def enzo_m_gen(fname, field_add):
reg, ds1 = enzo_grid_generate(fname, field_add)
amr = AMRGrid.from_yt(
ds1, quantity_mapping={'density': ('gas', 'dust_density')})
'''
levels = ds.index.max_level
amr = AMRGrid()
for ilevel in range(levels):
level = amr.add_level()
for igrid in ds.index.select_grids(ilevel):
print igrid
grid = level.add_grid()
grid.xmin,grid.xmax = igrid.LeftEdge[0].in_units('cm'),igrid.RightEdge[0].in_units('cm')
grid.ymin,grid.ymax = igrid.LeftEdge[1].in_units('cm'),igrid.RightEdge[1].in_units('cm')
grid.zmin,grid.zmax = igrid.LeftEdge[2].in_units('cm'),igrid.RightEdge[2].in_units('cm')
grid.quantities["density"] = np.transpose(np.array(igrid[("gas","metal_density")].in_units('g/cm**3')*cfg.par.dusttometals_ratio))
grid.nx,grid.ny,grid.nz = igrid[("gas","metal_density")].shape
'''
m = Model()
m.set_amr_grid(amr)
#CMB DISABLED -- UNCOMMENT THIS TO FIX THIS. The main issue is
#that I'm not sure what shape to give to the np.repeat
#array of energy_density_absorbed; I think it needs to be the ARM Grid shape but i'm not quite sure if it needs to be an AMRGrid()
#energy_density_absorbed=energy_density_absorbed_by_CMB()
#energy_density_absorbed =np.repeat(energy_density_absorbed.value,reg.index.num_grids)#amr['density'].shape)
d = SphericalDust(cfg.par.dustdir + cfg.par.dustfile)
if cfg.par.SUBLIMATION == True:
d.set_sublimation_temperature(
'fast', temperature=cfg.par.SUBLIMATION_TEMPERATURE)
m.add_density_grid(amr["density"], d)
#uncomment when we're ready to put CMB in (and comment out previous line)
#m.add_density_grid(amr['density'],d,specific_energy=energy_density_absorbed)
#m.set_specific_energy_type('additional')
center = ds1.arr([cfg.model.x_cent, cfg.model.y_cent, cfg.model.z_cent],
'code_length')
[xcent, ycent, zcent
] = center.in_units('cm') #boost needs to be in cm since that's what the
boost = np.array([xcent, ycent, zcent])
dx = ds1.domain_width[0].in_units('cm')
dy = ds1.domain_width[1].in_units('cm')
dz = ds1.domain_width[2].in_units('cm')
return m, xcent, ycent, zcent, dx, dy, dz, reg, ds1, boost
def enzo_m_gen(fname, field_add):
reg, ds1 = enzo_grid_generate(fname, field_add)
amr = yt_dataset_to_amr_grid_xyz(
ds1, quantity_mapping={'density': ('gas', 'dust_density')})
m = Model()
#save in the m__dict__ that we're in an amr geometry
m.__dict__['grid_type'] = 'amr'
m.set_amr_grid(amr)
#CMB DISABLED -- UNCOMMENT THIS TO FIX THIS. The main issue is
#that I'm not sure what shape to give to the np.repeat
#array of energy_density_absorbed; I think it needs to be the ARM Grid shape but i'm not quite sure if it needs to be an AMRGrid()
#energy_density_absorbed=energy_density_absorbed_by_CMB()
#energy_density_absorbed =np.repeat(energy_density_absorbed.value,reg.index.num_grids)#amr['density'].shape)
d = SphericalDust(cfg.par.dustdir + cfg.par.dustfile)
if cfg.par.SUBLIMATION == True:
d.set_sublimation_temperature(
'fast', temperature=cfg.par.SUBLIMATION_TEMPERATURE)
m.add_density_grid(amr["density"], d)
#uncomment when we're ready to put CMB in (and comment out previous line)
#m.add_density_grid(amr['density'],d,specific_energy=energy_density_absorbed)
#m.set_specific_energy_type('additional')
center = ds1.arr([cfg.model.x_cent, cfg.model.y_cent, cfg.model.z_cent],
'code_length')
[xcent, ycent, zcent
] = center.in_units('cm') #boost needs to be in cm since that's what the
boost = np.array([xcent, ycent, zcent])
dx = ds1.domain_width[0].in_units('cm')
dy = ds1.domain_width[1].in_units('cm')
dz = ds1.domain_width[2].in_units('cm')
return m, xcent, ycent, zcent, dx, dy, dz, reg, ds1, boost
d = IsotropicDust(nu, albedo, chi)
nr = 10
nt = 10
np = 10
r = arange(nr) * au / 2
t = arange(nt) / (nt - 1.) * pi
p = arange(np) / (np - 1.) * 2 * pi
m.set_spherical_polar_grid(r, t, p)
dens = zeros((nr - 1, nt - 1, np - 1)) + 1.0e-17
m.add_density_grid(dens, d)
source = m.add_spherical_source()
source.luminosity = lsun
source.radius = rsun
source.temperature = 4000.
