from hyperion.model import AnalyticalYSOModel from hyperion.util.constants import pc, lsun, rsun, msun, au,pi from hyperion.model import ModelOutput import numpy as np m = AnalyticalYSOModel() m.set_n_photons(initial=100000, imaging=100000) m.star.luminosity = 5 * lsun m.star.radius = 2 * rsun m.star.temperature = 6000. disk = m.add_flared_disk() disk.mass = 0.01 * msun # Disk mass disk.rmin = 10 * rsun # Inner radius disk.rmax = 300 * au # Outer radius disk.r_0 = 100. * au # Radius at which h_0 is defined disk.h_0 = 5 * au # Disk scaleheight at r_0 disk.p = -1 # Radial surface density exponent disk.beta = 1.25 # Disk flaring power #envelope = m.add_power_law_envelope() #envelope.mass = 0.001 * msun # Envelope mass #envelope.rmin = au # Inner radius #envelope.rmax = 10000 * au # Outer radius #envelope.power = -2 # Radial power #envelope.r_0 = au # Inner density radius #dust disk.dust = 'www003.hdf5' #envelope.dust = 'kmh.hdf5' #cavity.dust = 'kmh_hdf5'
# Set up grid
m.set_spherical_polar_grid_auto(400, 100, 1)
# Don't compute temperatures
m.set_n_initial_iterations(0)
# Don't re-emit photons
m.set_kill_on_absorb(True)
# Use raytracing (only important for source here, since no dust emission)
m.set_raytracing(True)
# Compute images using monochromatic radiative transfer
m.set_monochromatic(True, wavelengths=[1.])
# Set up image
i = m.add_peeled_images()
i.set_image_limits(-13 * rsun, 13 * rsun, -13. * rsun, 13 * rsun)
i.set_image_size(256, 256)
i.set_viewing_angles([60.], [20.])
i.set_wavelength_range(1, 1, 1)
# Set number of photons
m.set_n_photons(imaging_sources=10000000, imaging_dust=0,
raytracing_sources=100000, raytracing_dust=0)
# Write out the model and run it in parallel
m.write('pure_scattering.rtin')
m.run('pure_scattering.rtout', mpi=True)
cavity.r_0 = envelope.rmax
cavity.rho_0 = 5e4 * 3.32e-24
cavity.rho_exp = 0.
cavity.dust = 'kmh_lite.hdf5'
# Use raytracing to improve s/n of thermal/source emission
m.set_raytracing(True)
# Use the modified random walk
m.set_mrw(True, gamma=2.)
# Set up grid
m.set_spherical_polar_grid_auto(399, 199, 1)
# Set up SED
sed = m.add_peeled_images(sed=True, image=False)
sed.set_viewing_angles(np.linspace(0., 90., 10), np.repeat(45., 10))
sed.set_wavelength_range(150, 0.02, 2000.)
# Set number of photons
m.set_n_photons(initial=1e6, imaging=1e6,
raytracing_sources=1e4, raytracing_dust=1e6)
# Set number of temperature iterations and convergence criterion
m.set_n_initial_iterations(10)
m.set_convergence(True, percentile=99.0, absolute=2.0, relative=1.1)
# Write out file
m.write('class1_example.rtin')
m.run('class1_example.rtout', mpi=True)
disk.beta = 1.25
disk.dust = 'kmh_lite.hdf5'
# Use raytracing to improve s/n of thermal/source emission
m.set_raytracing(True)
# Use the modified random walk
m.set_mrw(True, gamma=2.)
