblat, Sig0, n, cosi, dist, ebv, rv = par
Hfrac = 0.30
lim = 3
lim2 = 2
else:
if model == 'befavor':
Mstar, oblat, Hfrac, cosi, dist, ebv = par
if model == 'befavor_new':
Mstar, oblat, t, cosi, dist, ebv = par
if model == 'aara' or model == 'acol' or\
model == 'bcmi':
Mstar, oblat, Hfrac, Sig0, Rd, n, cosi, dist, ebv = par
if model == 'bcmi_pol' or model == 'aara_pol':
Mstar, oblat, Hfrac, Sig0, Rd, n, cosi = par
if model == 'beatlas':
Mstar, oblat, Sig0, n, cosi, dist, ebv = par
Hfrac = 0.30
lim = 2
lim2 = 1
rv = 3.1
# Rpole, Lstar, Teff = vkg.geneve_par(Mstar, oblat, Hfrac, folder_tables)
# t = np.max(np.array([hfrac2tms(hfrac), 0.]))
if model != 'befavor_new':
t = np.max(np.array([hfrac2tms(Hfrac), 0.]))
Rpole, logL = geneva_interp_fast(Mstar,
oblat,
t,
neighbours_only=True,
isRpole=False)
if model != 'bcmi_pol' and model != 'aara_pol':
norma = (10. / dist)**2 # (Lstar*Lsun) / (4. * pi * (dist*pc)**2)
uplim = dlogF == 0
keep = np.logical_not(uplim)
# ***
chain = np.load(npy)
par_list = chain[:, -1, :]
# interpolate model
if model == 'beatlas':
logF_mod = griddataBAtlas(minfo, logF_grid, par[:-lim], listpar, dims,
isig)
elif model == 'befavor_new':
# print(isig, par[:-lim])
logF_mod = bat.griddataBA(minfo,
logF_grid,
par[:-lim],
isig=isig,
silent=True)
elif model == 'bcmi_pol' or model == 'aara_pol':
logF_mod = griddataBA(minfo, logF_grid, par, listpar, dims)
else:
logF_mod = griddataBA(minfo, logF_grid, par[:-lim], listpar, dims)
logF_list = np.zeros([len(par_list), len(logF_mod)])
chi2 = np.zeros(len(logF_list))
for i in range(len(par_list)):
if model == 'beatlas':
logF_list[i] = griddataBAtlas(minfo, logF_grid, par_list[i, :-lim],
listpar, dims, isig)
elif model == 'befavor_new':
logF_list[i] = bat.griddataBA(minfo,
logF_grid,
par_list[i, :-lim],
isig=isig,
silent=True)
elif model == 'bcmi_pol' or model == 'aara_pol':
logF_list[i] = griddataBA(minfo, logF_grid, par_list[i, :],
listpar, dims)
else:
logF_list[i] = griddataBA(minfo, logF_grid, par_list[i, :-lim],
listpar, dims)
# convert to physical units
if model != 'bcmi_pol' and model != 'aara_pol':
logF_mod += np.log10(norma)
logF_list += np.log10(norma)
for j in range(len(logF_list)):
chi2[j] = np.sum(
(logF[keep] - logF_list[j][keep])**2 / (dlogF[keep])**2)
flux = 10.**logF
dflux = dlogF * flux
flux_mod = 10.**logF_mod
if model != 'bcmi_pol' and model != 'aara_pol':
flux_mod = pyasl.unred(lbd * 1e4, flux_mod, ebv=-1 * ebv, R_V=rv)
# Plot definitions
bottom, left = 0.79, 0.51 #0.80, 0.48 # 0.75, 0.48
width, height = 0.96 - left, 0.97 - bottom
plt.axes([left, bottom, width, height])