m.set_n_photons(initial=1000000, imaging=0)
m.set_convergence(True, percentile=99., absolute=2., relative=1.02)
m.write("test_spherical.rtin")
m.run("test_spherical.rtout", mpi=False)
n = ModelOutput('test_spherical.rtout')
from hyperion.util.constants import au, lsun, rsun
from hyperion.dust import SphericalDust
# Model
m = Model()
dist = 20000 * au
x = np.linspace(-dist, dist, 101)
y = np.linspace(-dist, dist, 101)
z = np.linspace(-dist, dist, 101)
m.set_cartesian_grid(x,y,z)
# Dust
d = SphericalDust('kmh.hdf5')
d.set_sublimation_temperature('fast', temperature=1600.)
m.add_density_grid(np.ones((100,100,100)) * 1.e-18,'kmh.hdf5')
# Alpha centauri
sourceA = m.add_spherical_source()
sourceA.luminosity = 1.519 * lsun
sourceA.radius = 1.227 * rsun
sourceA.temperature = 5790.
sourceA.position = (0., 0., 0.)
# Beta centauri
sourceB = m.add_spherical_source()
sourceB.luminosity = 0.5 * lsun
sourceB.radius = 0.865 * rsun
sourceB.temperature = 5260.
sourceB.position = (-11.2 * au, 0., 0.)
def arepo_m_gen(fname, field_add):
reg, ds, dustdens = arepo_vornoi_grid_generate(fname, field_add)
xcent = ds.quan(cfg.model.x_cent, 'code_length').to('cm') #proper cm
ycent = ds.quan(cfg.model.y_cent, 'code_length').to('cm')
zcent = ds.quan(cfg.model.z_cent, 'code_length').to('cm')
boost = np.array([xcent, ycent, zcent])
print('[arepo_tributary/vornoi_m_gen]: boost = ', boost)
#========================================================================
#Initialize Hyperion Model
#========================================================================
m = Model()
#because we boost the stars to a [0,0,0] coordinate center, we
#want to make sure our vornoi tesslation is created in the same manner.
particle_x = reg["gascoordinates"][:, 0].to('cm')
particle_y = reg["gascoordinates"][:, 1].to('cm')
particle_z = reg["gascoordinates"][:, 2].to('cm')
#just for the sake of symmetry, pass on a dx,dy,dz since it can be
#used optionally downstream in other functions.
dx = 2. * ds.quan(cfg.par.zoom_box_len, 'kpc').to('cm')
dy = 2. * ds.quan(cfg.par.zoom_box_len, 'kpc').to('cm')
dz = 2. * ds.quan(cfg.par.zoom_box_len, 'kpc').to('cm')
print('[arepo_tributary] boost = ', boost)
print('[arepo_tributary] xmin (pc)= ', (xcent - dx / 2.).to('pc'))
print('[arepo_tributary] xmax (pc)= ', (xcent + dx / 2.).to('pc'))
print('[arepo_tributary] ymin (pc)= ', (ycent - dy / 2.).to('pc'))
print('[arepo_tributary] ymax (pc)= ', (ycent + dy / 2.).to('pc'))
print('[arepo_tributary] zmin (pc)= ', (zcent - dz / 2.).to('pc'))
print('[arepo_tributary] zmax (pc)= ', (zcent + dz / 2.).to('pc'))
x_pos_boost = (particle_x - xcent).to('cm')
y_pos_boost = (particle_y - ycent).to('cm')
z_pos_boost = (particle_z - zcent).to('cm')
m.set_voronoi_grid(x_pos_boost.value, y_pos_boost.value, z_pos_boost.value)
#get CMB:
energy_density_absorbed = energy_density_absorbed_by_CMB()
specific_energy = np.repeat(energy_density_absorbed.value, dustdens.shape)
if cfg.par.PAH == True:
# load PAH fractions for usg, vsg, and big (grain sizes)
frac = cfg.par.PAH_frac
# Normalize to 1
total = np.sum(list(frac.values()))
frac = {k: v / total for k, v in frac.items()}
for size in frac.keys():
d = SphericalDust(cfg.par.dustdir + '%s.hdf5' % size)
if cfg.par.SUBLIMATION == True:
d.set_sublimation_temperature(
'fast', temperature=cfg.par.SUBLIMATION_TEMPERATURE)
#m.add_density_grid(dustdens * frac[size], cfg.par.dustdir+'%s.hdf5' % size)
m.add_density_grid(dustdens * frac[size],
d,
specific_energy=specific_energy)
m.set_enforce_energy_range(cfg.par.enforce_energy_range)
else:
d = SphericalDust(cfg.par.dustdir + cfg.par.dustfile)
if cfg.par.SUBLIMATION == True:
d.set_sublimation_temperature(
'fast', temperature=cfg.par.SUBLIMATION_TEMPERATURE)
m.add_density_grid(dustdens, d, specific_energy=specific_energy)
#m.add_density_grid(dustdens,cfg.par.dustdir+cfg.par.dustfile)
m.set_specific_energy_type('additional')
return m, xcent, ycent, zcent, dx.value, dy.value, dz.value, reg, ds, boost
from hyperion.util.constants import au, lsun, rsun
from hyperion.dust import SphericalDust
# Model
m = Model()
dist = 20000 * au
x = np.linspace(-dist, dist, 101)
y = np.linspace(-dist, dist, 101)
z = np.linspace(-dist, dist, 101)
m.set_cartesian_grid(x, y, z)
# Dust
d = SphericalDust("kmh.hdf5")
d.set_sublimation_temperature("fast", temperature=1600.0)
