def dump_data(pf,model):
ad = pf.all_data()
particle_fh2 = ad["gasfh2"]
particle_fh1 = np.ones(len(particle_fh2))-particle_fh2
particle_gas_mass = ad["gasmasses"]
particle_star_mass = ad["starmasses"]
particle_star_metallicity = ad["starmetals"]
particle_stellar_formation_time = ad["starformationtime"]
particle_sfr = ad['gassfr'].in_units('Msun/yr')
#these are in try/excepts in case we're not dealing with gadget and yt 3.x
try: grid_gas_mass = ad["gassmoothedmasses"]
except: grid_gas_mass = -1
try: grid_gas_metallicity = ad["gassmoothedmetals"]
except: grid_gas_metallicity = -1
try: grid_star_mass = ad["starsmoothedmasses"]
except: grid_star_mass = -1
#get tdust
m = ModelOutput(model.outputfile+'.sed')
oct = m.get_quantities()
tdust_pf = oct.to_yt()
tdust_ad = tdust_pf.all_data()
tdust = tdust_ad[ ('gas', 'temperature')]
try: outfile = cfg.model.PD_output_dir+"grid_physical_properties."+cfg.model.snapnum_str+'_galaxy'+cfg.model.galaxy_num_str+".npz"
except:
outfile = cfg.model.PD_output_dir+"grid_physical_properties."+cfg.model.snapnum_str+".npz"
np.savez(outfile,particle_fh2=particle_fh2,particle_fh1 = particle_fh1,particle_gas_mass = particle_gas_mass,particle_star_mass = particle_star_mass,particle_star_metallicity = particle_star_metallicity,particle_stellar_formation_time = particle_stellar_formation_time,grid_gas_metallicity = grid_gas_metallicity,grid_gas_mass = grid_gas_mass,grid_star_mass = grid_star_mass,particle_sfr = particle_sfr,tdust = tdust)
def getRadialDensity(rtout, angle, plotdir):
"""
"""
import numpy as np
from hyperion.model import ModelOutput
m = ModelOutput(rtout)
q = m.get_quantities()
r_wall = q.r_wall; theta_wall = q.t_wall; phi_wall = q.p_wall
# get the cell coordinates
rc = r_wall[0:len(r_wall)-1] + 0.5*(r_wall[1:len(r_wall)]-r_wall[0:len(r_wall)-1])
thetac = theta_wall[0:len(theta_wall)-1] + \
0.5*(theta_wall[1:len(theta_wall)]-theta_wall[0:len(theta_wall)-1])
phic = phi_wall[0:len(phi_wall)-1] + \
0.5*(phi_wall[1:len(phi_wall)]-phi_wall[0:len(phi_wall)-1])
#
rho = q['density'].array[0]
# find the closest angle in the thetac grid
ind = np.argsort(abs(thetac-angle*np.pi/180.))[0]
return rc, rho[0,ind,:]
import numpy as np
from hyperion.model import ModelOutput
from hyperion.util.constants import au, lsun
RES = 256
mo = ModelOutput('bm2_eff_vor_temperature.rtout')
g = mo.get_quantities()
from scipy.spatial import cKDTree
sites = np.array([g.x, g.y, g.z]).transpose()
tree = cKDTree(sites)
ymin, ymax = 0 * au, 60 * au
zmin, zmax = 0 * au, 60 * au
y = np.linspace(ymin, ymax, RES)
z = np.linspace(zmin, zmax, RES)
Y, Z = np.meshgrid(y, z)
YR = Y.ravel()
ZR = Z.ravel()
for x_cut in [10 * au, 26.666667 * au]:
XR = np.ones(YR.shape) * x_cut
map_sites = np.array([XR, YR, ZR]).transpose()
ax_top = grid[i].twiny()
ax_top.set_xticks(r_ticks)
ax_top.set_xticklabels([])
grid[i].tick_params('both',labelsize=14)
for i in range(4,8):
# get the H-band simulated image
m = ModelOutput(filename[i-4])
image = m.get_image(group=0, inclination=0, distance=178 * pc, units='MJy/sr')
# Find the closest wavelength
iwav = np.argmin(np.abs(wave - image.wav))
# Calculate the image width in arcseconds given the distance used above
# get the max radius
rmax = max(m.get_quantities().r_wall)
w = np.degrees(rmax / image.distance) * 3600.
# Image in the unit of MJy/sr
# Change it into erg/s/cm2/Hz/sr
factor = 1e-23*1e6
# avoid zero in log
# flip the image, because the setup of inclination is upside down
val = image.val[::-1, :, iwav] * factor + 1e-30
if size != 'full':
pix_e2c = (w-size/2.)/w * len(val[:,0])/2
val = val[pix_e2c:-pix_e2c, pix_e2c:-pix_e2c]
w = size/2.
if convolve:
def temp_hyperion(rtout,outdir, bb_dust=False):
import numpy as np
import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt
import os
from hyperion.model import ModelOutput
import astropy.constants as const
from matplotlib.colors import LogNorm
# seaborn colormap
import seaborn.apionly as sns
# constants setup
AU = const.au.cgs.value
# misc variable setup
print_name = os.path.splitext(os.path.basename(rtout))[0]
m = ModelOutput(rtout)
q = m.get_quantities()
# get the grid info
ri, thetai = q.r_wall, q.t_wall
rc = 0.5*( ri[0:len(ri)-1] + ri[1:len(ri)] )
thetac = 0.5*( thetai[0:len(thetai)-1] + thetai[1:len(thetai)] )
# get the temperature profile
# and average across azimuthal angle
# temperature array in [phi, theta, r]
temp = q['temperature'][0].array.T
temp2d = np.sum(temp**2, axis=2)/np.sum(temp, axis=2)
temp2d_exp = np.hstack((temp2d,temp2d,temp2d[:,0:1]))
thetac_exp = np.hstack((thetac-np.pi/2, thetac+np.pi/2, thetac[0]-np.pi/2))
mag = 1
fig = plt.figure(figsize=(mag*8,mag*6))
ax = fig.add_subplot(111, projection='polar')
# cmap = sns.cubehelix_palette(light=1, as_cmap=True)
cmap = plt.cm.CMRmap
im = ax.pcolormesh(thetac_exp, rc/AU, temp2d_exp, cmap=cmap, norm=LogNorm(vmin=5, vmax=100))
#
# cmap = plt.cm.RdBu_r
# im = ax.pcolormesh(thetac_exp, np.log10(rc/AU), temp2d_exp/10, cmap=cmap, norm=LogNorm(vmin=0.1, vmax=10))
#
print temp2d_exp.min(), temp2d_exp.max()
im.set_edgecolor('face')
ax.set_xlabel(r'$\rm{Polar\,angle\,(Degree)}$',fontsize=20)
# ax.set_ylabel(r'$\rm{Radius\,(AU)}$',fontsize=20, labelpad=-140, color='grey')
# ax.set_ylabel('',fontsize=20, labelpad=-140, color='grey')
ax.tick_params(labelsize=16)
ax.tick_params(axis='y', colors='grey')
ax.set_yticks(np.hstack((np.arange(0,(int(max(rc)/AU/10000.)+1)*10000, 10000),max(rc)/AU)))
#
# ax.set_yticks(np.log10(np.array([1, 10, 100, 1000, 10000, max(rc)/AU])))
#
ax.set_yticklabels([])
ax.grid(True, color='LightGray', linewidth=1.5)
# ax.grid(True, color='k', linewidth=1)
ax.set_xticklabels([r'$\rm{90^{\circ}}$',r'$\rm{45^{\circ}}$',r'$\rm{0^{\circ}}$',r'$\rm{-45^{\circ}}$',\
r'$\rm{-90^{\circ}}$',r'$\rm{-135^{\circ}}$',r'$\rm{180^{\circ}}$',r'$\rm{135^{\circ}}$'])
cb = fig.colorbar(im, pad=0.1)
cb.ax.set_ylabel(r'$\rm{Averaged\,Temperature\,(K)}$',fontsize=20)
cb.set_ticks([5,10,20,30,40,50,60,70,80,90,100])
cb.set_ticklabels([r'$\rm{5}$',r'$\rm{10}$',r'$\rm{20}$',r'$\rm{30}$',r'$\rm{40}$',r'$\rm{50}$',r'$\rm{60}$',r'$\rm{70}$',r'$\rm{80}$',r'$\rm{90}$',r'$\rm{>100}$'])
#
# cb.ax.set_ylabel(r'$\rm{log(T/10)}$',fontsize=20)
# cb.set_ticks([0.1, 10**-0.5, 1, 10**0.5, 10])
# cb.set_ticklabels([r'$\rm{-1}$',r'$\rm{-0.5}$',r'$\rm{0}$',r'$\rm{0.5}$',r'$\rm{\geq 1}$'])
#
cb_obj = plt.getp(cb.ax.axes, 'yticklabels')
plt.setp(cb_obj,fontsize=20)
# fix the tick label font
ticks_font = mpl.font_manager.FontProperties(family='STIXGeneral',size=20)
for label in ax.get_yticklabels():
label.set_fontproperties(ticks_font)
fig.savefig(outdir+print_name+'_temperature.png', format='png', dpi=300, bbox_inches='tight')
fig.clf()
# Plot the radial temperature profile
fig = plt.figure(figsize=(12,9))
ax = fig.add_subplot(111)
plot_grid = [0,99,199]
label_grid = [r'$\rm{outflow}$', r'$\rm{45^{\circ}}$', r'$\rm{midplane}$']
alpha = np.linspace(0.3,1.0,len(plot_grid))
color_list = [[0.8507598215729224, 0.6322174528970308, 0.6702243543099417],\
[0.5687505862870377, 0.3322661256969763, 0.516976691731939],\
[0.1750865648952205, 0.11840023306916837, 0.24215989137836502]]
for i in plot_grid:
temp_rad, = ax.plot(np.log10(rc/AU), np.log10(temp2d[:,i]),'-',color=color_list[plot_grid.index(i)],\
linewidth=2, markersize=3,label=label_grid[plot_grid.index(i)])
# plot the theoretical prediction for black body dust without considering the extinction
if bb_dust == True:
from hyperion.model import Model
sigma = const.sigma_sb.cgs.value
lsun = const.L_sun.cgs.value
dum = Model()
dum.use_sources(rtout)
L_cen = dum.sources[0].luminosity/lsun
t_bbdust = (L_cen*lsun/(16*np.pi*sigma*rc**2))**(0.25)
temp_bbdust, = ax.plot(np.log10(rc/AU), np.log10(t_bbdust), '--', color='r', linewidth=2.5,label=r'$\rm{blackbody\,dust}$')
ax.legend(loc='upper right', numpoints=1, fontsize=24)
ax.set_xlabel(r'$\rm{log\,R\,(AU)}$',fontsize=24)
ax.set_ylabel(r'$\rm{log\,T\,(K)}$',fontsize=24)
[ax.spines[axis].set_linewidth(2) for axis in ['top','bottom','left','right']]
ax.minorticks_on()
ax.tick_params('both',labelsize=24,width=2,which='major',pad=15,length=5)
ax.tick_params('both',labelsize=24,width=2,which='minor',pad=15,length=2.5)
# fix the tick label font
ticks_font = mpl.font_manager.FontProperties(family='STIXGeneral',size=24)
for label in ax.get_xticklabels():
label.set_fontproperties(ticks_font)
for label in ax.get_yticklabels():
label.set_fontproperties(ticks_font)
ax.set_ylim([0,4])
fig.gca().set_xlim(left=np.log10(0.05))
# ax.set_xlim([np.log10(0.8),np.log10(10000)])
fig.savefig(outdir+print_name+'_temp_radial.pdf',format='pdf',dpi=300,bbox_inches='tight')
fig.clf()
def extract_hyperion(filename,indir=None,outdir=None,dstar=178.0,wl_aper=None,save=True):
def l_bol(wl,fv,dist=178.0):
import numpy as np
import astropy.constants as const
# wavelength unit: um
# Flux density unit: Jy
#
# constants setup
#
c = const.c.cgs.value
pc = const.pc.cgs.value
PI = np.pi
SL = const.L_sun.cgs.value
# Convert the unit from Jy to erg s-1 cm-2 Hz-1
fv = np.array(fv)*1e-23
freq = c/(1e-4*np.array(wl))
diff_dum = freq[1:]-freq[0:-1]
freq_interpol = np.hstack((freq[0:-1]+diff_dum/2.0,freq[0:-1]+diff_dum/2.0,freq[0],freq[-1]))
freq_interpol = freq_interpol[np.argsort(freq_interpol)[::-1]]
fv_interpol = np.empty(len(freq_interpol))
# calculate the histogram style of spectrum
#
for i in range(0,len(fv)):
if i == 0:
fv_interpol[i] = fv[i]
else:
fv_interpol[2*i-1] = fv[i-1]
fv_interpol[2*i] = fv[i]
fv_interpol[-1] = fv[-1]
dv = freq_interpol[0:-1]-freq_interpol[1:]
dv = np.delete(dv,np.where(dv==0))
fv = fv[np.argsort(freq)]
freq = freq[np.argsort(freq)]
return (np.trapz(fv,freq)*4.*PI*(dist*pc)**2)/SL
import matplotlib.pyplot as plt
import numpy as np
import os
from hyperion.model import ModelOutput
from hyperion.model import Model
from scipy.interpolate import interp1d
from hyperion.util.constants import pc, c, lsun
# Read in the observation data and calculate the noise & variance
if indir == None:
indir = '/Users/yaolun/bhr71/'
if outdir == None:
outdir = '/Users/yaolun/bhr71/hyperion/'
# assign the file name from the input file
print_name = os.path.splitext(os.path.basename(filename))[0]
#
[wl_pacs,flux_pacs,unc_pacs] = np.genfromtxt(indir+'BHR71_centralSpaxel_PointSourceCorrected_CorrectedYES_trim_continuum.txt',\
dtype='float',skip_header=1).T
# Convert the unit from Jy to erg cm-2 Hz-1
flux_pacs = flux_pacs*1e-23
[wl_spire,flux_spire] = np.genfromtxt(indir+'BHR71_spire_corrected_continuum.txt',dtype='float',skip_header=1).T
flux_spire = flux_spire*1e-23
wl_obs = np.hstack((wl_pacs,wl_spire))
flux_obs = np.hstack((flux_pacs,flux_spire))
[wl_pacs_data,flux_pacs_data,unc_pacs_data] = np.genfromtxt(indir+'BHR71_centralSpaxel_PointSourceCorrected_CorrectedYES_trim.txt',\
dtype='float').T
[wl_spire_data,flux_spire_data] = np.genfromtxt(indir+'BHR71_spire_corrected.txt',\
dtype='float').T
[wl_pacs_flat,flux_pacs_flat,unc_pacs_flat] = np.genfromtxt(indir+'BHR71_centralSpaxel_PointSourceCorrected_CorrectedYES_trim_flat_spectrum.txt',\
dtype='float',skip_header=1).T
[wl_spire_flat,flux_spire_flat] = np.genfromtxt(indir+'BHR71_spire_corrected_flat_spectrum.txt',dtype='float',skip_header=1).T
# Convert the unit from Jy to erg cm-2 Hz-1
flux_pacs_flat = flux_pacs_flat*1e-23
flux_spire_flat = flux_spire_flat*1e-23
flux_pacs_data = flux_pacs_data*1e-23
flux_spire_data = flux_spire_data*1e-23
wl_pacs_noise = wl_pacs_data
flux_pacs_noise = flux_pacs_data-flux_pacs-flux_pacs_flat
wl_spire_noise = wl_spire_data
flux_spire_noise = flux_spire_data-flux_spire-flux_spire_flat
# Read in the Spitzer IRS spectrum
[wl_irs, flux_irs]= (np.genfromtxt(indir+'bhr71_spitzer_irs.txt',skip_header=2,dtype='float').T)[0:2]
# Convert the unit from Jy to erg cm-2 Hz-1
flux_irs = flux_irs*1e-23
# Remove points with zero or negative flux
ind = flux_irs > 0
wl_irs = wl_irs[ind]
flux_irs = flux_irs[ind]
# Calculate the local variance (for spire), use the instrument uncertainty for pacs
#
wl_noise_5 = wl_spire_noise[(wl_spire_noise > 194)*(wl_spire_noise <= 304)]
flux_noise_5 = flux_spire_noise[(wl_spire_noise > 194)*(wl_spire_noise <= 304)]
wl_noise_6 = wl_spire_noise[wl_spire_noise > 304]
flux_noise_6 = flux_spire_noise[wl_spire_noise > 304]
wl_noise = [wl_pacs_data[wl_pacs_data<=190.31],wl_noise_5,wl_noise_6]
flux_noise = [unc_pacs[wl_pacs_data<=190.31],flux_noise_5,flux_noise_6]
sig_num = 20
sigma_noise = []
for i in range(0,len(wl_noise)):
sigma_dum = np.zeros([len(wl_noise[i])])
for iwl in range(0,len(wl_noise[i])):
if iwl < sig_num/2:
sigma_dum[iwl] = np.std(np.hstack((flux_noise[i][0:sig_num/2],flux_noise[i][0:sig_num/2-iwl])))
elif len(wl_noise[i])-iwl < sig_num/2:
sigma_dum[iwl] = np.std(np.hstack((flux_noise[i][iwl:],flux_noise[i][len(wl_noise[i])-sig_num/2:])))
else:
sigma_dum[iwl] = np.std(flux_noise[i][iwl-sig_num/2:iwl+sig_num/2])
sigma_noise = np.hstack((sigma_noise,sigma_dum))
sigma_noise = np.array(sigma_noise)
# Read in the photometry data
phot = np.genfromtxt(indir+'bhr71.txt',dtype=None,skip_header=1,comments='%')
wl_phot = []
flux_phot = []
flux_sig_phot = []
note = []
for i in range(0,len(phot)):
wl_phot.append(phot[i][0])
flux_phot.append(phot[i][1])
flux_sig_phot.append(phot[i][2])
note.append(phot[i][4])
wl_phot = np.array(wl_phot)
# Convert the unit from Jy to erg cm-2 Hz-1
flux_phot = np.array(flux_phot)*1e-23
flux_sig_phot = np.array(flux_sig_phot)*1e-23
# Print the observed L_bol
wl_tot = np.hstack((wl_irs,wl_obs,wl_phot))
flux_tot = np.hstack((flux_irs,flux_obs,flux_phot))
flux_tot = flux_tot[np.argsort(wl_tot)]
wl_tot = wl_tot[np.argsort(wl_tot)]
l_bol_obs = l_bol(wl_tot,flux_tot*1e23)
# Open the model
m = ModelOutput(filename)
if wl_aper == None:
wl_aper = [3.6, 4.5, 5.8, 8.0, 10, 16, 20, 24, 35, 70, 100, 160, 250, 350, 500, 850]
# Create the plot
mag = 1.5
fig = plt.figure(figsize=(8*mag,6*mag))
ax_sed = fig.add_subplot(1, 1, 1)
# Plot the observed SED
# plot the observed spectra
pacs, = ax_sed.plot(np.log10(wl_pacs),np.log10(c/(wl_pacs*1e-4)*flux_pacs),'-',color='DimGray',linewidth=1.5*mag, alpha=0.7)
spire, = ax_sed.plot(np.log10(wl_spire),np.log10(c/(wl_spire*1e-4)*flux_spire),'-',color='DimGray',linewidth=1.5*mag, alpha=0.7)
irs, = ax_sed.plot(np.log10(wl_irs),np.log10(c/(wl_irs*1e-4)*flux_irs),'-',color='DimGray',linewidth=1.5*mag, alpha=0.7)
# ax_sed.text(0.75,0.9,r'$\rm{L_{bol}= %5.2f L_{\odot}}$' % l_bol_obs,fontsize=mag*16,transform=ax_sed.transAxes)
# plot the observed photometry data
photometry, = ax_sed.plot(np.log10(wl_phot),np.log10(c/(wl_phot*1e-4)*flux_phot),'s',mfc='DimGray',mec='k',markersize=8)
ax_sed.errorbar(np.log10(wl_phot),np.log10(c/(wl_phot*1e-4)*flux_phot),\
yerr=[np.log10(c/(wl_phot*1e-4)*flux_phot)-np.log10(c/(wl_phot*1e-4)*(flux_phot-flux_sig_phot)),\
np.log10(c/(wl_phot*1e-4)*(flux_phot+flux_sig_phot))-np.log10(c/(wl_phot*1e-4)*flux_phot)],\
fmt='s',mfc='DimGray',mec='k',markersize=8)
# Extract the SED for the smallest inclination and largest aperture, and
# scale to 300pc. In Python, negative indices can be used for lists and
# arrays, and indicate the position from the end. So to get the SED in the
# largest aperture, we set aperture=-1.
# aperture group is aranged from smallest to infinite
sed_inf = m.get_sed(group=0, inclination=0, aperture=-1, distance=dstar * pc)
# l_bol_sim = l_bol(sed_inf.wav, sed_inf.val/(c/sed_inf.wav*1e4)*1e23)
# print sed.wav, sed.val
# print 'Bolometric luminosity of simulated spectrum: %5.2f lsun' % l_bol_sim
# plot the simulated SED
# sim, = ax_sed.plot(np.log10(sed_inf.wav), np.log10(sed_inf.val), '-', color='k', linewidth=1.5*mag, alpha=0.7)
# get flux at different apertures
flux_aper = np.empty_like(wl_aper)
unc_aper = np.empty_like(wl_aper)
for i in range(0, len(wl_aper)):
sed_dum = m.get_sed(group=i+1, inclination=0, aperture=-1, distance=dstar * pc)
# use a rectangle function the average the simulated SED
# apply the spectral resolution
if (wl_aper[i] < 50.) & (wl_aper[i] >= 5):
res = 60.
elif wl_aper[i] < 5:
res = 10.
else:
res = 1000.
ind = np.where((sed_dum.wav < wl_aper[i]*(1+1./res)) & (sed_dum.wav > wl_aper[i]*(1-1./res)))
if len(ind[0]) != 0:
flux_aper[i] = np.mean(sed_dum.val[ind])
else:
f = interp1d(sed_dum.wav, sed_dum.val)
flux_aper[i] = f(wl_aper[i])
# perform the same procedure of flux extraction of aperture flux with observed spectra
wl_aper = np.array(wl_aper)
obs_aper_wl = wl_aper[(wl_aper >= min(wl_irs)) & (wl_aper <= max(wl_spire))]
obs_aper_sed = np.empty_like(obs_aper_wl)
sed_tot = c/(wl_tot*1e-4)*flux_tot
# wl_tot and flux_tot are already hstacked and sorted by wavelength
for i in range(0, len(obs_aper_wl)):
if (obs_aper_wl[i] < 50.) & (obs_aper_wl[i] >= 5):
res = 60.
elif obs_aper_wl[i] < 5:
res = 10.
else:
res = 1000.
ind = np.where((wl_tot < obs_aper_wl[i]*(1+1./res)) & (wl_tot > obs_aper_wl[i]*(1-1./res)))
if len(ind[0]) != 0:
obs_aper_sed[i] = np.mean(sed_tot[ind])
else:
f = interp1d(wl_tot, sed_tot)
obs_aper_sed[i] = f(wl_aper[i])
aper_obs, = ax_sed.plot(np.log10(obs_aper_wl),np.log10(obs_aper_sed), 's-', mec='None', mfc='r', color='r',markersize=10, linewidth=1.5)
# # interpolate the uncertainty (maybe not the best way to do this)
# print sed_dum.unc
# f = interp1d(sed_dum.wav, sed_dum.unc)
# unc_aper[i] = f(wl_aper[i])
# if wl_aper[i] == 9.7:
# ax_sed.plot(np.log10(sed_dum.wav), np.log10(sed_dum.val), '-', linewidth=1.5*mag)
# print l_bol(sed_dum.wav, sed_dum.val/(c/sed_dum.wav*1e4)*1e23)
aper, = ax_sed.plot(np.log10(wl_aper),np.log10(flux_aper),'o-', mec='Blue', mfc='None', color='b',markersize=12, markeredgewidth=3, linewidth=1.7)
# calculate the bolometric luminosity of the aperture
l_bol_sim = l_bol(wl_aper, flux_aper/(c/np.array(wl_aper)*1e4)*1e23)
print 'Bolometric luminosity of simulated spectrum: %5.2f lsun' % l_bol_sim
# print out the sed into ascii file for reading in later
if save == True:
# unapertured SED
foo = open(outdir+print_name+'_sed_inf.txt','w')
foo.write('%12s \t %12s \n' % ('wave','vSv'))
for i in range(0, len(sed_inf.wav)):
foo.write('%12g \t %12g \n' % (sed_inf.wav[i], sed_inf.val[i]))
foo.close()
# SED with convolution of aperture sizes
foo = open(outdir+print_name+'_sed_w_aperture.txt','w')
foo.write('%12s \t %12s \n' % ('wave','vSv'))
for i in range(0, len(wl_aper)):
foo.write('%12g \t %12g \n' % (wl_aper[i], flux_aper[i]))
foo.close()
# Read in and plot the simulated SED produced by RADMC-3D using the same parameters
# [wl,fit] = np.genfromtxt(indir+'hyperion/radmc_comparison/spectrum.out',dtype='float',skip_header=3).T
# l_bol_radmc = l_bol(wl,fit*1e23/dstar**2)
# radmc, = ax_sed.plot(np.log10(wl),np.log10(c/(wl*1e-4)*fit/dstar**2),'-',color='DimGray', linewidth=1.5*mag, alpha=0.5)
# print the L bol of the simulated SED (both Hyperion and RADMC-3D)
# lg_sim = ax_sed.legend([sim,radmc],[r'$\rm{L_{bol,sim}=%5.2f~L_{\odot},~L_{center}=9.18~L_{\odot}}$' % l_bol_sim, \
# r'$\rm{L_{bol,radmc3d}=%5.2f~L_{\odot},~L_{center}=9.18~L_{\odot}}$' % l_bol_radmc],\
# loc='lower right',fontsize=mag*16)
# read the input central luminosity by reading in the source information from output file
dum = Model()
dum.use_sources(filename)
L_cen = dum.sources[0].luminosity/lsun
# lg_sim = ax_sed.legend([sim],[r'$\rm{L_{bol,sim}=%5.2f~L_{\odot},~L_{center}=%5.2f~L_{\odot}}$' % (l_bol_sim, L_cen)], \
# loc='lower right',fontsize=mag*16)
# lg_sim = ax_sed.legend([sim],[r'$\rm{L_{bol,sim}=%5.2f~L_{\odot},~L_{bol,obs}=%5.2f~L_{\odot}}$' % (l_bol_sim, l_bol_obs)], \
# loc='lower right',fontsize=mag*16)
# text = ax_sed.text(0.2 ,0.05 ,r'$\rm{L_{bol,simulation}=%5.2f~L_{\odot},~L_{bol,observation}=%5.2f~L_{\odot}}$' % (l_bol_sim, l_bol_obs),fontsize=mag*16,transform=ax_sed.transAxes)
# text.set_bbox(dict( edgecolor='k',facecolor='None',alpha=0.3,pad=10.0))
# plot setting
ax_sed.set_xlabel(r'$\rm{log\,\lambda\,({\mu}m)}$',fontsize=mag*20)
ax_sed.set_ylabel(r'$\rm{log\,\nu S_{\nu}\,(erg\,cm^{-2}\,s^{-1})}$',fontsize=mag*20)
[ax_sed.spines[axis].set_linewidth(1.5*mag) for axis in ['top','bottom','left','right']]
ax_sed.minorticks_on()
ax_sed.tick_params('both',labelsize=mag*18,width=1.5*mag,which='major',pad=15,length=5*mag)
ax_sed.tick_params('both',labelsize=mag*18,width=1.5*mag,which='minor',pad=15,length=2.5*mag)
ax_sed.set_ylim([-13,-7.5])
ax_sed.set_xlim([0,3])
# lg_data = ax_sed.legend([sim, aper], [r'$\rm{w/o~aperture}$', r'$\rm{w/~aperture}$'], \
# loc='upper left', fontsize=14*mag, framealpha=0.3, numpoints=1)
lg_data = ax_sed.legend([irs, photometry, aper, aper_obs],\
[r'$\rm{observation}$',\
r'$\rm{photometry}$',r'$\rm{F_{aper,sim}}$',r'$\rm{F_{aper,obs}}$'],\
loc='upper left',fontsize=14*mag,numpoints=1,framealpha=0.3)
# plt.gca().add_artist(lg_sim)
# Write out the plot
fig.savefig(outdir+print_name+'_sed.pdf',format='pdf',dpi=300,bbox_inches='tight')
fig.clf()
# Package for matching the colorbar
from mpl_toolkits.axes_grid1 import make_axes_locatable
# Extract the image for the first inclination, and scale to 300pc. We
# have to specify group=1 as there is no image in group 0.
image = m.get_image(group=len(wl_aper)+1, inclination=0, distance=dstar * pc, units='MJy/sr')
# image = m.get_image(group=14, inclination=0, distance=dstar * pc, units='MJy/sr')
# Open figure and create axes
# fig = plt.figure(figsize=(8, 8))
fig, axarr = plt.subplots(3, 3, sharex='col', sharey='row',figsize=(13.5,12))
# Pre-set maximum for colorscales
VMAX = {}
# VMAX[3.6] = 10.
# VMAX[24] = 100.
# VMAX[160] = 2000.
# VMAX[500] = 2000.
VMAX[100] = 10.
VMAX[250] = 100.
VMAX[500] = 2000.
VMAX[1000] = 2000.
# We will now show four sub-plots, each one for a different wavelength
# for i, wav in enumerate([3.6, 24, 160, 500]):
# for i, wav in enumerate([100, 250, 500, 1000]):
# for i, wav in enumerate([4.5, 9.7, 24, 40, 70, 100, 250, 500, 1000]):
for i, wav in enumerate([3.6, 8.0, 9.7, 24, 40, 100, 250, 500, 1000]):
# ax = fig.add_subplot(3, 3, i + 1)
ax = axarr[i/3, i%3]
# Find the closest wavelength
iwav = np.argmin(np.abs(wav - image.wav))
# Calculate the image width in arcseconds given the distance used above
rmax = max(m.get_quantities().r_wall)
w = np.degrees(rmax / image.distance) * 3600.
# w = np.degrees((1.5 * pc) / image.distance) * 60.
# Image in the unit of MJy/sr
# Change it into erg/s/cm2/Hz/sr
factor = 1e-23*1e6
# avoid zero in log
val = image.val[:, :, iwav] * factor + 1e-30
# This is the command to show the image. The parameters vmin and vmax are
# the min and max levels for the colorscale (remove for default values).
im = ax.imshow(np.log10(val), vmin= -22, vmax= -12,
cmap=plt.cm.jet, origin='lower', extent=[-w, w, -w, w], aspect=1)
# Colorbar setting
# create an axes on the right side of ax. The width of cax will be 5%
# of ax and the padding between cax and ax will be fixed at 0.05 inch.
if (i+1) % 3 == 0:
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
cb = fig.colorbar(im, cax=cax)
cb.solids.set_edgecolor("face")
cb.ax.minorticks_on()
cb.ax.set_ylabel(r'$\rm{log(I_{\nu})\,[erg\,s^{-2}\,cm^{-2}\,Hz^{-1}\,sr^{-1}]}$',fontsize=12)
cb_obj = plt.getp(cb.ax.axes, 'yticklabels')
plt.setp(cb_obj,fontsize=12)
if (i+1) == 7:
# Finalize the plot
ax.set_xlabel('RA Offset (arcsec)', fontsize=14)
ax.set_ylabel('Dec Offset (arcsec)', fontsize=14)
ax.tick_params(axis='both', which='major', labelsize=16)
ax.set_adjustable('box-forced')
ax.text(0.7,0.88,str(wav) + r'$\rm{\,\mu m}$',fontsize=18,color='white',weight='bold',transform=ax.transAxes)
fig.subplots_adjust(hspace=0,wspace=-0.2)
# Adjust the spaces between the subplots
# plt.tight_layout()
fig.savefig(outdir+print_name+'_cube_plot.png', format='png', dpi=300, bbox_inches='tight')
fig.clf()
class Hyperion2LIME:
"""
Class for importing Hyperion result to LIME
IMPORTANT: LIME uses SI units, while Hyperion uses CGS units.
"""
def __init__(self,
rtout,
velfile,
cs,
age,
omega,
rmin=0,
mmw=2.37,
g2d=100,
truncate=None,
debug=False,
load_full=True,
fix_tsc=True,
hybrid_tsc=False,
interpolate=False,
TSC_dir='',
tsc_outdir=''):
self.rtout = rtout
self.velfile = velfile
if load_full:
self.hyperion = ModelOutput(rtout)
self.hy_grid = self.hyperion.get_quantities()
self.rmin = rmin * 1e2 # rmin defined in LIME, which use SI unit
self.mmw = mmw
self.g2d = g2d
self.cs = cs # in km/s
self.age = age # in year
# YLY update - add omega
self.omega = omega
self.r_inf = self.cs * 1e5 * self.age * yr # in cm
# option to truncate the sphere to be a cylinder
# the value is given in au to specify the radius of the truncated cylinder viewed from the observer
self.truncate = truncate
# debug option: print out every call to getDensity, getVelocity and getAbundance
self.debug = debug
# option to use simple Trapezoid rule average for getting density, temperature, and velocity
self.interpolate = interpolate
self.tsc2d = getTSC(age,
cs,
omega,
velfile=velfile,
TSC_dir=TSC_dir,
outdir=tsc_outdir,
outname='tsc_regrid')
# # velocity grid construction
# if load_full:
# # ascii.read() fails for large file. Use pandas instead
# self.tsc = pd.read_csv(velfile, skiprows=1, delim_whitespace=True, header=None)
# self.tsc.columns = ['lp', 'xr', 'theta', 'ro', 'ur', 'utheta', 'uphi']
#
# self.xr = np.unique(self.tsc['xr']) # reduce radius: x = r/(a*t) = r/r_inf
# self.xr_wall = np.hstack(([2*self.xr[0]-self.xr[1]],
# (self.xr[:-1]+self.xr[1:])/2,
# [2*self.xr[-1]-self.xr[-2]]))
# self.theta = np.unique(self.tsc['theta'])
# self.theta_wall = np.hstack(([2*self.theta[0]-self.theta[1]],
# (self.theta[:-1]+self.theta[1:])/2,
# [2*self.theta[-1]-self.theta[-2]]))
# self.nxr = len(self.xr)
# self.ntheta = len(self.theta)
#
# # the output of TSC fortran binary is in mass density
# self.tsc_rho2d = 1/(4*np.pi*G*(self.age*yr)**2)/mh/mmw * np.array(self.tsc['ro']).reshape([self.nxr, self.ntheta])
#
# # self.vr2d = np.array(self.tsc['ur']).reshape([self.nxr, self.ntheta]) * self.cs*1e5
# # self.vtheta2d = np.array(self.tsc['utheta']).reshape([self.nxr, self.ntheta]) * self.cs*1e5
# # self.vphi2d = np.array(self.tsc['uphi']).reshape([self.nxr, self.ntheta]) * self.cs*1e5
#
# # in unit of km/s
# self.vr2d = np.reshape(self.tsc['ur'].to_numpy(), (self.nxr, self.ntheta)) * np.float64(self.cs)
# self.vtheta2d = np.reshape(self.tsc['utheta'].to_numpy(), (self.nxr, self.ntheta)) * np.float64(self.cs)
# self.vphi2d = np.reshape(self.tsc['uphi'].to_numpy(), (self.nxr, self.ntheta)) * np.float64(self.cs)
#
# if fix_tsc:
# # fix the discontinuity in v_r
# # vr = vr + offset * log(xr)/log(xr_break) for xr >= xr_break
# for i in range(self.ntheta):
# dvr = abs((self.vr2d[1:,i] - self.vr2d[:-1,i])/self.vr2d[1:,i])
# break_pt = self.xr[1:][(dvr > 0.05) & (self.xr[1:] > 1e-3) & (self.xr[1:] < 1-2e-3)]
# if len(break_pt) > 0:
# offset = self.vr2d[(self.xr < break_pt),i].max() - self.vr2d[(self.xr > break_pt),i].min()
# self.vr2d[(self.xr >= break_pt),i] = self.vr2d[(self.xr >= break_pt),i] + offset*np.log10(self.xr[self.xr >= break_pt])/np.log10(break_pt)
# # YLY update - 091118
# # fix the discontinuity in v_phi
# for i in range(self.ntheta):
# dvr = abs((self.vphi2d[1:,i] - self.vphi2d[:-1,i])/self.vphi2d[1:,i])
# break_pt = self.xr[1:][(dvr > 0.1) & (self.xr[1:] > 1e-3) & (self.xr[1:] < 1-2e-3)]
# if len(break_pt) > 0:
# offset = self.vphi2d[(self.xr < break_pt),i].min() - self.vphi2d[(self.xr > break_pt),i].max()
# self.vphi2d[(self.xr >= break_pt),i] = self.vphi2d[(self.xr >= break_pt),i] + offset*np.log10(self.xr[self.xr >= break_pt])/np.log10(break_pt)
#
# # hybrid TSC kinematics that switches to angular momentum conservation within the centrifugal radius
# if hybrid_tsc:
# from scipy.interpolate import interp1d
# for i in range(self.ntheta):
# rCR = self.omega**2 * G**3 * (0.975*(self.cs*1e5)**3/G*(self.age*3600*24*365))**3 * np.sin(self.theta[i])**4 / (16*(self.cs*1e5)**8)
# if rCR/self.r_inf >= self.xr.min():
# f_vr = interp1d(self.xr, self.vr2d[:,i])
# vr_rCR = f_vr(rCR/self.r_inf)
# f_vphi = interp1d(self.xr, self.vphi2d[:,i])
# vphi_rCR = f_vphi(rCR/self.r_inf)
#
# # radius in cylinderical coordinates
# wCR = np.sin(self.theta[i]) * rCR
# J = vphi_rCR * wCR
# M = (vr_rCR**2 + vphi_rCR**2) * wCR / (2*G)
#
# w = self.xr*np.sin(self.theta[i])*self.r_inf
# self.vr2d[(self.xr <= rCR/self.r_inf), i] = -( 2*G*M/w[self.xr <= rCR/self.r_inf] - J**2/(w[self.xr <= rCR/self.r_inf])**2 )**0.5
# self.vphi2d[(self.xr <= rCR/self.r_inf), i] = J/(w[self.xr <= rCR/self.r_inf])
#
# self.tsc2d = {'vr2d': self.vr2d, 'vtheta2d': self.vtheta2d, 'vphi2d': self.vphi2d}
def Cart2Spherical(self, x, y, z, unit_convert=True):
"""
if unit_convert, the inputs (x, y, z) are meter.
The outputs are in cm.