# Set up grid
m.set_spherical_polar_grid_auto(399, 199, 1)
# Set up SED for 10 viewing angles
sed = m.add_peeled_images(sed=True, image=False)
sed.set_viewing_angles(np.linspace(0., 90., 10), np.repeat(45., 10))
sed.set_wavelength_range(150, 0.02, 2000.)
sed.set_track_origin('basic')
# Set number of photons
m.set_n_photons(initial=1e5,
imaging=1e6,
raytracing_sources=1e4,
raytracing_dust=1e6)
# Set number of temperature iterations
m.set_n_initial_iterations(5)
# Write out file
m.write('class2_sed.rtin')
m.run('class2_sed.rtout', mpi=True)
image = m.add_peeled_images()
image.set_wavelength_range(100, 2.0, 670.0)
# pixel number
image.set_image_size(300, 300)
image.set_image_limits(-R_env_max, R_env_max, -R_env_max, R_env_max)
image.set_viewing_angles([82.0], [0.0])
image.set_uncertainties(True)
# output as 64-bit
image.set_output_bytes(8)
# Radiative transfer setting
# number of photons for temp and image
m.set_raytracing(True)
m.set_n_photons(initial=100000,
imaging=100000,
raytracing_sources=100000,
raytracing_dust=100000)
# number of iteration to compute dust specific energy (temperature)
m.set_n_initial_iterations(20)
m.set_convergence(True, percentile=99., absolute=1.5, relative=1.02)
m.set_mrw(True) # Gamma = 1 by default
# Output setting
# Density
m.conf.output.output_density = 'last'
# Density difference (shows where dust was destroyed)
m.conf.output.output_density_diff = 'none'
# Energy absorbed (using pathlengths)
m.conf.output.output_specific_energy = 'last'
def ShellThree_Only(dust_file_name, fnu, nu, stellar_luminoisity, stellar_temperature, stellar_radius, total_shell_mass, uant_distance, expansion_velocity, MaxCSE_outer_rad, Photon_Number, CPU_Number): # Initalize the model model = AnalyticalYSOModel() #We use the YSO model with modified parameters to match an AGB star with a detached shell # Set the stellar parameters model.star.spectrum = (nu, fnu) model.star.luminosity = stellar_luminoisity #model.star.mass = 2 * msun #Guesstimate 1.3 - 3 Msun #Not needed for RT modelling normally. Very difficult to estimate. model.star.radius = stellar_radius #Power-law spherically symmetric envelope - Only Detached Shell - Need to add constant outflow envelope on top of this to account for the ML after the thermal pulse. envelope_shell = model.add_power_law_envelope() envelope_shell.mass = total_shell_mass envelope_shell.rmin = (41.5 * uant_distance) * au # Shell 3 inner radius converted to au - from GD+2001&2003 AND M2010 envelope_shell.rmax = (44.5 * uant_distance) * au # Shell 3 outer radius converted to au - from GD+2001&2003 AND M2010 envelope_shell.r_0 = envelope_shell.rmin envelope_shell.power = -2 # Radial power #Constant Outflow envelope envelope_shell.dust = dust_file_name #Using a standard Hyperion Dust model. Needs to be modified a lot for U Ant envelope_CSE_Max = (MaxCSE_outer_rad * uant_distance) * au # Outer radius- estimating the total emission will be ~ this radius - pre&post thermal pulse. Units = cm # Set up grid to run the model on model.set_spherical_polar_grid_auto(100, 1, 1) #(n_r, n_theta, n_phi) - in a spherical envelope theta and phi are uniform and 1. n_r = number of r radii to seperate the grid to. # Use raytracing to improve s/n of thermal/source emission model.set_raytracing(raytracing=True) # Set up SED - Get SED output sed = model.add_peeled_images(sed=True, image=False) sed.set_uncertainties(uncertainties=True) sed.set_viewing_angles([45], [45]) #(np.linspace(0., 90., 10), np.repeat(45., 10)) #Veiw the source at 45degrees from the pole and theta = 45 sed.set_wavelength_range(100, 0.3, 1200.) #(n_wav, wav_min, wav_max) #Get the SED from 1micron to 2000micron in 100 wavelengths #Set up Image - Get image output - RT calculated for bins of wavelengths - PACS 70 image_70 = model.add_peeled_images(sed=False, image=True) image_70.set_uncertainties(uncertainties=True) image_70.set_viewing_angles([45], [45]) #(np.linspace(0., 90., 10), np.repeat(45., 10)) #Veiw the source at 45degrees from the pole and theta = 45 image_70.set_wavelength_range(30, 60, 90) #(n_wav, wav_min, wav_max) - PACS 70 filter profile limits 60 - 90 and get 30 image cube to get images at ~1 micron per image image_70.set_image_size(400, 400) #Size of image in pixels image_70.set_image_limits(-1.5 * envelope_CSE_Max, 1.5 * envelope_CSE_Max, -1.5 * envelope_CSE_Max, 1.5 * envelope_CSE_Max) #Set up Image - Get image output - RT calculated for bins of wavelengths - PACS 160 image_160 = model.add_peeled_images(sed=False, image=True) image_160.set_uncertainties(uncertainties=True) image_160.set_viewing_angles([45], [45]) #(np.linspace(0., 90., 10), np.repeat(45., 10)) #Veiw the source at 45degrees from the pole and theta = 45 image_160.set_wavelength_range(30, 130, 220) #(n_wav, wav_min, wav_max) - PACS 160 filter profile limits 130 - 220 and get 30 image cube to get images at ~3 micron per image image_160.set_image_size(400, 400) #Size of image in pixels image_160.set_image_limits(-1.5 * envelope_CSE_Max, 1.5 * envelope_CSE_Max, -1.5 * envelope_CSE_Max, 1.5 * envelope_CSE_Max) #Set up Image - Get image output - RT calculated for bins of wavelengths - SCUBA-2 450 image_450 = model.add_peeled_images(sed=False, image=True) image_450.set_uncertainties(uncertainties=True) image_450.set_viewing_angles([45], [45]) #(np.linspace(0., 90., 10), np.repeat(45., 10)) #Veiw the source at 45degrees from the pole and theta = 45 image_450.set_wavelength_range(30, 410, 480) #(n_wav, wav_min, wav_max) - - SCUBA-2 450 filter profile limits 790 - 940 and get 30 image cube to get images at ~2 micron per image image_450.set_image_size(400, 400) #Size of image in pixels image_450.set_image_limits(-1.5 * envelope_CSE_Max, 1.5 * envelope_CSE_Max, -1.5 * envelope_CSE_Max, 1.5 * envelope_CSE_Max) #Set up Image - Get image output - RT calculated for bins of wavelengths - SCUBA-2 850 image_850 = model.add_peeled_images(sed=False, image=True) image_850.set_uncertainties(uncertainties=True) image_850.set_viewing_angles([45], [45]) #(np.linspace(0., 90., 10), np.repeat(45., 10)) #Veiw the source at 45degrees from the pole and theta = 45 image_850.set_wavelength_range(30, 790, 940) #(n_wav, wav_min, wav_max) - SCUBA-2 850 filter profile limits 790 - 940 and get 30 image cube to get images at ~5 micron per image image_850.set_image_size(400, 400) #Size of image in pixels image_850.set_image_limits(-1.5 * envelope_CSE_Max, 1.5 * envelope_CSE_Max, -1.5 * envelope_CSE_Max, 1.5 * envelope_CSE_Max) # Set number of photons model.set_n_photons(initial=Photon_Number, imaging=Photon_Number, raytracing_sources=Photon_Number, raytracing_dust=Photon_Number) # Set number of temperature iterations and convergence criterion model.set_n_initial_iterations(5) model.set_convergence(True, percentile=99.0, absolute=2.0, relative=1.1) # Write out file Input_ShellThree_Only = 'Model_ShellThree_Only.rtin' #File which saves all the input above so that fortran can run internally for RT modelling Output_ShellThree_Only = 'Model_ShellThree_Only.rtout' model.write(Input_ShellThree_Only) model.run(Output_ShellThree_Only, mpi=True, n_processes=CPU_Number) return Input_ShellThree_Only, Output_ShellThree_Only
def setup_model(parfile, output, imaging=True):
# Read in model parameters
par = read_parfile(parfile, nested=True)
# Find all dust files
dust_files = {}
for par_name in par:
if 'dust' in par[par_name]:
dust_file = par[par_name]['dust']
dust_files[dust_file] = SphericalDust(dust_file)
# Find dimensionality of problem:
if 'disk' in par:
ndim = 2
optimize = False
elif 'cavity' in par:
ndim = 2
optimize = False
elif 'envelope' in par and 'rc' in par['envelope']:
ndim = 2
optimize = False
else:
ndim = 1
optimize = True
# Set up model
m = AnalyticalYSOModel(output)
if not 'star' in par:
raise Exception("Cannot compute a model without a central source")
# Set radius and luminosity
m.star.radius = par['star']['radius'] * rsun
m.star.luminosity = 4. * pi * (par['star']['radius'] * rsun) ** 2. \
* sigma * par['star']['temperature'] ** 4.