m.add_density_grid(np.ones((100, 100, 100)) * 1.0e-18, "kmh.hdf5")
# Alpha centauri
sourceA = m.add_spherical_source()
sourceA.luminosity = 1.519 * lsun
sourceA.radius = 1.227 * rsun
sourceA.temperature = 5790.0
sourceA.position = (0.0, 0.0, 0.0)
# Beta centauri
sourceB = m.add_spherical_source()
sourceB.luminosity = 0.5 * lsun
sourceB.radius = 0.865 * rsun
sourceB.temperature = 5260.0
sourceB.position = (-11.2 * au, 0.0, 0.0)
# the dust is directly behind, and the source is moving towards the dust, we # should see red-shifted emission from the source and blue-shifted scattered # light emission. import numpy as np from hyperion.model import Model from hyperion.util.constants import c m = Model() m.set_cartesian_grid([-1., 0, 1], [-1., 1.], [-1., 1]) density = np.zeros(m.grid.shape) density[:, :, 0] = 1. m.add_density_grid(density, 'kmh_lite.hdf5') # narrow emission line spectrum at 1 micron wav = np.array([0.9999, 1.0001]) fnu = np.array([1., 1.]) nu = c / (wav * 1.e-4) s = m.add_spherical_source() s.position = 0.5, 0., 0. s.velocity = -1e8, 0., 0. s.spectrum = nu[::-1], fnu[::-1] s.luminosity = 1 s.radius = 0.1 # Set up images
in_box = (x > 0.) & (x < 60 * au) & (y > 0.) & (y < 60 * au) & (z > 0.) & (z < 60 * au)
density[in_box] = rho_0
# Set up sphere 1
in_sphere_1 = (x - 10 * au) ** 2 + (y - 15 * au) ** 2 + (z - 20 * au) ** 2 < r_1 ** 2
density[in_sphere_1] = rho_1
# Set up sphere 2
in_sphere_2 = (x - 26.666667 * au) ** 2 + (y - 31.666667 * au) ** 2 + (z - 28.333333 * au) ** 2 < r_2 ** 2
density[in_sphere_2] = rho_2
# Remove dust close to source
in_rsub = np.sqrt(x * x + y * y + z * z) < RSUB
density[in_rsub] = 0.
m.add_density_grid(density, d)
# m.set_propagation_check_frequency(1.0)
# Set up illuminating source:
s = m.add_spherical_source()
s.radius = 6.6 * rsun
s.temperature = 33000.
s.luminosity = 4 * pi * s.radius ** 2 * sigma * s.temperature ** 4
# Set up number of photons
m.set_n_photons(initial=NPHOTONS, imaging=0)
# Write out and run
m.write(os.path.join('models', 'bm2_eff_vor_temperature.rtin'), overwrite=True)
m.run(os.path.join('models', 'bm2_eff_vor_temperature.rtout'), mpi=True, overwrite=True)
def enzo_m_gen(fname,field_add):
#add the fields in pd format
pf = field_add(fname)
ad = pf.all_data()
#cutout
center = pf.arr([cfg.model.x_cent,cfg.model.y_cent,cfg.model.z_cent],'code_length')
box_len = pf.quan(cfg.par.zoom_box_len,'kpc').in_units('code_length')
min_region = [center[0]-box_len,center[1]-box_len,center[2]-box_len]
max_region = [center[0]+box_len,center[1]+box_len,center[2]+box_len]
region = pf.region(center,min_region,max_region)
pf = region.ds
proj_plots(pf)
#def. dust density
def _dust_density(field, data):
return data[('gas', 'metal_density')].in_units("g/cm**3")*cfg.par.dusttometals_ratio
pf.add_field(('gas', 'dust_density'), function=_dust_density, units = 'g/cm**3')
amr = AMRGrid.from_yt(pf, quantity_mapping={'density':('gas','dust_density')})
'''
levels = pf.index.max_level
amr = AMRGrid()
for ilevel in range(levels):
level = amr.add_level()
for igrid in pf.index.select_grids(ilevel):
print igrid
grid = level.add_grid()
grid.xmin,grid.xmax = igrid.LeftEdge[0].in_units('cm'),igrid.RightEdge[0].in_units('cm')
grid.ymin,grid.ymax = igrid.LeftEdge[1].in_units('cm'),igrid.RightEdge[1].in_units('cm')
grid.zmin,grid.zmax = igrid.LeftEdge[2].in_units('cm'),igrid.RightEdge[2].in_units('cm')
grid.quantities["density"] = np.transpose(np.array(igrid[("gas","metal_density")].in_units('g/cm**3')*cfg.par.dusttometals_ratio))
grid.nx,grid.ny,grid.nz = igrid[("gas","metal_density")].shape
'''
m = Model()
m.set_amr_grid(amr)
energy_density_absorbed=energy_density_absorbed_by_CMB()
energy_density_absorbed = np.repeat(energy_density_absorbed.value,amr['density'].shape)
d = SphericalDust(cfg.par.dustdir+cfg.par.dustfile)
if cfg.par.SUBLIMATION == True:
d.set_sublimation_temperature('fast',temperature=cfg.par.SUBLIMATION_TEMPERATURE)
m.add_density_grid(amr['density'],d,specific_energy=energy_density_absorbed)
m.set_specific_energy_type('additional')
#m.add_density_grid(amr['density'], cfg.par.dustdir+cfg.par.dustfile)
#define the random things needed for parsing out the output args
#center = pf.domain_center
[xcent,ycent,zcent] = center
boost = np.array([xcent,ycent,zcent])
dx = pf.domain_width.in_units('cm')
dy = pf.domain_width.in_units('cm')