"""
if unit_convert:
x, y, z = x * 1e2, y * 1e2, z * 1e2
r_in = (x**2 + y**2 + z**2)**0.5
if r_in != 0:
t_in = np.arccos(z / r_in)
else:
t_in = 0
# if x != 0:
# p_in = np.sign(y)*np.arctan(y/x) # the input phi is irrelevant in axisymmetric model
# else:
# p_in = np.sign(y)*np.pi/2
p_in = np.arctan2(y, x)
if r_in < self.rmin:
r_in = self.rmin
return (r_in, t_in, p_in)
def Spherical2Cart(self, r, t, p):
"""
This is only valid for axisymmetric model
"""
x = r * np.sin(t) * np.cos(p)
y = r * np.sin(t) * np.sin(p)
z = r * np.cos(t)
return (x, y, z)
def Spherical2Cart_vector(self, coord_sph, v_sph):
r, theta, phi = coord_sph
vr, vt, vp = v_sph
transform = np.matrix([[
np.sin(theta) * np.cos(phi),
np.cos(theta) * np.cos(phi), -np.sin(phi)
],
[
np.sin(theta) * np.sin(phi),
np.cos(theta) * np.sin(phi),
np.cos(phi)
], [np.cos(theta), -np.sin(theta), 0]])
v_cart = transform.dot(np.array([vr, vt, vp]))
return list(map(float, np.asarray(v_cart).flatten()))
def locateCell(self, coord, wall_grid):
"""
return the indice of cell at given coordinates
"""
r, t, p = coord
r_wall, t_wall, p_wall = wall_grid
r_ind = min(np.argsort(abs(r_wall - r))[:2])
t_ind = min(np.argsort(abs(t_wall - t))[:2])
p_ind = min(np.argsort(abs(p_wall - p))[:2])
return (r_ind, t_ind, p_ind)
# def interpolateCell(self, coord, cube, wall_grid):
# """
# return the interpolated value at given data cube at given coordinates
# """
# r, t, p = coord
# r_wall, t_wall, p_wall = wall_grid
#
# # the cell center
# r_ind = min(np.argsort(abs(r_wall-r))[:2])
# t_ind = min(np.argsort(abs(t_wall-t))[:2])
# p_ind = min(np.argsort(abs(p_wall-p))[:2])
#
# # simple Trapezoid rule
# val_dum = 0
# for ri in r_ind:
# for ti in t_ind:
# for pi in p_ind:
# val_dum += cube[ri, ti, pi]
# val = val_dum/8.0
#
# return val
def locateCell2d(self, coord, wall_grid):
"""
return the indice of cell at given coordinates
"""
r, t = coord
r_wall, t_wall = wall_grid
r_ind = min(np.argsort(abs(r_wall - r))[:2])
t_ind = min(np.argsort(abs(t_wall - t))[:2])
return (r_ind, t_ind)
# def interpolateCell2d(self, coord, cube, wall_grid):
# """
# return the interpolated value at given data cube at given coordinates
# """
# r, t= coord
# r_wall, t_wall = wall_grid
#
# r_ind = np.argsort(abs(r_wall-r))[:2]
# t_ind = np.argsort(abs(t_wall-t))[:2]
#
# # simple Trapezoid rule
# val = (cube[r_ind[0], t_ind[0]] + cube[r_ind[0], t_ind[1]] +
# cube[r_ind[1], t_ind[0]] + cube[r_ind[1], t_ind[1]])/4.0
#
# return val
def getDensity(self, x, y, z, version='gridding', theta_cav=None):
(r_in, t_in, p_in) = self.Cart2Spherical(x, y, z)
if self.truncate != None:
if (y**2 + z**2)**0.5 > self.truncate * au_si:
return 0.0
if version == 'hyperion':
r_wall = self.hy_grid.r_wall
t_wall = self.hy_grid.t_wall
p_wall = self.hy_grid.p_wall
self.rho = self.hy_grid.quantities['density'][0].T
if not self.interpolate:
indice = self.locateCell((r_in, t_in, p_in),
(r_wall, t_wall, p_wall))
rho = self.rho[indice]
else:
rho = self.interpolateCell((r_in, t_in, p_in), self.rho,
(r_wall, t_wall, p_wall))
# LIME needs molecule number density per cubic meter
# if self.debug:
# foo = open('density.log', 'a')
# foo.write('%e \t %e \t %e \t %e\n' % (x,y,z,float(self.rho[indice])*self.g2d/mh/self.mmw*1e6))
# foo.close()
return float(rho) * self.g2d / mh / self.mmw * 1e6
elif version == 'gridding':
# check for cavity
# determine whether the cell is in the cavity
# if (theta_cav != None) and (theta_cav != 0):
# # using R = 10000 AU as the reference point
# c0 = (10000.*au_cgs)**(-0.5)*\
# np.sqrt(1/np.sin(np.radians(theta_cav))**3-1/np.sin(np.radians(theta_cav)))
#
# # related coordinates
# w = abs(r_in*np.cos(np.pi/2 - t_in))
# _z = r_in*np.sin(np.pi/2 - t_in)
#
# # condition for open cavity
# z_cav = c0*abs(w)**1.5
# cav_con = abs(_z) > abs(z_cav)
#
# if cav_con:
# # this is still wrong, because in the "correct" model setup. The cavity does not have zero density.
# rho = 0.0
# return float(rho)
# isothermal solution
if r_in > self.r_inf:
rho = (self.cs * 1e5)**2 / (2 * np.pi * G *
(r_in)**2) / mh / self.mmw * 1e6
# TSC solution
else:
if not self.interpolate:
ind = self.locateCell2d(
(r_in, t_in), (self.tsc2d['xr_wall'] * self.r_inf,
self.tsc2d['theta_wall']))
rho = self.tsc2d['rho2d'][
ind] * 1e6 # has been divided by "mh" and "mmw"
else:
rho = self.interpolateCell2d(
(r_in, t_in), self.tsc2d['rho2d'],
(self.tsc2d['xr_wall'] * self.r_inf,
self.tsc2d['theta_wall'])) * 1e6
return float(rho)
def getTemperature(self, x, y, z, external_heating=False, r_break=None):
r_wall = self.hy_grid.r_wall
t_wall = self.hy_grid.t_wall
p_wall = self.hy_grid.p_wall
self.temp = self.hy_grid.quantities['temperature'][0].T
if self.truncate != None:
if (y**2 + z**2)**0.5 > self.truncate * au_si:
return 0.0
(r_in, t_in, p_in) = self.Cart2Spherical(x, y, z)
if not self.interpolate:
indice = self.locateCell((r_in, t_in, p_in),
(r_wall, t_wall, p_wall))
temp = self.temp[indice]
else:
temp = self.interpolateCell((r_in, t_in, p_in), cube,
(r_wall, t_wall, p_wall))
# if external_heating:
# # get the temperature at the outermost radius
# indice_lowT = self.locateCell(((r_wall[-1]+r_wall[-2])/2, t_in, p_in), (r_wall, t_wall, p_wall))
# lowT = self.temp[indice_lowT]
# # the inner radius where the temperature correction starts to apply
# # User-defined value
# # r_break = 13000*au_cgs
# # r_break = 2600*au_cgs
# r_break = r_break*au_cgs
#
# if (lowT < 15) and (r_in >= r_break):
# dT = (r_in - r_break)*(15-lowT)/((r_wall[-1]+r_wall[-2])/2 - r_break)
# if float(temp) + float(dT) >= 0.0:
# return float(temp) + float(dT)
# else:
# return 0.0
# test for a different approach of external heating
if external_heating:
from scipy.interpolate import interp1d
# get the temperature at the outermost radius
rc = (r_wall[1:] + r_wall[:-1]) / 2
indice_Tmin = self.locateCell((rc.max(), t_in, p_in),
(r_wall, t_wall, p_wall))
Tmin = self.temp[indice_Tmin]
# set an inner radius that the external heating will apply for skipping the disk, where temperature may be lower than 10 K
# take two times the centrifugal radius
rCen = self.omega**2 * G**3 * (0.975 * (self.cs * 1e5)**3 / G *
(self.age * yr))**3 / (
16 * (self.cs * 1e5)**8)
r_ext_min = 2 * rCen
if (temp < 10.0) and (r_in >= r_ext_min) and (Tmin < 15.0):
rc = (r_wall[1:] + r_wall[:-1]) / 2
f_temp = interp1d(
self.temp[(rc > r_ext_min), indice_Tmin[1],
indice_Tmin[2]], rc[rc > r_ext_min])
r10K = f_temp(10.0)
dT = (r_in - r10K) / (rc.max() - r10K) * (15.0 - Tmin)
temp = float(temp) + float(dT)
if float(temp) >= 0.0:
return float(temp)
else:
return 0.0
def getVelocity(self,
x,
y,
z,
sph=False,
unit_convert=True,
vr_factor=1.0,
vr_offset=0.0):
"""
cs: effecitve sound speed in km/s;
age: the time since the collapse began in year.
vr_offset: in km/s
"""
(r_in, t_in, p_in) = self.Cart2Spherical(x,
y,
z,
unit_convert=unit_convert)
if self.truncate != None:
if (y**2 + z**2)**0.5 > self.truncate * au_si:
v_out = [0.0, 0.0, 0.0]
return v_out
# outside of infall radius, the envelope is static
# if r_in > self.r_inf:
# v_sph = [0.0+vr_offset*1e3, 0.0, 0.0]
# v_out = self.Spherical2Cart_vector((r_in, t_in, p_in), v_sph)
# return v_out
# if the input radius is smaller than the minimum in xr array,
# use the minimum in xr array instead.
# UPDATE (081518): return zero velocity instead
if r_in < self.tsc2d['xr_wall'].min() * self.r_inf:
r_in = self.tsc2d['xrc'].min() * self.r_inf
v_out = [0.0, 0.0, 0.0]
return v_out
if not self.interpolate:
ind = self.locateCell2d(
(r_in, t_in),
(self.tsc2d['xr_wall'] * self.r_inf, self.tsc2d['theta_wall']))
v_sph = list(
map(float, [
self.tsc2d['vr2d'][ind] * 1e5 / 1e2,
self.tsc2d['vtheta2d'][ind] * 1e5 / 1e2,
self.tsc2d['vphi2d'][ind] * 1e5 / 1e2
]))
else:
vr = self.interpolateCell2d((r_in, t_in), self.tsc2d['vr2d'],
(self.tsc2d['xr_wall'] * self.r_inf,
self.tsc2d['theta_wall'])) * 1e5
vtheta = self.interpolateCell2d(
(r_in, t_in), self.tsc2d['vtheta2d'],
(self.tsc2d['xr_wall'] * self.r_inf,
self.tsc2d['theta_wall'])) * 1e5
vphi = self.interpolateCell2d((r_in, t_in), self.tsc2d['vphi2d'],
(self.tsc2d['xr_wall'] * self.r_inf,
self.tsc2d['theta_wall'])) * 1e5
v_sph = list(map(float, [vr / 1e2, vtheta / 1e2, vphi / 1e2]))
# test for artifically reducing the radial velocity
v_sph[0] = v_sph[0] * vr_factor # + vr_offset*1e3
# flatten out at the vr_offset
# if v_sph[0] > vr_offset*1e3:
# v_sph[0] = vr_offset*1e3 # Note infall velocity should be negative
# A hybrid outer envelope model: -0.5 km/s uniformly within 1e4 AU and static beyond.
# static envelope beyond 3000 AU
# the vr_offset has a parabolic curve as a function of radius (e.g. Keto+2015)
# parameter is taken from Keto+2015. y = a(r - r_max)^2
# vr is negative
if v_sph[0] > vr_offset * 1e3:
v_sph[0] = 50.0 * (r_in - (r_wall[-1] + r_wall[-2]) / 2)**2 * 1e3
if sph:
return v_sph
v_out = self.Spherical2Cart_vector((r_in, t_in, p_in), v_sph)
if self.debug:
foo = open('velocity.log', 'a')
foo.write('%e \t %e \t %e \t %f \t %f \t %f\n' %
(x, y, z, v_out[0], v_out[1], v_out[2]))
foo.close()
return v_out
def getFFVelocity(self,
x,
y,
z,
J,
M,
sph=False,
unit_convert=True,
vr_factor=1.0):
"""
cs: effecitve sound speed in km/s;
age: the time since the collapse began in year.
"""
(r_in, t_in, p_in) = self.Cart2Spherical(x,
y,
z,
unit_convert=unit_convert)
if self.truncate != None:
if (y**2 + z**2)**0.5 > self.truncate * au_si:
v_out = [0.0, 0.0, 0.0]
return v_out
# if the input radius is smaller than the minimum in xr array,
# use the minimum in xr array instead.
# UPDATE: return zero velocity instead
if r_in < self.xr_wall.min() * self.r_inf:
r_in = self.xr.min() * self.r_inf
v_out = [0.0, 0.0, 0.0]
return v_out
# use the Sakai model
M = M * MS
# centrifugal barrier
cb = J**2 / (2 * G * M)
if 2 * G * M / r_in - J**2 / r_in**2 >= 0:
vr = (2 * G * M / r_in - J**2 / r_in**2)**0.5 * vr_factor
else:
vr = 0.0
# let vk = vp at CB
M_k = J**2 / (G * cb)
vp = J / r_in
vk = (G * M_k / r_in)**0.5
if r_in >= cb:
v_sph = [-vr / 1e2, 0.0, vp / 1e2]
else:
v_sph = [-vr / 1e2, 0.0, vk / 1e2]
if sph:
return v_sph
v_out = self.Spherical2Cart_vector((r_in, t_in, p_in), v_sph)
if self.debug:
foo = open('velocity.log', 'a')
foo.write('%e \t %e \t %e \t %f \t %f \t %f\n' %
(x, y, z, v_out[0], v_out[1], v_out[2]))
foo.close()
return v_out
def getVelocity2(self, x, y, z, sph=False, unit_convert=True):
"""
new method to interpolate the velocity
cs: effecitve sound speed in km/s;
age: the time since the collapse began in year.
"""
(r_in, t_in, p_in) = self.Cart2Spherical(x,
y,
z,
unit_convert=unit_convert)
if self.truncate != None:
if (y**2 + z**2)**0.5 > self.truncate * au_si:
v_out = [0.0, 0.0, 0.0]
return v_out
# outside of infall radius, the envelope is static
if r_in > self.r_inf:
v_out = [0.0, 0.0, 0.0]
return v_out
# if the input radius is smaller than the minimum in xr array,
# use the minimum in xr array instead.
if r_in < self.tsc2d['xr_wall'].min() * self.r_inf:
r_in = self.tsc2d['xrc'].min() * self.r_inf
# TODO: raise warning
# r, t = 10*au, np.radians(30.)
# print(r, t)
r_corners = np.argsort(abs(r_in - self.tsc2d['xrc'] * self.r_inf))[:2]
theta_corners = np.argsort(abs(t_in - self.tsc2d['thetac']))[:2]
# print(r_corners, theta_corners)
# initialize the velocity vector in spherical coordinates
# TODO: use scipy interp2d
v_sph = []
for k in ['vr2d', 'vtheta2d', 'vphi2d']:
f = interp2d(self.tsc2d['xrc'][r_corners] * self.r_inf,
self.tsc2d['thetac'][theta_corners],
self.tsc2d[k][np.ix_(r_corners, theta_corners)])
v_sph.append(float(f(r_in, t_in) * 1e5 / 1e2))
# v_r, v_theta, v_phi = 0.0, 0.0, 0.0
# for rc in r_corners:
# for tc in theta_corners:
# v_r += self.vr2d[rc, tc]
# v_theta += self.vtheta2d[rc, tc]
# v_phi += self.vphi2d[rc, tc]
# v_r = v_r/4
# v_theta = v_theta/4
# v_phi = v_phi/4
#
# v_sph = list(map(float, [v_r/1e2, v_theta/1e2, v_phi/1e2])) # convert to SI unit (meter)
if sph:
return v_sph
v_out = self.Spherical2Cart_vector((r_in, t_in, p_in), v_sph)
if self.debug:
foo = open('velocity.log', 'a')
foo.write('%e \t %e \t %e \t %f \t %f \t %f\n' %
(x, y, z, v_out[0], v_out[1], v_out[2]))
foo.close()
return v_out
def getAbundance(self, x, y, z, config, tol=10, theta_cav=None):
# tol: the size (in AU) of the linear region between two steps
# (try to avoid "cannot find cell" problem in LIME)
# a_params = [abundance at outer region,
# fraction of outer abundance to the inner abundance,
# the ratio of the outer radius of the inner region to the infall radius]
# abundances = [3.5e-8, 3.5e-9] # inner, outer
if self.truncate != None:
if (y**2 + z**2)**0.5 > self.truncate * au_si:
return 0.0
tol = tol * au_cgs
(r_in, t_in, p_in) = self.Cart2Spherical(x, y, z)
# determine whether the cell is in the cavity
if (theta_cav != None) and (theta_cav != 0):
# using R = 10000 AU as the reference point
c0 = (10000.*au_cgs)**(-0.5)*\
np.sqrt(1/np.sin(np.radians(theta_cav))**3-1/np.sin(np.radians(theta_cav)))
# related coordinates
w = abs(r_in * np.cos(np.pi / 2 - t_in))
_z = r_in * np.sin(np.pi / 2 - t_in)
# condition for open cavity
z_cav = c0 * abs(w)**1.5
cav_con = abs(_z) > abs(z_cav)
if cav_con:
abundance = 0.0
return float(abundance)
# single negative drop case
# TODO: adopt a more generic model name, but keep backward compatability.
if (config['a_model'] == 'neg_step1') or (config['a_model']
== 'step1'):
a0 = float(config['a_params0'])
a1 = float(config['a_params1'])
a2 = float(config['a_params2'])
if (r_in - a2 * self.r_inf) > tol / 2:
abundance = a0
elif abs(r_in - a2 * self.r_inf) <= tol / 2:
abundance = a0 * a1 + (r_in - (a2 * self.r_inf - tol / 2)) * (
a0 - a0 * a1) / tol
else:
abundance = a0 * a1
elif (config['a_model'] == 'neg_step2') or (config['a_model']
== 'step2'):
a0 = float(config['a_params0'])
a1 = float(config['a_params1'])
a2 = float(config['a_params2'])
a3 = float(config['a_params3'])
a4 = float(config['a_params4'])
if (r_in - a2 * self.r_inf) > tol / 2:
abundance = a0
# linear interpolation from the outer region to the first step
elif abs(r_in - a2 * self.r_inf) <= tol / 2:
abundance = a0 * a1 + (r_in - (a2 * self.r_inf - tol / 2)) * (
a0 - a0 * a1) / tol
# first step
elif (r_in - a4 * au_cgs) > tol / 5 / 2 and (a2 * self.r_inf -
r_in) > tol / 2:
abundance = a0 * a1
# linear interpolation from the first step to the second step
elif abs(r_in - a4 * au_cgs) <= tol / 5 / 2:
abundance = a0 * a3 + (r_in - (a4 * au_cgs - tol / 5 / 2)) * (
a0 * a1 - a0 * a3) / (tol / 5)
else:
abundance = a0 * a3
elif (config['a_model'] == 'drop'):
a0 = float(config['a_params0'])
a1 = float(config['a_params1'])
a2 = float(config['a_params2'])
a3 = float(config['a_params3'])
a4 = float(config['a_params4'])
if (r_in - a2 * au_cgs) > tol / 2:
abundance = a0
# linear interpolation from the outer region to the first step
elif abs(r_in - a2 * au_cgs) <= tol / 2:
abundance = a1 + (r_in -
(a2 * au_cgs - tol / 2)) * (a0 - a1) / tol
# first step
elif (r_in - a4 * au_cgs) > tol / 5 / 2 and (a2 * au_cgs -
r_in) > tol / 2:
abundance = a1
# linear interpolation from the first step to the second step
elif abs(r_in - a4 * au_cgs) <= tol / 5 / 2:
abundance = a3 + (r_in - (a4 * au_cgs - tol / 5 / 2)) * (
a1 - a3) / (tol / 5)
else:
abundance = a3
elif (config['a_model'] == 'drop2'):
a0 = float(config['a_params0'])
a1 = float(config['a_params1'])
a2 = float(config['a_params2'])
a3 = float(config['a_params3'])
a4 = float(config['a_params4'])
if (r_in - a2 * au_cgs) > tol / 2:
abundance = a0
# linear interpolation from the outer region to the first step
elif abs(r_in - a2 * au_cgs) <= tol / 2:
abundance = a1 + (r_in -
(a2 * au_cgs - tol / 2)) * (a0 - a1) / tol
# first step
elif (r_in - a4 * au_cgs) > tol / 5 / 2 and (a2 * au_cgs -
r_in) > tol / 2:
abundance = a1
# linear interpolation from the first step to the second step
elif abs(r_in - a4 * au_cgs) <= tol / 5 / 2:
abundance = a3 + (r_in - (a4 * au_cgs - tol / 5 / 2)) * (
a1 - a3) / (tol / 5)
elif r_in >= 13 * au_cgs:
abundance = a3
else:
abundance = 1e-20
elif (config['a_model'] == 'drop3'):
a0 = float(config['a_params0']) # undelepted abundance
a1 = float(config['a_params1']) # depleted abundance
a2 = float(config['a_params2']) # evaporation temperature (K)
a3 = float(config['a_params3']) # depletion density (cm-3)
a4 = float(config['a_params4']
) # the temperature H2O starts to destory HCO+
if a4 == -1:
a4 = np.inf
temp = self.getTemperature(x, y, z)
density = self.getDensity(x, y, z) / 1e6
if (temp <= a2) and (density >= a3):
abundance = a1
elif (temp <= a4):
abundance = a0
else:
abundance = 1e-20
elif config['a_model'] == 'uniform':
abundance = float(config['a_params0'])
elif config['a_model'] == 'lognorm':
a0 = float(config['a_params0'])
a1 = float(config['a_params1'])
a2 = float(config['a_params2'])
a3 = float(config['a_params3']) # r_in for power law decrease
if r_in >= a2 * self.r_inf:
abundance = a0
elif (r_in < a2 * self.r_inf) & (r_in > a3 * au_cgs):
abundance = a0 * a1 + a0 * (1 - a1) / (
np.log10(self.r_inf * a2) - np.log10(a3 * au_cgs)) * (
np.log10(r_in) - np.log10(a3 * au_cgs))
else:
abundance = a0 * a1
elif config['a_model'] == 'powerlaw':
a0 = float(config['a_params0'])
a1 = float(config['a_params1'])
a2 = float(config['a_params2'])
a3 = float(config['a_params3'])
a4 = float(config['a_params4'])
# re-define rMin
# rmin = 100*au_cgs
rmin = self.rmin
if r_in >= a2 * self.r_inf:
abundance = a0
elif (r_in >= rmin) and (r_in < a2 * self.r_inf):
# y = Ax^a3+B
A = a0 * (1 - a1) / ((a2 * self.r_inf)**a3 - rmin**a3)
B = a0 - a0 * (1 - a1) * (a2 * self.r_inf)**a3 / (
(a2 * self.r_inf)**a3 - rmin**a3)
abundance = A * r_in**a3 + B
else:
abundance = a0 * a1
# option to cap the maximum value of abundance
if a4 > 0:
if abundance > abs(a4):
abundance = abs(a4)
elif config['a_model'] == 'powerlaw2':
a0 = float(config['a_params0'])
a1 = float(config['a_params1'])
a2 = float(config['a_params2'])
a3 = float(config['a_params3'])
a4 = float(config['a_params4'])
# re-define rMin
# rmin = 100*au_cgs
rmin = self.rmin
if r_in >= a2 * self.r_inf:
abundance = a0
elif (r_in >= rmin) and (r_in < a2 * self.r_inf):
# y = Ax^a3+B
A = a0 * (1 - a1) / ((a2 * self.r_inf)**a3 - rmin**a3)
B = a0 - a0 * (1 - a1) * (a2 * self.r_inf)**a3 / (
(a2 * self.r_inf)**a3 - rmin**a3)
abundance = A * r_in**a3 + B
else:
abundance = a0 * a1
# add the evaporation zone
# TODO: parametrize the setup
if (r_in <= 100 * au_cgs) and (r_in >= 13 * au_cgs):
abundance = 1e-10
# option to cap the maximum value of abundance
if a4 > 0:
if abundance > abs(a4):
abundance = abs(a4)
elif config['a_model'] == 'chem':
a0 = float(config['a_params0']) # peak abundance
a1 = float(config['a_params1']) # inner abundance
a2 = float(config['a_params2']) # peak radius
a3 = float(config['a_params3']) # inner decrease power
a4 = float(config['a_params4']) # outer decrease power
# radius of the evaporation front, determined by the extent of COM emission
rCOM = 100 * au_cgs
if r_in >= a2 * self.r_inf:
# y = Ax^a, a < 0
A_out = a0 / (a2 * self.r_inf)**a4
abundance = A_out * r_in**a4
elif (r_in >= rCOM) and (r_in < a2 * self.r_inf):
# y = Ax^a, a > 0
A_in = a0 / (a2 * self.r_inf)**a3
abundance = A_in * r_in**a3
else:
abundance = a1
elif config['a_model'] == 'chem2':
a0 = float(config['a_params0']) # peak abundance
a1 = float(config['a_params1']) # inner abundance
a2 = float(config['a_params2']) # inner peak radius [AU]
a3 = float(config['a_params3']) # outer peak radius [AU]
a4 = config[
'a_params4'] # inner/outer radius of the evaporation region
# radius of the evaporation front, determined by the extent of COM emission
if (a4 == '-1') or (a4
== '2.0/-2.0'): # for backward compatability
rCOM = 100 * au_cgs
rCen = 13 * au_cgs
else:
rCen = float(a4.split(',')[0]) * au_cgs
rCOM = float(a4.split(',')[1]) * au_cgs
# innerExpo, outerExpo = [float(i) for i in config['a_params4'].split('/')]
# fix the decreasing/increasing powers
innerExpo = 2.0
outerExpo = -2.0
if r_in >= a3 * au_cgs:
# y = Ax^a, a < 0
A_out = a0 / (a3 * au_cgs)**outerExpo
abundance = A_out * r_in**outerExpo
elif (r_in < a3 * au_cgs) and (r_in >= a2 * au_cgs):
abundance = a0
elif (r_in >= rCOM) and (r_in < a2 * au_cgs):
# y = Ax^a, a > 0
A_in = a0 / (a2 * au_cgs)**innerExpo
abundance = A_in * r_in**innerExpo
elif (r_in >= rCen) and (r_in < rCOM): # centrifugal radius
abundance = a1
else:
abundance = 1e-20
elif config['a_model'] == 'chem3':
a0 = float(config['a_params0']) # peak abundance
a1 = float(config['a_params1']) # inner abundance
a2 = list(map(float, config['a_params2'].split(
','))) # inner/outer radius for the maximum abundance [AU]
a3 = list(map(float, config['a_params3'].split(
','))) # inner/outer radius for the evaporation zone [AU]
a4 = list(map(float, config['a_params4'].split(
','))) # inner/outer decreasing power
# radius of the evaporation front, determined by the extent of COM emission
rEvap_inner = a3[0] * au_cgs
rEvap_outer = a3[1] * au_cgs
# test the case of a broken power law for the freeze-out zone
# In this case, there will be three values for both a2 and a4
# The input powers are stored as -
# innerExpo for all powers except for the last one
# outerExpo for the last power
# fix the decreasing/increasing powers
innerExpo = a4[:-1]
outerExpo = a4[-1]
# calculate the constants for each freeze-out zone
A = []
for i, (r_out,
pow) in enumerate(zip(a2[:-1][::-1], innerExpo[::-1])):
if i == 0:
previous_pow = 0.0
_A = (r_out * au_cgs)**(-pow)
else:
_A = _A * (r_out * au_cgs)**(previous_pow - pow)
previous_pow = pow
A.append(a0 * _A)
A = A[::-1]
if r_in >= a2[-1] * au_cgs:
# y = Ax^a, a < 0
A_out = a0 / (a2[-1] * au_cgs)**outerExpo
abundance = A_out * r_in**outerExpo
elif (r_in < a2[-1] * au_cgs) and (r_in >= a2[-2] * au_cgs):
abundance = a0
# freeze-out zone
elif (r_in >= rEvap_outer) and (r_in < a2[-2] * au_cgs):
# y = Ax^a, a > 0
# determine which freeze-out zone
ind_zone = a2[:-1].index(
min([
rr for i, rr in enumerate(a2[:-1])
if rr * au_cgs - r_in > 0
]))
A_in = A[ind_zone]
Expo = innerExpo[ind_zone]
# A_in = a0 / (a2[0]*au_cgs)**innerExpo
# abundance = A_in * r_in**innerExpo
abundance = A_in * r_in**Expo
elif (r_in >= rEvap_inner) and (r_in <
rEvap_outer): # centrifugal radius
abundance = a1
else:
abundance = 0.0
elif config[
'a_model'] == 'chem4': # mostly for CS, which has two evaporation fronts, one for CO, and one for CS.
a0 = float(config['a_params0']) # peak abundance
a1 = list(map(
float, config['a_params1'].split(','))) # TWO inner abundance
a2 = list(map(float, config['a_params2'].split(
','))) # inner/outer radius for the maximum abundance [AU]
a3 = list(
map(float, config['a_params3'].split(','))
) # inner/middle/outer radius for the evaporation zone [AU]
a4 = list(map(float, config['a_params4'].split(
','))) # inner/outer decreasing power
# radius of the evaporation front, determined by the extent of COM emission
rEvap_inner = a3[0] * au_cgs
rEvap_middle = a3[1] * au_cgs
rEvap_outer = a3[2] * au_cgs
# test the case of a broken power law for the freeze-out zone
# In this case, there will be three values for both a2 and a4
# The input powers are stored as -
# innerExpo for all powers except for the last one
# outerExpo for the last power
# fix the decreasing/increasing powers
innerExpo = a4[:-1]
outerExpo = a4[-1]
# calculate the constants for each freeze-out zone
A = []
for i, (r_out,
pow) in enumerate(zip(a2[:-1][::-1], innerExpo[::-1])):
if i == 0:
previous_pow = 0.0
_A = (r_out * au_cgs)**(-pow)
else:
_A = _A * (r_out * au_cgs)**(previous_pow - pow)
previous_pow = pow
A.append(a0 * _A)
A = A[::-1]
if r_in >= a2[-1] * au_cgs:
# y = Ax^a, a < 0
A_out = a0 / (a2[-1] * au_cgs)**outerExpo
abundance = A_out * r_in**outerExpo
elif (r_in < a2[-1] * au_cgs) and (r_in >= a2[-2] * au_cgs):
abundance = a0
# freeze-out zone
elif (r_in >= rEvap_outer) and (r_in < a2[-2] * au_cgs):
# y = Ax^a, a > 0
# determine which freeze-out zone
ind_zone = a2[:-1].index(
min([
rr for i, rr in enumerate(a2[:-1])
if rr * au_cgs - r_in > 0
]))
A_in = A[ind_zone]
Expo = innerExpo[ind_zone]
# A_in = a0 / (a2[0]*au_cgs)**innerExpo
# abundance = A_in * r_in**innerExpo
abundance = A_in * r_in**Expo
elif (r_in >= rEvap_middle) and (
r_in < rEvap_outer): # 1st evaporation zone
abundance = a1[1]
elif (r_in >= rEvap_inner) and (
r_in < rEvap_middle): # 1st evaporation zone
abundance = a1[0]
else:
abundance = 0.0
elif config['a_model'] == 'interp':
filename = config['a_params0']
adata = io.ascii.read(filename, names=['radius', 'abundance'])
f_a = interp1d(adata['radius'], adata['abundance'])
if (r_in < adata['radius'].min() * au_cgs) or (
r_in > adata['radius'].max() * au_cgs):
abundance = 1e-40
else:
abundance = f_a(r_in / au_cgs)
else:
print('Cannot recognize the input a_model', config['a_model'])
return False
if self.debug:
foo = open('abundance.log', 'a')
foo.write('%e \t %e \t %e \t %f\n' % (x, y, z, abundance))
foo.close()
# uniform abundance
# abundance = 3.5e-9
return float(abundance)
def radialSmoothing(self,
x,
y,
z,
variable,
kernel='boxcar',
smooth_factor=2,
config=None):
# convert the coordinates from Cartian to spherical
(r_in, t_in, p_in) = self.Cart2Spherical(x, y, z)
# r-array for smoothing
smoothL = r_in / smooth_factor * au_cgs
r_arr = np.arange(r_in - smoothL / 2, r_in + smoothL / 2,
smoothL / 50) # 50 bins
# setup the smoothing kernel
# it is not really a smoothing kernel, more like a local mean
def averageKernel(kernel, r, var):
if kernel == 'boxcar':
out = np.mean(var)
return out
# run the corresponding look-up function for the desired variable
var_arr = np.empty_like(r_arr)
for i, r in enumerate(r_arr):
(xd, yd, zd) = self.Spherical2Cart(r, t_in, p_in)
if variable == 'abundance':
var_arr[i] = self.getAbundance(xd / 1e2, yd / 1e2, zd / 1e2,
config)
var = averageKernel(kernel, r, var_arr)
return float(var)
def DIG_source_add(m, reg, df_nu, boost):
print("--------------------------------")
print("Adding DIG to Source List in source_creation")
print("--------------------------------")
print("Getting the specific energy dumped in each grid cell")
try:
rtout = cfg.model.outputfile + '_DIG_energy_dumped.sed'
try:
grid_properties = np.load(cfg.model.PD_output_dir +
"/grid_physical_properties." +
cfg.model.snapnum_str + '_galaxy' +
cfg.model.galaxy_num_str + ".npz")
except:
grid_properties = np.load(cfg.model.PD_output_dir +
"/grid_physical_properties." +
cfg.model.snapnum_str + ".npz")
cell_info = np.load(cfg.model.PD_output_dir + "/cell_info." +
cfg.model.snapnum_str + "_" +
cfg.model.galaxy_num_str + ".npz")
except:
print(
"ERROR: Can't proceed with DIG nebular emission calculation. Code is unable to find the required files."
)
print(
"Make sure you have the rtout.sed, grid_physical_properties.npz and cell_info.npz for the corresponding galaxy."
)
return
m_out = ModelOutput(rtout)
oct = m_out.get_quantities()
grid = oct
order = find_order(grid.refined)
refined = grid.refined[order]
quantities = {}
for field in grid.quantities:
quantities[('gas', field)] = grid.quantities[field][0][order][~refined]
cell_width = cell_info["fw1"][:, 0]
mass = (quantities['gas', 'density'] * units.g /
units.cm**3).value * (cell_width**3)
specific_energy = (quantities['gas', 'specific_energy'] * units.erg /
units.s / units.g).value
specific_energy = (specific_energy * mass) # in ergs/s
mask = np.where(
mass != 0)[0] # Masking out all grid cells that have no gas mass
pos = cell_info["fc1"][mask] - boost
cell_width = cell_width[mask]
met = grid_properties["grid_gas_metallicity"][:, mask]
met = np.transpose(met)
specific_energy = specific_energy[mask]
mask = []
logU_arr = []
lam_arr = []
fnu_arr = []
for i in range(len(cell_width)):
# Getting shape of the incident spectrum by taking a distance weighted average of the CLOUDY output spectrum of nearby young stars.
sed_file_name = cfg.model.PD_output_dir + "neb_seds_galaxy_" + cfg.model.galaxy_num_str + ".npz"
lam, fnu = get_DIG_sed_shape(
pos[i], cell_width[i], sed_file=sed_file_name
) # Returns input SED shape, lam in Angstrom, fnu in Lsun/Hz
# If the gas cell has no young stars within a specified distance (stars_max_dist) then skip it.
if len(np.atleast_1d(fnu)) == 1:
continue
# Calulating the ionization parameter by extrapolating the specfic energy beyind the lyman limit
# using the SED shape calculated above.
logU = get_DIG_logU(lam, fnu, specific_energy[i], cell_width[i])
lam_arr.append(lam)
fnu_arr.append(fnu)
# Only cells with ionization parameter greater than the parameter DIG_min_logU are considered
# for nebular emission calculation. This is done so as to speed up the calculation by ignoring
# the cells that do not have enough energy to prduce any substantial emission
if logU > cfg.par.DIG_min_logU:
mask.append(i)
lam_arr.append(lam)
fnu_arr.append(fnu)
logU_arr.append(logU)
cell_width = cell_width[mask]
met = met[mask]
lam_arr = np.array(lam_arr)
fnu_arr = np.array(fnu_arr)
logU = np.array(logU_arr)
pos = pos[mask]
if (len(mask)) == 0:
print(
"No gas particles fit the criteria for calculating DIG. Skipping DIG calculation"
)
return
print(
"----------------------------------------------------------------------------------"
)
print("Calculating nebular emission from Diffused Ionized Gas for " +
str(len(mask)) + " gas cells")
print(
"----------------------------------------------------------------------------------"
)
fnu_arr_neb = sg.get_dig_seds(lam_arr, fnu_arr, logU, cell_width, met)
nu = 1.e8 * constants.c.cgs.value / lam
for i in range(len(logU)):
fnu = fnu_arr_neb[i, :]
nu, fnu = wavelength_compress(nu, fnu, df_nu)
nu = nu[::-1]
fnu = fnu[::-1]
lum = np.absolute(np.trapz(fnu, x=nu)) * constants.L_sun.cgs.value
source = m.add_point_source()
source.luminosity = lum # [ergs/s]
source.spectrum = (nu[::-1], fnu[::-1])
source.position = pos[i] # [cm]
def alma_cavity(freq,
outdir,
vlim,
units='MJy/sr',
pix=300,
filename=None,
label=None):
import numpy as np
import matplotlib.pyplot as plt
import astropy.constants as const
from hyperion.model import ModelOutput
from matplotlib.ticker import MaxNLocator
# constants setup
c = const.c.cgs.value
pc = const.pc.cgs.value
au = const.au.cgs.value
# Image in the unit of MJy/sr
# Change it into erg/s/cm2/Hz/sr
if units == 'erg/s/cm2/Hz/sr':
factor = 1e-23 * 1e6
cb_label = r'$\rm{I_{\nu}\,(erg\,s^{-1}\,cm^{-2}\,Hz^{-1}\,sr^{-1})}$'
elif units == 'MJy/sr':
factor = 1
cb_label = r'$\rm{I_{\nu}\,(MJy\,sr^{-1})}$'
if filename == None:
# input files setup
filename_reg = '/Users/yaolun/test/model12.rtout'
filename_r2 = '/Users/yaolun/test/model13.rtout'
filename_r15 = '/Users/yaolun/test/model17.rtout'
filename_uni = '/Users/yaolun/test/model62.rtout'
else:
filename_reg = filename['reg']
filename_r2 = filename['r2']
filename_r15 = filename['r15']
filename_uni = filename['uni']
if label == None:
label_reg = r'$\rm{const.+r^{-2}}$'
label_r2 = r'$\rm{r^{-2}}$'
label_r15 = r'$\rm{r^{-1.5}}$'
label_uni = r'$\rm{uniform}$'
else:
label_reg = label['reg']
label_r2 = label['r2']
label_r15 = label['r15']
label_uni = label['uni']
wl_aper = [
3.6, 4.5, 5.8, 8.0, 8.5, 9, 9.7, 10, 10.5, 11, 16, 20, 24, 35, 70, 100,
160, 250, 350, 500, 850
]
wav = c / freq / 1e9 * 1e4
# wav = 40
# read in
# regular cavity setting
m_reg = ModelOutput(filename_reg)
image_reg = m_reg.get_image(group=len(wl_aper) + 1,
inclination=0,
distance=178.0 * pc,
units='MJy/sr')
# Calculate the image width in arcseconds given the distance used above
rmax = max(m_reg.get_quantities().r_wall)
w = np.degrees(rmax / image_reg.distance) * 3600.