# Interpolate and set spectrum
nu, fnu = interp_atmos(par['star']['temperature'])
m.star.spectrum = (nu, fnu)
subtract_from_ambient = []
if 'disk' in par:
# Add the flared disk component
disk = m.add_flared_disk()
# Basic parameters
disk.mass = par['disk']['mass'] * msun
disk.rmax = par['disk']['rmax'] * au
disk.p = par['disk']['p']
disk.beta = par['disk']['beta']
disk.h_0 = par['disk']['h100'] * au
disk.r_0 = 100. * au
# Set inner and outer walls to be spherical
disk.cylindrical_inner_rim = False
disk.cylindrical_outer_rim = False
# Set dust
disk.dust = dust_files[par['disk']['dust']]
# Inner radius
if 'rmin' in par['disk']:
disk.rmin = par['disk']['rmin'] * OptThinRadius(TSUB)
else:
disk.rmin = OptThinRadius(TSUB)
# Settling
if 'eta' in par['disk']:
raise Exception("Dust settling not implemented")
# Accretion luminosity
if 'lacc' in par['disk']:
raise Exception("Accretion luminosity not implemented")
# m.setup_magnetospheric_accretion(par['disk']['lacc'] * lsun,
# par['disk']['rtrunc'],
# par['star']['fspot'], disk)
subtract_from_ambient.append(disk)
if 'envelope' in par:
if 'rc' in par['envelope']: # Ulrich envelope
envelope = m.add_ulrich_envelope()
envelope.rho_0 = par['envelope']['rho_0']
envelope.rc = par['envelope']['rc'] * au
elif 'power' in par['envelope']: # Power-law envelope
envelope = m.add_power_law_envelope()
envelope.power = par['envelope']['power']
envelope.rho_0 = par['envelope']['rho_0']
envelope.r_0 = 1000. * au
# Set dust
envelope.dust = dust_files[par['envelope']['dust']]
# Inner radius
if 'rmin' in par['envelope']:
envelope.rmin = par['envelope']['rmin'] * OptThinRadius(TSUB)
else:
envelope.rmin = OptThinRadius(TSUB)
subtract_from_ambient.append(envelope)
if 'cavity' in par:
if not 'envelope' in par:
raise Exception("Can't have a bipolar cavity without an envelope")
# Add the bipolar cavity component
cavity = envelope.add_bipolar_cavity()
# Basic parameters
cavity.power = par['cavity']['power']
cavity.r_0 = 10000 * au
cavity.theta_0 = par['cavity']['theta_0']
cavity.rho_0 = par['cavity']['rho_0']
cavity.rho_exp = 0.
