def export_to_fits(cli):
#
# Read in the model:
#
file = filename(cli, "plot")
file += ".rtout"
model = ModelOutput(file)
#
# Write fits file:
#
if(cli.mode == "images"):
los = [0 for i in range(3)]
los[0] = 'x'
los[1] = 'y'
los[2] = 'z'
for k in range(0, 3):
image = model.get_image(distance=1*pc, units='MJy/sr', inclination=0, component='total', group=k)
Nwavelength=image.val.shape[2]
for i in range(0, Nwavelength):
file = filename(cli, "fits")
file += "_wavelength=" + str(image.wav[i]) + "micron_los=" + los[k] + ".fits"
fits.writeto(file, image.val[:, :, i], clobber=True)
if(cli.verbose):
print(" The fits file was written to", file)
else:
print("ERROR: The specified mode", mode, "is not available. Use 'images' only.")
def get_image(filename, dist):
try:
m = ModelOutput(filename)
return m.get_image(inclination='all',
distance=luminosity_distance,
units='Jy')
except (OSError, ValueError) as e:
print("OS Error in reading in: " + filename)
pass
def test_docs_example(self):
import numpy as np
from hyperion.model import ModelOutput
from hyperion.util.constants import pc
from fluxcompensator.cube import *
from fluxcompensator.psf import *
from fluxcompensator.utils.resolution import *
# read in from HYPERION
m = ModelOutput(
os.path.join(os.path.dirname(__file__), 'hyperion_output.rtout'))
array = m.get_image(group=0,
inclination=0,
distance=300 * pc,
units='ergs/cm^2/s')
# initial FluxCompensator array
c = SyntheticCube(input_array=array,
unit_out='ergs/cm^2/s',
name='test_cube')
# dered with provided extinction law
ext = c.extinction(A_v=20.)
# change resolution to 10-times of the initial
zoom = ext.change_resolution(new_resolution=10 *
ext.resolution['arcsec'],
grid_plot=True)
import fluxcompensator.database.missions as PSFs
# call object from the psf database
psf_object = getattr(PSFs, 'PACS1_PSF')
# convolve with PSF
psf = zoom.convolve_psf(psf_object)
import fluxcompensator.database.missions as filters
# call object from the filter database
filter_input = getattr(filters, 'PACS1_FILTER')
# convolve with filter
filtered = psf.convolve_filter(filter_input,
plot_rebin=None,
plot_rebin_dpi=None)
# add noise
noise = filtered.add_noise(mu_noise=0,
sigma_noise=5e-15,
diagnostics=None)
def extract(model):
# Extract model name
model_name = os.path.basename(model).replace('.rtout', '').replace('external_', '')
m = ModelOutput(model)
wav, flux = m.get_image(group=0, units='MJy/sr', distance=1000. * kpc) # distance should not matter as long as it is large
flux = flux[0, :, :, :]
# Convolve with filters
flux_conv = np.zeros((len(filters), flux.shape[0], flux.shape[1]))
for i, filtname in enumerate(filters):
transmission = rebin_filter(filtname, c / (wav * 1.e-4))
flux_conv[i, :, :] = np.sum(transmission[np.newaxis, np.newaxis:] * flux, axis=2)
pyfits.writeto('models/external/external_%s.fits' % model_name, flux, clobber=True)
pyfits.writeto('models/external/external_%s_conv.fits' % model_name, flux_conv, clobber=True)
def setup_method(self, method):
import numpy as np
from hyperion.model import ModelOutput
from hyperion.util.constants import kpc
from fluxcompensator.cube import *
# read in from HYPERION
m = ModelOutput(
os.path.join(os.path.dirname(__file__), 'hyperion_output.rtout'))
array = m.get_image(group=0,
inclination=0,
distance=10 * kpc,
units='ergs/cm^2/s')
# initial FluxCompensator array
self.FC_object = SyntheticCube(input_array=array,
unit_out='ergs/cm^2/s',
name='test_cube')
import numpy as np
import matplotlib.pyplot as plt
from hyperion.model import ModelOutput
from hyperion.util.integrate import integrate_loglog
# Use LaTeX for plots
plt.rc('text', usetex=True)
# Open the output file
m = ModelOutput('example_isrf.rtout')
# Get an all-sky flux map
image = m.get_image(units='ergs/cm^2/s/Hz', inclination=0)
# Compute the frequency-integrated flux
fint = np.zeros(image.val.shape[:-1])
for (j, i) in np.ndindex(fint.shape):
fint[j, i] = integrate_loglog(image.nu, image.val[j, i, :])
# Find the area of each pixel
l = np.radians(np.linspace(180., -180., fint.shape[1] + 1))
b = np.radians(np.linspace(-90., 90., fint.shape[0] + 1))
dl = l[1:] - l[:-1]
db = np.sin(b[1:]) - np.sin(b[:-1])
DL, DB = np.meshgrid(dl, db)
area = np.abs(DL * DB)
# Compute the intensity
intensity = fint / area
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_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()
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()
type=float,
help='Minimum of colorbar scale, in units of ergs/s.')