# w = np.degrees((1.5 * pc) / image_reg.distance) * 60.
pix_num = len(image_reg.val[:, 0, 0])
pix2arcsec = 2 * w / pix_num
pix2au = np.radians(2 * w / pix_num / 3600.) * image_reg.distance / au
iwav = np.argmin(np.abs(wav - image_reg.wav))
# avoid zero in log
val_reg = image_reg.val[:, :, iwav] * factor + 1e-30
# r^-2 cavity setting
m_r2 = ModelOutput(filename_r2)
image_r2 = m_r2.get_image(group=len(wl_aper) + 1,
inclination=0,
distance=178.0 * pc,
units='MJy/sr')
# Calculate the image width in arcseconds given the distance used above
rmax = max(m_r2.get_quantities().r_wall)
w = np.degrees(rmax / image_r2.distance) * 3600.
pix_num = len(image_reg.val[:, 0, 0])
pix2arcsec = 2 * w / pix_num
pix2au = np.radians(2 * w / pix_num / 3600.) * image_reg.distance / au
iwav = np.argmin(np.abs(wav - image_r2.wav))
# avoid zero in log
val_r2 = image_r2.val[:, :, iwav] * factor + 1e-30
# r^-1.5 cavity setting
m_r15 = ModelOutput(filename_r15)
image_r15 = m_r15.get_image(group=len(wl_aper) + 1,
inclination=0,
distance=178.0 * pc,
units='MJy/sr')
# Calculate the image width in arcseconds given the distance used above
rmax = max(m_r15.get_quantities().r_wall)
w = np.degrees(rmax / image_r15.distance) * 3600.
pix_num = len(image_reg.val[:, 0, 0])
pix2arcsec = 2 * w / pix_num
pix2au = np.radians(2 * w / pix_num / 3600.) * image_reg.distance / au
iwav = np.argmin(np.abs(wav - image_r15.wav))
# avoid zero in log
val_r15 = image_r15.val[:, :, iwav] * factor + 1e-30
# uniform cavity setting
m_uni = ModelOutput(filename_uni)
image_uni = m_uni.get_image(group=len(wl_aper) + 1,
inclination=0,
distance=178.0 * pc,
units='MJy/sr')
# Calculate the image width in arcseconds given the distance used above
rmax = max(m_uni.get_quantities().r_wall)
w = np.degrees(rmax / image_uni.distance) * 3600.
print w
pix_num = len(image_reg.val[:, 0, 0])
pix2arcsec = 2 * w / pix_num
pix2au = np.radians(2 * w / pix_num / 3600.) * image_reg.distance / au
iwav = np.argmin(np.abs(wav - image_uni.wav))
# avoid zero in log
val_uni = image_uni.val[:, :, iwav] * factor + 1e-30
# 1-D radial intensity profile
# get y=0 plane, and plot it
fig = plt.figure(figsize=(8, 6))
ax = fig.add_subplot(111)
reg, = ax.plot(np.linspace(-pix / 2, pix / 2, num=pix) * pix2arcsec,
val_reg[:, pix / 2 - 1],
color='b',
linewidth=2)
r2, = ax.plot(np.linspace(-pix / 2, pix / 2, num=pix) * pix2arcsec,
val_r2[:, pix / 2 - 1],
color='r',
linewidth=1.5)
r15, = ax.plot(np.linspace(-pix / 2, pix / 2, num=pix) * pix2arcsec,
val_r15[:, pix / 2 - 1],
'--',
color='r',
linewidth=1.5)
uni, = ax.plot(np.linspace(-pix / 2, pix / 2, num=pix) * pix2arcsec,
val_uni[:, pix / 2 - 1],
color='k',
linewidth=1.5)
ax.legend([reg, r2, r15, uni], [label_reg, label_r2, label_r15, label_uni],\
numpoints=1, loc='lower center', fontsize=18)
ax.set_xlim([-1, 1])
ax.set_xlabel(r'$\rm{offset\,(arcsec)}$', fontsize=24)
# ax.set_ylabel(r'$\rm{I_{\nu}~(erg~s^{-1}~cm^{-2}~Hz^{-1}~sr^{-1})}$', fontsize=16)
ax.set_ylabel(cb_label, fontsize=24)
[
ax.spines[axis].set_linewidth(2)
for axis in ['top', 'bottom', 'left', 'right']
]
ax.minorticks_on()
ax.tick_params('both',
labelsize=16,
width=2,
which='major',
pad=10,
length=5)
ax.tick_params('both',
labelsize=16,
width=2,
which='minor',
pad=10,
length=2.5)
fig.savefig(outdir + 'cavity_intensity_' + str(freq) + '.pdf',
format='pdf',
dpi=300,
bbox_inches='tight')
# 2-D intensity map
from mpl_toolkits.axes_grid1 import AxesGrid
image_grid = [val_reg, val_uni, val_r2, val_r15]
label_grid = [label_reg, label_r2, label_r15, label_uni]
fig = plt.figure(figsize=(30, 30))
grid = AxesGrid(
fig,
142, # similar to subplot(142)
nrows_ncols=(2, 2),
axes_pad=0,
share_all=True,
label_mode="L",
cbar_location="right",
cbar_mode="single",
)
for i in range(4):
offset = np.linspace(-pix / 2, pix / 2, num=pix) * pix2arcsec
trim = np.where(abs(offset) <= 2)
im = grid[i].pcolor(np.linspace(-pix/2,pix/2,num=pix)*pix2arcsec, np.linspace(-pix/2,pix/2,num=pix)*pix2arcsec,\
image_grid[i], cmap=plt.cm.jet, vmin=vlim[0], vmax=vlim[1])#vmin=(image_grid[i][trim,trim]).min(), vmax=(image_grid[i][trim,trim]).max())
grid[i].set_xlim([-20, 20])
grid[i].set_ylim([-20, 20])
grid[i].set_xlabel(r'$\rm{RA\,offset\,(arcsec)}$', fontsize=14)
grid[i].set_ylabel(r'$\rm{Dec\,offset\,(arcsec)}$', fontsize=14)
# lg = grid[i].legend([label_grid[i]], loc='upper center', numpoints=1, fontsize=16)
# for text in lg.get_texts():
# text.set_color('w')
grid[i].text(0.5,
0.8,
label_grid[i],
color='w',
weight='heavy',
fontsize=18,
transform=grid[i].transAxes,
ha='center')
grid[i].locator_params(axis='x', nbins=5)
grid[i].locator_params(axis='y', nbins=5)
[
grid[i].spines[axis].set_linewidth(1.2)
for axis in ['top', 'bottom', 'left', 'right']
]
grid[i].tick_params('both',
labelsize=12,
width=1.2,
which='major',
pad=10,
color='white',
length=5)
grid[i].tick_params('both',
labelsize=12,
width=1.2,
which='minor',
pad=10,
color='white',
length=2.5)
# fix the overlap tick labels
if i != 0:
x_nbins = len(grid[i].get_xticklabels())
y_nbins = len(grid[i].get_yticklabels())
grid[i].yaxis.set_major_locator(MaxNLocator(nbins=5,
prune='upper'))
if i != 2:
grid[i].xaxis.set_major_locator(
MaxNLocator(nbins=5, prune='lower'))
[grid[0].spines[axis].set_color('white') for axis in ['bottom', 'right']]
[grid[1].spines[axis].set_color('white') for axis in ['bottom', 'left']]
[grid[2].spines[axis].set_color('white') for axis in ['top', 'right']]
[grid[3].spines[axis].set_color('white') for axis in ['top', 'left']]
# ax.set_aspect('equal')
cb = grid.cbar_axes[0].colorbar(im)
cb.solids.set_edgecolor("face")
cb.ax.minorticks_on()
cb.ax.set_ylabel(cb_label, fontsize=12)
cb_obj = plt.getp(cb.ax.axes, 'yticklabels')
plt.setp(cb_obj, fontsize=12)
# fig.text(0.5, -0.05 , r'$\rm{RA~offset~(arcsec)}$', fontsize=12, ha='center')
# fig.text(0, 0.5, r'$\rm{Dec~offset~(arcsec)}$', fontsize=12, va='center', rotation='vertical')
fig.savefig(outdir + 'cavity_2d_intensity_' + str(freq) + '.png',
format='png',
dpi=300,
bbox_inches='tight')
def get_physical_props_single(yso, grids, cell_sizes, save_dir, oversample=[3],
dust_out=None, logger=get_logger(__name__)):
"""Calculate and write the physical properties a model with one source.
Parameters:
yso: the model parameters.
grids: grids where the model will be evaluated
template: filename of the *define_model.c* file.
logger: logging system.
"""
# Models with more than one source should be treated in another function
# because the oversampling should be different.
# FITS list
fitslist = []
# Validate oversample
if len(oversample)==1 and len(oversample)!=len(cell_sizes):
oversample = oversample * len(cell_sizes)
elif len(oversample)==len(cell_sizes):
pass
else:
raise ValueError('The length of oversample != number of grids')
# Temperature function
if yso.params.get('DEFAULT', 'quantities_from'):
hmodel = yso.params.get('DEFAULT', 'quantities_from')
logger.info('Loading Hyperion model: %s', os.path.basename(hmodel))
hmodel = ModelOutput(os.path.expanduser(hmodel))
q = hmodel.get_quantities()
temperature = np.sum(q['temperature'].array[1:], axis=0)
r, th = q.r*u.cm, q.t*u.rad
temp_func = get_temp_func(yso.params, temperature, r, th)
elif dust_out is not None:
logger.info('Loading Hyperion model: %s', os.path.basename(dust_out))
hmodel = ModelOutput(os.path.expanduser(dust_out))
q = hmodel.get_quantities()
temperature = np.sum(q['temperature'].array[1:], axis=0)
r, th = q.r*u.cm, q.t*u.rad
temp_func = get_temp_func(yso.params, temperature, r, th)
else:
raise NotImplementedError
# Start from smaller to larger grid
inv_i = len(grids) - 1
for i,(grid,cellsz) in enumerate(zip(grids, cell_sizes)):
# Initialize grid axes
print '='*80
logger.info('Working on grid: %i', i)
logger.info('Oversampling factor: %i', oversample[i])
logger.info('Grid cell size: %i', cellsz)
# Multiply by units
x = grid[0]['x'] * grid[1]['x']
y = grid[0]['y'] * grid[1]['y']
z = grid[0]['z'] * grid[1]['z']
xi = np.unique(x)
yi = np.unique(y)
zi = np.unique(z)
# Density and velocity
dens, (vx, vy, vz), temp = phys_oversampled_cart(
xi, yi, zi, yso, temp_func, oversample=oversample[i],
logger=logger)
# Replace out of range values
dens[dens.cgs<=0./u.cm**3] = 10./u.cm**3
temp[np.logical_or(np.isnan(temp.value), temp.value<2.7)] = 2.7 * u.K
# Replace the inner region by rebbining the previous grid
if i>0:
# Walls of central cells
j = cell_sizes.index(cellsz)
xlen = cell_sizes[j-1] * len(xprev) * u.au
nxmid = int(xlen.value) / cellsz
xw = np.linspace(-0.5*xlen.value, 0.5*xlen.value, nxmid+1) * u.au
ylen = cell_sizes[j-1] * len(yprev) * u.au
nymid = int(ylen.value) / cellsz
yw = np.linspace(-0.5*ylen.value, 0.5*ylen.value, nymid+1) * u.au
zlen = cell_sizes[j-1] * len(zprev) * u.au
nzmid = int(zlen.value) / cellsz
zw = np.linspace(-0.5*zlen.value, 0.5*zlen.value, nzmid+1) * u.au
if nxmid==nymid==nzmid==0:
logger.warning('The inner grid is smaller than current grid size')
else:
logger.info('The inner %ix%ix%i cells will be replaced', nxmid,
nymid, nzmid)
# Rebin previous grid
# Density
vol_prev = (cell_sizes[j-1]*u.au)**3
vol = (cellsz * u.au)**3
N_cen = vol_prev.cgs * rebin_regular_nd(dens_prev.cgs.value,
zprev.cgs.value, yprev.cgs.value, xprev.cgs.value,
bins=(zw.cgs.value,yw.cgs.value,xw.cgs.value),
statistic='sum') * dens_prev.cgs.unit
dens_cen = N_cen / vol
dens_cen = dens_cen.to(dens.unit)
# Temperature
T_cen = rebin_regular_nd(temp_prev.value * dens_prev.cgs.value,
zprev.cgs.value, yprev.cgs.value, xprev.cgs.value,
bins=(zw.cgs.value,yw.cgs.value, xw.cgs.value),
statistic='sum') * temp_prev.unit * dens_prev.cgs.unit
T_cen = vol_prev.cgs * T_cen / N_cen.cgs
T_cen = T_cen.to(temp.unit)
# Replace
dens[len(zi)/2-nzmid/2:len(zi)/2+nzmid/2,
len(yi)/2-nymid/2:len(yi)/2+nymid/2,
len(xi)/2-nxmid/2:len(xi)/2+nxmid/2] = dens_cen
temp[len(zi)/2-nzmid/2:len(zi)/2+nzmid/2,
len(yi)/2-nymid/2:len(yi)/2+nymid/2,
len(xi)/2-nxmid/2:len(xi)/2+nxmid/2] = T_cen
dens_prev = dens
temp_prev = temp
xprev = xi
yprev = yi
zprev = zi
# Linewidth and abundance
linewidth = yso.linewidth(x, y, z, temp).to(u.cm/u.s)
# Abundance per molecule
abns = {}
abn_fmt = 'abn_%s_%i'
j = 1
for section in yso.params.sections():
if not section.lower().startswith('abundance'):
continue
mol = yso[section, 'molecule']
abns[abn_fmt % (mol, inv_i)] = yso.abundance(x, y, z, temp,
index=j, ignore_min=False)
j = j+1
# Write FITS
kw = {'temp%i'%inv_i: temp.value, 'dens%i'%inv_i: dens.cgs.value,
'vx%i'%inv_i: vx.cgs.value, 'vy%i'%inv_i: vy.cgs.value,
'vz%i'%inv_i: vz.cgs.value,
'lwidth%i'%inv_i: linewidth.cgs.value}
kw.update(abns)
fitsnames = write_fits(os.path.expanduser(save_dir),
**kw)
fitslist += fitsnames
inv_i = inv_i - 1
return fitslist
def hyperion_image(rtout, wave, plotdir, printname, dstar=200., group=0, marker=0,
size='full', convolve=False, unit=None, scalebar=None):
# to avoid X server error
import matplotlib as mpl
mpl.use('Agg')
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
import astropy.constants as const
from hyperion.model import ModelOutput
# Package for matching the colorbar
from mpl_toolkits.axes_grid1 import make_axes_locatable
pc = const.pc.cgs.value
if unit == None:
unit = 'erg\,s^{-1}\,cm^{-2}\,Hz^{-1}\,sr^{-1}'
m = ModelOutput(rtout)
# Extract the image.
image = m.get_image(group=group, inclination=0, distance=dstar * pc, units='MJy/sr')
# print np.shape(image.val)
# Open figure and create axes
fig = plt.figure(figsize=(8,8))
ax = fig.add_subplot(111)
# Find the closest wavelength
iwav = np.argmin(np.abs(wave - image.wav))
# Calculate the image width in arcseconds given the distance used above
# get the max radius
rmax = max(m.get_quantities().r_wall)
w = np.degrees(rmax / image.distance) * 3600.
# Image in the unit of MJy/sr
# Change it into erg/s/cm2/Hz/sr
# factor = 1e-23*1e6
factor = 1
# avoid zero in log
# flip the image, because the setup of inclination is upside down
val = image.val[::-1, :, iwav] * factor + 1e-30
if convolve:
from astropy.convolution import convolve, Gaussian2DKernel
img_res = 2*w/len(val[:,0])
kernel = Gaussian2DKernel(0.27/2.354/img_res)
val = convolve(val, kernel)
if size != 'full':
pix_e2c = (w-size/2.)/w * len(val[:,0])/2
val = val[pix_e2c:-pix_e2c, pix_e2c:-pix_e2c]
w = size/2.
# This is the command to show the image. The parameters vmin and vmax are
# the min and max levels for the colorscale (remove for default values).
# cmap = sns.cubehelix_palette(start=0.1, rot=-0.7, gamma=0.2, as_cmap=True)
cmap = plt.cm.CMRmap
im = ax.imshow(val,
# norm=mpl.colors.LogNorm(vmin=1.515e-01, vmax=4.118e+01),
norm=mpl.colors.LogNorm(vmin=1e-04, vmax=1e+01),
cmap=cmap, origin='lower', extent=[-w, w, -w, w], aspect=1)
# draw the flux extraction regions
# x = 100
# y = 100
# area = x*y / 4.25e10
# offset = 50
#
# pos_n = (len(val[0,:])/2.-1,len(val[0,:])/2.-1 + offset*len(val[0,:])/2/w)
# pos_s = (len(val[0,:])/2.-1,len(val[0,:])/2.-1 - offset*len(val[0,:])/2/w)
#
# import matplotlib.patches as patches
# ax.add_patch(patches.Rectangle((-x/2, -y), x, y, fill=False, edgecolor='lime'))
# ax.add_patch(patches.Rectangle((-x/2, 0), x, y, fill=False, edgecolor='lime'))
# plot the marker for center position by default or user input offset
ax.plot([0],[-marker], '+', color='lime', markersize=10, mew=2)
ax.set_xlim([-w,w])
ax.set_ylim([-w,w])
# ax.plot([0],[-10], '+', color='m', markersize=10, mew=2)
print(w)
# plot scalebar
if scalebar != None:
ax.plot([0.85*w-scalebar, 0.85*w], [-0.8*w, -0.8*w], color='w', linewidth=3)
# add text
ax.text(0.85*w-scalebar/2, -0.9*w, r'$\rm{'+str(scalebar)+"\,arcsec}$",
color='w', fontsize=18, fontweight='bold', ha='center')
# fix the tick label font
ticks_font = mpl.font_manager.FontProperties(family='STIXGeneral',size=16)
for label in ax.get_xticklabels():
label.set_fontproperties(ticks_font)
for label in ax.get_yticklabels():
label.set_fontproperties(ticks_font)
# Colorbar setting
# create an axes on the right side of ax. The width of cax will be 5%
# of ax and the padding between cax and ax will be fixed at 0.05 inch.
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
cb = fig.colorbar(im, cax=cax)
cb.solids.set_edgecolor("face")
cb.ax.minorticks_on()
cb.ax.set_ylabel(r'$\rm{Intensity\,['+unit+']}$',fontsize=16)
cb.ax.tick_params('both', width=1.5, which='major', length=3)
cb.ax.tick_params('both', width=1.5, which='minor', length=2)
cb_obj = plt.getp(cb.ax.axes, 'yticklabels')
plt.setp(cb_obj,fontsize=18)
# fix the tick label font
ticks_font = mpl.font_manager.FontProperties(family='STIXGeneral',size=18)
for label in cb.ax.get_yticklabels():
label.set_fontproperties(ticks_font)
ax.set_xlabel(r'$\rm{RA\,Offset\,[arcsec]}$', fontsize=16)
ax.set_ylabel(r'$\rm{Dec\,Offset\,[arcsec]}$', fontsize=16)
# set the frame color
ax.spines['bottom'].set_color('white')
ax.spines['top'].set_color('white')
ax.spines['left'].set_color('white')
ax.spines['right'].set_color('white')
ax.tick_params(axis='both', which='major', width=1.5, labelsize=18, color='white', length=5)
ax.text(0.7,0.88,str(wave) + r'$\rm{\,\mu m}$',fontsize=20,color='white', transform=ax.transAxes)
fig.savefig(plotdir+printname+'_image_'+str(wave)+'.pdf', format='pdf', dpi=300, bbox_inches='tight')
fig.clf()
def hyperion_image(rtout, wave, plotdir, printname, dstar=178., group=0, marker=0,
size='full', convolve=False, unit=None):
# to avoid X server error
import matplotlib as mpl
mpl.use('Agg')
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
import astropy.constants as const
from hyperion.model import ModelOutput
# Package for matching the colorbar
from mpl_toolkits.axes_grid1 import make_axes_locatable
pc = const.pc.cgs.value
if unit == None:
unit = r'$\rm{log(I_{\nu})\,[erg\,s^{-1}\,cm^{-2}\,Hz^{-1}\,sr^{-1}]}$'
m = ModelOutput(rtout)
# Extract the image.
image = m.get_image(group=group, inclination=0, distance=dstar * pc, units='mJy')
print np.shape(image.val)
# Open figure and create axes
fig = plt.figure(figsize=(8,8))
ax = fig.add_subplot(111)
# Find the closest wavelength
iwav = np.argmin(np.abs(wave - image.wav))
# Calculate the image width in arcseconds given the distance used above
# get the max radius
rmax = max(m.get_quantities().r_wall)
w = np.degrees(rmax / image.distance) * 3600.
# Image in the unit of MJy/sr
# Change it into erg/s/cm2/Hz/sr
# factor = 1e-23*1e6
factor = 1
# avoid zero in log
# flip the image, because the setup of inclination is upside down
val = image.val[::-1, :, iwav] * factor + 1e-30
if convolve:
from astropy.convolution import convolve, Gaussian2DKernel
img_res = 2*w/len(val[:,0])
kernel = Gaussian2DKernel(0.27/2.354/img_res)
val = convolve(val, kernel)
if size != 'full':
pix_e2c = (w-size/2.)/w * len(val[:,0])/2
val = val[pix_e2c:-pix_e2c, pix_e2c:-pix_e2c]
w = size/2.
# This is the command to show the image. The parameters vmin and vmax are
# the min and max levels for the colorscale (remove for default values).
# cmap = sns.cubehelix_palette(start=0.1, rot=-0.7, gamma=0.2, as_cmap=True)
cmap = plt.cm.CMRmap
# im = ax.imshow(np.log10(val), vmin= -20, vmax= -15,
# cmap=cmap, origin='lower', extent=[-w, w, -w, w], aspect=1)
im = ax.imshow(val,
cmap=cmap, origin='lower', extent=[-w, w, -w, w], aspect=1)
print val.max()
# plot the marker for center position by default or user input offset
ax.plot([0],[-marker], '+', color='ForestGreen', markersize=10, mew=2)
ax.set_xlim([-w,w])
ax.set_ylim([-w,w])
# ax.plot([0],[-10], '+', color='m', markersize=10, mew=2)
# fix the tick label font
ticks_font = mpl.font_manager.FontProperties(family='STIXGeneral',size=14)
for label in ax.get_xticklabels():
label.set_fontproperties(ticks_font)
for label in ax.get_yticklabels():
label.set_fontproperties(ticks_font)
# Colorbar setting
# create an axes on the right side of ax. The width of cax will be 5%
# of ax and the padding between cax and ax will be fixed at 0.05 inch.
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
cb = fig.colorbar(im, cax=cax)
cb.solids.set_edgecolor("face")
cb.ax.minorticks_on()
cb.ax.set_ylabel(unit,fontsize=18)
cb_obj = plt.getp(cb.ax.axes, 'yticklabels')
plt.setp(cb_obj,fontsize=14)
# fix the tick label font
ticks_font = mpl.font_manager.FontProperties(family='STIXGeneral',size=14)
for label in cb.ax.get_yticklabels():
label.set_fontproperties(ticks_font)
ax.set_xlabel(r'$\rm{RA\,Offset\,(arcsec)}$', fontsize=18)
ax.set_ylabel(r'$\rm{Dec\,Offset\,(arcsec)}$', fontsize=18)
ax.tick_params(axis='both', which='major', labelsize=18)
ax.text(0.7,0.88,str(wave) + r'$\rm{\,\mu m}$',fontsize=20,color='white', transform=ax.transAxes)
fig.savefig(plotdir+printname+'_image_'+str(wave)+'.pdf', format='pdf', dpi=300, bbox_inches='tight')
fig.clf()
def azimuthal_simulation(rtout, beam_size, wave, dist=200., group=22):
"""
rtout: the filepath to the output file of Hyperion
beam_size: the beam size used for the width of annulus
dist: the physical distance to the source
group: the group which contains image
"""
import numpy as np
import matplotlib.pyplot as plt
import astropy.constants as const
from hyperion.model import ModelOutput
# constant setup
pc = const.pc.cgs.value
au = const.au.cgs.value
output = {'wave': wave, 'annuli': [], 'flux_annuli': []}
# Read in the Hyperion output file
m = ModelOutput(rtout)
# get image
image = m.get_image(group=5, inclination=0, distance=dist*pc, units='Jy')
# Calculate the image width in arcsec given the distance to the source
rmax = max(m.get_quantities().r_wall)
w = np.degrees(rmax / image.distance) * 3600
# grid of radii of annulus
annuli = np.linspace(beam_size/2., np.floor((w-beam_size/2.)/beam_size)*beam_size, np.floor((w-beam_size/2.)/beam_size)) # plot
fig = plt.figure(figsize=(8,6))
ax = fig.add_subplot(111)
# iternate through wavelength
if type(wave) == int or type(wave) == float:
wave = [wave]
color_list = plt.cm.viridis(np.linspace(0, 1, len(wave)+1))
for i in range(len(wave)):
wav = wave[i]
# Find the closest wavelength
iwav = np.argmin(np.abs(wav - image.wav))
# avoid zero when log, and flip the image
val = image.val[::-1, :, iwav]
# determine the center of the image
npix = len(val[:,0])
center = np.array([npix/2. + 0.5, npix/2. + 0.5])
scale = 2*rmax/npix
# create index array of the image
x = np.empty_like(val)
for j in range(len(val[0,:])):
x[:,j] = j
flux_annuli = np.empty_like(annuli)
for k in range(len(annuli)):
flux_annuli[k] = np.sum(val[(((x-center[0])**2+(x.T-center[1])**2)**0.5*2*w/npix >= annuli[k]-beam_size/2.) & \
(((x-center[0])**2+(x.T-center[1])**2)**0.5*2*w/npix < annuli[k]+beam_size/2.)])
output['annuli'].append(np.array(annuli))
output['flux_annuli'].append(flux_annuli)
flux_annuli = flux_annuli/np.nanmax(flux_annuli)
ax.plot(np.log10(annuli*dist), np.log10(flux_annuli), 'o-', color=color_list[i], \
markersize=3, mec='None', label=r'$\rm{'+str(wav)+'\,\mu m}$')
ax.axvline(np.log10((w-beam_size/2.)*dist), linestyle='--', color='k')
ax.axvline(np.log10(w*dist), linestyle='-', color='k')
ax.legend(loc='best', fontsize=12, numpoints=1, ncol=2)
ax.set_xlabel(r'$\rm{log(Radius)\,[AU]}$', fontsize=18)
ax.set_ylabel(r'${\rm log(}F/F_{\rm max})$', fontsize=18)
fig.gca().set_ylim(top=0.1)
[ax.spines[axis].set_linewidth(1.5) for axis in ['top','bottom','left','right']]
ax.minorticks_on()
ax.tick_params('both',labelsize=18,width=1.5,which='major',pad=15,length=5)
ax.tick_params('both',labelsize=18,width=1.5,which='minor',pad=15,length=2.5)
fig.savefig('/Users/yaolun/test/annuli_profile.pdf', format='pdf', dpi=300, bbox_inches='tight')
fig.clf()
return output
def azimuthal_avg_radial_intensity(wave, rtout, plotname, dstar,
annulus_width=10, rrange=[10,200], group=8, obs=None,
other_obs=None):
"""
The 'obs' option only works for Herschel PACS/SPIRE image.
The 'obs' option now accept
"""
import numpy as np
import matplotlib as mpl
# to avoid X server error
mpl.use('Agg')
from astropy.io import ascii, fits
import matplotlib.pyplot as plt
from photutils import aperture_photometry as ap
from photutils import CircularAperture, CircularAnnulus
from astropy import units as u
from astropy.coordinates import SkyCoord
from astropy import wcs
from hyperion.model import ModelOutput
import astropy.constants as const
import os
pc = const.pc.cgs.value
AU = const.au.cgs.value
# radial grid in arcsec
# make the annulus center on
r = np.arange(rrange[0], rrange[1], annulus_width, dtype=float) - annulus_width*0.5
# r = np.arange(rrange[0], rrange[1], annulus_width, dtype=float) - annulus_width*0.55
# source_center = '12 01 36.3 -65 08 53.0'
def ExtractIntensityObs(rrange, annulus_width, obs):
import numpy as np
from astropy.io import fits
from astropy.coordinates import SkyCoord
from astropy import wcs
from photutils import aperture_photometry as ap
from photutils import CircularAperture, CircularAnnulus
r = np.arange(rrange[0], rrange[1], annulus_width, dtype=float) - annulus_width*0.5
# r = np.arange(rrange[0], rrange[1], annulus_width, dtype=float) - annulus_width*0.55
imgpath = obs['imgpath']
source_center = obs['source_center']
# Read in data and set up coversions
im_hdu = fits.open(imgpath)
im = im_hdu[1].data
wave = im_hdu[0].header['WAVELNTH']
# error
if (wave < 200.0) & (wave > 70.0):
im_err = im_hdu[5].data
elif (wave > 200.0) & (wave < 670.0):
im_err = im_hdu[2].data
else:
im_err_exten = raw_input('The extension that includes the image error: ')
im_err = im_hdu[int(im_err_exten)].data
w = wcs.WCS(im_hdu[1].header)
coord = SkyCoord(source_center, unit=(u.hourangle, u.deg))
pixcoord = w.wcs_world2pix(coord.ra.degree, coord.dec.degree, 1)
pix2arcsec = abs(im_hdu[1].header['CDELT1'])*3600.
# determine whether need to convert the unit
factor = 1
print 'Image unit is ', im_hdu[1].header['BUNIT']
if im_hdu[1].header['BUNIT'] != 'Jy/pixel':
print 'Image unit is ', im_hdu[1].header['BUNIT']
if im_hdu[1].header['BUNIT'] == 'MJy/sr':
# convert intensity unit from MJy/sr to Jy/pixel
factor = 1e6/4.25e10*abs(im_hdu[1].header['CDELT1']*im_hdu[1].header['CDELT2'])*3600**2
else:
factor = raw_input('What is the conversion factor to Jy/pixel?')
I = np.empty_like(r[:-1])
I_low = np.empty_like(r[:-1])
I_hi = np.empty_like(r[:-1])
I_err = np.empty_like(r[:-1])
# for calculating the uncertainty from the variation within each annulus
# construct the x- and y-matrix
grid_x, grid_y = np.meshgrid(np.linspace(0,len(im[0,:])-1,len(im[0,:])),
np.linspace(0,len(im[:,0])-1,len(im[:,0])))
grid_dist = ((grid_x-pixcoord[0])**2+(grid_y-pixcoord[1])**2)**0.5
# iteration
for ir in range(len(r)-1):
aperture = CircularAnnulus((pixcoord[0],pixcoord[1]), r_in=r[ir]/pix2arcsec, r_out=r[ir+1]/pix2arcsec)
phot = ap(im, aperture, error=im_err)
I[ir] = phot['aperture_sum'].data * factor / aperture.area()
# uncertainty
im_dum = np.where((grid_dist < r[ir+1]/pix2arcsec) & (grid_dist >= r[ir]/pix2arcsec), im, np.nan)
# estimate the uncertainty by offsetting the annulus by +/- 1 pixel
offset = -1
if r[ir]/pix2arcsec + offset < 0:
offset = -r[ir]/pix2arcsec
aperture = CircularAnnulus((pixcoord[0],pixcoord[1]),
r_in=r[ir]/pix2arcsec + offset, r_out=r[ir+1]/pix2arcsec + offset)
phot = ap(im, aperture, error=im_err)
I_low[ir] = phot['aperture_sum'].data * factor / aperture.area()
offset = 1
aperture = CircularAnnulus((pixcoord[0],pixcoord[1]),
r_in=r[ir]/pix2arcsec + offset, r_out=r[ir+1]/pix2arcsec + offset)
phot = ap(im, aperture, error=im_err)
I_hi[ir] = phot['aperture_sum'].data * factor / aperture.area()
I_err = (abs(I_low - I) + abs(I_hi - I))/2.
return r, I, I_err
if obs != None:
I_obs = []
for o in obs:
if 'label' not in o.keys():
label_dum = r'$\rm{observation}$'
color_dum = 'g'
linestyle_dum = '-'
rrange_dum = rrange
annulus_width_dum = annulus_width
else:
label_dum = o['label']
color_dum = o['plot_color']
linestyle_dum = o['plot_linestyle']
rrange_dum = o['rrange']
annulus_width_dum = o['annulus_width']
r_dum, I_dum, I_err_dum = ExtractIntensityObs(rrange_dum, annulus_width_dum, o)
# determine the label
I_obs.append({'imgpath':o['imgpath'], 'r':r_dum, 'I':I_dum, 'I_err':I_err_dum, 'label': label_dum,
'plot_color':color_dum, 'plot_linestyle':linestyle_dum})
# The first image should be the one to be compared primarily, and written out
I = I_obs[0]['I']
I_err = I_obs[0]['I_err']
imgpath = I_obs[0]['imgpath']
#
# read in from RTout
rtout = ModelOutput(rtout)
im = rtout.get_image(group=group, inclination=0, distance=dstar*pc, units='Jy', uncertainties=True)
factor = 1
# Find the closest wavelength
iwav = np.argmin(np.abs(wave - im.wav))
# avoid zero when log, and flip the image
val = im.val[::-1, :, iwav]
unc = im.unc[::-1, :, iwav]
w = np.degrees(max(rtout.get_quantities().r_wall) / im.distance) * 3600
npix = len(val[:,0])
pix2arcsec = 2*w/npix
I_sim = np.empty_like(r[:-1])
I_sim_hi = np.empty_like(r[:-1])
I_sim_low = np.empty_like(r[:-1])
I_sim_err = np.empty_like(r[:-1])
# for calculating the uncertainty from the variation within each annulus
# construct the x- and y-matrix
grid_x, grid_y = np.meshgrid(np.linspace(0,npix-1,npix),
np.linspace(0,npix-1,npix))
dist_x = abs(grid_x - ((npix-1)/2.))
dist_y = abs(grid_y - ((npix-1)/2.))
grid_dist = (dist_x**2+dist_y**2)**0.5
# iteration
for ir in range(len(r)-1):
aperture = CircularAnnulus((npix/2.+0.5, npix/2.+0.5),
r_in=r[ir]/pix2arcsec, r_out=r[ir+1]/pix2arcsec)
phot = ap(val, aperture, error=unc)
I_sim[ir] = phot['aperture_sum'].data / aperture.area()
# uncertainty
im_dum = np.where((grid_dist < r[ir+1]/pix2arcsec) & (grid_dist >= r[ir]/pix2arcsec), val, np.nan)
# I_sim_err[ir] = phot['aperture_sum_err'].data / aperture.area()
# I_sim_err[ir] = (np.nanstd(im_dum)**2+phot['aperture_sum_err'].data**2)**0.5 * factor / aperture.area()
offset = -1
aperture = CircularAnnulus((npix/2.+0.5, npix/2.+0.5),
r_in=r[ir]/pix2arcsec + offset, r_out=r[ir+1]/pix2arcsec + offset)
phot = ap(val, aperture, error=unc)
I_sim_low[ir] = phot['aperture_sum'].data * factor / aperture.area()
offset = 1
aperture = CircularAnnulus((npix/2.+0.5, npix/2.+0.5),
r_in=r[ir]/pix2arcsec + offset, r_out=r[ir+1]/pix2arcsec + offset)
phot = ap(val, aperture, error=unc)
I_sim_hi[ir] = phot['aperture_sum'].data * factor / aperture.area()
I_sim_err = (abs(I_sim_low - I_sim)+ abs(I_sim_hi - I_sim))/2.
if obs != None:
# write the numbers into file
foo = open(plotname+'_radial_profile_'+str(wave)+'um.txt', 'w')
# print some header info
foo.write('# wavelength '+str(wave)+' um \n')
foo.write('# image file '+os.path.basename(imgpath)+' \n')
foo.write('# annulus width '+str(annulus_width)+' arcsec \n')
# write profiles
foo.write('r_in \t I \t I_err \t I_sim \t I_sim_err \n')
foo.write('# [arcsec] \t [Jy/pixel] \t [Jy/pixel] \t [Jy/pixel] \t [Jy/pixel] \n')
for i in range(len(I)):
foo.write('%f \t %e \t %e \t %e \t %e \n' % (r[i]+annulus_width/2., I[i], I_err[i], I_sim[i], I_sim_err[i]))
foo.close()
else:
# write the numbers into file
foo = open(plotname+'_radial_profile_'+str(wave)+'um.txt', 'w')
# print some header info
foo.write('# wavelength '+str(wave)+' um \n')
foo.write('# annulus width '+str(annulus_width)+' arcsec \n')
# write profiles
foo.write('r_in \t I_sim \t I_sim_err \n')
foo.write('# [arcsec] \t [Jy/pixel] \t [Jy/pixel] \n')
for i in range(len(I_sim)):
foo.write('%f \t %e \t %e \n' % (r[i]+annulus_width/2., I_sim[i], I_sim_err[i]))
foo.close()
# plot
fig = plt.figure(figsize=(8,6))
ax = fig.add_subplot(111)
I_sim_hi = np.log10((I_sim+I_sim_err)/I_sim.max())-np.log10(I_sim/I_sim.max())
I_sim_low = np.log10(I_sim/I_sim.max())-np.log10((I_sim-I_sim_err)/I_sim.max())
i_sim = ax.errorbar(np.log10(r[:-1]*dstar), np.log10(I_sim/I_sim.max()), color='b',
yerr=(I_sim_low, I_sim_hi), marker='o', linestyle='-', mec='None', markersize=5,
ecolor='b', elinewidth=1.5, capthick=1.5, barsabove=True)
if obs != None:
plot_profile = []
plot_label = []
for o in I_obs:
I_hi = np.log10((o['I']+o['I_err'])/o['I'].max())-np.log10(o['I']/o['I'].max())
I_low = np.log10(o['I']/o['I'].max())-np.log10((o['I']-o['I_err'])/o['I'].max())
i = ax.errorbar(np.log10(o['r'][:-1]*dstar), np.log10(o['I']/o['I'].max()), color=o['plot_color'],
yerr=(I_low, I_hi), marker='o', linestyle=o['plot_linestyle'], mec='None', markersize=5,
ecolor=o['plot_color'], elinewidth=1.5, capthick=1.5, barsabove=True)
plot_profile.append(i)
plot_label.append(o['label'])
plot_profile.append(i_sim)
plot_label.append(r'$\rm{simulation}$')
ax.legend(plot_profile, plot_label,
fontsize=16, numpoints=1, loc='best')
else:
ax.legend([i_sim], [r'$\rm{simulation}$'], fontsize=16, numpoints=1, loc='best')
# limit radius
ax.axvline([np.log10(100*dstar)], color='k', linestyle='--', linewidth=1)
#
[ax.spines[axis].set_linewidth(1.5) for axis in ['top','bottom','left','right']]
ax.minorticks_on()
ax.tick_params('both',labelsize=18,width=1.5,which='major',pad=10,length=5)
ax.tick_params('both',labelsize=18,width=1.5,which='minor',pad=10,length=2.5)
ax.set_xlabel(r'$\rm{log(\it{b})\,[\rm{AU}]}$', fontsize=18)
ax.set_ylabel(r'$\rm{log(I\,/\,I_{max})}$', fontsize=18)
# fix the tick label font
ticks_font = mpl.font_manager.FontProperties(family='STIXGeneral',size=18)
for label in ax.get_xticklabels():
label.set_fontproperties(ticks_font)
for label in ax.get_yticklabels():
label.set_fontproperties(ticks_font)
fig.savefig(plotname+'_radial_profile_'+str(wave)+'um.pdf', format='pdf', dpi=300, bbox_inches='tight')
fig.clf()
class YSOModelSim(object):
def __init__(self,name,folder,T=9000,M_sun=5.6,L_sun=250,disk_mass=0.01,disk_rmax=100,
env=True,env_type='power',rc=400,mdot=1e-8,env_mass=0.1,env_rmin=30,env_rmax=5000,cav=True,cav_r0=500,cav_rho_0=8e-24,cav_theta=25,env_power=-1.5,
Npix=149,angles=[20.,45.,60.,80],angles2=[60.,60.,60.,60.], amb_dens=8e-24, disk="Flared",disk_rmin=1., amb_rmin=1., amb_rmax=1000., innerdustfile='OH5.hdf5',
outerdustfile='d03_5.5_3.0_A.hdf5',beta=1.1):
self.name=name
self.folder=folder
self.T=T
self.M_sun=M_sun*msun
self.L_sun=L_sun*lsun
self.disk_mass=disk_mass*msun
self.disk_rmax=disk_rmax*au
self.disk_rmin=disk_rmin*au
self.disk_h_0 = OptThinRadius(1600)
self.env=env
self.disk=disk
self.env_type=env_type
self.env_mass=env_mass*msun
self.env_rmin=env_rmin*au
self.env_rmax=env_rmax*au
self.mdot=mdot #*msun/yr*self.M_sun # disk accretion rate
self.rc=rc*au
self.cav=cav
self.cav_rho_0=cav_rho_0
self.cav_r0=cav_r0*au
self.cav_theta=cav_theta
self.Npix=Npix
self.angles=angles
self.angles2=angles2
self.amb_dens=amb_dens
self.amb_rmin=amb_rmin
self.amb_rmax=amb_rmax*au
self.env_power=env_power
self.dustfile=innerdustfile
self.dustfile_out=outerdustfile
self.limval = max(self.env_rmax,1000*au)
self.beta = beta
def modelDump(self):
sp.call('rm %s.mod ' % (self.folder+self.name),shell=True)
pickle.dump(self,open(self.folder+self.name+'.mod','wb'))
time.sleep(2)
def modelPrint(self):
#string= self.folder+ self.name+'\n'
string="T="+str(self.T)+"K"+'\n'
string+= "M="+str(self.M_sun/msun)+'Msun'+'\n'
string+= "L="+str(self.L_sun/lsun)+'Lsun'+'\n'
string+= "Disk="+str(self.disk)+'\n'
string+= "Disk_mass="+str(self.disk_mass/msun)+'Msun'+'\n'
string+= "Disk_rmax="+str(self.disk_rmax/au)+'AU'+'\n'
string+= "Disk_rmin="+str(self.disk_rmin/au)+'AU'+'\n'
string+= "env="+str(self.env)+'\n'
string+= "env_type="+self.env_type+'\n'
string+= "env_mass="+str(self.env_mass/msun)+'Msun'+'\n'
string+= "env_rmax="+str(self.env_rmax/au)+'AU'+'\n'
string+= "env_rmin="+str(self.env_rmin/au)+'AU'+'\n'
if self.env_type == 'ulrich' and self.env==True:
string+= "mass_ulrich="+str((8.*np.pi*self.env_rho_0*self.rc**3*pow(self.env_rmax/self.rc,1.5)/(3.*np.sqrt(2)))/msun)+'Msun'+'\n'
string+= "mdot="+str(self.mdot)+'Msun/yr'+'\n' # (only if env_type="Ulrich")
string+= "rc="+str(self.rc/au)+'AU'+'\n' # (only if env_type="Ulrich")
string+= "cav="+str(self.cav)+'\n'
string+= "cav_theta="+str(self.cav_theta)+'\n'
string+= "cav_r0="+str(self.cav_r0/au)+'\n'
string+= "env_power="+str(self.env_power)+'\n'
string+= "disk_h_0="+str(self.disk_h_0)+'\n'
string+= "dustfile="+self.dustfile+'\n'
string+= "dustfile_out="+self.dustfile_out+'\n'
string+= "amb_dens="+str(self.amb_dens)+'\n'
string+= "amb_rmin="+str(self.amb_rmin)+'\n'
string+= "amb_rmax="+str(self.amb_rmax/au)+'\n'
string+= "angles="+str(self.angles)+'\n'
print string
return string
def dust_gen(self,dustfile,dustfile_out='d03_5.5_3.0_A.hdf5'):
### first, we need to load Tracy's dust files and manipulate them to feed to Hyperion
### wavelength (microns),Cext,Csca,Kappa,g,pmax,theta (ignored)
### albedo = Csca/Cext
### opacity kappa is in cm^2/gm, dust_gas extinction opactity (absorption+scattering) - assumes gas-to=dust raio of 100
### see Whitney et al. 2003a
# tracy_dust = np.loadtxt('Tracy_models/OH5.par')
# ### format for dust: d = HenyeyGreensteinDust(nu, albedo, chi, g, p_lin_max)
# nu = const.c.value/ (tracy_dust[:,0]*1e-6)
# albedo = tracy_dust[:,2]/tracy_dust[:,1]
# chi = tracy_dust[:,3]
# g = tracy_dust[:,4]
# p_lin_max = tracy_dust[:,5]
# ### flip the table to have an increasing frequency
# nu = nu[::-1]
# albedo = albedo[::-1]
# chi=chi[::-1]
# g=g[::-1]
# p_lin_max=p_lin_max[::-1]
# ### create the dust model
# d = HenyeyGreensteinDust(nu, albedo, chi, g, p_lin_max)
# d.optical_properties.extrapolate_wav(0.001,1.e7)
# d.plot('OH5.png')
# d.write('OH5.hdf5')
self.d = SphericalDust()
self.d.read(dustfile)
self.d.plot(str(dustfile.split(',')[:-1])+'.png')
self.d_out = SphericalDust()
self.d_out.read(dustfile_out)
#self.d_out.read(dustfile)
self.d_out.plot(str(dustfile_out.split(',')[:-1])+'.png')
def initModel(self):
### Use Tracy parameter file to set up the model
self.dust_gen(self.dustfile,self.dustfile_out)
mi = AnalyticalYSOModel()
mi.star.temperature = self.T
mi.star.mass = self.M_sun
mi.star.luminosity = self.L_sun
mi.star.radius=np.sqrt(mi.star.luminosity/(4.0*np.pi*sigma*mi.star.temperature**4))
#m.star.luminosity = 4.0*np.pi*m.star.radius**2*sigma*m.star.temperature**4
print mi.star.luminosity/lsun
self.luminosity=mi.star.luminosity/lsun
if self.disk=="Flared":
print "Adding flared disk"
disk = mi.add_flared_disk()
disk.dust=self.d
if self.dustfile == 'd03_5.5_3.0_A.hdf5':
disk.mass=self.disk_mass/100.