dz = pf.domain_width.in_units('cm')
return m,xcent,ycent,zcent,dx,dy,dz,pf,boost
import random
random.seed('hyperion') # ensure that random numbers are the same every time
import numpy as np
from hyperion.model import Model
from hyperion.util.constants import pc, lsun
# Define cell walls
x = np.linspace(-10., 10., 101) * pc
y = np.linspace(-10., 10., 101) * pc
z = np.linspace(-10., 10., 101) * pc
# Initialize model and set up density grid
m = Model()
m.set_cartesian_grid(x, y, z)
m.add_density_grid(np.ones((100, 100, 100)) * 1.e-20, 'kmh_lite.hdf5')
# Generate random sources
for i in range(100):
s = m.add_point_source()
xs = random.uniform(-10., 10.) * pc
ys = random.uniform(-10., 10.) * pc
zs = random.uniform(-10., 10.) * pc
s.position = (xs, ys, zs)
s.luminosity = 10. ** random.uniform(0., 3.) * lsun
s.temperature = random.uniform(3000., 8000.)
# Specify that the specific energy and density are needed
m.conf.output.output_specific_energy = 'last'
m.conf.output.output_density = 'last'
def sph_m_gen(fname,field_add):
refined,dustdens,fc1,fw1,reg,ds = yt_octree_generate(fname,field_add)
if float(yt.__version__[0:3]) >= 4:
xmin = (fc1[:,0]-fw1[:,0]/2.).to('cm') #in proper cm
xmax = (fc1[:,0]+fw1[:,0]/2.).to('cm')
ymin = (fc1[:,1]-fw1[:,1]/2.).to('cm')
ymax = (fc1[:,1]+fw1[:,1]/2.).to('cm')
zmin = (fc1[:,2]-fw1[:,2]/2.).to('cm')
zmax = (fc1[:,2]+fw1[:,2]/2.).to('cm')
else:
xmin = (fc1[:,0]-fw1[:,0]/2.).convert_to_units('cm') #in proper cm
xmax = (fc1[:,0]+fw1[:,0]/2.).convert_to_units('cm')
ymin = (fc1[:,1]-fw1[:,1]/2.).convert_to_units('cm')
ymax = (fc1[:,1]+fw1[:,1]/2.).convert_to_units('cm')
zmin = (fc1[:,2]-fw1[:,2]/2.).convert_to_units('cm')
zmax = (fc1[:,2]+fw1[:,2]/2.).convert_to_units('cm')
#dx,dy,dz are the edges of the parent grid
dx = (np.max(xmax)-np.min(xmin)).value
dy = (np.max(ymax)-np.min(ymin)).value
dz = (np.max(zmax)-np.min(zmin)).value
xcent = float(ds.quan(cfg.model.x_cent,"code_length").to('cm').value)
ycent = float(ds.quan(cfg.model.y_cent,"code_length").to('cm').value)
zcent = float(ds.quan(cfg.model.z_cent,"code_length").to('cm').value)
boost = np.array([xcent,ycent,zcent])
print ('[sph_tributary] boost = ',boost)
print ('[sph_tributary] xmin (pc)= ',np.min(xmin.to('pc')))
print ('[sph_tributary] xmax (pc)= ',np.max(xmax.to('pc')))
print ('[sph_tributary] ymin (pc)= ',np.min(ymin.to('pc')))
print ('[sph_tributary] ymax (pc)= ',np.max(ymax.to('pc')))
print ('[sph_tributary] zmin (pc)= ',np.min(zmin.to('pc')))
print ('[sph_tributary] zmax (pc)= ',np.max(zmax.to('pc')))
#Tom Robitaille's conversion from z-first ordering (yt's default) to
#x-first ordering (the script should work both ways)
refined_array = np.array(refined)
refined_array = np.squeeze(refined_array)
order = find_order(refined_array)
refined_reordered = []
dustdens_reordered = np.zeros(len(order))
for i in range(len(order)):
refined_reordered.append(refined[order[i]])
dustdens_reordered[i] = dustdens[order[i]]
refined = refined_reordered
dustdens=dustdens_reordered
#hyperion octree stats
max_level = hos.hyperion_octree_stats(refined)
pto.test_octree(refined,max_level)
if float(yt.__version__[0:3]) >= 4:
dump_cell_info(refined,fc1.to('cm'),fw1.to('cm'),xmin,xmax,ymin,ymax,zmin,zmax)
else:
dump_cell_info(refined,fc1.convert_to_units('cm'),fw1.convert_to_units('cm'),xmin,xmax,ymin,ymax,zmin,zmax)
np.save('refined.npy',refined)
np.save('density.npy',dustdens)
#========================================================================
#Initialize Hyperion Model
#========================================================================
m = Model()
#save in the m__dict__ that we're in an oct geometry
m.__dict__['grid_type']='oct'
print ('Setting Octree Grid with Parameters: ')
#m.set_octree_grid(xcent,ycent,zcent,
# dx,dy,dz,refined)
m.set_octree_grid(0,0,0,dx/2,dy/2,dz/2,refined)
#get CMB:
energy_density_absorbed=energy_density_absorbed_by_CMB()
specific_energy = np.repeat(energy_density_absorbed.value,dustdens.shape)
if cfg.par.otf_extinction == False:
if cfg.par.PAH == True:
# load PAH fractions for usg, vsg, and big (grain sizes)
frac = cfg.par.PAH_frac
# Normalize to 1
total = np.sum(list(frac.values()))
frac = {k: v / total for k, v in frac.items()}
for size in frac.keys():
d = SphericalDust(cfg.par.dustdir+'%s.hdf5'%size)
if cfg.par.SUBLIMATION == True:
d.set_sublimation_temperature('fast',temperature=cfg.par.SUBLIMATION_TEMPERATURE)
#m.add_density_grid(dustdens * frac[size], cfg.par.dustdir+'%s.hdf5' % size)
m.add_density_grid(dustdens*frac[size],d,specific_energy=specific_energy)
m.set_enforce_energy_range(cfg.par.enforce_energy_range)
else:
d = SphericalDust(cfg.par.dustdir+cfg.par.dustfile)
if cfg.par.SUBLIMATION == True:
d.set_sublimation_temperature('fast',temperature=cfg.par.SUBLIMATION_TEMPERATURE)
m.add_density_grid(dustdens,d,specific_energy=specific_energy)
#m.add_density_grid(dustdens,cfg.par.dustdir+cfg.par.dustfile)
else: #instead of using a constant extinction law across the
#entire galaxy, we'll compute it on a cell-by-cell basis by
#using information about the grain size distribution from
#the simulation itself.
print("==============================================\n")
print("Entering OTF Extinction Calculation\n")
print("Note: For very high-resolution grids, this may cause memory issues due to adding ncells dust grids")
print("==============================================\n")
ad = ds.all_data()
nsizes = ad['PartType3','Dust_Size'].shape[1]
ncells = reg.parameters["octree_of_sizes"].shape[0]
#ensure that the grid has particles
for isize in range(nsizes):
try:
assert (np.sum(reg.parameters["octree_of_sizes"][:,isize]) > 0)
except AssertionError:
raise AssertionError("[sph_tributary:] The grain size distribution smoothed onto the octree has deposited no particles. Try either increasing your box size, or decreasing n_ref in parameters_master. Alternatively, run the simulation with otf_extinction=False")
#define the grid of sizes that will be used in tributary_dust_add
grid_of_sizes = reg.parameters["octree_of_sizes"]
active_dust_add(ds,m,grid_of_sizes,nsizes,dustdens,specific_energy,refined)
m.set_specific_energy_type('additional')
return m,xcent,ycent,zcent,dx,dy,dz,reg,ds,boost
import numpy as np
from hyperion.model import Model
from hyperion.util.constants import pc, lsun
# Initialize model
m = Model()
# Set one-cell cartesian grid
w = np.linspace(-pc, pc, 32)
m.set_cartesian_grid(w, w, w)
# Add density grid with constant density
m.add_density_grid(np.ones(m.grid.shape) * 4.0e-20, "kmh_lite.hdf5")
# Add a point source in the center
s = m.add_point_source()
s.luminosity = 1000 * lsun
s.temperature = 6000.0
# Add 10 SEDs for different viewing angles
image = m.add_peeled_images(sed=True, image=False)
image.set_wavelength_range(250, 0.01, 5000.0)
image.set_viewing_angles(np.linspace(0.0, 90.0, 10), np.repeat(20.0, 10))
image.set_track_origin("basic")
# Add multi-wavelength image for a single viewing angle
image = m.add_peeled_images(sed=False, image=True)
image.set_wavelength_range(30, 1.0, 1000.0)
image.set_viewing_angles([30.0], [20.0])
image.set_image_size(200, 200)
def setup_model(outdir,record_dir,outname,params,dust_file,tsc=True,idl=False,plot=False,\
low_res=True,flat=True,scale=1,radmc=False,mono=False,record=True,dstar=178.,\
aperture=None,dyn_cav=False,fix_params=None,alma=False,power=2,better_im=False,ellipsoid=False,\
TSC_dir='~/programs/misc/TSC/', IDL_path='/Applications/exelis/idl83/bin/idl',auto_disk=0.25):
"""
params = dictionary of the model parameters
alma keyword is obsoleted
outdir: The directory for storing Hyperion input files
record_dir: The directory contains "model_list.txt" for recording parameters
TSC_dir: Path the TSC-related IDL routines
IDL_path: The IDL executable
"""
import numpy as np
import astropy.constants as const
import scipy as sci
# to avoid X server error
import matplotlib as mpl
mpl.use('Agg')
#
import matplotlib.pyplot as plt
import os
from matplotlib.colors import LogNorm
from scipy.integrate import nquad
from hyperion.model import Model
from record_hyperion import record_hyperion
from outflow_inner_edge import outflow_inner_edge
from pprint import pprint
# import pdb
# pdb.set_trace()
# Constants setup
c = const.c.cgs.value
AU = 1.49598e13 # Astronomical Unit [cm]
pc = 3.08572e18 # Parsec [cm]
MS = 1.98892e33 # Solar mass [g]
LS = 3.8525e33 # Solar luminosity [erg/s]
RS = 6.96e10 # Solar radius [cm]
G = 6.67259e-8 # Gravitational constant [cm3/g/s^2]
yr = 60*60*24*365 # Years in seconds
PI = np.pi # PI constant
sigma = const.sigma_sb.cgs.value # Stefan-Boltzmann constant
mh = const.m_p.cgs.value + const.m_e.cgs.value
g2d = 100.