parser.add_argument('--vmax',
type=float,
help='Maximum of colorbar scale, in units of ergs/s.')
args = parser.parse_args()
m = ModelOutput(pathch(args.infile))
if args.outfile is None:
args.outfile = os.path.dirname(args.infile)
# 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(units='ergs/s')
# Open figure and create axes
fig = plt.figure()
ax = fig.add_subplot(111)
# Calculate the image width in kpc
w = image.x_max * u.cm
w = w.to(u.kpc)
# Find the closest wavelength
iwav = np.argmin(np.abs(args.wav - image.wav))
print('Input wavelength: {}'.format(args.wav))
print('Closest: {}'.format(image.wav[iwav]))
image_data = image.val[0, :, :, iwav]
default_image_suffix = '{:.4f}um'.format(image.wav[iwav])
def convolve(image_file, filterfilenames, filter_data):
# Load the model output object
m = ModelOutput(image_file)
# Get the image
image = m.get_image(units='ergs/s')
# Get image bounds for correct scaling
w = image.x_max * u.cm
w = w.to(u.kpc)
# This is where the convolved images will go
image_data = []
# List the filters that shouldn't be used in convolution
skip_conv = ['arbitrary.filter', 'pdfilters.dat']
# Loop through the filters and match wavelengths to those in the image
for i in range(len(filterfilenames)):
# Skip "arbitrary.filter" if it is selected
if filterfilenames[i] in skip_conv:
print(" Skipping convolution of default filter")
continue
print("\n Convolving filter {}...".format(filterfilenames[i]))
wavs = filter_data[i][:, 0]
# Figure out which indices of the image wavelengths correspond to
# this filter
indices = []
for wav in wavs:
diffs = np.abs(image.wav - wav)
# Make sure the closest wavelength is *really* close --- there
# could be rounding errors, but we don't want to accidentally grab
# the wrong wavelength
if min(diffs) <= 1e-10:
indices.append(diffs.argmin())
if len(indices) != len(wavs):
raise ValueError(
"Filter wavelength mismatch with available image wavelengths")
# Get the monochromatic images at each wavelength in the filter
images = [image.val[0, :, :, j] for j in indices]
print(' Found {} monochromatic images'.format(len(images)))
# Show wavelengths and weights from filter file
wavelengths = [image.wav[j] for j in indices]
weights = filter_data[i][:, 1]
print('\n Wavelength Weight')
print(' ---------- ------')
for k in range(len(wavelengths)):
print(' {:.2E} {:.2E}'.format(
wavelengths[k], weights[k]))
# Apply appropriate transmissivities from filter file
image_data.append(np.average(images, axis=0, weights=weights))
# Save the image data and filter information as an .hdf5 file
f = h5py.File(
cfg.model.PD_output_dir + "convolved." + cfg.model.snapnum_str +
".hdf5", "w")
f.create_dataset("image_data", data=image_data)
f['image_data'].attrs['width'] = w.value
f['image_data'].attrs['width_unit'] = np.bytes_('kpc')
# Don't add the names of filters that were skipped
trimmed_names = list(set(filterfilenames) - set(skip_conv))
f.create_dataset("filter_names", data=trimmed_names)
for i in range(len(filterfilenames)):
f.create_dataset(filterfilenames[i], data=filter_data[i])