else: disk.mass=self.disk_mass
disk.rmin=OptThinRadius(1600) #self.disk_rmin
print "disk.rmin = ",disk.rmin,disk.rmin/au
disk.rmax=self.disk_rmax
disk.r_0 = self.disk_rmin
disk.h_0 = disk.r_0/10. #self.disk_h_0*au
disk.beta=self.beta
disk.p = -1.
elif self.disk=="Alpha":
print "Adding alpha disk"
disk = mi.add_alpha_disk()
disk.dust=self.d
if self.dustfile == 'd03_5.5_3.0_A.hdf5':
disk.mass=self.disk_mass/100.
else: disk.mass=self.disk_mass
disk.rmin=OptThinRadius(1600)
disk.rmax=self.disk_rmax
disk.r_0 = self.disk_rmin
disk.h_0 = disk.r_0/10. #self.disk_h_0*au
disk.beta=1.1
disk.p = -1
disk.mdot=self.mdot
disk.star = mi.star
#print 'Disk density:',disk.rho_0
if self.env==True and self.env_type=='power':
envelope=mi.add_power_law_envelope()
envelope.dust=self.d_out
envelope.r_0=self.env_rmin
#envelope.r_0 = OptThinRadius(1600)
if self.dustfile_out == 'd03_5.5_3.0_A.hdf5':
envelope.mass=self.env_mass/100.
else: envelope.mass=self.env_mass
envelope.rmin=self.env_rmin
envelope.rmax=self.env_rmax
envelope.power=self.env_power
#print 'Envelope rho:',envelope.rho_0
elif self.env==True and self.env_type=='ulrich':
envelope=mi.add_ulrich_envelope()
envelope.dust=self.d_out
envelope.mdot=1e-6*msun/yr # has little impact on the fluxes, so fixed
envelope.rc=self.rc
envelope.rmin=self.env_rmin
envelope.rmax=self.env_rmax
if self.env==True:
self.env_rho_0 = envelope.rho_0
print 'Envelope rho:',envelope.rho_0
#print "Rho_0 = ",envelope.rho_0
if self.cav==True:
cavity=envelope.add_bipolar_cavity()
cavity.dust=self.d_out
cavity.power=1.5
cavity.cap_to_envelope_density=True ### prevents the cavity density to go above the envelope's density
cavity.r_0=self.cav_r0
cavity.theta_0=self.cav_theta
cavity.rho_0=self.cav_rho_0 #in g/cm^3
cavity.rho_exp=0.0
# if self.env==True:
# ambient=mi.add_ambient_medium(subtract=[envelope,disk])
# if self.dustfile_out == 'd03_5.5_3.0_A.hdf5':
# ambient.rho=self.amb_dens/100.
# else: ambient.rho=self.amb_dens
# ambient.rmin=OptThinRadius(1600.)
# ambient.rmax=self.env_rmax
# ambient.dust=self.d_out
'''*** Grid parameters ***'''
mi.set_spherical_polar_grid_auto(199,49,1)
# Specify that the specific energy and density are needed
mi.conf.output.output_specific_energy = 'last'
mi.conf.output.output_density = 'last'
'''**** Output Data ****'''
image = mi.add_peeled_images(sed=True,image=False)
image.set_wavelength_range(150,1,3000)
#image.set_image_size(self.Npix,self.Npix)
#image.set_image_limits(-self.limval,self.limval,-self.limval,self.limval)
image.set_aperture_range(1,100000.*au,100000.*au)
image.set_viewing_angles(self.angles,self.angles2)
#image.set_track_origin('detailed')
image.set_uncertainties(True)
''' Use the modified random walk
*** Advanced ***'
YES = DIFFUSION = Whether to use the diffusion
'''
if self.env==True:
#mi.set_pda(True)
mi.set_mrw(True)
else:
mi.set_pda(False)
mi.set_mrw(False)
# Use raytracing to improve s/n of thermal/source emission
mi.set_raytracing(True)
'''**** Preliminaries ****'''
mi.set_n_initial_iterations(5)
mi.set_n_photons(initial=1e6,imaging=1e6,raytracing_sources=1e5,raytracing_dust=1e6)
mi.set_convergence(True, percentile=99.0, absolute=2.0, relative=1.1)
self.m = mi
def runModel(self):
self.initModel()
self.m.write(self.folder+self.name+'.rtin')
self.m.run(self.folder+self.name+'.rtout', mpi=True,n_processes=6)
def plotData(self,ax,sourcename):
if sourcename != 'None':
folder_export="/n/a2/mrizzo/Dropbox/SOFIA/Processed_Data/"
sourcetable = pickle.load(open(folder_export+"totsourcetable_fits.data","r"))
markers = ['v','p','D','^','h','o','*','x','d','<']
TwoMASS = ['j','h','ks']
uTwoMASS = ["e_"+col for col in TwoMASS]
wlTwoMASS = [1.3,1.6,2.2]
colTwoMASS = colors[0]
markerTwoMASS = markers[0]
labelTwoMASS = '2MASS'
Spitzer = ['i1','i2','i3','i4','m1','m2']
uSpitzer = ["e_"+col for col in Spitzer]
wlSpitzer = [3.6,4.5,5.8,8.,24,70]
colSpitzer = colors[1]
markerSpitzer = markers[1]
labelSpitzer = 'Spitzer'
WISE = ['w1','w2','w3','w4']
uWISE = ["e_"+col for col in WISE]
wlWISE = [3.4,4.6,12,22]
colWISE = colors[2]
labelWISE = 'WISE'
markerWISE = markers[2]
SOFIA = ['F11','F19','F31','F37']
uSOFIA = ["e_"+col for col in SOFIA]
wlSOFIA = [11.1,19.7,31.5,37.1]
colSOFIA = colors[3]
markerSOFIA = markers[3]
labelSOFIA = 'SOFIA'
IRAS = ['Fnu_12','Fnu_25','Fnu_60','Fnu_100']
uIRAS = ["e_"+col for col in IRAS]
wlIRAS = [12,25,60,100]
colIRAS = colors[4]
markerIRAS = markers[4]
labelIRAS = 'IRAS'
AKARI = ['S65','S90','S140','S160']
uAKARI = ["e_"+col for col in AKARI]
wlAKARI = [65,90,140,160]
colAKARI = colors[5]
markerAKARI = markers[5]
labelAKARI = 'AKARI'
ENOCH = ['Fp']
uENOCH = ["e_"+col for col in ENOCH]
wlENOCH = [1300]
colENOCH = colors[6]
markerENOCH = markers[6]
labelENOCH = 'ENOCH'
HERSCHEL = ['H70','H160','H250','H350','H500']
uHERSCHEL = ["e_"+col for col in HERSCHEL]
wlHERSCHEL = [70,160,250,350,500]
colHERSCHEL = colors[7]
markerHERSCHEL = markers[7]
labelHERSCHEL = 'HERSCHEL'
SCUBA = ['S450','S850','S1300']
uSCUBA = ["e_"+col for col in SCUBA]
wlSCUBA = [450,850,1300]
colSCUBA = colors[8]
markerSCUBA = markers[8]
labelSCUBA = 'SCUBA'
alpha=1
sources = sourcetable.group_by('SOFIA_name')
for key,sourcetable in zip(sources.groups.keys,sources.groups):
if sourcename == sourcetable['SOFIA_name'][0]:
#print sourcetable['SOFIA_name'][0]
p.plotData(ax,sourcetable,markerTwoMASS,TwoMASS,uTwoMASS,wlTwoMASS,colTwoMASS,labelTwoMASS,alpha)
p.plotData(ax,sourcetable,markerSpitzer,Spitzer,uSpitzer,wlSpitzer,colSpitzer,labelSpitzer,alpha)
p.plotData(ax,sourcetable,markerWISE,WISE,uWISE,wlWISE,colWISE,labelWISE,alpha)
p.plotData(ax,sourcetable,markerSOFIA,SOFIA,uSOFIA,wlSOFIA,colSOFIA,labelSOFIA,alpha)
p.plotData(ax,sourcetable,markerIRAS,IRAS,uIRAS,wlIRAS,colIRAS,labelIRAS,alpha)
p.plotData(ax,sourcetable,markerAKARI,AKARI,uAKARI,wlAKARI,colAKARI,labelAKARI,alpha)
p.plotData(ax,sourcetable,markerENOCH,ENOCH,uENOCH,wlENOCH,colENOCH,labelENOCH,alpha)
p.plotData(ax,sourcetable,markerHERSCHEL,HERSCHEL,uHERSCHEL,wlHERSCHEL,colHERSCHEL,labelHERSCHEL,alpha)
p.plotData(ax,sourcetable,markerSCUBA,SCUBA,uSCUBA,wlSCUBA,colSCUBA,labelSCUBA,alpha)
def calcChi2(self,dist_pc=140,extinction=0, sourcename='Oph.1'):
self.dist=dist_pc*pc
self.extinction=extinction
chi = np.loadtxt('kmh94_3.1_full.chi')
wav = np.loadtxt('kmh94_3.1_full.wav')
Chi = interp1d(wav,chi,kind='linear')
modelname = self.folder+self.name
self.mo = ModelOutput(modelname+'.rtout')
# get the sed of all inclination
sed = self.mo.get_sed(aperture=-1, inclination='all', distance=self.dist,units='Jy')
# calculate the optical depth at all wavelengths
tau = self.extinction*Chi(sed.wav)/Chi(0.550)/1.086
# calculate extinction values
ext = np.array([np.exp(-tau) for i in range(sed.val.shape[0])])
# apply extinction to model
extinct_values = np.log10(sed.val.transpose()*ext.T)
# data points and errors
folder_export="/n/a2/mrizzo/Dropbox/SOFIA/Processed_Data/"
sourcetable = pickle.load(open(folder_export+"totsourcetable_fits.data","r"))
TwoMASS = ['j','h','ks']
uTwoMASS = ["e_"+col for col in TwoMASS]
wlTwoMASS = [1.3,1.6,2.2]
labelTwoMASS = '2MASS'
Spitzer = ['i1','i2','i3','i4']
uSpitzer = ["e_"+col for col in Spitzer]
wlSpitzer = [3.6,4.5,5.8,8.]
labelSpitzer = 'Spitzer'
SOFIA = ['F11','F19','F31','F37']
uSOFIA = ["e_"+col for col in SOFIA]
wlSOFIA = [11.1,19.7,31.5,37.1]
labelSOFIA = 'SOFIA'
sources = sourcetable.group_by('SOFIA_name')
for key,source in zip(sources.groups.keys,sources.groups):
if sourcename == source['SOFIA_name'][0]:
datapoints = source[TwoMASS+Spitzer+SOFIA]
dataerrors = source[uTwoMASS+uSpitzer+uSOFIA]
print p.nptable(datapoints),p.nptable(dataerrors)
# calculate log10 of quantities required for chi squared
logFnu = np.log10(p.nptable(datapoints))-0.5*(1./np.log(10.))*p.nptable(dataerrors)**2/p.nptable(datapoints)**2
varlogFnu = (1./np.log(10)/p.nptable(datapoints))**2*p.nptable(dataerrors)**2
print extinct_values,extinct_values.shape
# for each inclination, calculate chi squared; need to interpolate to get model at required wavelengths
Ninc = extinct_values.shape[1]
chi2 = np.zeros(Ninc)
wl=wlTwoMASS+wlSpitzer+wlSOFIA
N = len(wl)
for j in range(Ninc):
interp_func = interp1d(sed.wav,extinct_values[:,j],kind='linear')
interp_vals = interp_func(wl)
chi2[j] = 1./N * np.sum((logFnu - interp_vals)**2/varlogFnu)
print chi2
def plotModel(self,dist_pc=140,inc=3,extinction=0,show=False,sourcename='Oph.1'):
self.dist=dist_pc*pc
self.inc=inc
self.extinction=extinction
modelname = self.folder+self.name
self.mo = ModelOutput(modelname+'.rtout')
#tracy_dust = np.loadtxt('Tracy_models/OH5.par')
chi = np.loadtxt('kmh94_3.1_full.chi')
wav = np.loadtxt('kmh94_3.1_full.wav')
Chi = interp1d(wav,chi,kind='linear')
fig = plt.figure(figsize=(20,14))
ax=fig.add_subplot(2,3,1)
sed = self.mo.get_sed(aperture=-1, inclination='all', distance=self.dist)
#print tracy_dust[11,1],Cext(sed.wav[-1]),Cext(sed.wav[-1])/tracy_dust[11,1]
tau = self.extinction*Chi(sed.wav)/Chi(0.550)/1.086
#print Cext(sed.wav)/tracy_dust[11,1]
ext = np.array([np.exp(-tau) for i in range(sed.val.shape[0])])
#print tau,np.exp(-tau)
ax.loglog(sed.wav, sed.val.transpose()*ext.T, color='black')
ax.set_title(modelname+'_seds, Av='+str(self.extinction))
ax.set_xlim(sed.wav.min(), 1300)
ax.set_ylim(1e-13, 1e-7)
ax.set_xlabel(r'$\lambda$ [$\mu$m]')
ax.set_ylabel(r'$\lambda F_\lambda$ [ergs/cm$^2/s$]')
self.plotData(ax,sourcename)
ax.set_xscale('log')
ax.set_yscale('log')
#ax.set_ylabel(r'$F_{Jy}$ [Jy]')
#plt.legend(loc=4)
ax=fig.add_subplot(2,3,2)
sed = self.mo.get_sed(aperture=-1, inclination=self.inc, distance=self.dist)
ext=np.exp(-tau)
ax.loglog(sed.wav, sed.val.transpose()*ext.T, lw=3,color='black',label='source_total')
ax.set_xlim(sed.wav.min(), 1300)
ax.set_ylim(1e-13, 1e-7) ### for lamFlam
sed = self.mo.get_sed(aperture=-1, inclination=self.inc, distance=self.dist,component='source_emit')
ax.loglog(sed.wav, sed.val.transpose()*ext.T, color='blue',label='source_emit')
sed = self.mo.get_sed(aperture=-1, inclination=self.inc, distance=self.dist,component='source_scat')
ax.loglog(sed.wav, sed.val.transpose()*ext.T, color='teal',label='source_scat')
sed = self.mo.get_sed(aperture=-1, inclination=self.inc, distance=self.dist,component='dust_emit')
ax.loglog(sed.wav, sed.val.transpose()*ext.T, color='red',label='dust_emit')
sed = self.mo.get_sed(aperture=-1, inclination=self.inc, distance=self.dist,component='dust_scat')
ax.loglog(sed.wav, sed.val.transpose()*ext.T, color='orange',label='dust_scat')
self.plotData(ax,sourcename)
ax.set_xscale('log')
ax.set_yscale('log')
ax.set_title('seds_inc=inc')
ax.set_xlabel(r'$\lambda$ [$\mu$m]')
ax.set_ylabel(r'$\lambda F_\lambda$ [ergs/cm$^2/s$]')
#ax.set_ylabel(r'$F_{Jy}$ [Jy]')
leg = ax.legend(loc=4,fontsize='small')
#leg = plt.gca().get_legend()
#plt.setp(leg.get_text(),fontsize='small')
# Extract the quantities
g = self.mo.get_quantities()
# Get the wall positions for r and theta
rw, tw = g.r_wall / au, g.t_wall
# Make a 2-d grid of the wall positions (used by pcolormesh)
R, T = np.meshgrid(rw, tw)
# Calculate the position of the cell walls in cartesian coordinates
X, Z = R * np.sin(T), R * np.cos(T)
# Make a plot in (x, z) space for different zooms
from matplotlib.colors import LogNorm,PowerNorm
# Make a plot in (r, theta) space
ax = fig.add_subplot(2, 3, 3)
if g.shape[-1]==2:
c = ax.pcolormesh(X, Z, g['temperature'][0].array[0, :, :]+g['temperature'][1].array[0, :, :],norm=PowerNorm(gamma=0.5,vmin=1,vmax=500))
else :
c = ax.pcolormesh(X, Z, g['temperature'][0].array[0, :, :],norm=PowerNorm(gamma=0.5,vmin=1,vmax=500))
#ax.set_xscale('log')
#ax.set_yscale('log')
ax.set_xlim(X.min(), X.max()/5.)
ax.set_ylim(Z.min()/10., Z.max()/10.)
ax.set_xlabel('x (au)')
ax.set_ylabel('z (au)')
#ax.set_yticks([np.pi, np.pi * 0.75, np.pi * 0.5, np.pi * 0.25, 0.])
#ax.set_yticklabels([r'$\pi$', r'$3\pi/4$', r'$\pi/2$', r'$\pi/4$', r'$0$'])
cb = fig.colorbar(c)
ax.set_title('Temperature structure')
cb.set_label('Temperature (K)')
#fig.savefig(modelname+'_temperature_spherical_rt.png', bbox_inches='tight')
ax = fig.add_subplot(2, 3, 4)
if g.shape[-1]==2:
c = ax.pcolormesh(X, Z, g['density'][0].array[0, :, :]+g['density'][1].array[0, :, :],norm=LogNorm(vmin=1e-22,vmax=g['density'][0].array[0, :, :].max()))
else :
c = ax.pcolormesh(X, Z, g['density'][0].array[0, :, :],norm=LogNorm(vmin=1e-22,vmax=g['density'][0].array[0, :, :].max()))
#ax.set_xscale('log')
#ax.set_yscale('log')
ax.set_xlim(X.min(), X.max()/5.)
ax.set_ylim(Z.min()/10., Z.max()/10.)
ax.set_xlabel('x (au)')
ax.set_ylabel('z (au)')
ax.set_title('Density structure')
cb = fig.colorbar(c)
cb.set_label('Density (g/cm2)')
### plot the convolved image with the 37 micron filter (manually set to slice 18 of the cube - this would change with wavelength coverage)
ax = fig.add_subplot(2, 3, 5)
self.image = self.mo.get_image(inclination=inc,distance=self.dist,units='Jy')
fits.writeto(modelname+'_inc_'+str(inc)+'.fits',self.image.val.swapaxes(0,2).swapaxes(1,2),clobber=True)
### need to convolve the image with a Gaussian PSF
pix = 2.*self.limval/au/self.Npix # in AU/pix
pix_asec = pix/(self.dist/pc) # in asec/pix
airy_asec = 3.5 #asec
airy_pix = airy_asec/pix_asec # in pix
gauss_pix = airy_pix/2.35 # in Gaussian std
print "Gaussian std: ",gauss_pix
from scipy.ndimage.filters import gaussian_filter as gauss
#print [(i,sed.wav[i]) for i in range(len(sed.wav))]
img37 = self.image.val[:,:,18]
convol = gauss(img37,gauss_pix,mode='constant',cval=0.0)
Nc = self.Npix/2
hw = min(int(20./pix_asec),Nc) #(max is Nc)
#ax.imshow(img37,norm=LogNorm(vmin=1e-20,vmax=img37.max()))
#ax.imshow(img37,interpolation='nearest')
#ax.imshow(convol,norm=LogNorm(vmin=1e-20,vmax=img37.max()))
#ax.imshow(convol,interpolation='nearest',norm=LogNorm(vmin=1e-20,vmax=img37.max()))
ax.imshow(convol[Nc-hw:Nc+hw,Nc-hw:Nc+hw],interpolation='nearest',origin='lower',cmap=plt.get_cmap('gray'))
airy_disk = plt.Circle((airy_pix*1.3,airy_pix*1.3),airy_pix,color=colors[3])
ax.add_artist(airy_disk)
ax.text(airy_pix*3,airy_pix*1.3/2.0,'SOFIA 37um Airy disk',color=colors[3])
ax.set_title('Convolved image')
fits.writeto(modelname+'_inc_'+str(inc)+'_convol37.fits',convol,clobber=True)
### draw a cross-section of the image to show the spatial extension in linear scale, to compare with what we observe in the model.
ax = fig.add_subplot(2, 3, 6)
ax.plot(range(Nc-hw,Nc+hw),convol[Nc-hw:Nc+hw,Nc-1],label='cross-section 1')
ax.plot(range(Nc-hw,Nc+hw),convol[Nc-1,Nc-hw:Nc+hw],label='cross-section 2')
maxconvol = convol[Nc-hw:Nc+hw,Nc-1].max()
gauss = np.exp( -(np.array(range(-hw,hw))**2 / (2. * gauss_pix**2)))
gauss/= gauss.max()
gauss*=maxconvol
ax.plot(range(Nc-hw,Nc+hw),gauss,label='SOFIA beam')
leg = ax.legend(loc=2,fontsize='small')
#leg = plt.gca().get_legend()
#plt.setp(leg.get_text(),fontsize='small')
ax.set_title('Cross section at the center')
string=self.modelPrint()
fig.text(0.0,0.14,string+'Av='+str(self.extinction)+'\n'+'dist='+str(self.dist/pc)+'\n',color='r')
fig.savefig(modelname+'.png', bbox_inches='tight',dpi=300)
if show:
plt.show()
def plotSim(self,dist_pc=140,inc=3,extinction=0,show=False):
self.dist=dist_pc*pc
self.inc=inc
self.extinction=extinction
modelname = self.folder+self.name
self.mo = ModelOutput(modelname+'.rtout')
#tracy_dust = np.loadtxt('Tracy_models/OH5.par')
#chi = np.loadtxt('kmh94_3.1_full.chi')
#wav = np.loadtxt('kmh94_3.1_full.wav')
#Chi = interp1d(wav,chi,kind='linear')
fig = plt.figure(figsize=(20,14))
ax=fig.add_subplot(1,3,1)
sed = self.mo.get_sed(aperture=-1, inclination='all', distance=self.dist)
#print tracy_dust[11,1],Cext(sed.wav[-1]),Cext(sed.wav[-1])/tracy_dust[11,1]
#tau = self.extinction*Chi(sed.wav)/Chi(0.550)/1.086
#print Cext(sed.wav)/tracy_dust[11,1]
#ext = np.array([np.exp(-tau) for i in range(sed.val.shape[0])])
#print tau,np.exp(-tau)
ax.loglog(sed.wav, sed.val.transpose(), color='black')
ax.set_title(modelname+'_seds, Av='+str(self.extinction))
ax.set_xlim(sed.wav.min(), 1300)
ax.set_ylim(1e-13, 1e-7)
ax.set_xlabel(r'$\lambda$ [$\mu$m]')
ax.set_ylabel(r'$\lambda F_\lambda$ [ergs/cm$^2/s$]')
#self.plotData(ax,sourcename)
ax.set_xscale('log')
ax.set_yscale('log')
#ax.set_ylabel(r'$F_{Jy}$ [Jy]')
#plt.legend(loc=4)
# ax=fig.add_subplot(2,3,2)
# sed = self.mo.get_sed(aperture=-1, inclination=self.inc, distance=self.dist)
# ext=np.exp(-tau)
# ax.loglog(sed.wav, sed.val.transpose()*ext.T, lw=3,color='black',label='source_total')
# ax.set_xlim(sed.wav.min(), 1300)
# ax.set_ylim(1e-13, 1e-7) ### for lamFlam
# sed = self.mo.get_sed(aperture=-1, inclination=self.inc, distance=self.dist,component='source_emit')
# ax.loglog(sed.wav, sed.val.transpose()*ext.T, color='blue',label='source_emit')
# sed = self.mo.get_sed(aperture=-1, inclination=self.inc, distance=self.dist,component='source_scat')
# ax.loglog(sed.wav, sed.val.transpose()*ext.T, color='teal',label='source_scat')
# sed = self.mo.get_sed(aperture=-1, inclination=self.inc, distance=self.dist,component='dust_emit')
# ax.loglog(sed.wav, sed.val.transpose()*ext.T, color='red',label='dust_emit')
# sed = self.mo.get_sed(aperture=-1, inclination=self.inc, distance=self.dist,component='dust_scat')
# ax.loglog(sed.wav, sed.val.transpose()*ext.T, color='orange',label='dust_scat')
# #self.plotData(ax,sourcename)
# ax.set_xscale('log')
# ax.set_yscale('log')
# ax.set_title('seds_inc=inc')
# ax.set_xlabel(r'$\lambda$ [$\mu$m]')
# ax.set_ylabel(r'$\lambda F_\lambda$ [ergs/cm$^2/s$]')
# #ax.set_ylabel(r'$F_{Jy}$ [Jy]')
# leg = ax.legend(loc=4,fontsize='small')
#leg = plt.gca().get_legend()
#plt.setp(leg.get_text(),fontsize='small')
# Extract the quantities
g = self.mo.get_quantities()
# Get the wall positions for r and theta
rw, tw = g.r_wall / au, g.t_wall
# Make a 2-d grid of the wall positions (used by pcolormesh)
R, T = np.meshgrid(rw, tw)
# Calculate the position of the cell walls in cartesian coordinates
X, Z = R * np.sin(T), R * np.cos(T)
# Make a plot in (x, z) space for different zooms
from matplotlib.colors import LogNorm,PowerNorm
# Make a plot in (r, theta) space
ax = fig.add_subplot(1, 3, 2)
if g.shape[-1]==2:
c = ax.pcolormesh(X, Z, g['temperature'][0].array[0, :, :]+g['temperature'][1].array[0, :, :],norm=PowerNorm(gamma=0.5,vmin=1,vmax=500))
else :
c = ax.pcolormesh(X, Z, g['temperature'][0].array[0, :, :],norm=PowerNorm(gamma=0.5,vmin=1,vmax=500))
#ax.set_xscale('log')
#ax.set_yscale('log')
ax.set_xlim(X.min(), X.max())
ax.set_ylim(Z.min(), Z.max())
ax.set_xlabel('x (au)')
ax.set_ylabel('z (au)')
#ax.set_yticks([np.pi, np.pi * 0.75, np.pi * 0.5, np.pi * 0.25, 0.])
#ax.set_yticklabels([r'$\pi$', r'$3\pi/4$', r'$\pi/2$', r'$\pi/4$', r'$0$'])
cb = fig.colorbar(c)
ax.set_title('Temperature structure')
cb.set_label('Temperature (K)')
#fig.savefig(modelname+'_temperature_spherical_rt.png', bbox_inches='tight')
ax = fig.add_subplot(1, 3, 3)
if g.shape[-1]==2:
c = ax.pcolormesh(X, Z, g['density'][0].array[0, :, :]+g['density'][1].array[0, :, :],norm=LogNorm(vmin=1e-22,vmax=g['density'][0].array[0, :, :].max()))
else :
c = ax.pcolormesh(X, Z, g['density'][0].array[0, :, :],norm=LogNorm(vmin=1e-22,vmax=g['density'][0].array[0, :, :].max()))
#ax.set_xscale('log')
#ax.set_yscale('log')
ax.set_xlim(X.min(), X.max())
ax.set_ylim(Z.min(), Z.max())
ax.set_xlabel('x (au)')
ax.set_ylabel('z (au)')
ax.set_title('Density structure')
cb = fig.colorbar(c)
cb.set_label('Density (g/cm2)')
# ### plot the convolved image with the 37 micron filter (manually set to slice 18 of the cube - this would change with wavelength coverage)
# ax = fig.add_subplot(2, 3, 5)
# self.image = self.mo.get_image(inclination=inc,distance=self.dist,units='Jy')
# fits.writeto(modelname+'_inc_'+str(inc)+'.fits',self.image.val.swapaxes(0,2).swapaxes(1,2),clobber=True)
# ### need to convolve the image with a Gaussian PSF
# pix = 2.*self.limval/au/self.Npix # in AU/pix
# pix_asec = pix/(self.dist/pc) # in asec/pix
# airy_asec = 3.5 #asec
# airy_pix = airy_asec/pix_asec # in pix
# gauss_pix = airy_pix/2.35 # in Gaussian std
# print "Gaussian std: ",gauss_pix
# from scipy.ndimage.filters import gaussian_filter as gauss
# #print [(i,sed.wav[i]) for i in range(len(sed.wav))]
# img37 = self.image.val[:,:,18]
# convol = gauss(img37,gauss_pix,mode='constant',cval=0.0)
# Nc = self.Npix/2
# hw = min(int(20./pix_asec),Nc) #(max is Nc)
# #ax.imshow(img37,norm=LogNorm(vmin=1e-20,vmax=img37.max()))
# #ax.imshow(img37,interpolation='nearest')
# #ax.imshow(convol,norm=LogNorm(vmin=1e-20,vmax=img37.max()))
# #ax.imshow(convol,interpolation='nearest',norm=LogNorm(vmin=1e-20,vmax=img37.max()))
# ax.imshow(convol[Nc-hw:Nc+hw,Nc-hw:Nc+hw],interpolation='nearest',origin='lower',cmap=plt.get_cmap('gray'))
# airy_disk = plt.Circle((airy_pix*1.3,airy_pix*1.3),airy_pix,color=colors[3])
# ax.add_artist(airy_disk)
# ax.text(airy_pix*3,airy_pix*1.3/2.0,'SOFIA 37um Airy disk',color=colors[3])
# ax.set_title('Convolved image')
# fits.writeto(modelname+'_inc_'+str(inc)+'_convol37.fits',convol,clobber=True)
# ### draw a cross-section of the image to show the spatial extension in linear scale, to compare with what we observe in the model.
# ax = fig.add_subplot(2, 3, 6)
# ax.plot(range(Nc-hw,Nc+hw),convol[Nc-hw:Nc+hw,Nc-1],label='cross-section 1')
# ax.plot(range(Nc-hw,Nc+hw),convol[Nc-1,Nc-hw:Nc+hw],label='cross-section 2')
# maxconvol = convol[Nc-hw:Nc+hw,Nc-1].max()
# gauss = np.exp( -(np.array(range(-hw,hw))**2 / (2. * gauss_pix**2)))
# gauss/= gauss.max()
# gauss*=maxconvol
# ax.plot(range(Nc-hw,Nc+hw),gauss,label='SOFIA beam')
# leg = ax.legend(loc=2,fontsize='small')
# #leg = plt.gca().get_legend()
# #plt.setp(leg.get_text(),fontsize='small')
# ax.set_title('Cross section at the center')
string=self.modelPrint()
fig.text(0.0,0.14,string+'Av='+str(self.extinction)+'\n'+'dist='+str(self.dist/pc)+'\n',color='r')
fig.savefig(modelname+'.png', bbox_inches='tight',dpi=300)
if show:
plt.show()
def azimuthal_simulation(rtout, beam_size, wave, dist=200., group=22):
"""
rtout: the filepath to the output file of Hyperion
beam_size: the beam size used for the width of annulus
dist: the physical distance to the source
group: the group which contains image
"""
import numpy as np
import matplotlib.pyplot as plt
import astropy.constants as const
from hyperion.model import ModelOutput
# constant setup
pc = const.pc.cgs.value
au = const.au.cgs.value
output = {'wave': wave, 'annuli': [], 'flux_annuli': []}
# Read in the Hyperion output file
m = ModelOutput(rtout)
# get image
image = m.get_image(group=5, inclination=0, distance=dist * pc, units='Jy')
# Calculate the image width in arcsec given the distance to the source
rmax = max(m.get_quantities().r_wall)
w = np.degrees(rmax / image.distance) * 3600
# grid of radii of annulus
annuli = np.linspace(beam_size / 2.,
np.floor(
(w - beam_size / 2.) / beam_size) * beam_size,
np.floor((w - beam_size / 2.) / beam_size)) # plot
fig = plt.figure(figsize=(8, 6))
ax = fig.add_subplot(111)
# iternate through wavelength
if type(wave) == int or type(wave) == float:
wave = [wave]
color_list = plt.cm.viridis(np.linspace(0, 1, len(wave) + 1))
for i in range(len(wave)):
wav = wave[i]
# Find the closest wavelength
iwav = np.argmin(np.abs(wav - image.wav))
# avoid zero when log, and flip the image
val = image.val[::-1, :, iwav]
# determine the center of the image
npix = len(val[:, 0])
center = np.array([npix / 2. + 0.5, npix / 2. + 0.5])
scale = 2 * rmax / npix
# create index array of the image
x = np.empty_like(val)
for j in range(len(val[0, :])):
x[:, j] = j
flux_annuli = np.empty_like(annuli)
for k in range(len(annuli)):
flux_annuli[k] = np.sum(val[(((x-center[0])**2+(x.T-center[1])**2)**0.5*2*w/npix >= annuli[k]-beam_size/2.) & \
(((x-center[0])**2+(x.T-center[1])**2)**0.5*2*w/npix < annuli[k]+beam_size/2.)])
output['annuli'].append(np.array(annuli))
output['flux_annuli'].append(flux_annuli)
flux_annuli = flux_annuli / np.nanmax(flux_annuli)
ax.plot(np.log10(annuli*dist), np.log10(flux_annuli), 'o-', color=color_list[i], \
markersize=3, mec='None', label=r'$\rm{'+str(wav)+'\,\mu m}$')
ax.axvline(np.log10((w - beam_size / 2.) * dist),
linestyle='--',
color='k')
ax.axvline(np.log10(w * dist), linestyle='-', color='k')
ax.legend(loc='best', fontsize=12, numpoints=1, ncol=2)
ax.set_xlabel(r'$\rm{log(Radius)\,[AU]}$', fontsize=18)
ax.set_ylabel(r'${\rm log(}F/F_{\rm max})$', fontsize=18)
fig.gca().set_ylim(top=0.1)
[
ax.spines[axis].set_linewidth(1.5)
for axis in ['top', 'bottom', 'left', 'right']
]
ax.minorticks_on()
ax.tick_params('both',
labelsize=18,
width=1.5,
which='major',
pad=15,
length=5)
ax.tick_params('both',
labelsize=18,
width=1.5,
which='minor',
pad=15,
length=2.5)
fig.savefig('/Users/yaolun/test/annuli_profile.pdf',
format='pdf',
dpi=300,
bbox_inches='tight')
fig.clf()
return output
m.set_spherical_polar_grid(r, t, p)
dens = zeros((nr - 1, nt - 1, np - 1)) + 1.0e-17
m.add_density_grid(dens, d)
source = m.add_spherical_source()
source.luminosity = lsun
source.radius = rsun
source.temperature = 4000.