mmw = 2.37 # Kauffmann 2008
m = Model()
# Create dust properties
# Hyperion needs nu, albedo, chi, g, p_lin_max
from hyperion.dust import HenyeyGreensteinDust
# Read in the dust opacity table used by RADMC-3D
dust = dict()
# [dust_radmc['wl'], dust_radmc['abs'], dust_radmc['scat'], dust_radmc['g']] = np.genfromtxt(dust_file,skip_header=2).T
[dust['nu'], dust['albedo'], dust['chi'], dust['g']] = np.genfromtxt(dust_file).T
# opacity per mass of dust?
# dust_hy = dict()
# dust_hy['nu'] = c/dust_radmc['wl']*1e4
# ind = np.argsort(dust_hy['nu'])
# dust_hy['nu'] = dust_hy['nu'][ind]
# dust_hy['albedo'] = (dust_radmc['scat']/(dust_radmc['abs']+dust_radmc['scat']))[ind]
# dust_hy['chi'] = (dust_radmc['abs']+dust_radmc['scat'])[ind]
# dust_hy['g'] = dust_radmc['g'][ind]
# dust_hy['p_lin_max'] = 0*dust_radmc['wl'][ind] # assume no polarization
# d = HenyeyGreensteinDust(dust_hy['nu'], dust_hy['albedo'], dust_hy['chi'], dust_hy['g'], dust_hy['p_lin_max'])
d = HenyeyGreensteinDust(dust['nu'], dust['albedo'], dust['chi'], dust['g'], dust['g']*0)
# dust sublimation option
d.set_sublimation_temperature('slow', temperature=1600.0)
d.set_lte_emissivities(n_temp=3000,
temp_min=0.1,
temp_max=2000.)
# try to solve the freq. problem
d.optical_properties.extrapolate_nu(3.28e15, 4e15)
#
d.write(outdir+os.path.basename(dust_file).split('.')[0]+'.hdf5')
d.plot(outdir+os.path.basename(dust_file).split('.')[0]+'.png')
plt.clf()
# Grids and Density
# Calculation inherited from the script used for RADMC-3D
# Grid Parameters
nx = 300L
if low_res == True:
nx = 100L
ny = 400L
nz = 50L
[nx, ny, nz] = [int(scale*nx), int(scale*ny), int(scale*nz)]
# TSC model input setting
# params = np.genfromtxt(indir+'/tsc_params.dat', dtype=None)
dict_params = params # input_reader(params_file)
# TSC model parameter
cs = dict_params['Cs']*1e5
t = dict_params['age'] # year
omega = dict_params['Omega0']
# calculate related parameters
M_env_dot = 0.975*cs**3/G
mstar = M_env_dot * t * yr
R_cen = omega**2 * G**3 * mstar**3 /(16*cs**8)
R_inf = cs * t * yr
# M_env_dot = dict_params['M_env_dot']*MS/yr
# R_cen = dict_params['R_cen']*AU
# R_inf = dict_params['R_inf']*AU
# protostar parameter
tstar = dict_params['tstar']
R_env_max = dict_params['R_env_max']*AU
theta_cav = dict_params['theta_cav']
rho_cav_center = dict_params['rho_cav_center']
rho_cav_edge = dict_params['rho_cav_edge']*AU
rstar = dict_params['rstar']*RS
# Mostly fixed parameter
M_disk = dict_params['M_disk']*MS
beta = dict_params['beta']
h100 = dict_params['h100']*AU
rho_cav = dict_params['rho_cav']
# make M_disk varies with mstar, which is the mass of star+disk
if auto_disk != None:
if M_disk != 0:
print 'M_disk is reset to %4f of mstar (star+disk)' % auto_disk
M_disk = mstar * auto_disk
else:
print 'M_disk = 0 is found. M_disk is set to 0.'