f.close()
import numpy as np
import matplotlib.pyplot as plt
from hyperion.model import ModelOutput
from hyperion.util.integrate import integrate_loglog
# Use LaTeX for plots
plt.rc('text', usetex=True)
# Open the output file
m = ModelOutput('example_isrf.rtout')
# Get an all-sky flux map
image = m.get_image(units='ergs/cm^2/s/Hz', inclination=0)
# Compute the frequency-integrated flux
fint = np.zeros(image.val.shape[:-1])
for (j, i) in np.ndindex(fint.shape):
fint[j, i] = integrate_loglog(image.nu, image.val[j, i, :])
# Find the area of each pixel
l = np.radians(np.linspace(180., -180., fint.shape[1] + 1))
b = np.radians(np.linspace(-90., 90., fint.shape[0] + 1))
dl = l[1:] - l[:-1]
db = np.sin(b[1:]) - np.sin(b[:-1])
DL, DB = np.meshgrid(dl, db)
area = np.abs(DL * DB)
# Compute the intensity
intensity = fint / area
outdir = '/Users/yaolun/test/' dist = 178. wave = 500. from hyperion.model import ModelOutput import astropy.constants as const import numpy as np import matplotlib.pyplot as plt from matplotlib import font_manager 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
import matplotlib.pyplot as plt
from hyperion.model import ModelOutput
from hyperion.util.constants import pc
mo = ModelOutput('pure_scattering.rtout')
image_fnu = mo.get_image(inclination=0, units='MJy/sr', distance=300. * pc)
image_pol = mo.get_image(inclination=0, stokes='linpol')
fig = plt.figure(figsize=(8, 8))
# Make total intensity sub-plot
ax = fig.add_axes([0.1, 0.3, 0.4, 0.4])
ax.imshow(image_fnu.val[:, :, 0], extent=[-13, 13, -13, 13],
interpolation='none', cmap=plt.cm.gist_heat,
origin='lower', vmin=0., vmax=4e9)
ax.set_xlim(-13., 13.)
ax.set_ylim(-13., 13.)
ax.set_xlabel("x (solar radii)")
ax.set_ylabel("y (solar radii)")
ax.set_title("Surface brightness")
# Make linear polarization sub-plot
ax = fig.add_axes([0.51, 0.3, 0.4, 0.4])
im = ax.imshow(image_pol.val[:, :, 0] * 100., extent=[-13, 13, -13, 13],
interpolation='none', cmap=plt.cm.gist_heat,
origin='lower', vmin=0., vmax=100.)
ax.set_xlim(-13., 13.)
ax.set_ylim(-13., 13.)
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.")
f2 = np.loadtxt(image_dat_file_2)
diff = f1 - f2
OUTPUT_DIR = '/home/cmcclellan1010/pdwork/output/'
np.savetxt(OUTPUT_DIR+'difference.dat', diff)
# Image data
path = '/home/cmcclellan1010/pdwork/output/manualconv/'
filename = 'example.134.rtout.image'
m = ModelOutput(path+filename)
redshift = 3.1
image_width = 200 #kpc
distance = Planck13.luminosity_distance(redshift).cgs.value
image = m.get_image(distance=distance, units='mJy')
w = image.x_max * u.cm
w = w.to(u.kpc)
# Plot the figure
fig = plt.figure()
ax = fig.add_subplot(111)
cax = ax.imshow(diff, cmap=plt.cm.viridis, origin='lower',
extent=[-w.value, w.value, -w.value, w.value])
plt.xlim([-image_width,image_width])
plt.ylim([-image_width,image_width])
ax.tick_params(axis='both', which='major', labelsize=10)
ax.set_xlabel('x kpc')
ax.set_xlabel('y kpc')
def extract(model):
# Check that file is valid
if not os.path.basename(model).startswith('basic_') or not os.path.basename(model).endswith('.rtout'):