m.set_n_photons(initial=1000000, imaging=0)
m.set_convergence(True, percentile=99., absolute=2., relative=1.02)
m.write("test_spherical.rtin")
m.run("test_spherical.rtout", mpi=False)
n = ModelOutput('test_spherical.rtout')
grid = n.get_quantities()
temp = grid.quantities['temperature'][0]
for i in range(9):
plt.imshow(temp[i,:,:],origin="lower",interpolation="nearest", \
vmin=temp.min(),vmax=temp.max())
plt.colorbar()
plt.show()
def extract_hyperion(filename,indir=None,outdir=None,dstar=178.0,wl_aper=None,save=True,filter_func=False,\
plot_all=False,clean=False,exclude_wl=[],log=True):
def l_bol(wl, fv, dist=178.0):
import numpy as np
import astropy.constants as const
# wavelength unit: um
# Flux density unit: Jy
#
# constants setup
#
c = const.c.cgs.value
pc = const.pc.cgs.value
PI = np.pi
SL = const.L_sun.cgs.value
# Convert the unit from Jy to erg s-1 cm-2 Hz-1
fv = np.array(fv) * 1e-23
freq = c / (1e-4 * np.array(wl))
diff_dum = freq[1:] - freq[0:-1]
freq_interpol = np.hstack(
(freq[0:-1] + diff_dum / 2.0, freq[0:-1] + diff_dum / 2.0, freq[0],
freq[-1]))
freq_interpol = freq_interpol[np.argsort(freq_interpol)[::-1]]
fv_interpol = np.empty(len(freq_interpol))
# calculate the histogram style of spectrum
#
for i in range(0, len(fv)):
if i == 0:
fv_interpol[i] = fv[i]
else:
fv_interpol[2 * i - 1] = fv[i - 1]
fv_interpol[2 * i] = fv[i]
fv_interpol[-1] = fv[-1]
dv = freq_interpol[0:-1] - freq_interpol[1:]
dv = np.delete(dv, np.where(dv == 0))
fv = fv[np.argsort(freq)]
freq = freq[np.argsort(freq)]
return (np.trapz(fv, freq) * 4. * PI * (dist * pc)**2) / SL
# to avoid X server error
import matplotlib as mpl
mpl.use('Agg')
#
import matplotlib.pyplot as plt
import numpy as np
import os
from hyperion.model import ModelOutput, Model
from scipy.interpolate import interp1d
from hyperion.util.constants import pc, c, lsun, au
from astropy.io import ascii
import sys
sys.path.append(os.path.expanduser('~') + '/programs/spectra_analysis/')
from phot_filter import phot_filter
from get_bhr71_obs import get_bhr71_obs
# seaborn colormap, because jet is bad obviously
import seaborn.apionly as sns
# Read in the observation data and calculate the noise & variance
if indir == None:
indir = '/Users/yaolun/bhr71/'
if outdir == None:
outdir = '/Users/yaolun/bhr71/hyperion/'
# assign the file name from the input file
print_name = os.path.splitext(os.path.basename(filename))[0]
# use a canned function to extract BHR71 observational data
bhr71 = get_bhr71_obs(indir) # unit in um, Jy
wl_tot, flux_tot, unc_tot = bhr71['spec']
flux_tot = flux_tot * 1e-23 # convert unit from Jy to erg s-1 cm-2 Hz-1
unc_tot = unc_tot * 1e-23
l_bol_obs = l_bol(wl_tot, flux_tot * 1e23)
wl_phot, flux_phot, flux_sig_phot = bhr71['phot']
flux_phot = flux_phot * 1e-23 # convert unit from Jy to erg s-1 cm-2 Hz-1
flux_sig_phot = flux_sig_phot * 1e-23
# Print the observed L_bol
# wl_tot = np.hstack((wl_irs,wl_obs,wl_phot))
# flux_tot = np.hstack((flux_irs,flux_obs,flux_phot))
# flux_tot = flux_tot[np.argsort(wl_tot)]
# wl_tot = wl_tot[np.argsort(wl_tot)]
# l_bol_obs = l_bol(wl_tot,flux_tot*1e23)
# Open the model
m = ModelOutput(filename)
if wl_aper == None:
wl_aper = [
3.6, 4.5, 5.8, 8.0, 10, 16, 20, 24, 35, 70, 100, 160, 250, 350,
500, 850
]
# Create the plot
mag = 1.5
fig = plt.figure(figsize=(8 * mag, 6 * mag))
ax_sed = fig.add_subplot(1, 1, 1)
# Plot the observed SED
# plot the observed spectra
if not clean:
color_seq = ['Green', 'Red', 'Blue']
else:
color_seq = ['DimGray', 'DimGray', 'DimGray']
# plot the observations
if log:
pacs, = ax_sed.plot(np.log10(wl_tot[(wl_tot>40) & (wl_tot<190.31)]),\
np.log10(c/(wl_tot[(wl_tot>40) & (wl_tot<190.31)]*1e-4)*flux_tot[(wl_tot>40) & (wl_tot<190.31)]),\
'-',color=color_seq[0],linewidth=1.5*mag, alpha=0.7)
spire, = ax_sed.plot(np.log10(wl_tot[wl_tot > 194]),np.log10(c/(wl_tot[wl_tot > 194]*1e-4)*flux_tot[wl_tot > 194]),\
'-',color=color_seq[1],linewidth=1.5*mag, alpha=0.7)
irs, = ax_sed.plot(np.log10(wl_tot[wl_tot < 40]),np.log10(c/(wl_tot[wl_tot < 40]*1e-4)*flux_tot[wl_tot < 40]),\
'-',color=color_seq[2],linewidth=1.5*mag, alpha=0.7)
photometry, = ax_sed.plot(np.log10(wl_phot),
np.log10(c / (wl_phot * 1e-4) * flux_phot),
's',
mfc='DimGray',
mec='k',
markersize=8)
# plot the observed photometry data
ax_sed.errorbar(np.log10(wl_phot),np.log10(c/(wl_phot*1e-4)*flux_phot),\
yerr=[np.log10(c/(wl_phot*1e-4)*flux_phot)-np.log10(c/(wl_phot*1e-4)*(flux_phot-flux_sig_phot)),\
np.log10(c/(wl_phot*1e-4)*(flux_phot+flux_sig_phot))-np.log10(c/(wl_phot*1e-4)*flux_phot)],\
fmt='s',mfc='DimGray',mec='k',markersize=8)
else:
pacs, = ax_sed.plot(np.log10(wl_tot[(wl_tot>40) & (wl_tot<190.31)]),\
c/(wl_tot[(wl_tot>40) & (wl_tot<190.31)]*1e-4)*flux_tot[(wl_tot>40) & (wl_tot<190.31)],\
'-',color=color_seq[0],linewidth=1.5*mag, alpha=0.7)
spire, = ax_sed.plot(np.log10(wl_tot[wl_tot > 194]),c/(wl_tot[wl_tot > 194]*1e-4)*flux_tot[wl_tot > 194],\
'-',color=color_seq[1],linewidth=1.5*mag, alpha=0.7)
irs, = ax_sed.plot(np.log10(wl_tot[wl_tot < 40]),c/(wl_tot[wl_tot < 40]*1e-4)*flux_tot[wl_tot < 40],\
'-',color=color_seq[2],linewidth=1.5*mag, alpha=0.7)
photometry, = ax_sed.plot(wl_phot,
c / (wl_phot * 1e-4) * flux_phot,
's',
mfc='DimGray',
mec='k',
markersize=8)
# plot the observed photometry data
ax_sed.errorbar(np.log10(wl_phot),c/(wl_phot*1e-4)*flux_phot,\
yerr=[c/(wl_phot*1e-4)*flux_phot-c/(wl_phot*1e-4)*(flux_phot-flux_sig_phot),\
c/(wl_phot*1e-4)*(flux_phot+flux_sig_phot)-c/(wl_phot*1e-4)*flux_phot],\
fmt='s',mfc='DimGray',mec='k',markersize=8)
if not clean:
ax_sed.text(0.75,
0.9,
r'$\rm{L_{bol}= %5.2f L_{\odot}}$' % l_bol_obs,
fontsize=mag * 16,
transform=ax_sed.transAxes)
# else:
# pacs, = ax_sed.plot(np.log10(wl_tot[(wl_tot>40) & (wl_tot<190.31)]),\
# np.log10(c/(wl_tot[(wl_tot>40) & (wl_tot<190.31)]*1e-4)*flux_tot[(wl_tot>40) & (wl_tot<190.31)]),\
# '-',color='DimGray',linewidth=1.5*mag, alpha=0.7)
# spire, = ax_sed.plot(np.log10(wl_tot[wl_tot > 194]),np.log10(c/(wl_tot[wl_tot > 194]*1e-4)*flux_tot[wl_tot > 194]),\
# '-',color='DimGray',linewidth=1.5*mag, alpha=0.7)
# irs, = ax_sed.plot(np.log10(wl_tot[wl_tot < 40]),np.log10(c/(wl_tot[wl_tot < 40]*1e-4)*flux_tot[wl_tot < 40]),\
# '-',color='DimGray',linewidth=1.5*mag, alpha=0.7)
# Extract the SED for the smallest inclination and largest aperture, and
# scale to 300pc. In Python, negative indices can be used for lists and
# arrays, and indicate the position from the end. So to get the SED in the
# largest aperture, we set aperture=-1.
# aperture group is aranged from smallest to infinite
sed_inf = m.get_sed(group=0,
inclination=0,
aperture=-1,
distance=dstar * pc,
uncertainties=True)
# plot the simulated SED
if clean == False:
sim, = ax_sed.plot(np.log10(sed_inf.wav),
np.log10(sed_inf.val),
'-',
color='GoldenRod',
linewidth=0.5 * mag)
ax_sed.fill_between(np.log10(sed_inf.wav), np.log10(sed_inf.val-sed_inf.unc), np.log10(sed_inf.val+sed_inf.unc),\
color='GoldenRod', alpha=0.5)
# get flux at different apertures
flux_aper = np.zeros_like(wl_aper, dtype=float)
unc_aper = np.zeros_like(wl_aper, dtype=float)
a = np.zeros_like(wl_aper) + 1
color_list = plt.cm.jet(np.linspace(0, 1, len(wl_aper) + 1))
for i in range(0, len(wl_aper)):
if wl_aper[i] in exclude_wl:
continue
# if (wl_aper[i] == 5.8) or (wl_aper[i] == 8.0) or (wl_aper[i] == 10.5) or (wl_aper[i] == 11):
# continue
sed_dum = m.get_sed(group=i + 1,
inclination=0,
aperture=-1,
distance=dstar * pc,
uncertainties=True)
if plot_all == True:
ax_sed.plot(np.log10(sed_dum.wav),
np.log10(sed_dum.val),
'-',
color=color_list[i])
ax_sed.fill_between(np.log10(sed_dum.wav), np.log10(sed_dum.val-sed_dum.unc), np.log10(sed_dum.val+sed_dum.unc),\
color=color_list[i], alpha=0.5)
if filter_func == False:
# use a rectangle function the average the simulated SED
# apply the spectral resolution
if (wl_aper[i] < 50.) & (wl_aper[i] >= 5):
res = 60.
elif wl_aper[i] < 5:
res = 10.
else:
res = 1000.
ind = np.where((sed_dum.wav < wl_aper[i] * (1 + 1. / res))
& (sed_dum.wav > wl_aper[i] * (1 - 1. / res)))
if len(ind[0]) != 0:
flux_aper[i] = np.mean(sed_dum.val[ind])
unc_aper[i] = np.mean(sed_dum.unc[ind])
else:
f = interp1d(sed_dum.wav, sed_dum.val)
f_unc = interp1d(sed_dum.wav, sed_dum.unc)
flux_aper[i] = f(wl_aper[i])
unc_aper[i] = f_unc(wl_aper[i])
else:
# apply the filter function
# decide the filter name
if wl_aper[i] == 70:
fil_name = 'Herschel PACS 70um'
elif wl_aper[i] == 100:
fil_name = 'Herschel PACS 100um'
elif wl_aper[i] == 160:
fil_name = 'Herschel PACS 160um'
elif wl_aper[i] == 250:
fil_name = 'Herschel SPIRE 250um'
elif wl_aper[i] == 350:
fil_name = 'Herschel SPIRE 350um'
elif wl_aper[i] == 500:
fil_name = 'Herschel SPIRE 500um'
elif wl_aper[i] == 3.6:
fil_name = 'IRAC Channel 1'
elif wl_aper[i] == 4.5:
fil_name = 'IRAC Channel 2'
elif wl_aper[i] == 5.8:
fil_name = 'IRAC Channel 3'
elif wl_aper[i] == 8.0:
fil_name = 'IRAC Channel 4'
elif wl_aper[i] == 24:
fil_name = 'MIPS 24um'
elif wl_aper[i] == 850:
fil_name = 'SCUBA 850WB'
else:
fil_name = None
if fil_name != None:
filter_func = phot_filter(fil_name)
# Simulated SED should have enough wavelength coverage for applying photometry filters.
f = interp1d(sed_dum.wav, sed_dum.val)
f_unc = interp1d(sed_dum.wav, sed_dum.unc)
flux_aper[i] = np.trapz(
filter_func['wave'] / 1e4,
f(filter_func['wave'] / 1e4) *
filter_func['transmission']) / np.trapz(
filter_func['wave'] / 1e4, filter_func['transmission'])
unc_aper[i] = abs(
np.trapz((filter_func['wave'] / 1e4)**2,
(f_unc(filter_func['wave'] / 1e4) *
filter_func['transmission'])**2))**0.5 / abs(
np.trapz(filter_func['wave'] / 1e4,
filter_func['transmission']))
else:
# use a rectangle function the average the simulated SED
# apply the spectral resolution
if (wl_aper[i] < 50.) & (wl_aper[i] >= 5):
res = 60.
elif wl_aper[i] < 5:
res = 10.
else:
res = 1000.
ind = np.where((sed_dum.wav < wl_aper[i] * (1 + 1. / res))
& (sed_dum.wav > wl_aper[i] * (1 - 1. / res)))
if len(ind[0]) != 0:
flux_aper[i] = np.mean(sed_dum.val[ind])
unc_aper[i] = np.mean(sed_dum.unc[ind])
else:
f = interp1d(sed_dum.wav, sed_dum.val)
f_unc = interp1d(sed_dum.wav, sed_dum.unc)
flux_aper[i] = f(wl_aper[i])
unc_aper[i] = f_unc(wl_aper[i])
# temperory step: solve issue of uncertainty greater than the value
for i in range(len(wl_aper)):
if unc_aper[i] >= flux_aper[i]:
unc_aper[i] = flux_aper[i] - 1e-20
# perform the same procedure of flux extraction of aperture flux with observed spectra
wl_aper = np.array(wl_aper, dtype=float)
obs_aper_wl = wl_aper[(wl_aper >= min(wl_tot)) & (wl_aper <= max(wl_tot))]
obs_aper_sed = np.zeros_like(obs_aper_wl)
obs_aper_sed_unc = np.zeros_like(obs_aper_wl)
sed_tot = c / (wl_tot * 1e-4) * flux_tot
sed_unc_tot = c / (wl_tot * 1e-4) * unc_tot
# wl_tot and flux_tot are already hstacked and sorted by wavelength
for i in range(0, len(obs_aper_wl)):
if obs_aper_wl[i] in exclude_wl:
continue
if filter_func == False:
# use a rectangle function the average the simulated SED
# apply the spectral resolution
if (obs_aper_wl[i] < 50.) & (obs_aper_wl[i] >= 5):
res = 60.
elif obs_aper_wl[i] < 5:
res = 10.
else:
res = 1000.
ind = np.where((wl_tot < obs_aper_wl[i] * (1 + 1. / res))
& (wl_tot > obs_aper_wl[i] * (1 - 1. / res)))
if len(ind[0]) != 0:
obs_aper_sed[i] = np.mean(sed_tot[ind])
obs_aper_sed_unc[i] = np.mean(sed_unc_tot[ind])
else:
f = interp1d(wl_tot, sed_tot)
f_unc = interp1d(wl_tot, sed_unc_tot)
obs_aper_sed[i] = f(obs_aper_wl[i])
obs_aper_sed_unc[i] = f_unc(obs_aper_wl[i])
else:
# apply the filter function
# decide the filter name
if obs_aper_wl[i] == 70:
fil_name = 'Herschel PACS 70um'
elif obs_aper_wl[i] == 100:
fil_name = 'Herschel PACS 100um'
elif obs_aper_wl[i] == 160:
fil_name = 'Herschel PACS 160um'
elif obs_aper_wl[i] == 250:
fil_name = 'Herschel SPIRE 250um'
elif obs_aper_wl[i] == 350:
fil_name = 'Herschel SPIRE 350um'
elif obs_aper_wl[i] == 500:
fil_name = 'Herschel SPIRE 500um'
elif obs_aper_wl[i] == 3.6:
fil_name = 'IRAC Channel 1'
elif obs_aper_wl[i] == 4.5:
fil_name = 'IRAC Channel 2'
elif obs_aper_wl[i] == 5.8:
fil_name = 'IRAC Channel 3'
elif obs_aper_wl[i] == 8.0:
fil_name = 'IRAC Channel 4'
elif obs_aper_wl[i] == 24:
fil_name = 'MIPS 24um'
# elif obs_aper_wl[i] == 850:
# fil_name = 'SCUBA 850WB'
# do not have SCUBA spectra
else:
fil_name = None
# print obs_aper_wl[i], fil_name
if fil_name != None:
filter_func = phot_filter(fil_name)
# Observed SED needs to be trimmed before applying photometry filters
filter_func = filter_func[(filter_func['wave']/1e4 >= min(wl_tot))*\
((filter_func['wave']/1e4 >= 54.8)+(filter_func['wave']/1e4 <= 36.0853))*\
((filter_func['wave']/1e4 <= 95.05)+(filter_func['wave']/1e4 >=103))*\
((filter_func['wave']/1e4 <= 190.31)+(filter_func['wave']/1e4 >= 195))*\
(filter_func['wave']/1e4 <= max(wl_tot))]
f = interp1d(wl_tot, sed_tot)
f_unc = interp1d(wl_tot, sed_unc_tot)
obs_aper_sed[i] = np.trapz(
filter_func['wave'] / 1e4,
f(filter_func['wave'] / 1e4) *
filter_func['transmission']) / np.trapz(
filter_func['wave'] / 1e4, filter_func['transmission'])
obs_aper_sed_unc[i] = abs(
np.trapz((filter_func['wave'] / 1e4)**2,
(f_unc(filter_func['wave'] / 1e4) *
filter_func['transmission'])**2))**0.5 / abs(
np.trapz(filter_func['wave'] / 1e4,
filter_func['transmission']))
else:
# use a rectangle function the average the simulated SED
# apply the spectral resolution
if (obs_aper_wl[i] < 50.) & (obs_aper_wl[i] >= 5):
res = 60.
elif obs_aper_wl[i] < 5:
res = 10.
else:
res = 1000.
ind = np.where((wl_tot < obs_aper_wl[i] * (1 + 1. / res))
& (wl_tot > obs_aper_wl[i] * (1 - 1. / res)))
if len(ind[0]) != 0:
obs_aper_sed[i] = np.mean(sed_tot[ind])
obs_aper_sed_unc[i] = np.mean(sed_unc_tot[ind])
else:
f = interp1d(wl_tot, sed_tot)
f_unc = interp1d(wl_tot, sed_unc_tot)
obs_aper_sed[i] = f(obs_aper_wl[i])
obs_aper_sed_unc[i] = f_unc(obs_aper_wl[i])
# if clean == False:
# if log:
# aper_obs = ax_sed.errorbar(np.log10(obs_aper_wl), np.log10(obs_aper_sed), \
# yerr=[np.log10(obs_aper_sed)-np.log10(obs_aper_sed-obs_aper_sed_unc), np.log10(obs_aper_sed+obs_aper_sed_unc)-np.log10(obs_aper_sed)],\
# fmt='s', mec='Magenta', mfc='Magenta', markersize=10, elinewidth=3, ecolor='Magenta',capthick=3,barsabove=True)
# aper = ax_sed.errorbar(np.log10(wl_aper), np.log10(flux_aper),\
# yerr=[np.log10(flux_aper)-np.log10(flux_aper-unc_aper), np.log10(flux_aper+unc_aper)-np.log10(flux_aper)],\
# fmt='o', mfc='None', mec='k', ecolor='Black', markersize=12, markeredgewidth=3, elinewidth=3, barsabove=True)
# else:
# aper_obs = ax_sed.errorbar(obs_aper_wl, obs_aper_sed, yerr=obs_aper_sed_unc,\
# fmt='s', mec='Magenta', mfc='Magenta', markersize=10, elinewidth=3, ecolor='Magenta',capthick=3,barsabove=True)
# aper = ax_sed.errorbar(wl_aper, flux_aper, yerr=unc_aper,\
# fmt='o', mfc='None', mec='k', ecolor='Black', markersize=12, markeredgewidth=3, elinewidth=3, barsabove=True)
# else:
if log:
aper_obs = ax_sed.errorbar(np.log10(obs_aper_wl), np.log10(obs_aper_sed),\
yerr=[np.log10(obs_aper_sed)-np.log10(obs_aper_sed-obs_aper_sed_unc), np.log10(obs_aper_sed+obs_aper_sed_unc)-np.log10(obs_aper_sed)],\
fmt='s', mec='None', mfc='r', markersize=10, linewidth=1.5, ecolor='Red', elinewidth=3, capthick=3, barsabove=True)
aper = ax_sed.errorbar(np.log10(wl_aper),np.log10(flux_aper),\
yerr=[np.log10(flux_aper)-np.log10(flux_aper-unc_aper), np.log10(flux_aper+unc_aper)-np.log10(flux_aper)],\
fmt='o', mec='Blue', mfc='None', color='b',markersize=12, markeredgewidth=2.5, linewidth=1.7, ecolor='Blue', elinewidth=3, barsabove=True)
ax_sed.set_ylim([-14, -7])
ax_sed.set_xlim([0, 3])
else:
aper_obs = ax_sed.errorbar(np.log10(obs_aper_wl), obs_aper_sed, yerr=obs_aper_sed_unc,\
fmt='s', mec='None', mfc='r', markersize=10, linewidth=1.5, ecolor='Red', elinewidth=3, capthick=3, barsabove=True)
aper = ax_sed.errorbar(np.log10(wl_aper),flux_aper, yerr=unc_aper,\
fmt='o', mec='Blue', mfc='None', color='b',markersize=12, markeredgewidth=2.5, linewidth=1.7, ecolor='Blue', elinewidth=3, barsabove=True)
# ax_sed.set_xlim([1, 1000])
ax_sed.set_xlim([0, 3])
# ax_sed.set_ylim([1e-14, 1e-8])
# calculate the bolometric luminosity of the aperture
# print flux_aper
l_bol_sim = l_bol(wl_aper,
flux_aper / (c / np.array(wl_aper) * 1e4) * 1e23)
print 'Bolometric luminosity of simulated spectrum: %5.2f lsun' % l_bol_sim
# print out the sed into ascii file for reading in later
if save == True:
# unapertured SED
foo = open(outdir + print_name + '_sed_inf.txt', 'w')
foo.write('%12s \t %12s \t %12s \n' % ('wave', 'vSv', 'sigma_vSv'))
for i in range(0, len(sed_inf.wav)):
foo.write('%12g \t %12g \t %12g \n' %
(sed_inf.wav[i], sed_inf.val[i], sed_inf.unc[i]))
foo.close()
# SED with convolution of aperture sizes
foo = open(outdir + print_name + '_sed_w_aperture.txt', 'w')
foo.write('%12s \t %12s \t %12s \n' % ('wave', 'vSv', 'sigma_vSv'))
for i in range(0, len(wl_aper)):
foo.write('%12g \t %12g \t %12g \n' %
(wl_aper[i], flux_aper[i], unc_aper[i]))
foo.close()
# Read in and plot the simulated SED produced by RADMC-3D using the same parameters
# [wl,fit] = np.genfromtxt(indir+'hyperion/radmc_comparison/spectrum.out',dtype='float',skip_header=3).T
# l_bol_radmc = l_bol(wl,fit*1e23/dstar**2)
# radmc, = ax_sed.plot(np.log10(wl),np.log10(c/(wl*1e-4)*fit/dstar**2),'-',color='DimGray', linewidth=1.5*mag, alpha=0.5)
# print the L bol of the simulated SED (both Hyperion and RADMC-3D)
# lg_sim = ax_sed.legend([sim,radmc],[r'$\rm{L_{bol,sim}=%5.2f\,L_{\odot},\,L_{center}=9.18\,L_{\odot}}$' % l_bol_sim, \
# r'$\rm{L_{bol,radmc3d}=%5.2f\,L_{\odot},\,L_{center}=9.18\,L_{\odot}}$' % l_bol_radmc],\
# loc='lower right',fontsize=mag*16)
# read the input central luminosity by reading in the source information from output file
dum = Model()
dum.use_sources(filename)
L_cen = dum.sources[0].luminosity / lsun
# legend
lg_data = ax_sed.legend([irs, photometry, aper, aper_obs],\
[r'$\rm{observation}$',\
r'$\rm{photometry}$',r'$\rm{F_{aper,sim}}$',r'$\rm{F_{aper,obs}}$'],\
loc='upper left',fontsize=14*mag,numpoints=1,framealpha=0.3)
if clean == False:
lg_sim = ax_sed.legend([sim],[r'$\rm{L_{bol,sim}=%5.2f\,L_{\odot},\,L_{center}=%5.2f\,L_{\odot}}$' % (l_bol_sim, L_cen)], \
loc='lower right',fontsize=mag*16)
plt.gca().add_artist(lg_data)
# plot setting
ax_sed.set_xlabel(r'$\rm{log\,\lambda\,({\mu}m)}$', fontsize=mag * 20)
ax_sed.set_ylabel(r'$\rm{log\,\nu S_{\nu}\,(erg/cm^{2}/s)}$',
fontsize=mag * 20)
[
ax_sed.spines[axis].set_linewidth(1.5 * mag)
for axis in ['top', 'bottom', 'left', 'right']
]
ax_sed.minorticks_on()
ax_sed.tick_params('both',
labelsize=mag * 18,
width=1.5 * mag,
which='major',
pad=15,
length=5 * mag)
ax_sed.tick_params('both',
labelsize=mag * 18,
width=1.5 * mag,
which='minor',
pad=15,
length=2.5 * mag)
# fix the tick label font
ticks_font = mpl.font_manager.FontProperties(family='STIXGeneral',
size=mag * 18)
for label in ax_sed.get_xticklabels():
label.set_fontproperties(ticks_font)
for label in ax_sed.get_yticklabels():
label.set_fontproperties(ticks_font)
# if clean == False:
# lg_data = ax_sed.legend([irs, pacs, spire,photometry],[r'$\rm{{\it Spitzer}-IRS}$',r'$\rm{{\it Herschel}-PACS}$',r'$\rm{{\it Herschel}-SPIRE}$',r'$\rm{Photometry}$'],\
# loc='upper left',fontsize=14*mag,numpoints=1,framealpha=0.3)
# plt.gca().add_artist(lg_sim)
# else:
# lg_data = ax_sed.legend([irs, photometry, aper, aper_obs],\
# [r'$\rm{observation}$',\
# r'$\rm{photometry}$',r'$\rm{F_{aper,sim}}$',r'$\rm{F_{aper,obs}}$'],\
# loc='upper left',fontsize=14*mag,numpoints=1,framealpha=0.3)
# Write out the plot
fig.savefig(outdir + print_name + '_sed.pdf',
format='pdf',
dpi=300,
bbox_inches='tight')
fig.clf()
# Package for matching the colorbar
from mpl_toolkits.axes_grid1 import make_axes_locatable
# Extract the image for the first inclination, and scale to 300pc. We
# have to specify group=1 as there is no image in group 0.
image = m.get_image(group=len(wl_aper) + 1,
inclination=0,
distance=dstar * pc,
units='MJy/sr')
# image = m.get_image(group=14, inclination=0, distance=dstar * pc, units='MJy/sr')
# Open figure and create axes
# fig = plt.figure(figsize=(8, 8))
fig, axarr = plt.subplots(3,
3,
sharex='col',
sharey='row',
figsize=(13.5, 12))
# Pre-set maximum for colorscales
VMAX = {}
# VMAX[3.6] = 10.
# VMAX[24] = 100.
# VMAX[160] = 2000.
# VMAX[500] = 2000.
VMAX[100] = 10.
VMAX[250] = 100.
VMAX[500] = 2000.
VMAX[1000] = 2000.
# We will now show four sub-plots, each one for a different wavelength
# for i, wav in enumerate([3.6, 24, 160, 500]):
# for i, wav in enumerate([100, 250, 500, 1000]):
# for i, wav in enumerate([4.5, 9.7, 24, 40, 70, 100, 250, 500, 1000]):
for i, wav in enumerate([3.6, 8.0, 9.7, 24, 40, 100, 250, 500, 1000]):
# ax = fig.add_subplot(3, 3, i + 1)
ax = axarr[i / 3, i % 3]
# Find the closest wavelength
iwav = np.argmin(np.abs(wav - image.wav))
# Calculate the image width in arcseconds given the distance used above
# get the max radius
rmax = max(m.get_quantities().r_wall)
w = np.degrees(rmax / image.distance) * 3600.
# Image in the unit of MJy/sr
# Change it into erg/s/cm2/Hz/sr
factor = 1e-23 * 1e6
# avoid zero in log
# flip the image, because the setup of inclination is upside down
val = image.val[::-1, :, iwav] * factor + 1e-30
# val = image.val[:, :, iwav] * factor + 1e-30
# This is the command to show the image. The parameters vmin and vmax are
# the min and max levels for the colorscale (remove for default values).
# cmap = sns.cubehelix_palette(start=0.1, rot=-0.7, gamma=0.2, as_cmap=True)
cmap = plt.cm.CMRmap
im = ax.imshow(np.log10(val),
vmin=-22,
vmax=-12,
cmap=cmap,
origin='lower',
extent=[-w, w, -w, w],
aspect=1)
# fix the tick label font
ticks_font = mpl.font_manager.FontProperties(family='STIXGeneral',
size=14)
for label in ax.get_xticklabels():
label.set_fontproperties(ticks_font)
for label in ax.get_yticklabels():
label.set_fontproperties(ticks_font)
# Colorbar setting
# create an axes on the right side of ax. The width of cax will be 5%
# of ax and the padding between cax and ax will be fixed at 0.05 inch.
if (i + 1) % 3 == 0:
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
cb = fig.colorbar(im, cax=cax)
cb.solids.set_edgecolor("face")
cb.ax.minorticks_on()
cb.ax.set_ylabel(
r'$\rm{log(I_{\nu})\,[erg\,s^{-1}\,cm^{-2}\,Hz^{-1}\,sr^{-1}]}$',
fontsize=12)
cb_obj = plt.getp(cb.ax.axes, 'yticklabels')
plt.setp(cb_obj, fontsize=12)
# fix the tick label font
ticks_font = mpl.font_manager.FontProperties(family='STIXGeneral',
size=12)
for label in cb.ax.get_yticklabels():
label.set_fontproperties(ticks_font)
if (i + 1) == 7:
# Finalize the plot
ax.set_xlabel(r'$\rm{RA\,Offset\,(arcsec)}$', fontsize=14)
ax.set_ylabel(r'$\rm{Dec\,Offset\,(arcsec)}$', fontsize=14)
ax.tick_params(axis='both', which='major', labelsize=16)
ax.set_adjustable('box-forced')
ax.text(0.7,
0.88,
str(wav) + r'$\rm{\,\mu m}$',
fontsize=16,
color='white',
transform=ax.transAxes)
fig.subplots_adjust(hspace=0, wspace=-0.2)
# Adjust the spaces between the subplots
# plt.tight_layout()
fig.savefig(outdir + print_name + '_cube_plot.png',
format='png',
dpi=300,
bbox_inches='tight')
fig.clf()
def set_physical_props(yso, grids, cell_sizes, save_dir, oversample=3,
dust_out=None, logger=get_logger(__name__)):
"""Calculate and write the physical properties of the model.
Parameters:
yso: the model parameters.
grids: grids where the model will be evaluated
template: filename of the *define_model.c* file.
logger: logging system.
"""
# Load temperature function
if yso.params.get('DEFAULT', 'quantities_from'):
hmodel = yso.params.get('DEFAULT', 'quantities_from')
logger.info('Loading Hyperion model: %s', os.path.basename(hmodel))
hmodel = ModelOutput(os.path.expanduser(hmodel))
q = hmodel.get_quantities()
temperature = np.sum(q['temperature'].array[1:], axis=0)
r, th = q.r*u.cm, q.t*u.rad
temp_func = get_temp_func(yso.params, temperature, r, th)
elif dust_out is not None:
logger.info('Loading Hyperion model: %s', os.path.basename(dust_out))
hmodel = ModelOutput(os.path.expanduser(dust_out))
q = hmodel.get_quantities()
temperature = np.sum(q['temperature'].array[1:], axis=0)
r, th = q.r*u.cm, q.t*u.rad
temp_func = get_temp_func(yso.params, temperature, r, th)
else:
raise NotImplementedError
# Open template
fitslist = []
# Start from smaller to larger grid
for i,grid,cellsz in zip(range(len(grids))[::-1], grids, cell_sizes):
print '='*80
logger.info('Working on grid: %i', i)
logger.info('Grid cell size: %i', cellsz)
x = grid[0]['x'] * grid[1]['x']
y = grid[0]['y'] * grid[1]['y']
z = grid[0]['z'] * grid[1]['z']
xi = np.unique(x)
yi = np.unique(y)
zi = np.unique(z)
# Density and velocity
dens, (vx, vy, vz), temp = phys_oversampled_cart(xi, yi, zi, yso,
temp_func, oversample=oversample if i!=2 else 5, logger=logger)
dens[dens.cgs<=0./u.cm**3] = 10./u.cm**3
temp[np.isnan(temp.value)] = 2.7 * u.K
# Replace the inner region by rebbining the previous grid
if i<len(grids)-1:
# Walls of central cells
j = cell_sizes.index(cellsz)
xlen = cell_sizes[j-1] * len(xprev) * u.au
nxmid = int(xlen.value) / cellsz
xw = np.linspace(-0.5*xlen.value, 0.5*xlen.value, nxmid+1) * u.au
ylen = cell_sizes[j-1] * len(yprev) * u.au
nymid = int(ylen.value) / cellsz
yw = np.linspace(-0.5*ylen.value, 0.5*ylen.value, nymid+1) * u.au
zlen = cell_sizes[j-1] * len(zprev) * u.au
nzmid = int(zlen.value) / cellsz
zw = np.linspace(-0.5*zlen.value, 0.5*zlen.value, nzmid+1) * u.au
if nxmid==nymid==nzmid==0:
logger.warning('The inner grid is smaller than current grid size')
else:
logger.info('The inner %ix%ix%i cells will be replaced', nxmid,
nymid, nzmid)
# Rebin previous grid
# Density
vol_prev = (cell_sizes[j-1]*u.au)**3
vol = (cellsz * u.au)**3
N_cen = vol_prev.cgs * rebin_regular_nd(dens_prev.cgs.value,
zprev.cgs.value, yprev.cgs.value, xprev.cgs.value,
bins=(zw.cgs.value,yw.cgs.value,xw.cgs.value),
statistic='sum') * dens_prev.cgs.unit
dens_cen = N_cen / vol
dens_cen = dens_cen.to(dens.unit)
# Temperature
T_cen = rebin_regular_nd(temp_prev.value * dens_prev.cgs.value,
zprev.cgs.value, yprev.cgs.value, xprev.cgs.value,
bins=(zw.cgs.value,yw.cgs.value, xw.cgs.value),
statistic='sum') * temp_prev.unit * dens_prev.cgs.unit
T_cen = vol_prev.cgs * T_cen / N_cen.cgs
T_cen = T_cen.to(temp.unit)
# Replace
dens[len(zi)/2-nzmid/2:len(zi)/2+nzmid/2,
len(yi)/2-nymid/2:len(yi)/2+nymid/2,
len(xi)/2-nxmid/2:len(xi)/2+nxmid/2] = dens_cen
temp[len(zi)/2-nzmid/2:len(zi)/2+nzmid/2,
len(yi)/2-nymid/2:len(yi)/2+nymid/2,
len(xi)/2-nxmid/2:len(xi)/2+nxmid/2] = T_cen
dens_prev = dens
temp_prev = temp
xprev = xi
yprev = yi
zprev = zi
# Abundance
abundance = yso.abundance(temp)
# Linewidth
amu = 1.660531e-24 * u.g
atoms = yso.params.getfloat('Velocity', 'atoms')
c_s2 = ct.k_B * temp / (atoms * amu)
linewidth = np.sqrt(yso.params.getquantity('Velocity', 'linewidth')**2
+ c_s2)
# Write FITS
fitsnames = write_fits(os.path.expanduser(save_dir),
**{'temp%i'%i: temp.value, 'dens%i'%i: dens.cgs.value,
'vx%i'%i: vx.cgs.value, 'vy%i'%i: vy.cgs.value, 'vz%i'%i:
vz.cgs.value, 'abn%i'%i: abundance,
'lwidth%i'%i: linewidth.cgs.value})
fitslist += fitsnames
return fitslist
def inspect_output(rtout,plotdir,quantities=None):
import numpy as np
import hyperion as hp
import os
import matplotlib.pyplot as plt
from hyperion.model import ModelOutput
import astropy.constants as const
import matplotlib as mat
from matplotlib.colors import LogNorm
# Constants setup
c = const.c.cgs.value
AU = const.au.cgs.value # Astronomical Unit [cm]
pc = const.pc.cgs.value # Parsec [cm]
MS = const.M_sun.cgs.value # Solar mass [g]
LS = const.L_sun.cgs.value # Solar luminosity [erg/s]
RS = const.R_sun.cgs.value # Solar radius [cm]
G = const.G.cgs.value # Gravitational constant [cm^3/g/s^2]
yr = 60*60*24*365. # Years in seconds [s]
PI = np.pi # PI constant
sigma = const.sigma_sb.cgs.value # Stefan-Boltzmann constant
mh = const.m_p.cgs.value + const.m_e.cgs.value # Mass of Hydrogen atom [g]
# Get the dir path of rtout file
indir = os.path.dirname(rtout)
m = ModelOutput(rtout)
grid = m.get_quantities()
rc = 0.5*(grid.r_wall[0:-1]+grid.r_wall[1:])
thetac = 0.5*(grid.t_wall[0:-1]+grid.t_wall[1:])
phic = 0.5*(grid.p_wall[0:-1]+grid.p_wall[1:])
print 'Only works for density now'
if quantities == None:
quantities = input('What quantity you want to take a look at? ')
elif quantities == 'density':
rho = grid[quantities][0].array.T
rho2d = np.sum(rho**2,axis=2)/np.sum(rho,axis=2)
# Read in TSC-only envelope
rho_tsc = np.genfromtxt(indir+'/rhoenv.dat').T
# extrapolate
def poly(x, y, x0, deg=1):
import numpy as np
p = np.polyfit(x, y, deg)
y0 = 0
for i in range(0, len(p)):
y0 = y0 + p[i]*x0**(len(p)-i-1)
return y0
print 'Warning: hard coded infall radius (3500 AU) is used for extrapolating TSC envelope'
r_inf = 3500 * AU
rhoenv = rho_tsc.copy()
for ithetac in range(0, len(thetac)):
rho_dum = np.log10(rhoenv[(rc > 1.1*r_inf) & (np.isnan(rhoenv[:,ithetac]) == False),ithetac])
rc_dum = np.log10(rc[(rc > 1.1*r_inf) & (np.isnan(rhoenv[:,ithetac]) == False)])
# rho_dum_nan = np.log10(rhoenv[(rc > 1.1*r_inf) & (np.isnan(rhoenv[:,ithetac]) == True),ithetac])
rc_dum_nan = np.log10(rc[(rc > 1.1*r_inf) & (np.isnan(rhoenv[:,ithetac]) == True)])
for i in range(0, len(rc_dum_nan)):
rho_extrapol = poly(rc_dum, rho_dum, rc_dum_nan[i])
rhoenv[(np.log10(rc) == rc_dum_nan[i]),ithetac] = 10**rho_extrapol
rho_tsc = rhoenv
rho_tsc3d = np.empty_like(rho)
for i in range(0, len(rho[0,0,:])):
rho_tsc3d[:,:,i] = rho_tsc
rho_tsc2d = np.sum(rho_tsc3d**2,axis=2)/np.sum(rho_tsc3d,axis=2)
rho2d_exp = np.hstack((rho2d,rho2d,rho2d[:,0:1]))
thetac_exp = np.hstack((thetac-PI/2, thetac+PI/2, thetac[0]-PI/2))
# Make the plot
fig = plt.figure(figsize=(8,6))
ax_env = fig.add_subplot(111,projection='polar')
zmin = 1e-22/mh
cmap = 'jet'
img_env = ax_env.pcolormesh(thetac_exp,rc/AU,rho2d_exp/mh,cmap=cmap,norm=LogNorm(vmin=zmin,vmax=np.nanmax(rho2d_exp/mh)))
ax_env.pcolormesh(thetac_exp-PI,rc/AU,rho2d_exp/mh,cmap=cmap,norm=LogNorm(vmin=zmin,vmax=np.nanmax(rho2d_exp/mh)))
ax_env.set_xlabel(r'$\mathrm{Polar~angle~(Degree)}$',fontsize=20)
ax_env.set_ylabel(r'$\mathrm{Radius~(AU)}$',fontsize=20)
ax_env.tick_params(labelsize=20)
# ax_env.set_yticks(np.arange(0,R_env_max/AU,R_env_max/AU/5))
ax_env.set_xticklabels([r'$\mathrm{90^{\circ}}$',r'$\mathrm{45^{\circ}}$',r'$\mathrm{0^{\circ}}$',r'$\mathrm{-45^{\circ}}$',\
r'$\mathrm{-90^{\circ}}$',r'$\mathrm{-135^{\circ}}$',r'$\mathrm{180^{\circ}}$',r'$\mathrm{135^{\circ}}$'])
ax_env.set_ylim([0,100])
ax_env.grid(True)
cb = fig.colorbar(img_env, pad=0.1)
cb.ax.set_ylabel(r'$\mathrm{Averaged~Density~(cm^{-3})}$',fontsize=20)
cb_obj = plt.getp(cb.ax.axes, 'yticklabels')
plt.setp(cb_obj,fontsize=20)
fig.savefig(plotdir+'dust_density.png',format='png',dpi=300,bbox_inches='tight')
fig.clf()
# Radial density plot
fig = plt.figure(figsize=(12,9))
ax = fig.add_subplot(111)
plot_grid = [0,19,39,59,79,99,119,139,159,179,199]
plot_grid = [0,39,79,119,159,199]
# c_range = range(len(plot_grid))
# cNorm = mat.colors.Normalize(vmin=0, vmax=c_range[-1])
# color map 1
# cm1 = plt.get_cmap('Blues')
# scalarMap1 = mat.cm.ScalarMappable(norm=cNorm, cmap=cm1)
# color map 2
# cm2 = plt.get_cmap('Reds')
# scalarMap2 = mat.cm.ScalarMappable(norm=cNorm, cmap=cm2)
alpha = np.linspace(0.3,1,len(plot_grid))
for i in plot_grid:
# colorVal1 = scalarMap1.to_rgba(c_range[plot_grid.index(i)])
# colorVal2 = scalarMap2.to_rgba(c_range[plot_grid.index(i)])
rho_plot, = ax.plot(np.log10(rc/AU), np.log10(rho2d[:,i]),'o',color='b',linewidth=1.5, markersize=3, alpha=alpha[plot_grid.index(i)])
tsc_only, = ax.plot(np.log10(rc/AU), np.log10(rho_tsc2d[:,i]),'-',color='r',linewidth=1.5, markersize=3, alpha=alpha[plot_grid.index(i)])
# lg = plt.legend([wrong, wrong2, wrong_mid, wrong2_mid],\
# [r'$\mathrm{Before~fixing~\theta~(pole)}$',r'$\mathrm{After~fixing~\theta~(pole)}$',r'$\mathrm{Before~fixing~\theta~(midplane)}$',r'$\mathrm{After~fixing~\theta~(midplane)}$'],\
# fontsize=20, numpoints=1)
ax.set_xlabel(r'$\mathrm{log(Radius)~(AU)}$',fontsize=20)
ax.set_ylabel(r'$\mathrm{log(Density)~(g~cm^{-3})}$',fontsize=20)
[ax.spines[axis].set_linewidth(1.5) for axis in ['top','bottom','left','right']]
ax.minorticks_on()
ax.tick_params('both',labelsize=18,width=1.5,which='major',pad=15,length=5)
ax.tick_params('both',labelsize=18,width=1.5,which='minor',pad=15,length=2.5)
ax.set_ylim([-23,-11])
ax.set_xlim([np.log10(0.8),np.log10(10000)])
fig.savefig(plotdir+'radial_density.pdf',format='pdf',dpi=300,bbox_inches='tight')
fig.clf()
def hyperion_image(rtout,
wave,
plotdir,
printname,
dstar=200.,
group=0,
marker=0,
size='full',
convolve=False,
unit=None,
scalebar=None):
# to avoid X server error
import matplotlib as mpl
mpl.use('Agg')
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
import astropy.constants as const
from hyperion.model import ModelOutput
# Package for matching the colorbar
from mpl_toolkits.axes_grid1 import make_axes_locatable
pc = const.pc.cgs.value
if unit == None:
unit = 'erg\,s^{-1}\,cm^{-2}\,Hz^{-1}\,sr^{-1}'
m = ModelOutput(rtout)
# Extract the image.
image = m.get_image(group=group,
inclination=0,
distance=dstar * pc,
units='MJy/sr')
# print np.shape(image.val)
# Open figure and create axes
fig = plt.figure(figsize=(8, 8))
ax = fig.add_subplot(111)
# Find the closest wavelength
iwav = np.argmin(np.abs(wave - image.wav))
# Calculate the image width in arcseconds given the distance used above
# get the max radius
rmax = max(m.get_quantities().r_wall)
w = np.degrees(rmax / image.distance) * 3600.