# ellipsoid cavity parameter
if ellipsoid == True:
a_out = 130 * 178. * AU
b_out = 50 * 178. * AU
z_out = a_out
# a_in = 77.5 * 178. * AU
# b_in = 30 * 178. * AU
a_in = dict_params['a_in'] * 178. * AU
b_in = a_in/a_out*b_out
z_in = a_in
# rho_cav_out = 1e4 * mh
# rho_cav_in = 1e3 * mh
rho_cav_out = dict_params['rho_cav_out'] * mh
rho_cav_in = dict_params['rho_cav_in'] * mh
# Calculate the dust sublimation radius
T_sub = 1600
a = 1 #in micron
# realistic dust
# d_sub = 2.9388e7*(a/0.1)**-0.2 * (4*np.pi*rstar**2*sigma*tstar**4/LS)**0.5 / T_sub**3 *AU
# black body dust
d_sub = (LS/16./np.pi/sigma/AU**2*(4*np.pi*rstar**2*sigma*tstar**4/LS)/T_sub**4)**0.5 *AU
# use the dust sublimation radius as the inner radius of disk and envelope
R_disk_min = d_sub
R_env_min = d_sub
rin = rstar
rout = R_env_max
R_disk_max = R_cen
# Do the variable conversion
# cs = (G * M_env_dot / 0.975)**(1/3.) # cm/s
# t = R_inf / cs / yr # in year
# mstar = M_env_dot * t * yr
# omega = (R_cen * 16*cs**8 / (G**3 * mstar**3))**0.5
# print the variables for radmc3d
print 'Dust sublimation radius %6f AU' % (d_sub/AU)
print 'M_star %4f Solar mass' % (mstar/MS)
print 'Infall radius %4f AU' % (R_inf / AU)
# if there is any parameter found in fix_params, then fix them
if fix_params != None:
if 'R_min' in fix_params.keys():
R_disk_min = fix_params['R_min']*AU
R_env_min = fix_params['R_min']*AU
# Make the Coordinates
#
ri = rin * (rout/rin)**(np.arange(nx+1).astype(dtype='float')/float(nx))
ri = np.hstack((0.0, ri))
thetai = PI*np.arange(ny+1).astype(dtype='float')/float(ny)
phii = PI*2.0*np.arange(nz+1).astype(dtype='float')/float(nz)
# Keep the constant cell size in r-direction at large radii
#
if flat == True:
ri_cellsize = ri[1:-1]-ri[0:-2]
ind = np.where(ri_cellsize/AU > 100.0)[0][0] # The largest cell size is 100 AU
ri = np.hstack((ri[0:ind],ri[ind]+np.arange(np.ceil((rout-ri[ind])/100/AU))*100*AU))
nxx = nx
nx = len(ri)-1
# Assign the coordinates of the center of cell as its coordinates.
#
rc = 0.5*( ri[0:nx] + ri[1:nx+1] )
thetac = 0.5*( thetai[0:ny] + thetai[1:ny+1] )
phic = 0.5*( phii[0:nz] + phii[1:nz+1] )
# phic = 0.5*( phii[0:nz-1] + phii[1:nz] )
# Make the dust density model
# Make the density profile of the envelope
#
total_mass = 0
if tsc == False:
print 'Calculating the dust density profile with infall solution...'
if theta_cav != 0:
# c0 = R_env_max**(-0.5)*np.sqrt(1/np.sin(np.radians(theta_cav))**3-1/np.sin(np.radians(theta_cav)))
# using R = 10000 AU as the reference point
c0 = (10000.*AU)**(-0.5)*np.sqrt(1/np.sin(np.radians(theta_cav))**3-1/np.sin(np.radians(theta_cav)))
else:
c0 = 0
rho_env = np.zeros([len(rc),len(thetac),len(phic)])
rho_disk = np.zeros([len(rc),len(thetac),len(phic)])
rho = np.zeros([len(rc),len(thetac),len(phic)])
if dyn_cav == True:
print 'WARNING: Calculation of interdependent cavity property has not implemented in infall-only solution!'