raise Exception("Only basic_*.rtout files should be specified")
# Extract model name
model_name = os.path.basename(model).replace('.rtout', '').replace('basic_', '')
m = ModelOutput('models/basic/basic_%s.rtout' % model_name)
for image_set in range(3):
if image_set == 0:
n_x = 1
n_y = 1
image_set_name = 'total'
elif image_set == 1:
n_x = 130
n_y = 1
image_set_name = 'lon'
elif image_set == 2:
n_x = 1
n_y = 40
image_set_name = 'lat'
flux = np.zeros((n_y, n_x, n_groups, n_wav))
print "Direct source emission"
try:
wav, nufnu_all = m.get_image(group=image_set, component='source_emit', units='MJy/sr', source_id='all')
except IOError:
return
for source_id in range(n_sources):
nufnu = nufnu_all[source_id, 0, :, :, :]
spec_type = t_default['Type'][source_id].strip().upper()
group_id = group(spec_type)
flux[:, :, group_id, :] += nufnu
print "Direct dust emission"
wav, nufnu_all = m.get_image(group=image_set, component='dust_emit', units='MJy/sr', dust_id='all')
for dust_id in range(n_dust):
nufnu = nufnu_all[dust_id, 0, :, :, :]
flux[:, :, 5 + dust_id, :] += nufnu
print "Scattered source emission"
wav, nufnu = m.get_image(group=image_set, component='source_scat', units='MJy/sr')
nufnu = nufnu[0, :, :, :]
flux[:, :, 8, :] += nufnu
print "Scattered dust emission"
wav, nufnu = m.get_image(group=image_set, component='dust_scat', units='MJy/sr')
nufnu = nufnu[0, :, :, :]
flux[:, :, 8, :] += nufnu
# Convolve with filters
flux_conv = np.zeros((len(filters), n_y, n_x, n_groups))
for i, filtname in enumerate(filters):
transmission = rebin_filter(filtname, c / (wav * 1.e-4))
flux_conv[i, :, :, :] = np.sum(transmission[np.newaxis, np.newaxis, np.newaxis, :] * flux, axis=3)
pyfits.writeto('models/basic/images_%s_%s.fits' % (image_set_name, model_name), flux, clobber=True)
pyfits.writeto('models/basic/images_%s_%s_conv.fits' % (image_set_name, model_name), flux_conv, clobber=True)
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
import matplotlib.pyplot as plt
from hyperion.model import ModelOutput
from hyperion.util.constants import pc
mo = ModelOutput('class1_example.rtout')
sed = mo.get_sed(aperture=-1, distance=140. * pc)
image = mo.get_image(inclination=0,distance=300*pc,units='Jy')
fig = plt.figure(figsize=(5, 4))
ax = fig.add_subplot(1, 1, 1)
ax.loglog(sed.wav, sed.val.transpose(), color='black')
ax.set_xlim(0.03, 2000.)
ax.set_ylim(2.e-15, 1e-8)
ax.set_xlabel(r'$\lambda$ [$\mu$m]')
ax.set_ylabel(r'$\lambda F_\lambda$ [ergs/cm$^2/s$]')
#ax2 = fig_add_subplot(1,1,2)
#ax.imshow(image,origin='lower')
fig.savefig('class1_example_sed.png', bbox_inches='tight')
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()
import pyfits
from hyperion.model import ModelOutput
from hyperion.util.constants import pc
# Open the model - we specify the name without the .rtout extension
m = ModelOutput('tutorial_model.rtout')
# 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
wav, nufnu = m.get_image(group=1, inclination=0, distance=300 * pc)