# Very important is that the cavity density should not be *larger* than
# the envelope density.
cavity.cap_to_envelope_density = True
# Set dust
cavity.dust = dust_files[par['cavity']['dust']]
subtract_from_ambient.append(cavity)
if 'ambient' in par:
# Add the ambient medium contribution
ambient = m.add_ambient_medium(subtract=subtract_from_ambient)
# Set the density, temperature, and dust properties
ambient.rho = par['ambient']['density']
ambient.temperature = par['ambient']['temperature']
ambient.dust = dust_files[par['ambient']['dust']]
# If there is an envelope, set the outer radius to where the
# optically thin temperature would transition to the ambient medium
# temperature
if 'envelope' in par:
# Find radius where the optically thin temperature drops to the
# ambient temperature. We can do this only if we've already set
# up all the sources of emission beforehand (which we have)
rmax_temp = OptThinRadius(ambient.temperature).evaluate(m.star, envelope.dust)
# Find radius where the envelope density drops to the ambient density
rmax_dens = envelope.outermost_radius(ambient.rho)
# If disk radius is larger than this, use that instead
if 'disk' in par:
if disk.rmax > rmax_dens:
rmax_dens = disk.rmax
# Pick the largest
if rmax_temp < rmax_dens:
print("Setting envelope outer radius to that where rho(r) = rho_amb")
envelope.rmax = rmax_dens
else:
print("Setting envelope outer radius to that where T_thin(r) = T_amb")
envelope.rmax = OptThinRadius(ambient.temperature)
ambient.rmax = envelope.rmax
else:
if 'disk' in par:
# Find radius where the optically thin temperature drops to the
# ambient temperature. We can do this only if we've already set
# up all the sources of emission beforehand (which we have)
rmax_temp = OptThinRadius(ambient.temperature).evaluate(m.star, ambient.dust)
# Find outer disk radius
rmax_dens = disk.rmax
# Pick the largest
if rmax_temp < rmax_dens:
print("Setting ambient outer radius to outer disk radius")
ambient.rmax = rmax_dens
else:
print("Setting ambient outer radius to that where T_thin(r) = T_amb")
ambient.rmax = OptThinRadius(ambient.temperature)
else:
ambient.rmax = OptThinRadius(ambient.temperature)
# The inner radius for the ambient medium should be the largest of
# the inner radii for the disk and envelope
if 'envelope' in par and 'rmin' in par['envelope']:
if 'disk' in par and 'rmin' in par['disk']:
ambient.rmin = max(par['disk']['rmin'], \
par['envelope']['rmin']) \
* OptThinRadius(TSUB)
else:
ambient.rmin = par['envelope']['rmin'] * OptThinRadius(TSUB)
elif 'disk' in par and 'rmin' in par['disk']:
ambient.rmin = par['disk']['rmin'] * OptThinRadius(TSUB)
else:
ambient.rmin = OptThinRadius(TSUB)
# The ambient medium needs to go out to sqrt(2.) times the envelope
# radius to make sure the slab is full (don't need to do sqrt(3)
# because we only need a cylinder along line of sight)
ambient.rmax *= np.sqrt(2.)
# Make sure that the temperature in the model is always at least
# the ambient temperature
m.set_minimum_temperature(ambient.temperature)
else:
# Make sure that the temperature in the model is always at least
# the CMB temperature
m.set_minimum_temperature(2.725)
if 'envelope' in par:
raise Exception("Can't have an envelope without an ambient medium")
# Use raytracing to improve s/n of thermal/source emission
m.set_raytracing(True)
# Use the modified random walk
m.set_mrw(True, gamma=2.)
# Use the partial diffusion approximation
m.set_pda(True)
# Improve s/n of scattering by forcing the first interaction
m.set_forced_first_scattering(True)
# Set up grid.
if ndim == 1:
m.set_spherical_polar_grid_auto(400, 1, 1)
else:
m.set_spherical_polar_grid_auto(400, 300, 1)
# Find the range of radii spanned by the grid
rmin, rmax = m.radial_range()
# Set up SEDs
image = m.add_peeled_images(sed=True, image=False)
image.set_wavelength_range(200, 0.01, 5000.)
if 'ambient' in par:
image.set_aperture_range(20, rmin, rmax / np.sqrt(2.))
else:
image.set_aperture_range(20, rmin, rmax)
image.set_output_bytes(8)
image.set_track_origin(True)
image.set_uncertainties(True)
image.set_stokes(True)
if ndim == 1:
# Viewing angle does not matter
image.set_viewing_angles([45.], [45.])
else:
# Use stratified random sampling to ensure that all models
# contain a viewing angle in each bin, but also ensure we have a
# continuum of viewing angles over all models
xi = np.random.uniform(0., 90./float(NVIEW), NVIEW)
theta = xi + np.linspace(0., 90. * (1. - 1./float(NVIEW)), NVIEW)
image.set_viewing_angles(theta, np.repeat(45., NVIEW))
if 'ambient' in par: # take a slab to avoid spherical geometrical effects
w = ambient.rmax / np.sqrt(2.)