# plot fit
if log_scale is True:
for i in range(len(par_list)):
if model != 'bcmi_pol' and model != 'aara_pol':
ebv_temp = np.copy(par_list[i][-lim2])
F_temp = pyasl.unred(lbd * 1e4,
10**logF_list[i],
ebv=-1 * ebv_temp,
R_V=rv)
plt.plot(lbd, lbd * F_temp, color='gray', alpha=0.1)
else:
F_temp = pyasl.unred(lbd * 1e4,
10**logF_list[i],
ebv=0,
R_V=rv)
plt.plot(lbd, F_temp, color='gray', alpha=0.1)
if model != 'bcmi_pol' and model != 'aara_pol':
plt.plot(lbd,
lbd * flux_mod,
color='red',
ls='-',
lw=3.5,
alpha=0.4,
label='$\mathrm{Best\, fit}$')
plt.errorbar(lbd,
lbd * flux,
yerr=lbd * dflux,
ls='',
marker='o',
alpha=0.5,
ms=10,
color='blue',
linewidth=2)
else:
plt.plot(lbd,
flux_mod,
color='red',
ls='-',
lw=3.5,
alpha=0.4,
label='$\mathrm{Best\, fit}$')
plt.errorbar(lbd,
flux,
yerr=dflux,
ls='',
marker='o',
alpha=0.5,
ms=10,
color='blue',
linewidth=2)
if model != 'bcmi_pol' and model != 'aara_pol':
plt.ylabel(
r'$\lambda F_{\lambda}\,\mathrm{[erg\, s^{-1}\, cm^{-2}]}$',
fontsize=17)
plt.xscale('log')
else:
plt.ylabel(r'$P [\%]$', fontsize=17)
plt.tick_params(labelbottom='off')
else:
for i in range(len(par_list)):
if model != 'bcmi_pol' and model != 'aara_pol':
ebv_temp = np.copy(par_list[i][-lim2])
F_temp = pyasl.unred(lbd * 1e4,
10**logF_list[i],
ebv=-1 * ebv_temp,
R_V=rv)
plt.plot(lbd, lbd * F_temp, color='gray', alpha=0.1)
else:
F_temp = pyasl.unred(lbd * 1e4,
10**logF_list[i],
ebv=0,
R_V=rv)
plt.plot(lbd, F_temp, color='gray', alpha=0.1)
if model != 'bcmi_pol' and model != 'aara_pol':
plt.plot(lbd,
lbd * flux_mod,
color='red',
ls='-',
lw=3.5,
alpha=0.4,
label='$\mathrm{Best\, fit}$')
plt.errorbar(lbd,
lbd * flux,
yerr=lbd * dflux,
ls='',
marker='o',
alpha=0.5,
ms=10,
color='blue',
linewidth=2)
plt.ylabel(
r'$\lambda F_{\lambda}\,\mathrm{[erg\, s^{-1}\, cm^{-2}]}$',
fontsize=20)
else:
plt.plot(lbd,
flux_mod,
color='red',
ls='-',
lw=3.5,
alpha=0.4,
label='$\mathrm{Best\, fit}$')
plt.errorbar(lbd,
flux,
yerr=dflux,
ls='',
marker='o',
alpha=0.5,
ms=10,
color='blue',
linewidth=2)
plt.ylabel(r'$P [\%]$', fontsize=20)
plt.tick_params(labelbottom='off')
plt.xscale('linear')
plt.xlim(min(lbd), max(lbd))
if model != 'bcmi_pol' and model != 'aara_pol':
plt.yscale('log')
if model == 'aara' or model == 'beatlas' or\
model == 'acol' or model == 'bcmi':
plt.xscale('log')
if log_scale is True:
plt.xscale('log')
else:
plt.xscale('linear')
if model == 'bcmi_pol' or model == 'aara_pol':
plt.xlim(0.9 * min(lbd), 1.1 * max(lbd))
plt.tick_params(direction='in',
length=6,
width=2,
colors='gray',
which='both')
plt.legend(loc='upper right')
return