# The image extracted above is a 3D array. We can write it out to FITS.
# We need to swap some of the directions around so as to be able to use
# the ds9 slider to change the wavelength of the image.
pyfits.writeto('image_cube.fits', nufnu.swapaxes(0, 2).swapaxes(1, 2), \
clobber=True)
# We can also just output one of the wavelengths
pyfits.writeto('image_slice.fits', nufnu[:, :, 0], clobber=True)
from hyperion.model import ModelOutput
from hyperion.util.constants import kpc
from astropy.io import fits
for tau in [0.1, 1.0, 20.]:
input_file = 'bm1_slab_effgrain_tau_{tau:05.2f}_images.rtout'.format(
tau=tau)
m = ModelOutput(input_file)
for iincl, theta in enumerate([0, 30, 60, 90, 120, 150, 180]):
image = m.get_image(inclination=iincl,
units='MJy/sr',
distance=10. * kpc)
for iwav, wav in enumerate([0.165, 0.570, 21.3, 161.6]):
output_file = 'images/bm1_slab_effgrain_tau_{tau:06.2f}_theta_{theta:03d}_wave_{wav:07.3f}.fits'.format(
tau=tau, theta=theta, wav=wav)
fits.writeto(output_file, image.val[:, :, iwav], clobber=True)
import numpy as np
import matplotlib.pyplot as plt
from hyperion.model import ModelOutput
from hyperion.util.constants import pc
# Create output directory if it does not already exist
if not os.path.exists('frames'):
os.mkdir('frames')
# Open model
m = ModelOutput('flyaround_cube.rtout')
# Read image from model
image = m.get_image(distance=300 * pc, units='MJy/sr')
# image.val is now an array with four dimensions (n_view, n_y, n_x, n_wav)
for iview in range(image.val.shape[0]):
# Open figure and create axes
fig = plt.figure(figsize=(3, 3))
ax = fig.add_subplot(1, 1, 1)
# This is the command to show the image. The parameters vmin and vmax are
# the min and max levels for the grayscale (remove for default values).
# The colormap is set here to be a heat map. Other possible heat maps
# include plt.cm.gray (grayscale), plt.cm.gist_yarg (inverted grayscale),
# plt.cm.jet (default, colorful). The np.sqrt() is used to plot the
# images on a sqrt stretch.
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()
dist = 178.
wave = 500.
from hyperion.model import ModelOutput
import astropy.constants as const
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import font_manager
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
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()
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()
from hyperion.model import ModelOutput
from hyperion.util.constants import kpc
from astropy.io import fits
for tau in [0.1, 1.0, 20.]:
input_file = 'bm1_slab_effgrain_tau_{tau:05.2f}_images.rtout'.format(tau=tau)
m = ModelOutput(input_file)
for iincl, theta in enumerate([0, 30, 60, 90, 120, 150, 180]):
image = m.get_image(inclination=iincl, units='MJy/sr', distance=10. * kpc)
for iwav, wav in enumerate([0.165, 0.570, 21.3, 161.6]):
output_file = 'images/bm1_slab_effgrain_tau_{tau:06.2f}_theta_{theta:03d}_wave_{wav:07.3f}.fits'.format(tau=tau, theta=theta, wav=wav)
fits.writeto(output_file, image.val[:, :, iwav], clobber=True)
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()
ax_top.set_xticklabels(r_tick_labels)
ax_top.tick_params('x', labelsize=14)
else:
r_ticks = scale(np.array([0, 1, 2, 3, 4]),
(np.log10(0.14), np.log10(41253)), (-w, w))
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
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
import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt
from hyperion.model import ModelOutput
from hyperion.util.constants import pc
# Create output directory if it does not already exist
if not os.path.exists('frames'):
os.mkdir('frames')
# Open model
m = ModelOutput('tutorial_model.rtout')
# Read image from model
wav, nufnu = m.get_image(group=2, distance=300 * pc)
# nufnu is now an array with four dimensions (n_view, n_wav, n_y, n_x)
# Fix the wavelength to the first one and cycle through viewing angles
iwav = 0
print "Wavelength is %g microns" % wav[iwav]
for iview in range(nufnu.shape[0]):
# Open figure and create axes
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
# This is the command to show the image. The parameters vmin and vmax are
# the min and max levels for the grayscale (remove for default values).
ax_top.set_xlabel(r'$\rm{log(radius)\,[AU]}$', fontsize=16)
ax_top.set_xticks(r_ticks)
ax_top.set_xticklabels(r_tick_labels)
ax_top.tick_params('x', labelsize=14)
else:
r_ticks = scale(np.array([0,1,2,3,4]), (np.log10(0.14), np.log10(41253)), (-w,w))
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
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()
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=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
from PIL import Image
from hyperion.model import ModelOutput
from hyperion.util.constants import pc
m = ModelOutput('simple_cube.rtout')
image = m.get_image(inclination=0, distance=300 * pc, units='MJy/sr')
# Extract the slices we want to use for red, green, and blue
r = image.val[:, :, 17]
g = image.val[:, :, 18]
b = image.val[:, :, 19]
# Now we need to rescale the values we want to the range 0 to 255, clip values
# outside the range, and convert to unsigned 8-bit integers. We also use a sqrt
# stretch (hence the ** 0.5)
r = np.clip((r / 0.5)**0.5 * 255., 0., 255.)