# Image in the unit of MJy/sr
# Change it into erg/s/cm2/Hz/sr
# factor = 1e-23*1e6
factor = 1
# avoid zero in log
# flip the image, because the setup of inclination is upside down
val = image.val[::-1, :, iwav] * factor + 1e-30
if convolve:
from astropy.convolution import convolve, Gaussian2DKernel
img_res = 2 * w / len(val[:, 0])
kernel = Gaussian2DKernel(0.27 / 2.354 / img_res)
val = convolve(val, kernel)
if size != 'full':
pix_e2c = (w - size / 2.) / w * len(val[:, 0]) / 2
val = val[pix_e2c:-pix_e2c, pix_e2c:-pix_e2c]
w = size / 2.
# This is the command to show the image. The parameters vmin and vmax are
# the min and max levels for the colorscale (remove for default values).
# cmap = sns.cubehelix_palette(start=0.1, rot=-0.7, gamma=0.2, as_cmap=True)
cmap = plt.cm.CMRmap
im = ax.imshow(
val,
# norm=mpl.colors.LogNorm(vmin=1.515e-01, vmax=4.118e+01),
norm=mpl.colors.LogNorm(vmin=1e-04, vmax=1e+01),
cmap=cmap,
origin='lower',
extent=[-w, w, -w, w],
aspect=1)
# draw the flux extraction regions
# x = 100
# y = 100
# area = x*y / 4.25e10
# offset = 50
#
# pos_n = (len(val[0,:])/2.-1,len(val[0,:])/2.-1 + offset*len(val[0,:])/2/w)
# pos_s = (len(val[0,:])/2.-1,len(val[0,:])/2.-1 - offset*len(val[0,:])/2/w)
#
# import matplotlib.patches as patches
# ax.add_patch(patches.Rectangle((-x/2, -y), x, y, fill=False, edgecolor='lime'))
# ax.add_patch(patches.Rectangle((-x/2, 0), x, y, fill=False, edgecolor='lime'))
# plot the marker for center position by default or user input offset
ax.plot([0], [-marker], '+', color='lime', markersize=10, mew=2)
ax.set_xlim([-w, w])
ax.set_ylim([-w, w])
# ax.plot([0],[-10], '+', color='m', markersize=10, mew=2)
print(w)
# plot scalebar
if scalebar != None:
ax.plot([0.85 * w - scalebar, 0.85 * w], [-0.8 * w, -0.8 * w],
color='w',
linewidth=3)
# add text
ax.text(0.85 * w - scalebar / 2,
-0.9 * w,
r'$\rm{' + str(scalebar) + "\,arcsec}$",
color='w',
fontsize=18,
fontweight='bold',
ha='center')
# fix the tick label font
ticks_font = mpl.font_manager.FontProperties(family='STIXGeneral', size=16)
for label in ax.get_xticklabels():
label.set_fontproperties(ticks_font)
for label in ax.get_yticklabels():
label.set_fontproperties(ticks_font)
# Colorbar setting
# create an axes on the right side of ax. The width of cax will be 5%
# of ax and the padding between cax and ax will be fixed at 0.05 inch.
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
cb = fig.colorbar(im, cax=cax)
cb.solids.set_edgecolor("face")
cb.ax.minorticks_on()
cb.ax.set_ylabel(r'$\rm{Intensity\,[' + unit + ']}$', fontsize=16)
cb.ax.tick_params('both', width=1.5, which='major', length=3)
cb.ax.tick_params('both', width=1.5, which='minor', length=2)
cb_obj = plt.getp(cb.ax.axes, 'yticklabels')
plt.setp(cb_obj, fontsize=18)
# fix the tick label font
ticks_font = mpl.font_manager.FontProperties(family='STIXGeneral', size=18)
for label in cb.ax.get_yticklabels():
label.set_fontproperties(ticks_font)
ax.set_xlabel(r'$\rm{RA\,Offset\,[arcsec]}$', fontsize=16)
ax.set_ylabel(r'$\rm{Dec\,Offset\,[arcsec]}$', fontsize=16)
# set the frame color
ax.spines['bottom'].set_color('white')
ax.spines['top'].set_color('white')
ax.spines['left'].set_color('white')
ax.spines['right'].set_color('white')
ax.tick_params(axis='both',
which='major',
width=1.5,
labelsize=18,
color='white',
length=5)
ax.text(0.7,
0.88,
str(wave) + r'$\rm{\,\mu m}$',
fontsize=20,
color='white',
transform=ax.transAxes)
fig.savefig(plotdir + printname + '_image_' + str(wave) + '.pdf',
format='pdf',
dpi=300,
bbox_inches='tight')
fig.clf()
import numpy as np
import glob
import matplotlib.pyplot as plt
from hyperion.model import ModelOutput
import sphviewer as sph
from sphviewer.tools import QuickView
run = '/blue/narayanan/c.lovell/simba/m100n1024/run_sed/snap_078_hires_orthogonal'
groupID = 3
fname = glob.glob('%s/gal_%i/snap078.galaxy*.rtout.sed' % (run, groupID))[0]
m = ModelOutput(fname)
_pf = m.get_quantities().to_yt()
ad = _pf.all_data()
_kpc = 3.08568025e+21
_temp = np.array(ad.to_dataframe('temperature'))
_mass = np.array(ad.to_dataframe('cell_mass'))
_density = np.array(ad.to_dataframe('density'))
_radius = np.array(ad.to_dataframe('radius')) / _kpc
_x = np.array(ad.to_dataframe('x')) / _kpc
_y = np.array(ad.to_dataframe('y')) / _kpc
_z = np.array(ad.to_dataframe('z')) / _kpc
_coods = np.squeeze(np.array([_x, _z, _y])).T
radius = 15 # kpc
mask = _radius < radius
print("radius:", radius)
import numpy as np
from hyperion.model import ModelOutput
import matplotlib.pyplot as plt
from yt.mods import write_bitmap, ColorTransferFunction
plt.rcParams['font.family'] = 'Arial'
# Read in model from Hyperion
m = ModelOutput('pla704850_lev7_129.rtout')
grid = m.get_quantities()
# Convert quantities to yt
pf = grid.to_yt()
# Instantiate the ColorTransferfunction.
tmin, tmax = 1.3, 2.3
tf_temp = ColorTransferFunction((tmin, tmax))
dmin, dmax = -20, -16
tf_dens = ColorTransferFunction((dmin, dmax))
# Set up the camera parameters: center, looking direction, width, resolution
c = (pf.domain_right_edge + pf.domain_left_edge) / 2.0
L = np.array([1.0, 1.0, 1.0])
W = 0.7 / pf["unitary"]
N = 512
# Create camera objects
cam_temp = pf.h.camera(c, L, W, N, tf_temp,
def plot_results(cli):
file = filename(cli, "plot")
file += ".rtout"
#
# Read in the model:
#
model = ModelOutput(file)
los = [0 for k in range(3)]
los[0] = '30degree'
los[1] = '80degree'
los[2] = '88degree'
if(cli.mode == "images"):
#
# Extract the quantities
#
g = model.get_quantities()
#
# Get the wall positions:
#
ww = g.w_wall / pc
zw = g.z_wall / pc
pw = g.p_wall
grid_Nw = len(ww) - 1
grid_Nz = len(zw) - 1
grid_Np = len(pw) - 1
#
# Graphics:
#
fig = plt.figure()
Imaxp = [0 for i in range(5)]
Imaxp[0] = 1e-15 # in W/cm^2
Imaxp[1] = 1e-14 # in W/cm^2
Imaxp[2] = 1e-15 # in W/cm^2
Imaxp[3] = 1e-15 # in W/cm^2
Imaxp[4] = 1e-18 # in W/cm^2
for k in range(0, 3):
if(cli.verbose):
print("Group: ", k)
image = model.get_image(distance=1e+7*pc, units='ergs/cm^2/s', inclination=0, component='total', group=k)
#source_emit = model.get_image(distance=1e+7*pc, units='MJy/sr', inclination=0, component='source_emit', group=k)
#dust_emit = model.get_image(distance=1e+7*pc, units='MJy/sr', inclination=0, component='dust_emit' , group=k)
#source_scat = model.get_image(distance=1e+7*pc, units='MJy/sr', inclination=0, component='source_scat', group=k)
#dust_scat = model.get_image(distance=1e+7*pc, units='MJy/sr', inclination=0, component='dust_scat' , group=k)
if(cli.verbose):
print(" Data cube: ", image.val.shape)
print(" Wavelengths =", image.wav)
print(" Uncertainties =", image.unc)
image_Nx=image.val.shape[0]
image_Ny=image.val.shape[1]
Nwavelength=image.val.shape[2]
if(cli.verbose):
print(" Image Nx =", image_Nx)
print(" Image Ny =", image_Ny)
print(" Nwavelength =", Nwavelength)
for i in range(0, Nwavelength):
if(cli.verbose):
print(" Image #", i,":")
print(" Wavelength =", image.wav[i])
image.val[:, :, i] *= 1e-4 # in W/m^2
#Imin = np.min(image.val[:, :, i])
#Imax = np.max(image.val[:, :, i])
#Imax = Imaxp[i]
#Imin = Imax/1e+20
Imax = np.max(image.val[:, :, i])/5
Imin = 0.0
if(cli.verbose):
print(" Intensity min data values =", np.min(image.val[:, :, i]))
print(" Intensity max data values =", np.max(image.val[:, :, i]))
print(" Intensity min color-table =", Imin)
print(" Intensity max color-table =", Imax)
#ax = fig.add_subplot(2, 1, 2)
ax = fig.add_subplot(1, 1, 1)
# 'hot', see http://wiki.scipy.org/Cookbook/Matplotlib/Show_colormaps
ax.imshow(image.val[:, :, i], vmin=Imin, vmax=Imax, cmap=plt.cm.hot, origin='lower')
ax.set_xticks([0,100,200,300,400,500], minor=False)
ax.set_yticks([0,100,200,300,400,500], minor=False)
ax.set_xlabel('x (pixel)')
ax.set_ylabel('y (pixel)')
ax.set_title(str(image.wav[i]) + ' microns' + '\n' + los[k], y=0.88, x=0.5, color='white')
#ax = fig.add_subplot(2, 1, 1)
#ax.imshow([np.logspace(np.log10(Imin+1e-10),np.log10(Imax/10),100),np.logspace(np.log10(Imin+1e-10),np.log10(Imax/10),100)], vmin=Imin, vmax=Imax/10, cmap=plt.cm.gist_heat)
#ax.set_xticks(np.logspace(np.log10(Imin+1e-10),np.log10(Imax/10),1), minor=False)
##ax.set_xticks(np.linspace(np.log10(Imin+1e-10),np.log10(Imax/10),10), minor=False)
#ax.set_yticks([], minor=False)
#ax.set_xlabel('flux (MJy/sr)')
#x = plt.colorbar()
#print(x)
file = filename(cli, "plot")
file += "_wavelength=" + str(image.wav[i]) + "micron_los=" + los[k] + ".png"
fig.savefig(file, bbox_inches='tight')
if(cli.verbose):
print(" The image graphics was written to", file)
plt.clf()
elif(cli.mode == "seds"):
#
# Graphics:
#
fig = plt.figure()
for k in range(0, 3):
if(cli.verbose):
print("Group: ", k)
sed = model.get_sed(distance=1e+7*pc, inclination=0, aperture=-1, group=k)
#units='ergs/cm^2/s' # = default, if distance is specified
ax = fig.add_subplot(1, 1, 1)
ax.loglog(sed.wav, sed.val)
ax.set_xlabel(r'$\lambda$ [$\mu$m]')
ax.set_ylabel(r'$\lambda F_\lambda$ [ergs/s/cm$^2$]')
ax.set_xlim(0.09, 1000.0)
ax.set_ylim(1.e-13, 1.e-7)
file = filename(cli, "plot")
file += "_los=" + los[k] + ".png"
fig.savefig(file)
if(cli.verbose):
print(" The sed graphics was written to", file)
plt.clf()
#
# Data files:
#
for k in range(0, 3):
sed = model.get_sed(distance=1e+7*pc, inclination=0, aperture=-1, group=k)
file = filename(cli, "plot")
file += "_los=" + los[k] + ".dat"
sedtable = open(file, 'w')
sedtable.write("# wavelength [micron] - flux [erg cm^-2 s^-1]\n")
for lp in range(0, len(sed.wav)):
l = len(sed.wav)-lp-1
line = str("%.4e" % sed.wav[l]) + " " + str("%.4e" % sed.val[l]) + "\n"
sedtable.write(line)
sedtable.close()
else:
print("ERROR: The specified mode", mode, "is not available. Use 'images' or 'seds' only.")
from mpl_toolkits.axes_grid1 import make_axes_locatable
# constant setup
pc = const.pc.cgs.value
m = ModelOutput(filename)
image = m.get_image(group=22,
inclination=0,
distance=dist * pc,
units='MJy/sr')
# Find the closest wavelength
iwav = np.argmin(np.abs(wave - image.wav))
# Calculate the image width in arcseconds given the distance used above
# get the max radius
rmax = max(m.get_quantities().r_wall)
w = np.degrees(rmax / image.distance) * 3600.
# Image in the unit of MJy/sr
# Change it into erg/s/cm2/Hz/sr
factor = 1e-23 * 1e6
# avoid zero in log
# flip the image, because the setup of inclination is upside down
val = image.val[::-1, :, iwav] * factor + 1e-30
# plot the image
fig = plt.figure(figsize=(8, 6))
ax = fig.add_subplot(111)
cmap = plt.cm.CMRmap
im = ax.imshow(np.log10(val),
def run_thermal_hyperion(self, nphot=1e6, mrw=False, pda=False, \
niterations=20, percentile=99., absolute=2.0, relative=1.02, \
max_interactions=1e8, mpi=False, nprocesses=None):
d = []
for i in range(len(self.grid.dust)):
d.append(IsotropicDust( \
self.grid.dust[i].nu[::-1].astype(numpy.float64), \
self.grid.dust[i].albedo[::-1].astype(numpy.float64), \
self.grid.dust[i].kext[::-1].astype(numpy.float64)))
m = HypModel()
if (self.grid.coordsystem == "cartesian"):
m.set_cartesian_grid(self.grid.w1*AU, self.grid.w2*AU, \
self.grid.w3*AU)
elif (self.grid.coordsystem == "cylindrical"):
m.set_cylindrical_polar_grid(self.grid.w1*AU, self.grid.w3*AU, \
self.grid.w2)
elif (self.grid.coordsystem == "spherical"):
m.set_spherical_polar_grid(self.grid.w1*AU, self.grid.w2, \
self.grid.w3)
for i in range(len(self.grid.density)):
if (self.grid.coordsystem == "cartesian"):
m.add_density_grid(numpy.transpose(self.grid.density[i], \
axes=(2,1,0)), d[i])
if (self.grid.coordsystem == "cylindrical"):
m.add_density_grid(numpy.transpose(self.grid.density[i], \
axes=(1,2,0)), d[i])
if (self.grid.coordsystem == "spherical"):
m.add_density_grid(numpy.transpose(self.grid.density[i], \
axes=(2,1,0)), d[i])
sources = []
for i in range(len(self.grid.stars)):
sources.append(m.add_spherical_source())
sources[i].luminosity = self.grid.stars[i].luminosity * L_sun
sources[i].radius = self.grid.stars[i].radius * R_sun
sources[i].temperature = self.grid.stars[i].temperature
m.set_mrw(mrw)
m.set_pda(pda)
m.set_max_interactions(max_interactions)
m.set_n_initial_iterations(niterations)
m.set_n_photons(initial=nphot, imaging=0)
m.set_convergence(True, percentile=percentile, absolute=absolute, \
relative=relative)
m.write("temp.rtin")
m.run("temp.rtout", mpi=mpi, n_processes=nprocesses)
n = ModelOutput("temp.rtout")
grid = n.get_quantities()
self.grid.temperature = []
temperature = grid.quantities['temperature']
for i in range(len(temperature)):
if (self.grid.coordsystem == "cartesian"):
self.grid.temperature.append(numpy.transpose(temperature[i], \
axes=(2,1,0)))
if (self.grid.coordsystem == "cylindrical"):
self.grid.temperature.append(numpy.transpose(temperature[i], \
axes=(2,0,1)))
if (self.grid.coordsystem == "spherical"):
self.grid.temperature.append(numpy.transpose(temperature[i], \
axes=(2,1,0)))
os.system("rm temp.rtin temp.rtout")
def azimuthal_avg_radial_intensity(wave,
imgpath,
source_center,
rtout,
plotname,
annulus_width=10,
group=8,
dstar=200.):
import numpy as np
import matplotlib as mpl
# to avoid X server error
mpl.use('Agg')
from astropy.io import ascii, fits
import matplotlib.pyplot as plt
from photutils import aperture_photometry as ap
from photutils import CircularAperture, CircularAnnulus
from astropy import units as u
from astropy.coordinates import SkyCoord
from astropy import wcs
from hyperion.model import ModelOutput
import astropy.constants as const
import os
pc = const.pc.cgs.value
AU = const.au.cgs.value
# source_center = '12 01 36.3 -65 08 53.0'
# Read in data and set up coversions
im_hdu = fits.open(imgpath)
im = im_hdu[1].data
# error
if (wave < 200.0) & (wave > 70.0):
im_err = im_hdu[5].data
elif (wave > 200.0) & (wave < 670.0):
im_err = im_hdu[5].data
else:
im_err_exten = raw_input(
'The extension that includes the image error: ')
im_err = im_hdu[int(im_err_exten)].data
w = wcs.WCS(im_hdu[1].header)
coord = SkyCoord(source_center, unit=(u.hourangle, u.deg))
pixcoord = w.wcs_world2pix(coord.ra.degree, coord.dec.degree, 1)
pix2arcsec = abs(im_hdu[1].header['CDELT1']) * 3600.
# convert intensity unit from MJy/sr to Jy/pixel
factor = 1e6 / 4.25e10 * abs(
im_hdu[1].header['CDELT1'] * im_hdu[1].header['CDELT2']) * 3600**2
# radial grid in arcsec
# annulus_width = 10
r = np.arange(10, 200, annulus_width, dtype=float)
I = np.empty_like(r[:-1])
I_err = np.empty_like(r[:-1])
# iteration
for ir in range(len(r) - 1):
aperture = CircularAnnulus((pixcoord[0], pixcoord[1]),
r_in=r[ir] / pix2arcsec,
r_out=r[ir + 1] / pix2arcsec)
# print aperture.r_in
phot = ap(im, aperture, error=im_err)
I[ir] = phot['aperture_sum'].data * factor / aperture.area()
I_err[ir] = phot['aperture_sum_err'].data * factor / aperture.area()
# print r[ir], I[ir]
# read in from RTout
rtout = ModelOutput(rtout)
# setting up parameters
# dstar = 200.
# group = 8
# wave = 500.0
im = rtout.get_image(group=group,
inclination=0,
distance=dstar * pc,
units='Jy',
uncertainties=True)
# Find the closest wavelength
iwav = np.argmin(np.abs(wave - im.wav))
# avoid zero when log, and flip the image
val = im.val[::-1, :, iwav]
unc = im.unc[::-1, :, iwav]
w = np.degrees(max(rtout.get_quantities().r_wall) / im.distance) * 3600
npix = len(val[:, 0])
pix2arcsec = 2 * w / npix
# radial grid in arcsec
# annulus_width = 10
r = np.arange(10, 200, annulus_width, dtype=float)
I_sim = np.empty_like(r[:-1])
I_sim_err = np.empty_like(r[:-1])
# iteration
for ir in range(len(r) - 1):
aperture = CircularAnnulus((npix / 2. + 0.5, npix / 2. + 0.5),
r_in=r[ir] / pix2arcsec,
r_out=r[ir + 1] / pix2arcsec)
# print aperture.r_in
phot = ap(val, aperture, error=unc)
I_sim[ir] = phot['aperture_sum'].data / aperture.area()
I_sim_err[ir] = phot['aperture_sum_err'].data / aperture.area()
# print r[ir], I_sim[ir]
# write the numbers into file
foo = open(plotname + '_radial_profile_' + str(wave) + 'um.txt', 'w')
# print some header info
foo.write('# wavelength ' + str(wave) + ' um \n')
foo.write('# image file ' + os.path.basename(imgpath) + ' \n')
foo.write('# annulus width ' + str(annulus_width) + ' arcsec \n')
# write profiles
foo.write('r_in[arcsec] \t I \t I_err \t I_sim \t I_sim_err \n')
for i in range(len(I)):
foo.write('%f \t %e \t %e \t %e \t %e \n' %
(r[i], I[i], I_err[i], I_sim[i], I_sim_err[i]))
foo.close()
# plot
fig = plt.figure(figsize=(8, 6))
ax = fig.add_subplot(111)
I_sim_hi = np.log10(
(I_sim + I_sim_err) / I_sim.max()) - np.log10(I_sim / I_sim.max())
I_sim_low = np.log10(I_sim / I_sim.max()) - np.log10(
(I_sim - I_sim_err) / I_sim.max())
I_hi = np.log10((I + I_err) / I.max()) - np.log10(I / I.max())
I_low = np.log10(I / I.max()) - np.log10((I - I_err) / I.max())
i_sim = ax.errorbar(np.log10(r[:-1] * dstar),
np.log10(I_sim / I_sim.max()),
yerr=(I_sim_low, I_sim_hi),
marker='o',
linestyle='-',
mec='None',
markersize=10)
i = ax.errorbar(np.log10(r[:-1] * dstar),
np.log10(I / I.max()),
yerr=(I_low, I_hi),
marker='o',
linestyle='-',
mec='None',
markersize=10)
ax.legend([i, i_sim], [r'$\rm{observation}$', r'$\rm{simulation}$'],
fontsize=16,
numpoints=1,
loc='upper right')
[
ax.spines[axis].set_linewidth(1.5)
for axis in ['top', 'bottom', 'left', 'right']
]
ax.minorticks_on()
ax.tick_params('both',
labelsize=18,
width=1.5,
which='major',
pad=10,
length=5)
ax.tick_params('both',
labelsize=18,
width=1.5,
which='minor',
pad=10,
length=2.5)
ax.set_xlabel(r'$\rm{log(Radius)\,[AU]}$', fontsize=18)
ax.set_ylabel(r'$\rm{log(I\,/\,I_{max})}$', fontsize=18)
# fix the tick label font
ticks_font = mpl.font_manager.FontProperties(family='STIXGeneral', size=18)
for label in ax.get_xticklabels():
label.set_fontproperties(ticks_font)
for label in ax.get_yticklabels():
label.set_fontproperties(ticks_font)
fig.savefig(plotname + '_radial_profile_' + str(wave) + 'um.pdf',
format='pdf',
dpi=300,
bbox_inches='tight')
fig.clf()
def extract_hyperion(filename,
indir=None,
outdir=None,
dstar=200.0,
aperture=None,
save=True,
filter_func=False,
plot_all=False,
clean=False,
exclude_wl=[],
log=True,
image=True,
obj='BHR71',
print_data_w_aper=False,
mag=1.5):
"""
filename: The path to Hyperion output file
indir: The path to the directory which contains observations data
outdir: The path to the directory for storing extracted plots and ASCII files
"""
def l_bol(wl, fv, dstar):
import numpy as np
import astropy.constants as const
# wavelength unit: um
# Flux density unit: Jy
# constants setup
#
c = const.c.cgs.value
pc = const.pc.cgs.value
PI = np.pi
SL = const.L_sun.cgs.value
# Convert the unit from Jy to erg s-1 cm-2 Hz-1
fv = np.array(fv) * 1e-23
freq = c / (1e-4 * np.array(wl))
diff_dum = freq[1:] - freq[0:-1]
freq_interpol = np.hstack(
(freq[0:-1] + diff_dum / 2.0, freq[0:-1] + diff_dum / 2.0, freq[0],
freq[-1]))
freq_interpol = freq_interpol[np.argsort(freq_interpol)[::-1]]
fv_interpol = np.empty(len(freq_interpol))
# calculate the histogram style of spectrum
#
for i in range(0, len(fv)):
if i == 0:
fv_interpol[i] = fv[i]
else:
fv_interpol[2 * i - 1] = fv[i - 1]
fv_interpol[2 * i] = fv[i]
fv_interpol[-1] = fv[-1]
dv = freq_interpol[0:-1] - freq_interpol[1:]
dv = np.delete(dv, np.where(dv == 0))
fv = fv[np.argsort(freq)]
freq = freq[np.argsort(freq)]
return (np.trapz(fv, freq) * 4. * PI * (dstar * pc)**2) / SL
# function for properly calculating uncertainty of spectrophotometry value
def unc_spectrophoto(wl, unc, trans):
# adopting smiliar procedure as Trapezoidal rule
# (b-a) * [ f(a) + f(b) ] / 2
#
return (np.sum(trans[:-1]**2 * unc[:-1]**2 * (wl[1:] - wl[:-1])**2) /
np.trapz(trans, x=wl)**2)**0.5
# to avoid X server error
import matplotlib as mpl
mpl.use('Agg')
#
import matplotlib.pyplot as plt
import numpy as np
import os
from hyperion.model import ModelOutput, Model
from scipy.interpolate import interp1d
from hyperion.util.constants import pc, c, lsun, au
from astropy.io import ascii
import sys
from phot_filter import phot_filter
from get_obs import get_obs
# Open the model
m = ModelOutput(filename)
# Read in the observation data and calculate the noise & variance
if indir == None:
indir = raw_input('Path to the observation data: ')
if outdir == None:
outdir = raw_input('Path for the output: ')
# assign the file name from the input file
print_name = os.path.splitext(os.path.basename(filename))[0]
# use a canned function to extract observational data
obs_data = get_obs(indir, obj=obj) # unit in um, Jy
wl_tot, flux_tot, unc_tot = obs_data['spec']
flux_tot = flux_tot * 1e-23 # convert unit from Jy to erg s-1 cm-2 Hz-1
unc_tot = unc_tot * 1e-23
l_bol_obs = l_bol(wl_tot, flux_tot * 1e23, dstar)
wl_phot, flux_phot, flux_sig_phot = obs_data['phot']
flux_phot = flux_phot * 1e-23 # convert unit from Jy to erg s-1 cm-2 Hz-1
flux_sig_phot = flux_sig_phot * 1e-23
if aperture == None:
aperture = {'wave': [3.6, 4.5, 5.8, 8.0, 8.5, 9, 9.7, 10, 10.5, 11, 16, 20, 24, 30, 70, 100, 160, 250, 350, 500, 850],\
'aperture': [7.2, 7.2, 7.2, 7.2, 7.2, 7.2, 7.2, 7.2, 7.2, 7.2, 20.4, 20.4, 20.4, 20.4, 24.5, 24.5, 24.5, 24.5, 24.5, 24.5, 24.5]}
# assign wl_aper and aper from dictionary of aperture
wl_aper = aperture['wave']
aper = aperture['aperture']
# create the non-repetitive aperture list and index array
aper_reduced = sorted(list(set(aper)))
index_reduced = np.arange(
1,
len(aper_reduced) +
1) # '+1': the zeroth slice corresponds to infinite aperture
# Create the plot
fig = plt.figure(figsize=(8 * mag, 6 * mag))
ax_sed = fig.add_subplot(1, 1, 1)
# Plot the observed SED
if not clean:
color_seq = ['Green', 'Red', 'Black']
else:
color_seq = ['DimGray', 'DimGray', 'DimGray']
# plot the observations
# plot in log scale
if log:
pacs, = ax_sed.plot(
np.log10(wl_tot[(wl_tot > 40) & (wl_tot < 190.31)]),
np.log10(c / (wl_tot[(wl_tot > 40) & (wl_tot < 190.31)] * 1e-4) *
flux_tot[(wl_tot > 40) & (wl_tot < 190.31)]),
'-',
color=color_seq[0],
linewidth=1.5 * mag,
alpha=0.7)
spire, = ax_sed.plot(np.log10(wl_tot[wl_tot > 194]),
np.log10(c / (wl_tot[wl_tot > 194] * 1e-4) *
flux_tot[wl_tot > 194]),
'-',
color=color_seq[1],
linewidth=1.5 * mag,
alpha=0.7)
irs, = ax_sed.plot(np.log10(wl_tot[wl_tot < 40]),
np.log10(c / (wl_tot[wl_tot < 40] * 1e-4) *
flux_tot[wl_tot < 40]),
'-',
color=color_seq[2],
linewidth=1.5 * mag,
alpha=0.7)
photometry, = ax_sed.plot(np.log10(wl_phot),
np.log10(c / (wl_phot * 1e-4) * flux_phot),
's',
mfc='DimGray',
mec='k',
markersize=8)
# plot the observed photometry data
ax_sed.errorbar(
np.log10(wl_phot),
np.log10(c / (wl_phot * 1e-4) * flux_phot),
yerr=[
np.log10(c / (wl_phot * 1e-4) * flux_phot) -
np.log10(c / (wl_phot * 1e-4) * (flux_phot - flux_sig_phot)),
np.log10(c / (wl_phot * 1e-4) * (flux_phot + flux_sig_phot)) -
np.log10(c / (wl_phot * 1e-4) * flux_phot)
],
fmt='s',
mfc='DimGray',
mec='k',
markersize=8)
# plot in normal scale
else:
pacs, = ax_sed.plot(
np.log10(wl_tot[(wl_tot > 40) & (wl_tot < 190.31)]),
c / (wl_tot[(wl_tot > 40) & (wl_tot < 190.31)] * 1e-4) *
flux_tot[(wl_tot > 40) & (wl_tot < 190.31)],
'-',
color=color_seq[0],
linewidth=1.5 * mag,
alpha=0.7)
spire, = ax_sed.plot(np.log10(wl_tot[wl_tot > 194]),
c / (wl_tot[wl_tot > 194] * 1e-4) *
flux_tot[wl_tot > 194],
'-',
color=color_seq[1],
linewidth=1.5 * mag,
alpha=0.7)
irs, = ax_sed.plot(np.log10(wl_tot[wl_tot < 40]),
c / (wl_tot[wl_tot < 40] * 1e-4) *
flux_tot[wl_tot < 40],
'-',
color=color_seq[2],
linewidth=1.5 * mag,
alpha=0.7)
photometry, = ax_sed.plot(wl_phot,
c / (wl_phot * 1e-4) * flux_phot,
's',
mfc='DimGray',
mec='k',
markersize=8)
# plot the observed photometry data
ax_sed.errorbar(
np.log10(wl_phot),
c / (wl_phot * 1e-4) * flux_phot,
yerr=[
c / (wl_phot * 1e-4) * flux_phot - c / (wl_phot * 1e-4) *
(flux_phot - flux_sig_phot), c / (wl_phot * 1e-4) *
(flux_phot + flux_sig_phot) - c / (wl_phot * 1e-4) * flux_phot
],
fmt='s',
mfc='DimGray',
mec='k',
markersize=8)
# if keyword 'clean' is not set, print L_bol derived from observations at upper right corner.
if not clean:
ax_sed.text(0.75,
0.9,
r'$\rm{L_{bol}= %5.2f L_{\odot}}$' % l_bol_obs,
fontsize=mag * 16,
transform=ax_sed.transAxes)
# getting SED with infinite aperture
sed_inf = m.get_sed(group=0,
inclination=0,
aperture=-1,
distance=dstar * pc,
uncertainties=True)
# plot the simulated SED with infinite aperture
if clean == False:
sim, = ax_sed.plot(np.log10(sed_inf.wav),
np.log10(sed_inf.val),
'-',
color='GoldenRod',
linewidth=0.5 * mag)
ax_sed.fill_between(np.log10(sed_inf.wav),
np.log10(sed_inf.val - sed_inf.unc),
np.log10(sed_inf.val + sed_inf.unc),
color='GoldenRod',
alpha=0.5)
#######################################
# get fluxes with different apertures #
#######################################
# this is non-reduced wavelength array because this is for printing out fluxes at all channels specified by users
flux_aper = np.zeros_like(wl_aper, dtype=float)
unc_aper = np.zeros_like(wl_aper, dtype=float)
a = np.zeros_like(wl_aper) + 1
color_list = plt.cm.jet(np.linspace(0, 1, len(wl_aper) + 1))
for i in range(0, len(wl_aper)):
# occasionally users might want not to report some wavelength channels
if wl_aper[i] in exclude_wl:
continue
# getting simulated SED from Hyperion output. (have to match with the reduced index)
sed_dum = m.get_sed(
group=index_reduced[np.where(aper_reduced == aper[i])],
inclination=0,
aperture=-1,
distance=dstar * pc,
uncertainties=True)
# plot the whole SED from this aperture (optional)
if plot_all == True:
ax_sed.plot(np.log10(sed_dum.wav),
np.log10(sed_dum.val),
'-',
color=color_list[i])
ax_sed.fill_between(np.log10(sed_dum.wav), np.log10(sed_dum.val-sed_dum.unc), np.log10(sed_dum.val+sed_dum.unc),\
color=color_list[i], alpha=0.5)
# Extracting spectrophotometry values from simulated SED
# Not using the photometry filer function to extract spectrophotometry values
# sort by wavelength first.
sort_wl = np.argsort(sed_dum.wav)
val_sort = sed_dum.val[sort_wl]
unc_sort = sed_dum.unc[sort_wl]
wav_sort = sed_dum.wav[sort_wl]
# Before doing that, convert vSv to F_lambda
flux_dum = val_sort / wav_sort
unc_dum = unc_sort / wav_sort
# If no using filter function to extract the spectrophotometry,
# then use the spectral resolution.
if filter_func == False:
# use a rectangle function the average the simulated SED
# apply the spectral resolution
if (wl_aper[i] < 50.) & (wl_aper[i] >= 5):
res = 60.
elif wl_aper[i] < 5:
res = 10.
else:
res = 1000.
ind = np.where((wav_sort < wl_aper[i] * (1 + 1. / res))
& (wav_sort > wl_aper[i] * (1 - 1. / res)))
if len(ind[0]) != 0:
flux_aper[i] = np.mean(flux_dum[ind])
unc_aper[i] = np.mean(unc_dum[ind])
else:
f = interp1d(wav_sort, flux_dum)
f_unc = interp1d(wav_sort, unc_dum)
flux_aper[i] = f(wl_aper[i])
unc_aper[i] = f_unc(wl_aper[i])
# Using photometry filter function to extract spectrophotometry values
else:
# apply the filter function
# decide the filter name
if wl_aper[i] == 70:
fil_name = 'Herschel PACS 70um'
elif wl_aper[i] == 100:
fil_name = 'Herschel PACS 100um'
elif wl_aper[i] == 160:
fil_name = 'Herschel PACS 160um'
elif wl_aper[i] == 250:
fil_name = 'Herschel SPIRE 250um'
elif wl_aper[i] == 350:
fil_name = 'Herschel SPIRE 350um'
elif wl_aper[i] == 500:
fil_name = 'Herschel SPIRE 500um'
elif wl_aper[i] == 3.6:
fil_name = 'IRAC Channel 1'
elif wl_aper[i] == 4.5:
fil_name = 'IRAC Channel 2'
elif wl_aper[i] == 5.8:
fil_name = 'IRAC Channel 3'
elif wl_aper[i] == 8.0:
fil_name = 'IRAC Channel 4'
elif wl_aper[i] == 24:
fil_name = 'MIPS 24um'
elif wl_aper[i] == 850:
fil_name = 'SCUBA 850WB'
else:
fil_name = None
if fil_name != None:
filter_func = phot_filter(fil_name, indir)
# Simulated SED should have enough wavelength coverage for applying photometry filters.
f = interp1d(wav_sort, flux_dum)
f_unc = interp1d(wav_sort, unc_dum)
flux_aper[i] = np.trapz(f(filter_func['wave']/1e4)*\
filter_func['transmission'],x=filter_func['wave']/1e4 )/\
np.trapz(filter_func['transmission'], x=filter_func['wave']/1e4)
# fix a bug
unc_aper[i] = unc_spectrophoto(
filter_func['wave'] / 1e4,
f_unc(filter_func['wave'] / 1e4),
filter_func['transmission'])
else:
# use a rectangle function the average the simulated SED
# apply the spectral resolution
if (wl_aper[i] < 50.) & (wl_aper[i] >= 5):
res = 60.
elif wl_aper[i] < 5:
res = 10.
else:
res = 1000.
ind = np.where((wav_sort < wl_aper[i] * (1 + 1. / res))
& (wav_sort > wl_aper[i] * (1 - 1. / res)))
if len(ind[0]) != 0:
flux_aper[i] = np.mean(flux_dum[ind])
unc_aper[i] = np.mean(unc_dum[ind])
else:
f = interp1d(wav_sort, flux_dum)
f_unc = interp1d(wav_sort, unc_dum)
flux_aper[i] = f(wl_aper[i])
unc_aper[i] = f_unc(wl_aper[i])
# temperory step: solve issue of uncertainty greater than the value
for i in range(len(wl_aper)):
if unc_aper[i] >= flux_aper[i]:
unc_aper[i] = flux_aper[i] - 1e-20
###########################
# Observations Extraction #
###########################
# perform the same procedure of flux extraction of aperture flux with observed spectra
# wl_aper = np.array(wl_aper, dtype=float)
obs_aper_wl = wl_aper[(wl_aper >= min(wl_tot)) & (wl_aper <= max(wl_tot))]
obs_aper_flux = np.zeros_like(obs_aper_wl)
obs_aper_unc = np.zeros_like(obs_aper_wl)
# have change the simulation part to work in F_lambda for fliter convolution
# flux_tot and unc_tot have units of erg/s/cm2/Hz. Need to convert it to F_lambda (erg/s/cm2/um)
fnu2fl = c / (wl_tot * 1e-4) / wl_tot
#
# wl_tot and flux_tot are already hstacked and sorted by wavelength
for i in range(0, len(obs_aper_wl)):
# sometime users want not report some wavelength channels
if obs_aper_wl[i] in exclude_wl:
continue
if filter_func == False:
# use a rectangle function the average the simulated SED
# apply the spectral resolution
if (obs_aper_wl[i] < 50.) & (obs_aper_wl[i] >= 5):
res = 60.
elif obs_aper_wl[i] < 5:
res = 10.
else:
res = 1000.