image.set_depth(-w, w)
else: # don't need to take a slab, as no ambient material or envelope
image.set_depth(-np.inf, np.inf)
# Set number of photons
if imaging:
n_imaging=1e6
n_raytracing_sources=10000
n_raytracing_dust=1e6
else:
n_imaging=0
n_raytracing_sources=0
n_raytracing_dust=0
if ndim == 1:
m.set_n_photons(initial=100, imaging=n_imaging,
raytracing_sources=n_raytracing_sources,
raytracing_dust=n_raytracing_dust)
else:
m.set_n_photons(initial=1000000, imaging=n_imaging,
raytracing_sources=n_raytracing_sources,
raytracing_dust=n_raytracing_dust)
# Set physical array output to 32-bit
m.set_output_bytes(4)
# Set maximum of 10^8 interactions per photon
m.set_max_interactions(1e8)
# Only request certain arrays to be output
m.conf.output.output_density = 'none'
m.conf.output.output_specific_energy = 'last'
m.conf.output.output_n_photons = 'none'
m.conf.output.output_density_diff = 'last'
# Set number of temperature iterations and convergence criterion
m.set_n_initial_iterations(10)
m.set_convergence(True, percentile=99.0, absolute=2.0, relative=1.1)
# Don't copy the full input into the output files
m.set_copy_input(False)
# Check whether the model is very optically thick
mf = m.to_model()
if 'envelope' in par:
from hyperion.model.helpers import tau_to_radius
surface = tau_to_radius(mf, tau=1., wav=5e3)
rtau = np.min(surface)
if rtau > rmin and optimize:
log.warn("tau_5mm > 1 for all (theta,phi) values - truncating "
"inner envelope from {0:.3f}au to {1:.3f}au".format(mf.grid.r_wall[1] / au, rtau / au))
for item in mf.grid['density']:
item.array[mf.grid.gr < rtau] = 0.
# Write out file
mf.write(copy=False, absolute_paths=False,
physics_dtype=np.float32, wall_dtype=float)
# Setting up images and SEDs image = m.add_peeled_images() image.set_wavelength_range(100, 2.0, 670.0) # pixel number image.set_image_size(300, 300) image.set_image_limits(-R_env_max, R_env_max, -R_env_max, R_env_max) image.set_viewing_angles([82.0], [0.0]) image.set_uncertainties(True) # output as 64-bit image.set_output_bytes(8) # Radiative transfer setting # number of photons for temp and image m.set_raytracing(True) m.set_n_photons(initial=100000, imaging=100000, raytracing_sources=100000, raytracing_dust=100000) # number of iteration to compute dust specific energy (temperature) m.set_n_initial_iterations(20) m.set_convergence(True, percentile=99., absolute=1.5, relative=1.02) m.set_mrw(True) # Gamma = 1 by default # Output setting # Density m.conf.output.output_density = 'last' # Density difference (shows where dust was destroyed) m.conf.output.output_density_diff = 'none' # Energy absorbed (using pathlengths) m.conf.output.output_specific_energy = 'last'
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
m.star.luminosity = lsun
# Set up a simple flared disk
d = m.add_flared_disk()
d.mass = 0.001 * msun
d.rmin = 0.1 * au
d.rmax = 100. * au
d.p = -1
d.beta = 1.25
d.h_0 = 0.01 * au
d.r_0 = au
d.dust = 'kmh_lite.hdf5'
# Specify that the specific energy and density are needed
m.conf.output.output_specific_energy = 'last'
m.conf.output.output_density = 'last'
# Set the number of photons
m.set_n_photons(initial=1000000, imaging=0)
# Set up the grid
m.set_spherical_polar_grid_auto(400, 300, 1)
# Use MRW and PDA
m.set_mrw(True)
m.set_pda(True)
# Write output and run model
m.write('quantity_spherical.rtin')
m.run('quantity_spherical.rtout', mpi=True)