r = np.array(r, dtype=np.uint8)
g = np.clip((g / 2)**0.5 * 255., 0., 255.)
g = np.array(g, dtype=np.uint8)
b = np.clip((b / 4.)**0.5 * 255., 0., 255.)
b = np.array(b, dtype=np.uint8)
# We now convert to image objects
image_r = Image.fromarray(r)
image_g = Image.fromarray(g)
image_b = Image.fromarray(b)
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 matplotlib.pyplot as plt
from hyperion.model import ModelOutput
from hyperion.util.constants import pc
mo = ModelOutput('pure_scattering.rtout')
wav, fnu = mo.get_image(inclination=0, units='MJy/sr', distance=300. * pc)
wav, pol = mo.get_image(inclination=0, stokes='linpol')
fig = plt.figure(figsize=(8, 8))
# Make total intensity sub-plot
ax = fig.add_axes([0.1, 0.3, 0.4, 0.4])
ax.imshow(fnu[:, :, 0], extent=[-13, 13, -13, 13],
interpolation='none', cmap=plt.cm.gist_heat,
origin='lower', vmin=0., vmax=4e9)
ax.set_xlim(-13., 13.)
ax.set_ylim(-13., 13.)
ax.set_xlabel("x (solar radii)")
ax.set_ylabel("y (solar radii)")
ax.set_title("Surface brightness")
# Make linear polarization sub-plot
ax = fig.add_axes([0.51, 0.3, 0.4, 0.4])
im = ax.imshow(pol[:, :, 0] * 100., extent=[-13, 13, -13, 13],
interpolation='none', cmap=plt.cm.gist_heat,
origin='lower', vmin=0., vmax=100.)
ax.set_xlim(-13., 13.)
ax.set_ylim(-13., 13.)
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()
import numpy as np
import matplotlib.pyplot as plt
from hyperion.model import ModelOutput
from hyperion.util.constants import pc
# Create output directory if it does not already exist
if not os.path.exists('frames'):
os.mkdir('frames')
# Open model
m = ModelOutput('flyaround_cube.rtout')
# Read image from model
image = m.get_image(distance=300 * pc, units='MJy/sr')
# image.val is now an array with four dimensions (n_view, n_y, n_x, n_wav)
for iview in range(image.val.shape[0]):
# Open figure and create axes
fig = plt.figure(figsize=(3, 3))
ax = fig.add_subplot(1, 1, 1)
# This is the command to show the image. The parameters vmin and vmax are
# the min and max levels for the grayscale (remove for default values).
# The colormap is set here to be a heat map. Other possible heat maps
# include plt.cm.gray (grayscale), plt.cm.gist_yarg (inverted grayscale),
# plt.cm.jet (default, colorful). The np.sqrt() is used to plot the
# images on a sqrt stretch.
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')
import numpy as np
import matplotlib.pyplot as plt
from hyperion.model import ModelOutput
from hyperion.util.constants import pc
# Open the model
m = ModelOutput('simple_cube.rtout')
# 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(inclination=0, distance=300 * pc, units='MJy/sr')
# Open figure and create axes
fig = plt.figure(figsize=(8, 8))
# Pre-set maximum for colorscales
VMAX = {}
VMAX[1] = 10.
VMAX[30] = 100.
VMAX[100] = 2000.
VMAX[300] = 2000.
# We will now show four sub-plots, each one for a different wavelength
for i, wav in enumerate([1, 30, 100, 300]):
ax = fig.add_subplot(2, 2, i + 1)
# Find the closest wavelength
iwav = np.argmin(np.abs(wav - image.wav))
def setup_method(self, method):
path = os.path.join(os.path.dirname(__file__), 'hyperion_output.rtout')
m = ModelOutput(path)
self.image = m.get_image(group=0, inclination=0, distance=8.5*kpc, units='ergs/cm^2/s')