ind = np.where((wl_tot < obs_aper_wl[i] * (1 + 1. / res))
& (wl_tot > obs_aper_wl[i] * (1 - 1. / res)))
if len(ind[0]) != 0:
obs_aper_flux[i] = np.mean(fnu2fl[ind] * flux_tot[ind])
obs_aper_unc[i] = np.mean(fnu2fl[ind] * unc_tot[ind])
else:
f = interp1d(wl_tot, fnu2fl * flux_tot)
f_unc = interp1d(wl_tot, fnu2fl * unc_tot)
obs_aper_flux[i] = f(obs_aper_wl[i])
obs_aper_unc[i] = f_unc(obs_aper_wl[i])
else:
# apply the filter function
# decide the filter name
if obs_aper_wl[i] == 70:
fil_name = 'Herschel PACS 70um'
elif obs_aper_wl[i] == 100:
fil_name = 'Herschel PACS 100um'
elif obs_aper_wl[i] == 160:
fil_name = 'Herschel PACS 160um'
elif obs_aper_wl[i] == 250:
fil_name = 'Herschel SPIRE 250um'
elif obs_aper_wl[i] == 350:
fil_name = 'Herschel SPIRE 350um'
elif obs_aper_wl[i] == 500:
fil_name = 'Herschel SPIRE 500um'
elif obs_aper_wl[i] == 3.6:
fil_name = 'IRAC Channel 1'
elif obs_aper_wl[i] == 4.5:
fil_name = 'IRAC Channel 2'
elif obs_aper_wl[i] == 5.8:
fil_name = 'IRAC Channel 3'
elif obs_aper_wl[i] == 8.0:
fil_name = 'IRAC Channel 4'
elif obs_aper_wl[i] == 24:
fil_name = 'MIPS 24um'
elif obs_aper_wl[i] == 850:
fil_name = 'SCUBA 850WB'
# do not have SCUBA spectra
else:
fil_name = None
if fil_name != None:
filter_func = phot_filter(fil_name, indir)
# Observed SED needs to be trimmed before applying photometry filters
filter_func = filter_func[(filter_func['wave']/1e4 >= min(wl_tot))*\
((filter_func['wave']/1e4 >= 54.8)+(filter_func['wave']/1e4 <= 36.0853))*\
((filter_func['wave']/1e4 <= 95.05)+(filter_func['wave']/1e4 >=103))*\
((filter_func['wave']/1e4 <= 190.31)+(filter_func['wave']/1e4 >= 195))*\
(filter_func['wave']/1e4 <= max(wl_tot))]
f = interp1d(wl_tot, fnu2fl * flux_tot)
f_unc = interp1d(wl_tot, fnu2fl * unc_tot)
obs_aper_flux[i] = np.trapz(f(filter_func['wave']/1e4)*filter_func['transmission'], x=filter_func['wave']/1e4)/\
np.trapz(filter_func['transmission'], x=filter_func['wave']/1e4)
obs_aper_unc[i] = unc_spectrophoto(
filter_func['wave'] / 1e4,
f_unc(filter_func['wave'] / 1e4),
filter_func['transmission'])
else:
# use a rectangle function the average the simulated SED
# apply the spectral resolution
if (obs_aper_wl[i] < 50.) & (obs_aper_wl[i] >= 5):
res = 60.
elif obs_aper_wl[i] < 5:
res = 10.
else:
res = 1000.
ind = np.where((wl_tot < obs_aper_wl[i] * (1 + 1. / res))
& (wl_tot > obs_aper_wl[i] * (1 - 1. / res)))
if len(ind[0]) != 0:
obs_aper_flux[i] = np.mean(fnu2fl[ind] * flux_tot[ind])
obs_aper_unc[i] = np.mean(fnu2fl[ind] * unc_tot[ind])
else:
f = interp1d(wl_tot, fnu2fl * flux_tot)
f_unc = interp1d(wl_tot, fnu2fl * unc_tot)
obs_aper_flux[i] = f(obs_aper_wl[i])
obs_aper_unc[i] = f_unc(obs_aper_wl[i])
# plot the aperture-extracted spectrophotometry fluxes from observed spectra and simulations
# in log-scale
if log:
aper_obs = ax_sed.errorbar(np.log10(obs_aper_wl), np.log10(obs_aper_flux * obs_aper_wl ),\
yerr=[np.log10(obs_aper_flux*obs_aper_wl)-np.log10(obs_aper_flux*obs_aper_wl-obs_aper_unc*obs_aper_wl), np.log10(obs_aper_flux*obs_aper_wl+obs_aper_unc*obs_aper_wl)-np.log10(obs_aper_flux*obs_aper_wl)],\
fmt='s', mec='None', mfc='r', markersize=10, linewidth=1.5, ecolor='Red', elinewidth=3, capthick=3, barsabove=True)
aper = ax_sed.errorbar(np.log10(wl_aper),np.log10(flux_aper*wl_aper),\
yerr=[np.log10(flux_aper*wl_aper)-np.log10(flux_aper*wl_aper-unc_aper*wl_aper), np.log10(flux_aper*wl_aper+unc_aper*wl_aper)-np.log10(flux_aper*wl_aper)],\
fmt='o', mec='Blue', mfc='None', color='b',markersize=12, markeredgewidth=2.5, linewidth=1.7, ecolor='Blue', elinewidth=3, barsabove=True)
ax_sed.set_ylim([-14, -7])
ax_sed.set_xlim([0, 3.2])
# in normal scale (normal in y-axis)
else:
aper_obs = ax_sed.errorbar(np.log10(obs_aper_wl), obs_aper_flux*obs_aper_wl, yerr=obs_aper_unc*obs_aper_wl,\
fmt='s', mec='None', mfc='r', markersize=10, linewidth=1.5, ecolor='Red', elinewidth=3, capthick=3, barsabove=True)
aper = ax_sed.errorbar(np.log10(wl_aper),flux_aper*wl_aper, yerr=unc_aper*wl_aper,\
fmt='o', mec='Blue', mfc='None', color='b',markersize=12, markeredgewidth=2.5, linewidth=1.7, ecolor='Blue', elinewidth=3, barsabove=True)
ax_sed.set_xlim([0, 3.2])
# calculate the bolometric luminosity of the aperture
# print flux_aper
l_bol_sim = l_bol(
wl_aper, flux_aper * wl_aper / (c / np.array(wl_aper) * 1e4) * 1e23,
dstar)
print 'Bolometric luminosity of simulated spectrum: %5.2f lsun' % l_bol_sim
# print out the sed into ascii file for reading in later
if save == True:
# unapertured SED
foo = open(outdir + print_name + '_sed_inf.txt', 'w')
foo.write('%12s \t %12s \t %12s \n' % ('wave', 'vSv', 'sigma_vSv'))
for i in range(0, len(sed_inf.wav)):
foo.write('%12g \t %12g \t %12g \n' %
(sed_inf.wav[i], sed_inf.val[i], sed_inf.unc[i]))
foo.close()
# SED with convolution of aperture sizes
foo = open(outdir + print_name + '_sed_w_aperture.txt', 'w')
foo.write('%12s \t %12s \t %12s \n' % ('wave', 'vSv', 'sigma_vSv'))
for i in range(0, len(wl_aper)):
foo.write('%12g \t %12g \t %12g \n' %
(wl_aper[i], flux_aper[i] * wl_aper[i],
unc_aper[i] * wl_aper[i]))
foo.close()
# print out the aperture-convolved fluxex from observations
if print_data_w_aper:
foo = open(outdir + print_name + '_obs_w_aperture.txt', 'w')
foo.write('%12s \t %12s \t %12s \n' % ('wave', 'Jy', 'sigma_Jy'))
for i in range(0, len(obs_aper_wl)):
foo.write('%12g \t %12g \t %12g \n' %
(obs_aper_wl[i], obs_aper_flux[i] * obs_aper_wl[i] /
(c / obs_aper_wl[i] * 1e4) * 1e23, obs_aper_unc[i] *
obs_aper_wl[i] / (c / obs_aper_wl[i] * 1e4) * 1e23))
foo.close()
# read the input central luminosity by reading in the source information from output file
dum = Model()
dum.use_sources(filename)
L_cen = dum.sources[0].luminosity / lsun
# legend
lg_data = ax_sed.legend([irs, photometry, aper, aper_obs], [
r'$\rm{observation}$', r'$\rm{photometry}$', r'$\rm{F_{aper,sim}}$',
r'$\rm{F_{aper,obs}}$'
],
loc='upper left',
fontsize=14 * mag,
numpoints=1,
framealpha=0.3)
if clean == False:
lg_sim = ax_sed.legend([sim],[r'$\rm{L_{bol,sim}=%5.2f\,L_{\odot},\,L_{center}=%5.2f\,L_{\odot}}$' % (l_bol_sim, L_cen)], \
loc='lower right',fontsize=mag*16)
plt.gca().add_artist(lg_data)
# plot setting
ax_sed.set_xlabel(r'$\rm{log\,\lambda\,[{\mu}m]}$', fontsize=mag * 20)
ax_sed.set_ylabel(r'$\rm{log\,\nu S_{\nu}\,[erg\,s^{-1}\,cm^{-2}]}$',
fontsize=mag * 20)
[
ax_sed.spines[axis].set_linewidth(1.5 * mag)
for axis in ['top', 'bottom', 'left', 'right']
]
ax_sed.minorticks_on()
ax_sed.tick_params('both',
labelsize=mag * 18,
width=1.5 * mag,
which='major',
pad=15,
length=5 * mag)
ax_sed.tick_params('both',
labelsize=mag * 18,
width=1.5 * mag,
which='minor',
pad=15,
length=2.5 * mag)
# fix the tick label font
ticks_font = mpl.font_manager.FontProperties(family='STIXGeneral',
size=mag * 18)
for label in ax_sed.get_xticklabels():
label.set_fontproperties(ticks_font)
for label in ax_sed.get_yticklabels():
label.set_fontproperties(ticks_font)
# Write out the plot
fig.savefig(outdir + print_name + '_sed.pdf',
format='pdf',
dpi=300,
bbox_inches='tight')
fig.clf()
# option for suppress image plotting (for speed)
if image:
# Package for matching the colorbar
from mpl_toolkits.axes_grid1 import make_axes_locatable, ImageGrid
# Users may change the unit: mJy, Jy, MJy/sr, ergs/cm^2/s, ergs/cm^2/s/Hz
# !!!
image = m.get_image(group=len(aper_reduced) + 1,
inclination=0,
distance=dstar * pc,
units='MJy/sr')
# Open figure and create axes
fig = plt.figure(figsize=(12, 12))
grid = ImageGrid(fig,
111,
nrows_ncols=(3, 3),
direction='row',
add_all=True,
label_mode='1',
share_all=True,
cbar_location='right',
cbar_mode='single',
cbar_size='3%',
cbar_pad=0)
for i, wav in enumerate([3.6, 8.0, 9.7, 24, 40, 100, 250, 500, 1000]):
ax = grid[i]
# Find the closest wavelength
iwav = np.argmin(np.abs(wav - image.wav))
# Calculate the image width in arcseconds given the distance used above
# get the max radius
rmax = max(m.get_quantities().r_wall)
w = np.degrees(rmax / image.distance) * 3600.
# Image in the unit of MJy/sr
# Change it into erg/s/cm2/Hz/sr
factor = 1e-23 * 1e6
# avoid zero in log
# flip the image, because the setup of inclination is upside down
val = image.val[::-1, :, iwav] * factor + 1e-30
# This is the command to show the image. The parameters vmin and vmax are
# the min and max levels for the colorscale (remove for default values).
cmap = plt.cm.CMRmap
im = ax.imshow(np.log10(val),
vmin=-22,
vmax=-12,
cmap=cmap,
origin='lower',
extent=[-w, w, -w, w],
aspect=1)
ax.set_xlabel(r'$\rm{RA\,Offset\,[arcsec]}$', fontsize=14)
ax.set_ylabel(r'$\rm{Dec\,Offset\,[arcsec]}$', fontsize=14)
# fix the tick label font
ticks_font = mpl.font_manager.FontProperties(family='STIXGeneral',
size=14)
for label in ax.get_xticklabels():
label.set_fontproperties(ticks_font)
for label in ax.get_yticklabels():
label.set_fontproperties(ticks_font)
# Colorbar setting
cb = ax.cax.colorbar(im)
cb.solids.set_edgecolor('face')
cb.ax.minorticks_on()
cb.ax.set_ylabel(
r'$\rm{log(I_{\nu})\,[erg\,s^{-1}\,cm^{-2}\,Hz^{-1}\,sr^{-1}]}$',
fontsize=18)
cb_obj = plt.getp(cb.ax.axes, 'yticklabels')
plt.setp(cb_obj, fontsize=18)
ticks_font = mpl.font_manager.FontProperties(family='STIXGeneral',
size=18)
for label in cb.ax.get_yticklabels():
label.set_fontproperties(ticks_font)
ax.tick_params(axis='both', which='major', labelsize=16)
ax.text(0.7,
0.88,
str(wav) + r'$\rm{\,\mu m}$',
fontsize=16,
color='white',
transform=ax.transAxes)
fig.savefig(outdir + print_name + '_image_gridplot.pdf',
format='pdf',
dpi=300,
bbox_inches='tight')
fig.clf()
def extract_hyperion(filename,indir=None,outdir=None,dstar=200.0,aperture=None,
save=True,filter_func=False,plot_all=False,clean=False,
exclude_wl=[],log=True,image=True,obj='BHR71',
print_data_w_aper=False,mag=1.5):
"""
filename: The path to Hyperion output file
indir: The path to the directory which contains observations data
outdir: The path to the directory for storing extracted plots and ASCII files
"""
def l_bol(wl,fv,dstar):
import numpy as np
import astropy.constants as const
# wavelength unit: um
# Flux density unit: Jy
# constants setup
#
c = const.c.cgs.value
pc = const.pc.cgs.value
PI = np.pi
SL = const.L_sun.cgs.value
# Convert the unit from Jy to erg s-1 cm-2 Hz-1
fv = np.array(fv)*1e-23
freq = c/(1e-4*np.array(wl))
diff_dum = freq[1:]-freq[0:-1]
freq_interpol = np.hstack((freq[0:-1]+diff_dum/2.0,freq[0:-1]+diff_dum/2.0,freq[0],freq[-1]))
freq_interpol = freq_interpol[np.argsort(freq_interpol)[::-1]]
fv_interpol = np.empty(len(freq_interpol))
# calculate the histogram style of spectrum
#
for i in range(0,len(fv)):
if i == 0:
fv_interpol[i] = fv[i]
else:
fv_interpol[2*i-1] = fv[i-1]
fv_interpol[2*i] = fv[i]
fv_interpol[-1] = fv[-1]
dv = freq_interpol[0:-1]-freq_interpol[1:]
dv = np.delete(dv,np.where(dv==0))
fv = fv[np.argsort(freq)]
freq = freq[np.argsort(freq)]
return (np.trapz(fv,freq)*4.*PI*(dstar*pc)**2)/SL
# function for properly calculating uncertainty of spectrophotometry value
def unc_spectrophoto(wl, unc, trans):
# adopting smiliar procedure as Trapezoidal rule
# (b-a) * [ f(a) + f(b) ] / 2
#
return ( np.sum( trans[:-1]**2 * unc[:-1]**2 * (wl[1:]-wl[:-1])**2 ) / np.trapz(trans, x=wl)**2 )**0.5
# to avoid X server error
import matplotlib as mpl
mpl.use('Agg')
#
import matplotlib.pyplot as plt
import numpy as np
import os
from hyperion.model import ModelOutput, Model
from scipy.interpolate import interp1d
from hyperion.util.constants import pc, c, lsun, au
from astropy.io import ascii
import sys
from phot_filter import phot_filter
from get_obs import get_obs
# Open the model
m = ModelOutput(filename)
# Read in the observation data and calculate the noise & variance
if indir == None:
indir = raw_input('Path to the observation data: ')
if outdir == None:
outdir = raw_input('Path for the output: ')
# assign the file name from the input file
print_name = os.path.splitext(os.path.basename(filename))[0]
# use a canned function to extract observational data
obs_data = get_obs(indir, obj=obj) # unit in um, Jy
wl_tot, flux_tot, unc_tot = obs_data['spec']
flux_tot = flux_tot*1e-23 # convert unit from Jy to erg s-1 cm-2 Hz-1
unc_tot = unc_tot*1e-23
l_bol_obs = l_bol(wl_tot, flux_tot*1e23, dstar)
wl_phot, flux_phot, flux_sig_phot = obs_data['phot']
flux_phot = flux_phot*1e-23 # convert unit from Jy to erg s-1 cm-2 Hz-1
flux_sig_phot = flux_sig_phot*1e-23
if aperture == None:
aperture = {'wave': [3.6, 4.5, 5.8, 8.0, 8.5, 9, 9.7, 10, 10.5, 11, 16, 20, 24, 35, 70, 100, 160, 250, 350, 500, 850],\
'aperture': [7.2, 7.2, 7.2, 7.2, 7.2, 7.2, 7.2, 7.2, 7.2, 7.2, 20.4, 20.4, 20.4, 20.4, 24.5, 24.5, 24.5, 24.5, 24.5, 24.5, 24.5]}
# assign wl_aper and aper from dictionary of aperture
wl_aper = aperture['wave']
aper = aperture['aperture']
# create the non-repetitive aperture list and index array
aper_reduced = list(set(aper))
index_reduced = np.arange(1, len(aper_reduced)+1) # '+1': the zeroth slice corresponds to infinite aperture
# Create the plot
fig = plt.figure(figsize=(8*mag,6*mag))
ax_sed = fig.add_subplot(1, 1, 1)
# Plot the observed SED
if not clean:
color_seq = ['Green','Red','Black']
else:
color_seq = ['DimGray','DimGray','DimGray']
# plot the observations
# plot in log scale
if log:
pacs, = ax_sed.plot(np.log10(wl_tot[(wl_tot>40) & (wl_tot<190.31)]),
np.log10(c/(wl_tot[(wl_tot>40) & (wl_tot<190.31)]*1e-4)*flux_tot[(wl_tot>40) & (wl_tot<190.31)]),
'-',color=color_seq[0],linewidth=1.5*mag, alpha=0.7)
spire, = ax_sed.plot(np.log10(wl_tot[wl_tot > 194]),np.log10(c/(wl_tot[wl_tot > 194]*1e-4)*flux_tot[wl_tot > 194]),
'-',color=color_seq[1],linewidth=1.5*mag, alpha=0.7)
irs, = ax_sed.plot(np.log10(wl_tot[wl_tot < 40]),np.log10(c/(wl_tot[wl_tot < 40]*1e-4)*flux_tot[wl_tot < 40]),
'-',color=color_seq[2],linewidth=1.5*mag, alpha=0.7)
photometry, = ax_sed.plot(np.log10(wl_phot),np.log10(c/(wl_phot*1e-4)*flux_phot),'s',mfc='DimGray',mec='k',markersize=8)
# plot the observed photometry data
ax_sed.errorbar(np.log10(wl_phot),np.log10(c/(wl_phot*1e-4)*flux_phot),
yerr=[np.log10(c/(wl_phot*1e-4)*flux_phot)-np.log10(c/(wl_phot*1e-4)*(flux_phot-flux_sig_phot)),
np.log10(c/(wl_phot*1e-4)*(flux_phot+flux_sig_phot))-np.log10(c/(wl_phot*1e-4)*flux_phot)],
fmt='s',mfc='DimGray',mec='k',markersize=8)
# plot in normal scale
else:
pacs, = ax_sed.plot(np.log10(wl_tot[(wl_tot>40) & (wl_tot<190.31)]),
c/(wl_tot[(wl_tot>40) & (wl_tot<190.31)]*1e-4)*flux_tot[(wl_tot>40) & (wl_tot<190.31)],
'-',color=color_seq[0],linewidth=1.5*mag, alpha=0.7)
spire, = ax_sed.plot(np.log10(wl_tot[wl_tot > 194]),c/(wl_tot[wl_tot > 194]*1e-4)*flux_tot[wl_tot > 194],
'-',color=color_seq[1],linewidth=1.5*mag, alpha=0.7)
irs, = ax_sed.plot(np.log10(wl_tot[wl_tot < 40]),c/(wl_tot[wl_tot < 40]*1e-4)*flux_tot[wl_tot < 40],
'-',color=color_seq[2],linewidth=1.5*mag, alpha=0.7)
photometry, = ax_sed.plot(wl_phot,c/(wl_phot*1e-4)*flux_phot,'s',mfc='DimGray',mec='k',markersize=8)
# plot the observed photometry data
ax_sed.errorbar(np.log10(wl_phot),c/(wl_phot*1e-4)*flux_phot,
yerr=[c/(wl_phot*1e-4)*flux_phot-c/(wl_phot*1e-4)*(flux_phot-flux_sig_phot),
c/(wl_phot*1e-4)*(flux_phot+flux_sig_phot)-c/(wl_phot*1e-4)*flux_phot],
fmt='s',mfc='DimGray',mec='k',markersize=8)
# if keyword 'clean' is not set, print L_bol derived from observations at upper right corner.
if not clean:
ax_sed.text(0.75,0.9,r'$\rm{L_{bol}= %5.2f L_{\odot}}$' % l_bol_obs,
fontsize=mag*16,transform=ax_sed.transAxes)
# getting SED with infinite aperture
sed_inf = m.get_sed(group=0, inclination=0, aperture=-1, distance=dstar*pc,
uncertainties=True)
# plot the simulated SED with infinite aperture
if clean == False:
sim, = ax_sed.plot(np.log10(sed_inf.wav), np.log10(sed_inf.val),
'-', color='GoldenRod', linewidth=0.5*mag)
ax_sed.fill_between(np.log10(sed_inf.wav), np.log10(sed_inf.val-sed_inf.unc),
np.log10(sed_inf.val+sed_inf.unc),color='GoldenRod', alpha=0.5)
#######################################
# get fluxes with different apertures #
#######################################
# this is non-reduced wavelength array because this is for printing out fluxes at all channels specified by users
flux_aper = np.zeros_like(wl_aper, dtype=float)
unc_aper = np.zeros_like(wl_aper, dtype=float)
a = np.zeros_like(wl_aper) + 1
color_list = plt.cm.jet(np.linspace(0, 1, len(wl_aper)+1))
for i in range(0, len(wl_aper)):
# occasionally users might want not to report some wavelength channels
if wl_aper[i] in exclude_wl:
continue
# getting simulated SED from Hyperion output. (have to match with the reduced index)
sed_dum = m.get_sed(group=index_reduced[np.where(aper_reduced == aper[i])],
inclination=0,aperture=-1,distance=dstar*pc, uncertainties=True)
# plot the whole SED from this aperture (optional)
if plot_all == True:
ax_sed.plot(np.log10(sed_dum.wav), np.log10(sed_dum.val),'-', color=color_list[i])
ax_sed.fill_between(np.log10(sed_dum.wav), np.log10(sed_dum.val-sed_dum.unc), np.log10(sed_dum.val+sed_dum.unc),\
color=color_list[i], alpha=0.5)
# Extracting spectrophotometry values from simulated SED
# Not using the photometry filer function to extract spectrophotometry values
# sort by wavelength first.
sort_wl = np.argsort(sed_dum.wav)
val_sort = sed_dum.val[sort_wl]
unc_sort = sed_dum.unc[sort_wl]
wav_sort = sed_dum.wav[sort_wl]
# Before doing that, convert vSv to F_lambda
flux_dum = val_sort / wav_sort
unc_dum = unc_sort / wav_sort
# If no using filter function to extract the spectrophotometry,
# then use the spectral resolution.
if filter_func == False:
# use a rectangle function the average the simulated SED
# apply the spectral resolution
if (wl_aper[i] < 50.) & (wl_aper[i] >= 5):
res = 60.
elif wl_aper[i] < 5:
res = 10.
else:
res = 1000.
ind = np.where((wav_sort < wl_aper[i]*(1+1./res)) & (wav_sort > wl_aper[i]*(1-1./res)))
if len(ind[0]) != 0:
flux_aper[i] = np.mean(flux_dum[ind])
unc_aper[i] = np.mean(unc_dum[ind])
else:
f = interp1d(wav_sort, flux_dum)
f_unc = interp1d(wav_sort, unc_dum)
flux_aper[i] = f(wl_aper[i])
unc_aper[i] = f_unc(wl_aper[i])
# Using photometry filter function to extract spectrophotometry values
else:
# apply the filter function
# decide the filter name
if wl_aper[i] == 70:
fil_name = 'Herschel PACS 70um'
elif wl_aper[i] == 100:
fil_name = 'Herschel PACS 100um'
elif wl_aper[i] == 160:
fil_name = 'Herschel PACS 160um'
elif wl_aper[i] == 250:
fil_name = 'Herschel SPIRE 250um'
elif wl_aper[i] == 350:
fil_name = 'Herschel SPIRE 350um'
elif wl_aper[i] == 500:
fil_name = 'Herschel SPIRE 500um'
elif wl_aper[i] == 3.6:
fil_name = 'IRAC Channel 1'
elif wl_aper[i] == 4.5:
fil_name = 'IRAC Channel 2'
elif wl_aper[i] == 5.8:
fil_name = 'IRAC Channel 3'
elif wl_aper[i] == 8.0:
fil_name = 'IRAC Channel 4'
elif wl_aper[i] == 24:
fil_name = 'MIPS 24um'
elif wl_aper[i] == 850:
fil_name = 'SCUBA 850WB'
else:
fil_name = None
if fil_name != None:
filter_func = phot_filter(fil_name)
# Simulated SED should have enough wavelength coverage for applying photometry filters.
f = interp1d(wav_sort, flux_dum)
f_unc = interp1d(wav_sort, unc_dum)
flux_aper[i] = np.trapz(f(filter_func['wave']/1e4)*\
filter_func['transmission'],x=filter_func['wave']/1e4 )/\
np.trapz(filter_func['transmission'], x=filter_func['wave']/1e4)
# fix a bug
unc_aper[i] = unc_spectrophoto(filter_func['wave']/1e4,
f_unc(filter_func['wave']/1e4), filter_func['transmission'])
else:
# use a rectangle function the average the simulated SED
# apply the spectral resolution
if (wl_aper[i] < 50.) & (wl_aper[i] >= 5):
res = 60.
elif wl_aper[i] < 5:
res = 10.
else:
res = 1000.
ind = np.where((wav_sort < wl_aper[i]*(1+1./res)) & (wav_sort > wl_aper[i]*(1-1./res)))
if len(ind[0]) != 0:
flux_aper[i] = np.mean(flux_dum[ind])
unc_aper[i] = np.mean(unc_dum[ind])
else:
f = interp1d(wav_sort, flux_dum)
f_unc = interp1d(wav_sort, unc_dum)
flux_aper[i] = f(wl_aper[i])
unc_aper[i] = f_unc(wl_aper[i])
# temperory step: solve issue of uncertainty greater than the value
for i in range(len(wl_aper)):
if unc_aper[i] >= flux_aper[i]:
unc_aper[i] = flux_aper[i] - 1e-20
###########################
# Observations Extraction #
###########################
# perform the same procedure of flux extraction of aperture flux with observed spectra
# wl_aper = np.array(wl_aper, dtype=float)
obs_aper_wl = wl_aper[(wl_aper >= min(wl_tot)) & (wl_aper <= max(wl_tot))]
obs_aper_flux = np.zeros_like(obs_aper_wl)
obs_aper_unc = np.zeros_like(obs_aper_wl)
# have change the simulation part to work in F_lambda for fliter convolution
# flux_tot and unc_tot have units of erg/s/cm2/Hz. Need to convert it to F_lambda (erg/s/cm2/um)
fnu2fl = c/(wl_tot*1e-4)/wl_tot
#
# wl_tot and flux_tot are already hstacked and sorted by wavelength
for i in range(0, len(obs_aper_wl)):
# sometime users want not report some wavelength channels
if obs_aper_wl[i] in exclude_wl:
continue
if filter_func == False:
# use a rectangle function the average the simulated SED
# apply the spectral resolution
if (obs_aper_wl[i] < 50.) & (obs_aper_wl[i] >= 5):
res = 60.
elif obs_aper_wl[i] < 5:
res = 10.
else:
res = 1000.
ind = np.where((wl_tot < obs_aper_wl[i]*(1+1./res)) & (wl_tot > obs_aper_wl[i]*(1-1./res)))
if len(ind[0]) != 0:
obs_aper_flux[i] = np.mean(fnu2fl[ind]*flux_tot[ind])
obs_aper_unc[i] = np.mean(fnu2fl[ind]*unc_tot[ind])
else:
f = interp1d(wl_tot, fnu2fl*flux_tot)
f_unc = interp1d(wl_tot, fnu2fl*unc_tot)
obs_aper_flux[i] = f(obs_aper_wl[i])
obs_aper_unc[i] = f_unc(obs_aper_wl[i])
else:
# apply the filter function
# decide the filter name
if obs_aper_wl[i] == 70:
fil_name = 'Herschel PACS 70um'
elif obs_aper_wl[i] == 100:
fil_name = 'Herschel PACS 100um'
elif obs_aper_wl[i] == 160:
fil_name = 'Herschel PACS 160um'
elif obs_aper_wl[i] == 250:
fil_name = 'Herschel SPIRE 250um'
elif obs_aper_wl[i] == 350:
fil_name = 'Herschel SPIRE 350um'
elif obs_aper_wl[i] == 500:
fil_name = 'Herschel SPIRE 500um'
elif obs_aper_wl[i] == 3.6:
fil_name = 'IRAC Channel 1'
elif obs_aper_wl[i] == 4.5:
fil_name = 'IRAC Channel 2'
elif obs_aper_wl[i] == 5.8:
fil_name = 'IRAC Channel 3'
elif obs_aper_wl[i] == 8.0:
fil_name = 'IRAC Channel 4'
elif obs_aper_wl[i] == 24:
fil_name = 'MIPS 24um'
elif obs_aper_wl[i] == 850:
fil_name = 'SCUBA 850WB'
# do not have SCUBA spectra
else:
fil_name = None
if fil_name != None:
filter_func = phot_filter(fil_name)
# Observed SED needs to be trimmed before applying photometry filters
filter_func = filter_func[(filter_func['wave']/1e4 >= min(wl_tot))*\
((filter_func['wave']/1e4 >= 54.8)+(filter_func['wave']/1e4 <= 36.0853))*\
((filter_func['wave']/1e4 <= 95.05)+(filter_func['wave']/1e4 >=103))*\
((filter_func['wave']/1e4 <= 190.31)+(filter_func['wave']/1e4 >= 195))*\
(filter_func['wave']/1e4 <= max(wl_tot))]
f = interp1d(wl_tot, fnu2fl*flux_tot)
f_unc = interp1d(wl_tot, fnu2fl*unc_tot)
obs_aper_flux[i] = np.trapz(f(filter_func['wave']/1e4)*filter_func['transmission'], x=filter_func['wave']/1e4)/\
np.trapz(filter_func['transmission'], x=filter_func['wave']/1e4)
obs_aper_unc[i] = unc_spectrophoto(filter_func['wave']/1e4, f_unc(filter_func['wave']/1e4), filter_func['transmission'])
else:
# use a rectangle function the average the simulated SED
# apply the spectral resolution
if (obs_aper_wl[i] < 50.) & (obs_aper_wl[i] >= 5):
res = 60.
elif obs_aper_wl[i] < 5:
res = 10.
else:
res = 1000.
ind = np.where((wl_tot < obs_aper_wl[i]*(1+1./res)) & (wl_tot > obs_aper_wl[i]*(1-1./res)))
if len(ind[0]) != 0:
obs_aper_flux[i] = np.mean(fnu2fl[ind]*flux_tot[ind])
obs_aper_unc[i] = np.mean(fnu2fl[ind]*unc_tot[ind])
else:
f = interp1d(wl_tot, fnu2fl*flux_tot)
f_unc = interp1d(wl_tot, fnu2fl*unc_tot)
obs_aper_flux[i] = f(obs_aper_wl[i])
obs_aper_unc[i] = f_unc(obs_aper_wl[i])
# plot the aperture-extracted spectrophotometry fluxes from observed spectra and simulations
# in log-scale
if log:
aper_obs = ax_sed.errorbar(np.log10(obs_aper_wl), np.log10(obs_aper_flux * obs_aper_wl ),\
yerr=[np.log10(obs_aper_flux*obs_aper_wl)-np.log10(obs_aper_flux*obs_aper_wl-obs_aper_unc*obs_aper_wl), np.log10(obs_aper_flux*obs_aper_wl+obs_aper_unc*obs_aper_wl)-np.log10(obs_aper_flux*obs_aper_wl)],\
fmt='s', mec='None', mfc='r', markersize=10, linewidth=1.5, ecolor='Red', elinewidth=3, capthick=3, barsabove=True)
aper = ax_sed.errorbar(np.log10(wl_aper),np.log10(flux_aper*wl_aper),\
yerr=[np.log10(flux_aper*wl_aper)-np.log10(flux_aper*wl_aper-unc_aper*wl_aper), np.log10(flux_aper*wl_aper+unc_aper*wl_aper)-np.log10(flux_aper*wl_aper)],\
fmt='o', mec='Blue', mfc='None', color='b',markersize=12, markeredgewidth=2.5, linewidth=1.7, ecolor='Blue', elinewidth=3, barsabove=True)
ax_sed.set_ylim([-14,-7])
ax_sed.set_xlim([0,3.2])
# in normal scale (normal in y-axis)
else:
aper_obs = ax_sed.errorbar(np.log10(obs_aper_wl), obs_aper_flux*obs_aper_wl, yerr=obs_aper_unc*obs_aper_wl,\
fmt='s', mec='None', mfc='r', markersize=10, linewidth=1.5, ecolor='Red', elinewidth=3, capthick=3, barsabove=True)
aper = ax_sed.errorbar(np.log10(wl_aper),flux_aper*wl_aper, yerr=unc_aper*wl_aper,\
fmt='o', mec='Blue', mfc='None', color='b',markersize=12, markeredgewidth=2.5, linewidth=1.7, ecolor='Blue', elinewidth=3, barsabove=True)
ax_sed.set_xlim([0,3.2])
# calculate the bolometric luminosity of the aperture
# print flux_aper
l_bol_sim = l_bol(wl_aper, flux_aper*wl_aper/(c/np.array(wl_aper)*1e4)*1e23, dstar)
print 'Bolometric luminosity of simulated spectrum: %5.2f lsun' % l_bol_sim
# print out the sed into ascii file for reading in later
if save == True:
# unapertured SED
foo = open(outdir+print_name+'_sed_inf.txt','w')
foo.write('%12s \t %12s \t %12s \n' % ('wave','vSv','sigma_vSv'))
for i in range(0, len(sed_inf.wav)):
foo.write('%12g \t %12g \t %12g \n' % (sed_inf.wav[i], sed_inf.val[i], sed_inf.unc[i]))
foo.close()
# SED with convolution of aperture sizes
foo = open(outdir+print_name+'_sed_w_aperture.txt','w')
foo.write('%12s \t %12s \t %12s \n' % ('wave','vSv','sigma_vSv'))
for i in range(0, len(wl_aper)):
foo.write('%12g \t %12g \t %12g \n' % (wl_aper[i], flux_aper[i]*wl_aper[i], unc_aper[i]*wl_aper[i]))
foo.close()
# print out the aperture-convolved fluxex from observations
if print_data_w_aper:
foo = open(outdir+print_name+'_obs_w_aperture.txt','w')
foo.write('%12s \t %12s \t %12s \n' % ('wave','Jy','sigma_Jy'))
for i in range(0, len(obs_aper_wl)):
foo.write('%12g \t %12g \t %12g \n' % (obs_aper_wl[i], obs_aper_flux[i]*obs_aper_wl[i]/(c/obs_aper_wl[i]*1e4)*1e23, obs_aper_unc[i]*obs_aper_wl[i]/(c/obs_aper_wl[i]*1e4)*1e23))
foo.close()
# read the input central luminosity by reading in the source information from output file
dum = Model()
dum.use_sources(filename)
L_cen = dum.sources[0].luminosity/lsun
# legend
lg_data = ax_sed.legend([irs, photometry, aper, aper_obs],
[r'$\rm{observation}$',
r'$\rm{photometry}$',r'$\rm{F_{aper,sim}}$',r'$\rm{F_{aper,obs}}$'],
loc='upper left',fontsize=14*mag,numpoints=1,framealpha=0.3)
if clean == False:
lg_sim = ax_sed.legend([sim],[r'$\rm{L_{bol,sim}=%5.2f\,L_{\odot},\,L_{center}=%5.2f\,L_{\odot}}$' % (l_bol_sim, L_cen)], \
loc='lower right',fontsize=mag*16)
plt.gca().add_artist(lg_data)
# plot setting
ax_sed.set_xlabel(r'$\rm{log\,\lambda\,[{\mu}m]}$',fontsize=mag*20)
ax_sed.set_ylabel(r'$\rm{log\,\nu S_{\nu}\,[erg\,s^{-1}\,cm^{-2}]}$',fontsize=mag*20)
[ax_sed.spines[axis].set_linewidth(1.5*mag) for axis in ['top','bottom','left','right']]
ax_sed.minorticks_on()
ax_sed.tick_params('both',labelsize=mag*18,width=1.5*mag,which='major',pad=15,length=5*mag)
ax_sed.tick_params('both',labelsize=mag*18,width=1.5*mag,which='minor',pad=15,length=2.5*mag)
# fix the tick label font
ticks_font = mpl.font_manager.FontProperties(family='STIXGeneral',size=mag*18)
for label in ax_sed.get_xticklabels():
label.set_fontproperties(ticks_font)
for label in ax_sed.get_yticklabels():
label.set_fontproperties(ticks_font)
# Write out the plot
fig.savefig(outdir+print_name+'_sed.pdf',format='pdf',dpi=300,bbox_inches='tight')
fig.clf()
# option for suppress image plotting (for speed)
if image:
# Package for matching the colorbar
from mpl_toolkits.axes_grid1 import make_axes_locatable, ImageGrid
# Users may change the unit: mJy, Jy, MJy/sr, ergs/cm^2/s, ergs/cm^2/s/Hz
# !!!
image = m.get_image(group=len(aper_reduced)+1, inclination=0,
distance=dstar*pc, units='MJy/sr')
# Open figure and create axes
fig = plt.figure(figsize=(12,12))
grid = ImageGrid(fig, 111,nrows_ncols=(3,3),direction='row',
add_all=True,label_mode='1',share_all=True,
cbar_location='right',cbar_mode='single',
cbar_size='3%',cbar_pad=0)
for i, wav in enumerate([3.6, 8.0, 9.7, 24, 40, 100, 250, 500, 1000]):
ax = grid[i]
# Find the closest wavelength
iwav = np.argmin(np.abs(wav - image.wav))
# Calculate the image width in arcseconds given the distance used above
# get the max radius
rmax = max(m.get_quantities().r_wall)
w = np.degrees(rmax / image.distance) * 3600.
# Image in the unit of MJy/sr
# Change it into erg/s/cm2/Hz/sr
factor = 1e-23*1e6
# avoid zero in log
# flip the image, because the setup of inclination is upside down
val = image.val[::-1, :, iwav] * factor + 1e-30
# This is the command to show the image. The parameters vmin and vmax are
# the min and max levels for the colorscale (remove for default values).
cmap = plt.cm.CMRmap
im = ax.imshow(np.log10(val), vmin= -22, vmax= -12,
cmap=cmap, origin='lower', extent=[-w, w, -w, w], aspect=1)
ax.set_xlabel(r'$\rm{RA\,Offset\,[arcsec]}$', fontsize=14)
ax.set_ylabel(r'$\rm{Dec\,Offset\,[arcsec]}$', fontsize=14)
# fix the tick label font
ticks_font = mpl.font_manager.FontProperties(family='STIXGeneral',size=14)
for label in ax.get_xticklabels():
label.set_fontproperties(ticks_font)
for label in ax.get_yticklabels():
label.set_fontproperties(ticks_font)
# Colorbar setting
cb = ax.cax.colorbar(im)
cb.solids.set_edgecolor('face')
cb.ax.minorticks_on()
cb.ax.set_ylabel(r'$\rm{log(I_{\nu})\,[erg\,s^{-1}\,cm^{-2}\,Hz^{-1}\,sr^{-1}]}$',fontsize=18)
cb_obj = plt.getp(cb.ax.axes, 'yticklabels')
plt.setp(cb_obj,fontsize=18)
ticks_font = mpl.font_manager.FontProperties(family='STIXGeneral',size=18)
for label in cb.ax.get_yticklabels():
label.set_fontproperties(ticks_font)
ax.tick_params(axis='both', which='major', labelsize=16)
ax.text(0.7,0.88,str(wav) + r'$\rm{\,\mu m}$',fontsize=16,color='white', transform=ax.transAxes)
fig.savefig(outdir+print_name+'_image_gridplot.pdf', format='pdf', dpi=300, bbox_inches='tight')
fig.clf()
def run_thermal_hyperion(self, nphot=1e6, mrw=False, pda=False, \
niterations=20, percentile=99., absolute=2.0, relative=1.02, \
max_interactions=1e8, mpi=False, nprocesses=None):
d = []
for i in range(len(self.grid.dust)):
d.append(IsotropicDust( \
self.grid.dust[i].nu[::-1].astype(numpy.float64), \
self.grid.dust[i].albedo[::-1].astype(numpy.float64), \
self.grid.dust[i].kext[::-1].astype(numpy.float64)))
m = HypModel()
if (self.grid.coordsystem == "cartesian"):
m.set_cartesian_grid(self.grid.w1*AU, self.grid.w2*AU, \
self.grid.w3*AU)
elif (self.grid.coordsystem == "cylindrical"):
m.set_cylindrical_polar_grid(self.grid.w1*AU, self.grid.w3*AU, \
self.grid.w2)
elif (self.grid.coordsystem == "spherical"):
m.set_spherical_polar_grid(self.grid.w1*AU, self.grid.w2, \
self.grid.w3)
for i in range(len(self.grid.density)):
if (self.grid.coordsystem == "cartesian"):
m.add_density_grid(numpy.transpose(self.grid.density[i], \
axes=(2,1,0)), d[i])
if (self.grid.coordsystem == "cylindrical"):
m.add_density_grid(numpy.transpose(self.grid.density[i], \
axes=(1,2,0)), d[i])
if (self.grid.coordsystem == "spherical"):
m.add_density_grid(numpy.transpose(self.grid.density[i], \
axes=(2,1,0)), d[i])
sources = []
for i in range(len(self.grid.stars)):
sources.append(m.add_spherical_source())
sources[i].luminosity = self.grid.stars[i].luminosity * L_sun
sources[i].radius = self.grid.stars[i].radius * R_sun
sources[i].temperature = self.grid.stars[i].temperature
m.set_mrw(mrw)
m.set_pda(pda)
m.set_max_interactions(max_interactions)
m.set_n_initial_iterations(niterations)
m.set_n_photons(initial=nphot, imaging=0)
m.set_convergence(True, percentile=percentile, absolute=absolute, \
relative=relative)
m.write("temp.rtin")
m.run("temp.rtout", mpi=mpi, n_processes=nprocesses)
n = ModelOutput("temp.rtout")
grid = n.get_quantities()
self.grid.temperature = []
temperature = grid.quantities['temperature']
for i in range(len(temperature)):
if (self.grid.coordsystem == "cartesian"):
self.grid.temperature.append(numpy.transpose(temperature[i], \
axes=(2,1,0)))
if (self.grid.coordsystem == "cylindrical"):
self.grid.temperature.append(numpy.transpose(temperature[i], \
axes=(2,0,1)))
if (self.grid.coordsystem == "spherical"):
self.grid.temperature.append(numpy.transpose(temperature[i], \
axes=(2,1,0)))
os.system("rm temp.rtin temp.rtout")
def DIG_source_add(m,reg,df_nu):
print("--------------------------------\n")
print("Adding DIG to Source List in source_creation\n")
print("--------------------------------\n")
print ("Getting specific energy dumped in each grid cell")
try:
rtout = cfg.model.outputfile + '.sed'
try:
grid_properties = np.load(cfg.model.PD_output_dir+"/grid_physical_properties."+cfg.model.snapnum_str+'_galaxy'+cfg.model.galaxy_num_str+".npz")
except:
grid_properties = np.load(cfg.model.PD_output_dir+"/grid_physical_properties."+cfg.model.snapnum_str+".npz")
cell_info = np.load(cfg.model.PD_output_dir+"/cell_info."+cfg.model.snapnum_str+"_"+cfg.model.galaxy_num_str+".npz")
except:
print ("ERROR: Can't proceed with DIG nebular emission calculation. Code is unable to find the required files.")
print ("Make sure you have the rtout.sed, grid_physical_properties.npz and cell_info.npz for the corresponding galaxy.")
return
m_out = ModelOutput(rtout)
oct = m_out.get_quantities()
grid = oct
order = find_order(grid.refined)
refined = grid.refined[order]
quantities = {}
for field in grid.quantities:
quantities[('gas', field)] = grid.quantities[field][0][order][~refined]
cell_width = cell_info["fw1"][:,0]
mass = (quantities['gas','density']*units.g/units.cm**3).value * (cell_width**3)
met = grid_properties["grid_gas_metallicity"]
specific_energy = (quantities['gas','specific_energy']*units.erg/units.s/units.g).value
specific_energy = (specific_energy * mass) # in ergs/s
# Black 1987 curve has a integrated ergs/s/cm2 of 0.0278 so the factor we need to multiply it by is given by this value
factor = specific_energy/(cell_width**2)/(0.0278)
mask1 = np.where(mass != 0 )[0]
mask = np.where((mass != 0 ) & (factor >= cfg.par.DIG_min_factor))[0] # Masking out all grid cells that have no gas mass and where the specific emergy is too low
print (len(factor), len(mask1), len(mask))
factor = factor[mask]
cell_width = cell_width[mask]
cell_x = (cell_info["xmax"] - cell_info["xmin"])[mask]
cell_y = (cell_info["ymax"] - cell_info["ymin"])[mask]
cell_z = (cell_info["zmax"] - cell_info["zmin"])[mask]
pos = np.vstack([cell_x, cell_y, cell_z]).transpose()
met = grid_properties["grid_gas_metallicity"][:, mask]
met = np.transpose(met)
fnu_arr = sg.get_dig_seds(factor, cell_width, met)
dat = np.load(cfg.par.pd_source_dir + "/powderday/nebular_emission/data/black_1987.npz")
spec_lam = dat["lam"]
nu = 1.e8 * constants.c.cgs.value / spec_lam
for i in range(len(factor)):
fnu = fnu_arr[i,:]
nu, fnu = wavelength_compress(nu,fnu,df_nu)
nu = nu[::-1]
fnu = fnu[::-1]
lum = np.absolute(np.trapz(fnu,x=nu))*constants.L_sun.cgs.value
source = m.add_point_source()
source.luminosity = lum # [ergs/s]
source.spectrum = (nu,fnu)
source.position = pos[i] # [cm]
m.set_spherical_polar_grid(r, t, p)
dens = zeros((nr-1,nt-1,np-1)) + 1.0e-17
m.add_density_grid(dens, d)
source = m.add_spherical_source()
source.luminosity = lsun
source.radius = rsun
source.temperature = 4000.
m.set_n_photons(initial=1000000, imaging=0)
m.set_convergence(True, percentile=99., absolute=2., relative=1.02)
m.write("test_spherical.rtin")
m.run("test_spherical.rtout", mpi=False)
n = ModelOutput('test_spherical.rtout')
grid = n.get_quantities()
temp = grid.quantities['temperature'][0]
for i in range(9):
plt.imshow(temp[i,:,:],origin="lower",interpolation="nearest", \
vmin=temp.min(),vmax=temp.max())
plt.colorbar()
plt.show()
def plot_results(cli):
file = filename(cli, "plot")
file += ".rtout"
#
# Read in the model:
#
model = ModelOutput(file)
if(cli.mode == "images"):
#
# Extract the quantities
#
g = model.get_quantities()
#
# Get the wall positions:
#
ww = g.w_wall / pc
zw = g.z_wall / pc
pw = g.p_wall
grid_Nw = len(ww) - 1
grid_Nz = len(zw) - 1
grid_Np = len(pw) - 1
#
# Graphics:
#
fig = plt.figure()
los = [0 for i in range(3)]
los[0] = 'x'
los[1] = 'y'
los[2] = 'z'
#Imaxp = [0 for i in range(4)]
##Imaxp[0] = 1e-4
#Imaxp[1] = 1e-5
#Imaxp[2] = 1e-7
#Imaxp[3] = 1e-8
for k in range(0, 3):
if(cli.verbose):
print("Group: ", k)
image = model.get_image(distance=1*pc, units='MJy/sr', inclination=0, component='total', group=k)
source_emit = model.get_image(distance=1*pc, units='MJy/sr', inclination=0, component='source_emit', group=k)
dust_emit = model.get_image(distance=1*pc, units='MJy/sr', inclination=0, component='dust_emit' , group=k)
source_scat = model.get_image(distance=1*pc, units='MJy/sr', inclination=0, component='source_scat', group=k)
dust_scat = model.get_image(distance=1*pc, units='MJy/sr', inclination=0, component='dust_scat' , group=k)
if(cli.verbose):
print(" Data cube: ", image.val.shape)
print(" Wavelengths =", image.wav)
print(" Uncertainties =", image.unc)
image_Nx=image.val.shape[0]
image_Ny=image.val.shape[1]
Nwavelength=image.val.shape[2]
if(cli.verbose):
print(" Image Nx =", image_Nx)
print(" Image Ny =", image_Ny)
print(" Nwavelength =", Nwavelength)
for i in range(0, Nwavelength):
if(cli.verbose):
print(" Image #", i,":")
print(" Wavelength =", image.wav[i])
Imin = np.min(image.val[:, :, i])
Imax = np.max(image.val[:, :, i])
# TODO: compute the mean value as well and use this for specifying the maximum value/color?!
if(cli.verbose):
print(" Intensity min =", Imin)
print(" Intensity max =", Imax)
#Imax=Imaxp[i]
#ax = fig.add_subplot(2, 1, 2)
ax = fig.add_subplot(1, 1, 1)
if(image.wav[i] < 10.0):
ax.imshow(source_scat.val[:, :, i] + dust_scat.val[:, :, i], vmin=Imin, vmax=Imax/10, cmap=plt.cm.gist_heat, origin='lower')
else:
ax.imshow(image.val[:, :, i], vmin=Imin, vmax=Imax/10, cmap=plt.cm.gist_heat, origin='lower')
ax.set_xticks([0,100,200,300], minor=False)
ax.set_yticks([0,100,200,300], minor=False)
ax.set_xlabel('x (pixel)')
ax.set_ylabel('y (pixel)')
ax.set_title(str(image.wav[i]) + ' microns' + '\n' + los[k] + '-direction', y=0.88, x=0.5, color='white')
#ax = fig.add_subplot(2, 1, 1)
#ax.imshow([np.logspace(np.log10(Imin+1e-10),np.log10(Imax/10),100),np.logspace(np.log10(Imin+1e-10),np.log10(Imax/10),100)], vmin=Imin, vmax=Imax/10, cmap=plt.cm.gist_heat)
#ax.set_xticks(np.logspace(np.log10(Imin+1e-10),np.log10(Imax/10),1), minor=False)
##ax.set_xticks(np.linspace(np.log10(Imin+1e-10),np.log10(Imax/10),10), minor=False)
#ax.set_yticks([], minor=False)
#ax.set_xlabel('flux (MJy/sr)')
file = filename(cli, "plot")
file += "_wavelength=" + str(image.wav[i]) + "micron_los=" + los[k] + ".png"
fig.savefig(file, bbox_inches='tight')
if(cli.verbose):
print(" The image graphics was written to", file)
plt.clf()
elif(cli.mode == "sed"):
#
# Graphics:
#
fig = plt.figure()
z_center = [0 for i in range(3)]
z_center[0] = '2.5'
z_center[1] = '5.0'
z_center[2] = '7.5'
for k in range(0, 3):
if(cli.verbose):
print("Group: ", k)
sed = model.get_sed(distance=1*pc, inclination=0, aperture=-1, group=k)
ax = fig.add_subplot(1, 1, 1)
ax.loglog(sed.wav, sed.val)
ax.set_xlabel(r'$\lambda$ [$\mu$m]')
ax.set_ylabel(r'$\lambda F_\lambda$ [ergs/s/cm$^2$]')
ax.set_xlim(0.01, 2000.0)
#ax.set_ylim(2.e-16, 2.e-9)
file = filename(cli, "plot")
file += "_z=" + z_center[k] + ".png"
fig.savefig(file)
if(cli.verbose):
print(" The sed graphics was written to", file)
plt.clf()
else:
print("ERROR: The specified mode", mode, "is not available. Use 'images' or 'sed' only.")
def hyperion_image(rtout,
wave,
plotdir,
printname,
dstar=178.,
group=0,
marker=0,
size='full',
convolve=False,
unit=None):
# to avoid X server error
import matplotlib as mpl
mpl.use('Agg')
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
import astropy.constants as const
from hyperion.model import ModelOutput
# Package for matching the colorbar
from mpl_toolkits.axes_grid1 import make_axes_locatable
pc = const.pc.cgs.value
if unit == None:
unit = r'$\rm{log(I_{\nu})\,[erg\,s^{-1}\,cm^{-2}\,Hz^{-1}\,sr^{-1}]}$'
m = ModelOutput(rtout)
# Extract the image.
image = m.get_image(group=group,
inclination=0,
distance=dstar * pc,
units='mJy')
print np.shape(image.val)
# Open figure and create axes
fig = plt.figure(figsize=(8, 8))
ax = fig.add_subplot(111)
# Find the closest wavelength
iwav = np.argmin(np.abs(wave - image.wav))
# Calculate the image width in arcseconds given the distance used above
# get the max radius
rmax = max(m.get_quantities().r_wall)
w = np.degrees(rmax / image.distance) * 3600.
# Image in the unit of MJy/sr
# Change it into erg/s/cm2/Hz/sr
# factor = 1e-23*1e6
factor = 1
# avoid zero in log
# flip the image, because the setup of inclination is upside down
val = image.val[::-1, :, iwav] * factor + 1e-30
if convolve:
from astropy.convolution import convolve, Gaussian2DKernel
img_res = 2 * w / len(val[:, 0])
kernel = Gaussian2DKernel(0.27 / 2.354 / img_res)
val = convolve(val, kernel)
if size != 'full':
pix_e2c = (w - size / 2.) / w * len(val[:, 0]) / 2
val = val[pix_e2c:-pix_e2c, pix_e2c:-pix_e2c]
w = size / 2.
# This is the command to show the image. The parameters vmin and vmax are
# the min and max levels for the colorscale (remove for default values).
# cmap = sns.cubehelix_palette(start=0.1, rot=-0.7, gamma=0.2, as_cmap=True)
cmap = plt.cm.CMRmap
# im = ax.imshow(np.log10(val), vmin= -20, vmax= -15,
# cmap=cmap, origin='lower', extent=[-w, w, -w, w], aspect=1)
im = ax.imshow(val,
cmap=cmap,
origin='lower',
extent=[-w, w, -w, w],
aspect=1)
print val.max()
# plot the marker for center position by default or user input offset
ax.plot([0], [-marker], '+', color='ForestGreen', markersize=10, mew=2)
ax.set_xlim([-w, w])
ax.set_ylim([-w, w])
# ax.plot([0],[-10], '+', color='m', markersize=10, mew=2)
# fix the tick label font
ticks_font = mpl.font_manager.FontProperties(family='STIXGeneral', size=14)
for label in ax.get_xticklabels():
label.set_fontproperties(ticks_font)
for label in ax.get_yticklabels():
label.set_fontproperties(ticks_font)
# Colorbar setting
# create an axes on the right side of ax. The width of cax will be 5%
# of ax and the padding between cax and ax will be fixed at 0.05 inch.
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
cb = fig.colorbar(im, cax=cax)
cb.solids.set_edgecolor("face")
cb.ax.minorticks_on()
cb.ax.set_ylabel(unit, fontsize=18)
cb_obj = plt.getp(cb.ax.axes, 'yticklabels')
plt.setp(cb_obj, fontsize=14)
# fix the tick label font
ticks_font = mpl.font_manager.FontProperties(family='STIXGeneral', size=14)
for label in cb.ax.get_yticklabels():
label.set_fontproperties(ticks_font)
ax.set_xlabel(r'$\rm{RA\,Offset\,(arcsec)}$', fontsize=18)
ax.set_ylabel(r'$\rm{Dec\,Offset\,(arcsec)}$', fontsize=18)
ax.tick_params(axis='both', which='major', labelsize=18)
ax.text(0.7,
0.88,
str(wave) + r'$\rm{\,\mu m}$',
fontsize=20,
color='white',
transform=ax.transAxes)
fig.savefig(plotdir + printname + '_image_' + str(wave) + '.pdf',
format='pdf',
dpi=300,
bbox_inches='tight')
fig.clf()
g2d = 100
mmw = 2.37
mh = const.m_p.cgs.value + const.m_e.cgs.value
AU = const.au.cgs.value
model = np.arange(99,133).astype('str')
# color map
cmap = plt.cm.viridis
color_array = [cmap(np.linspace(0, 0.9, len(model))[i]) for i in range(len(model))]
fig = plt.figure(figsize=(8,6))
ax = fig.add_subplot(111)
for i in range(len(model)):
m = ModelOutput('/home/bettyjo/yaolun/hyperion/bhr71/controlled/model'+model[i]+'/model'+model[i]+'.rtout')
q = m.get_quantities()
r = q.r_wall
rc = 0.5*(r[0:len(r)-1]+r[1:len(r)])
rho = q['density'][0].array
rho2d = np.sum(rho**2,axis=0)/np.sum(rho,axis=0)
plt.plot(np.log10(rc[rc > 0.14*AU]/AU), np.log10(rho2d[199,rc > 0.14*AU]/g2d/mmw/mh)-0.1*i, '-',
color=color_array[i], linewidth=1)
ax.set_ylim([-2,9])
ax.set_xlabel(r'$\rm{log(Radius)\,(AU)}$',fontsize=20)
ax.set_ylabel(r'$\rm{log(Dust\,Density)\,(cm^{-3})}$',fontsize=20)
[ax.spines[axis].set_linewidth(1.5) for axis in ['top','bottom','left','right']]
ax.minorticks_on()
ax.tick_params('both',labelsize=18,width=1.5,which='major',pad=15,length=5)
ax.tick_params('both',labelsize=18,width=1.5,which='minor',pad=15,length=2.5)
# fix the tick label font
def extract_hyperion(filename,
indir=None,
outdir=None,
dstar=178.0,
wl_aper=None,
save=True):
def l_bol(wl, fv, dist=178.0):
import numpy as np
import astropy.constants as const
# wavelength unit: um
# Flux density unit: Jy
#
# constants setup
#
c = const.c.cgs.value
pc = const.pc.cgs.value
PI = np.pi
SL = const.L_sun.cgs.value
# Convert the unit from Jy to erg s-1 cm-2 Hz-1
fv = np.array(fv) * 1e-23
freq = c / (1e-4 * np.array(wl))
diff_dum = freq[1:] - freq[0:-1]
freq_interpol = np.hstack(
(freq[0:-1] + diff_dum / 2.0, freq[0:-1] + diff_dum / 2.0, freq[0],
freq[-1]))
freq_interpol = freq_interpol[np.argsort(freq_interpol)[::-1]]
fv_interpol = np.empty(len(freq_interpol))
# calculate the histogram style of spectrum
#
for i in range(0, len(fv)):
if i == 0:
fv_interpol[i] = fv[i]
else:
fv_interpol[2 * i - 1] = fv[i - 1]
fv_interpol[2 * i] = fv[i]
fv_interpol[-1] = fv[-1]
dv = freq_interpol[0:-1] - freq_interpol[1:]
dv = np.delete(dv, np.where(dv == 0))
fv = fv[np.argsort(freq)]
freq = freq[np.argsort(freq)]
return (np.trapz(fv, freq) * 4. * PI * (dist * pc)**2) / SL
import matplotlib.pyplot as plt
import numpy as np
import os
from hyperion.model import ModelOutput
from hyperion.model import Model
from scipy.interpolate import interp1d
from hyperion.util.constants import pc, c, lsun
# Read in the observation data and calculate the noise & variance
if indir == None:
indir = '/Users/yaolun/bhr71/'
if outdir == None:
outdir = '/Users/yaolun/bhr71/hyperion/'
# assign the file name from the input file
print_name = os.path.splitext(os.path.basename(filename))[0]
#
[wl_pacs,flux_pacs,unc_pacs] = np.genfromtxt(indir+'BHR71_centralSpaxel_PointSourceCorrected_CorrectedYES_trim_continuum.txt',\
dtype='float',skip_header=1).T
# Convert the unit from Jy to erg cm-2 Hz-1
flux_pacs = flux_pacs * 1e-23
[wl_spire,
flux_spire] = np.genfromtxt(indir + 'BHR71_spire_corrected_continuum.txt',
dtype='float',
skip_header=1).T
flux_spire = flux_spire * 1e-23
wl_obs = np.hstack((wl_pacs, wl_spire))
flux_obs = np.hstack((flux_pacs, flux_spire))
[wl_pacs_data,flux_pacs_data,unc_pacs_data] = np.genfromtxt(indir+'BHR71_centralSpaxel_PointSourceCorrected_CorrectedYES_trim.txt',\
dtype='float').T
[wl_spire_data,flux_spire_data] = np.genfromtxt(indir+'BHR71_spire_corrected.txt',\
dtype='float').T
[wl_pacs_flat,flux_pacs_flat,unc_pacs_flat] = np.genfromtxt(indir+'BHR71_centralSpaxel_PointSourceCorrected_CorrectedYES_trim_flat_spectrum.txt',\
dtype='float',skip_header=1).T
[wl_spire_flat, flux_spire_flat
] = np.genfromtxt(indir + 'BHR71_spire_corrected_flat_spectrum.txt',
dtype='float',
skip_header=1).T
# Convert the unit from Jy to erg cm-2 Hz-1
flux_pacs_flat = flux_pacs_flat * 1e-23
flux_spire_flat = flux_spire_flat * 1e-23
flux_pacs_data = flux_pacs_data * 1e-23
flux_spire_data = flux_spire_data * 1e-23
wl_pacs_noise = wl_pacs_data
flux_pacs_noise = flux_pacs_data - flux_pacs - flux_pacs_flat
wl_spire_noise = wl_spire_data
flux_spire_noise = flux_spire_data - flux_spire - flux_spire_flat
# Read in the Spitzer IRS spectrum
[wl_irs, flux_irs] = (np.genfromtxt(indir + 'bhr71_spitzer_irs.txt',
skip_header=2,
dtype='float').T)[0:2]
# Convert the unit from Jy to erg cm-2 Hz-1
flux_irs = flux_irs * 1e-23
# Remove points with zero or negative flux
ind = flux_irs > 0
wl_irs = wl_irs[ind]
flux_irs = flux_irs[ind]
# Calculate the local variance (for spire), use the instrument uncertainty for pacs
#
wl_noise_5 = wl_spire_noise[(wl_spire_noise > 194) *
(wl_spire_noise <= 304)]
flux_noise_5 = flux_spire_noise[(wl_spire_noise > 194) *
(wl_spire_noise <= 304)]
wl_noise_6 = wl_spire_noise[wl_spire_noise > 304]
flux_noise_6 = flux_spire_noise[wl_spire_noise > 304]
wl_noise = [wl_pacs_data[wl_pacs_data <= 190.31], wl_noise_5, wl_noise_6]
flux_noise = [unc_pacs[wl_pacs_data <= 190.31], flux_noise_5, flux_noise_6]
sig_num = 20
sigma_noise = []
for i in range(0, len(wl_noise)):
sigma_dum = np.zeros([len(wl_noise[i])])
for iwl in range(0, len(wl_noise[i])):
if iwl < sig_num / 2:
sigma_dum[iwl] = np.std(
np.hstack((flux_noise[i][0:sig_num / 2],
flux_noise[i][0:sig_num / 2 - iwl])))
elif len(wl_noise[i]) - iwl < sig_num / 2:
sigma_dum[iwl] = np.std(
np.hstack(
(flux_noise[i][iwl:],
flux_noise[i][len(wl_noise[i]) - sig_num / 2:])))
else:
sigma_dum[iwl] = np.std(flux_noise[i][iwl - sig_num / 2:iwl +
sig_num / 2])
sigma_noise = np.hstack((sigma_noise, sigma_dum))
sigma_noise = np.array(sigma_noise)
# Read in the photometry data
phot = np.genfromtxt(indir + 'bhr71.txt',
dtype=None,
skip_header=1,
comments='%')
wl_phot = []
flux_phot = []
flux_sig_phot = []
note = []
for i in range(0, len(phot)):
wl_phot.append(phot[i][0])
flux_phot.append(phot[i][1])
flux_sig_phot.append(phot[i][2])
note.append(phot[i][4])
wl_phot = np.array(wl_phot)
# Convert the unit from Jy to erg cm-2 Hz-1
flux_phot = np.array(flux_phot) * 1e-23
flux_sig_phot = np.array(flux_sig_phot) * 1e-23
# Print the observed L_bol
wl_tot = np.hstack((wl_irs, wl_obs, wl_phot))
flux_tot = np.hstack((flux_irs, flux_obs, flux_phot))
flux_tot = flux_tot[np.argsort(wl_tot)]
wl_tot = wl_tot[np.argsort(wl_tot)]
l_bol_obs = l_bol(wl_tot, flux_tot * 1e23)
# Open the model
m = ModelOutput(filename)
if wl_aper == None:
wl_aper = [
3.6, 4.5, 5.8, 8.0, 10, 16, 20, 24, 35, 70, 100, 160, 250, 350,
500, 850
]
# Create the plot
mag = 1.5
fig = plt.figure(figsize=(8 * mag, 6 * mag))
ax_sed = fig.add_subplot(1, 1, 1)
# Plot the observed SED
# plot the observed spectra
pacs, = ax_sed.plot(np.log10(wl_pacs),
np.log10(c / (wl_pacs * 1e-4) * flux_pacs),
'-',
color='DimGray',
linewidth=1.5 * mag,
alpha=0.7)
spire, = ax_sed.plot(np.log10(wl_spire),
np.log10(c / (wl_spire * 1e-4) * flux_spire),
'-',
color='DimGray',
linewidth=1.5 * mag,
alpha=0.7)
irs, = ax_sed.plot(np.log10(wl_irs),
np.log10(c / (wl_irs * 1e-4) * flux_irs),
'-',
color='DimGray',
linewidth=1.5 * mag,
alpha=0.7)
# ax_sed.text(0.75,0.9,r'$\rm{L_{bol}= %5.2f L_{\odot}}$' % l_bol_obs,fontsize=mag*16,transform=ax_sed.transAxes)
# plot the observed photometry data
photometry, = ax_sed.plot(np.log10(wl_phot),
np.log10(c / (wl_phot * 1e-4) * flux_phot),
's',
mfc='DimGray',
mec='k',
markersize=8)
ax_sed.errorbar(np.log10(wl_phot),np.log10(c/(wl_phot*1e-4)*flux_phot),\
yerr=[np.log10(c/(wl_phot*1e-4)*flux_phot)-np.log10(c/(wl_phot*1e-4)*(flux_phot-flux_sig_phot)),\
np.log10(c/(wl_phot*1e-4)*(flux_phot+flux_sig_phot))-np.log10(c/(wl_phot*1e-4)*flux_phot)],\
fmt='s',mfc='DimGray',mec='k',markersize=8)
# Extract the SED for the smallest inclination and largest aperture, and
# scale to 300pc. In Python, negative indices can be used for lists and
# arrays, and indicate the position from the end. So to get the SED in the
# largest aperture, we set aperture=-1.
# aperture group is aranged from smallest to infinite
sed_inf = m.get_sed(group=0,
inclination=0,
aperture=-1,
distance=dstar * pc)
# l_bol_sim = l_bol(sed_inf.wav, sed_inf.val/(c/sed_inf.wav*1e4)*1e23)
# print sed.wav, sed.val
# print 'Bolometric luminosity of simulated spectrum: %5.2f lsun' % l_bol_sim
# plot the simulated SED
# sim, = ax_sed.plot(np.log10(sed_inf.wav), np.log10(sed_inf.val), '-', color='k', linewidth=1.5*mag, alpha=0.7)
# get flux at different apertures
flux_aper = np.empty_like(wl_aper)
unc_aper = np.empty_like(wl_aper)
for i in range(0, len(wl_aper)):
sed_dum = m.get_sed(group=i + 1,
inclination=0,
aperture=-1,
distance=dstar * pc)
# use a rectangle function the average the simulated SED
# apply the spectral resolution
if (wl_aper[i] < 50.) & (wl_aper[i] >= 5):
res = 60.
elif wl_aper[i] < 5:
res = 10.
else:
res = 1000.
ind = np.where((sed_dum.wav < wl_aper[i] * (1 + 1. / res))
& (sed_dum.wav > wl_aper[i] * (1 - 1. / res)))
if len(ind[0]) != 0:
flux_aper[i] = np.mean(sed_dum.val[ind])
else:
f = interp1d(sed_dum.wav, sed_dum.val)
flux_aper[i] = f(wl_aper[i])
# perform the same procedure of flux extraction of aperture flux with observed spectra
wl_aper = np.array(wl_aper)
obs_aper_wl = wl_aper[(wl_aper >= min(wl_irs))
& (wl_aper <= max(wl_spire))]
obs_aper_sed = np.empty_like(obs_aper_wl)
sed_tot = c / (wl_tot * 1e-4) * flux_tot
# wl_tot and flux_tot are already hstacked and sorted by wavelength
for i in range(0, len(obs_aper_wl)):
if (obs_aper_wl[i] < 50.) & (obs_aper_wl[i] >= 5):
res = 60.
elif obs_aper_wl[i] < 5:
res = 10.
else:
res = 1000.
ind = np.where((wl_tot < obs_aper_wl[i] * (1 + 1. / res))
& (wl_tot > obs_aper_wl[i] * (1 - 1. / res)))
if len(ind[0]) != 0:
obs_aper_sed[i] = np.mean(sed_tot[ind])
else:
f = interp1d(wl_tot, sed_tot)
obs_aper_sed[i] = f(wl_aper[i])
aper_obs, = ax_sed.plot(np.log10(obs_aper_wl),
np.log10(obs_aper_sed),
's-',
mec='None',
mfc='r',
color='r',
markersize=10,
linewidth=1.5)
# # interpolate the uncertainty (maybe not the best way to do this)
# print sed_dum.unc
# f = interp1d(sed_dum.wav, sed_dum.unc)
# unc_aper[i] = f(wl_aper[i])
# if wl_aper[i] == 9.7:
# ax_sed.plot(np.log10(sed_dum.wav), np.log10(sed_dum.val), '-', linewidth=1.5*mag)
# print l_bol(sed_dum.wav, sed_dum.val/(c/sed_dum.wav*1e4)*1e23)
aper, = ax_sed.plot(np.log10(wl_aper),
np.log10(flux_aper),
'o-',
mec='Blue',
mfc='None',
color='b',
markersize=12,
markeredgewidth=3,
linewidth=1.7)
# calculate the bolometric luminosity of the aperture
l_bol_sim = l_bol(wl_aper,
flux_aper / (c / np.array(wl_aper) * 1e4) * 1e23)
print 'Bolometric luminosity of simulated spectrum: %5.2f lsun' % l_bol_sim
# print out the sed into ascii file for reading in later
if save == True:
# unapertured SED
foo = open(outdir + print_name + '_sed_inf.txt', 'w')
foo.write('%12s \t %12s \n' % ('wave', 'vSv'))
for i in range(0, len(sed_inf.wav)):
foo.write('%12g \t %12g \n' % (sed_inf.wav[i], sed_inf.val[i]))
foo.close()
# SED with convolution of aperture sizes
foo = open(outdir + print_name + '_sed_w_aperture.txt', 'w')
foo.write('%12s \t %12s \n' % ('wave', 'vSv'))
for i in range(0, len(wl_aper)):
foo.write('%12g \t %12g \n' % (wl_aper[i], flux_aper[i]))
foo.close()
# Read in and plot the simulated SED produced by RADMC-3D using the same parameters
# [wl,fit] = np.genfromtxt(indir+'hyperion/radmc_comparison/spectrum.out',dtype='float',skip_header=3).T
# l_bol_radmc = l_bol(wl,fit*1e23/dstar**2)
# radmc, = ax_sed.plot(np.log10(wl),np.log10(c/(wl*1e-4)*fit/dstar**2),'-',color='DimGray', linewidth=1.5*mag, alpha=0.5)
# print the L bol of the simulated SED (both Hyperion and RADMC-3D)
# lg_sim = ax_sed.legend([sim,radmc],[r'$\rm{L_{bol,sim}=%5.2f~L_{\odot},~L_{center}=9.18~L_{\odot}}$' % l_bol_sim, \
# r'$\rm{L_{bol,radmc3d}=%5.2f~L_{\odot},~L_{center}=9.18~L_{\odot}}$' % l_bol_radmc],\
# loc='lower right',fontsize=mag*16)
# read the input central luminosity by reading in the source information from output file
dum = Model()
dum.use_sources(filename)
L_cen = dum.sources[0].luminosity / lsun
# lg_sim = ax_sed.legend([sim],[r'$\rm{L_{bol,sim}=%5.2f~L_{\odot},~L_{center}=%5.2f~L_{\odot}}$' % (l_bol_sim, L_cen)], \
# loc='lower right',fontsize=mag*16)
# lg_sim = ax_sed.legend([sim],[r'$\rm{L_{bol,sim}=%5.2f~L_{\odot},~L_{bol,obs}=%5.2f~L_{\odot}}$' % (l_bol_sim, l_bol_obs)], \
# loc='lower right',fontsize=mag*16)
# text = ax_sed.text(0.2 ,0.05 ,r'$\rm{L_{bol,simulation}=%5.2f~L_{\odot},~L_{bol,observation}=%5.2f~L_{\odot}}$' % (l_bol_sim, l_bol_obs),fontsize=mag*16,transform=ax_sed.transAxes)
# text.set_bbox(dict( edgecolor='k',facecolor='None',alpha=0.3,pad=10.0))
# plot setting
ax_sed.set_xlabel(r'$\rm{log\,\lambda\,({\mu}m)}$', fontsize=mag * 20)
ax_sed.set_ylabel(r'$\rm{log\,\nu S_{\nu}\,(erg\,cm^{-2}\,s^{-1})}$',
fontsize=mag * 20)
[
ax_sed.spines[axis].set_linewidth(1.5 * mag)
for axis in ['top', 'bottom', 'left', 'right']
]
ax_sed.minorticks_on()
ax_sed.tick_params('both',
labelsize=mag * 18,
width=1.5 * mag,
which='major',
pad=15,
length=5 * mag)
ax_sed.tick_params('both',
labelsize=mag * 18,
width=1.5 * mag,
which='minor',
pad=15,
length=2.5 * mag)
ax_sed.set_ylim([-13, -7.5])
ax_sed.set_xlim([0, 3])
# lg_data = ax_sed.legend([sim, aper], [r'$\rm{w/o~aperture}$', r'$\rm{w/~aperture}$'], \
# loc='upper left', fontsize=14*mag, framealpha=0.3, numpoints=1)
lg_data = ax_sed.legend([irs, photometry, aper, aper_obs],\
[r'$\rm{observation}$',\
r'$\rm{photometry}$',r'$\rm{F_{aper,sim}}$',r'$\rm{F_{aper,obs}}$'],\
loc='upper left',fontsize=14*mag,numpoints=1,framealpha=0.3)
# plt.gca().add_artist(lg_sim)
# Write out the plot
fig.savefig(outdir + print_name + '_sed.pdf',
format='pdf',
dpi=300,
bbox_inches='tight')
fig.clf()
# Package for matching the colorbar
from mpl_toolkits.axes_grid1 import make_axes_locatable
# Extract the image for the first inclination, and scale to 300pc. We
# have to specify group=1 as there is no image in group 0.
image = m.get_image(group=len(wl_aper) + 1,
inclination=0,
distance=dstar * pc,
units='MJy/sr')
# image = m.get_image(group=14, inclination=0, distance=dstar * pc, units='MJy/sr')
# Open figure and create axes
# fig = plt.figure(figsize=(8, 8))
fig, axarr = plt.subplots(3,
3,
sharex='col',
sharey='row',
figsize=(13.5, 12))
# Pre-set maximum for colorscales
VMAX = {}
# VMAX[3.6] = 10.
# VMAX[24] = 100.
# VMAX[160] = 2000.
# VMAX[500] = 2000.
VMAX[100] = 10.
VMAX[250] = 100.
VMAX[500] = 2000.
VMAX[1000] = 2000.
# We will now show four sub-plots, each one for a different wavelength
# for i, wav in enumerate([3.6, 24, 160, 500]):
# for i, wav in enumerate([100, 250, 500, 1000]):
# for i, wav in enumerate([4.5, 9.7, 24, 40, 70, 100, 250, 500, 1000]):
for i, wav in enumerate([3.6, 8.0, 9.7, 24, 40, 100, 250, 500, 1000]):
# ax = fig.add_subplot(3, 3, i + 1)
ax = axarr[i / 3, i % 3]
# Find the closest wavelength
iwav = np.argmin(np.abs(wav - image.wav))
# Calculate the image width in arcseconds given the distance used above
rmax = max(m.get_quantities().r_wall)
w = np.degrees(rmax / image.distance) * 3600.
# w = np.degrees((1.5 * pc) / image.distance) * 60.
# Image in the unit of MJy/sr
# Change it into erg/s/cm2/Hz/sr
factor = 1e-23 * 1e6
# avoid zero in log
val = image.val[:, :, iwav] * factor + 1e-30
# This is the command to show the image. The parameters vmin and vmax are
# the min and max levels for the colorscale (remove for default values).
im = ax.imshow(np.log10(val),
vmin=-22,
vmax=-12,
cmap=plt.cm.jet,
origin='lower',
extent=[-w, w, -w, w],
aspect=1)
# Colorbar setting
# create an axes on the right side of ax. The width of cax will be 5%
# of ax and the padding between cax and ax will be fixed at 0.05 inch.
if (i + 1) % 3 == 0:
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
cb = fig.colorbar(im, cax=cax)
cb.solids.set_edgecolor("face")
cb.ax.minorticks_on()
cb.ax.set_ylabel(
r'$\rm{log(I_{\nu})\,[erg\,s^{-2}\,cm^{-2}\,Hz^{-1}\,sr^{-1}]}$',
fontsize=12)
cb_obj = plt.getp(cb.ax.axes, 'yticklabels')
plt.setp(cb_obj, fontsize=12)
if (i + 1) == 7:
# Finalize the plot
ax.set_xlabel('RA Offset (arcsec)', fontsize=14)
ax.set_ylabel('Dec Offset (arcsec)', fontsize=14)
ax.tick_params(axis='both', which='major', labelsize=16)
ax.set_adjustable('box-forced')
ax.text(0.7,
0.88,
str(wav) + r'$\rm{\,\mu m}$',
fontsize=18,
color='white',
weight='bold',
transform=ax.transAxes)
fig.subplots_adjust(hspace=0, wspace=-0.2)
# Adjust the spaces between the subplots
# plt.tight_layout()
fig.savefig(outdir + print_name + '_cube_plot.png',
format='png',
dpi=300,
bbox_inches='tight')
fig.clf()
#Script showing how to extract some rtout files that were run on an
#octree format
from __future__ import print_function
from hyperion.model import ModelOutput
from hyperion.grid.yt3_wrappers import find_order
import astropy.units as u
import numpy as np
run = '/home/desika.narayanan/pd_git/tests/SKIRT/gizmo_mw_zoom/pd_skirt_comparison.134.rtout.sed'
m = ModelOutput(run)
oct = m.get_quantities()
#ds = oct.to_yt()
#ripped from hyperion/grid/yt3_wrappers.py -- we do this because
#something about load_octree in yt4.x is only returning the first cell
grid = oct
order = find_order(grid.refined)
refined = grid.refined[order]
quantities = {}
for field in grid.quantities:
quantities[('gas', field)] = np.atleast_2d(grid.quantities[field][0][order][~refined]).transpose()
specific_energy = quantities['gas','specific_energy']*u.erg/u.s/u.g
dust_temp = quantities['gas','temperature']*u.K
dust_density = quantities['gas','density']*u.g/u.cm**3
import numpy as np
from hyperion.model import ModelOutput
import matplotlib.pyplot as plt
from yt.mods import write_bitmap, ColorTransferFunction
plt.rcParams['font.family'] = 'Arial'
# Read in model from Hyperion
m = ModelOutput('pla704850_lev7_129.rtout')
grid = m.get_quantities()
# Convert quantities to yt
pf = grid.to_yt()
# Instantiate the ColorTransferfunction.
tmin, tmax = 1.3, 2.3
tf_temp = ColorTransferFunction((tmin, tmax))
dmin, dmax = -20, -16
tf_dens = ColorTransferFunction((dmin, dmax))
# Set up the camera parameters: center, looking direction, width, resolution
c = (pf.domain_right_edge + pf.domain_left_edge) / 2.0
L = np.array([1.0, 1.0, 1.0])
W = 0.7 / pf["unitary"]
N = 512
# Create camera objects
cam_temp = pf.h.camera(c,