# Normalization for the total disk mass
def f(w,z,beta,rstar,h100):
f = 2*PI*w*(1-np.sqrt(rstar/w))*(rstar/w)**(beta+1)*np.exp(-0.5*(z/(w**beta*h100/100**beta))**2)
return f
rho_0 = M_disk/(nquad(f,[[R_disk_min,R_disk_max],[-R_env_max,R_env_max]], args=(beta,rstar,h100)))[0]
i = 0
j = 0
if 'rho_cav_center' in locals() == False:
rho_cav_center = 5.27e-18 # 1.6e-17 # 5.27e-18
print 'Use 5.27e-18 as the default value for cavity center'
if 'rho_cav_edge' in locals() == False:
rho_cav_edge = 40*AU
print 'Use 40 AU as the default value for size of the inner region'
discont = 1
for ir in range(0,len(rc)):
for itheta in range(0,len(thetac)):
for iphi in range(0,len(phic)):
if rc[ir] > R_env_min:
# Envelope profile
w = abs(rc[ir]*np.cos(np.pi/2 - thetac[itheta]))
z = rc[ir]*np.sin(np.pi/2 - thetac[itheta])
if ellipsoid == False:
z_cav = c0*abs(w)**1.5
if z_cav == 0:
z_cav = R_env_max
cav_con = abs(z) > abs(z_cav)
else:
# condition for the outer ellipsoid
cav_con = (2*(w/b_out)**2 + ((abs(z)-z_out)/a_out)**2) < 1
if cav_con:
# open cavity
if ellipsoid == False:
if rho_cav_edge == 0:
rho_cav_edge = R_env_min
if (rc[ir] <= rho_cav_edge) & (rc[ir] >= R_env_min):
rho_env[ir,itheta,iphi] = g2d * rho_cav_center#*((rc[ir]/AU)**2)
else:
rho_env[ir,itheta,iphi] = g2d * rho_cav_center*discont*(rho_cav_edge/rc[ir])**power
i += 1
else:
# condition for the inner ellipsoid
if (2*(w/b_in)**2 + ((abs(z)-z_in)/a_in)**2) > 1:
rho_env[ir,itheta,iphi] = rho_cav_out
else:
rho_env[ir,itheta,iphi] = rho_cav_in
i +=1
else:
j += 1
mu = abs(np.cos(thetac[itheta]))
# Implement new root finding algorithm
roots = np.roots(np.array([1.0, 0.0, rc[ir]/R_cen-1.0, -mu*rc[ir]/R_cen]))
if len(roots[roots.imag == 0]) == 1:
if (abs(roots[roots.imag == 0]) - 1.0) <= 0.0:
mu_o_dum = roots[roots.imag == 0]
else:
mu_o_dum = -0.5
print 'Problem with cubic solving, cos(theta) = ', mu_o_dum
print 'parameters are ', np.array([1.0, 0.0, rc[ir]/R_cen-1.0, -mu*rc[ir]/R_cen])
else:
mu_o_dum = -0.5
for imu in range(0, len(roots)):
if roots[imu]*mu >= 0.0:
if (abs((abs(roots[imu]) - 1.0)) <= 1e-5):
mu_o_dum = 1.0 * np.sign(mu)
else:
mu_o_dum = roots[imu]
if mu_o_dum == -0.5:
print 'Problem with cubic solving, roots are: ', roots
mu_o = mu_o_dum.real
rho_env[ir,itheta,iphi] = M_env_dot/(4*PI*(G*mstar*R_cen**3)**0.5)*(rc[ir]/R_cen)**(-3./2)*(1+mu/mu_o)**(-0.5)*(mu/mu_o+2*mu_o**2*R_cen/rc[ir])**(-1)
# Disk profile
if ((w >= R_disk_min) and (w <= R_disk_max)) == True:
h = ((w/(100*AU))**beta)*h100
rho_disk[ir,itheta,iphi] = rho_0*(1-np.sqrt(rstar/w))*(rstar/w)**(beta+1)*np.exp(-0.5*(z/h)**2)
# Combine envelope and disk
rho[ir,itheta,iphi] = rho_disk[ir,itheta,iphi] + rho_env[ir,itheta,iphi]
else:
rho[ir,itheta,iphi] = 1e-30
# add the dust mass into the total count
cell_mass = rho[ir, itheta, iphi] * (1/3.)*(ri[ir+1]**3 - ri[ir]**3) * (phii[iphi+1]-phii[iphi]) * -(np.cos(thetai[itheta+1])-np.cos(thetai[itheta]))
total_mass = total_mass + cell_mass
rho_env = rho_env + 1e-40
rho_disk = rho_disk + 1e-40
rho = rho + 1e-40
# TSC model
else:
print 'Calculating the dust density profile with TSC solution...'
if theta_cav != 0:
# c0 = R_env_max**(-0.5)*np.sqrt(1/np.sin(np.radians(theta_cav))**3-1/np.sin(np.radians(theta_cav)))
c0 = (1e4*AU)**(-0.5)*np.sqrt(1/np.sin(np.radians(theta_cav))**3-1/np.sin(np.radians(theta_cav)))
else:
c0 = 0
# If needed, calculate the TSC model via IDL
#
if idl == True:
print 'Using IDL to calculate the TSC model. Make sure you are running this on mechine with IDL.'
import pidly
# idl = pidly.IDL('/Applications/exelis/idl82/bin/idl')
idl = pidly.IDL(IDL_path)
idl('.r '+TSC_dir+'tsc.pro')
# idl.pro('tsc_run', outdir=outdir, grid=[nxx,ny,nz], time=t, c_s=cs, omega=omega, rstar=rstar, renv_min=R_env_min, renv_max=R_env_max)
# idl.pro('tsc_run', outdir=outdir, grid=[nxx,ny,nz], time=t, c_s=cs, omega=omega, rstar=rstar, renv_min=R_env_min, renv_max=min([R_inf,max(ri)])) # min([R_inf,max(ri)])
#
# only run TSC calculation within infall radius
# modify the rc array
rc_idl = rc[(rc < min([R_inf,max(ri)]))]
idl.pro('tsc_run', outdir=outdir, rc=rc_idl, thetac=thetac, time=t, c_s=cs, omega=omega, renv_min=R_env_min)#, rstar=rstar, renv_min=R_env_min, renv_max=min([R_inf,max(ri)])) # min([R_inf,max(ri)])
else:
print 'Read the pre-computed TSC model.'
rc_idl = rc[(rc < min([R_inf,max(ri)]))]
# read in the exist file
rho_env_tsc_idl = np.genfromtxt(outdir+'rhoenv.dat').T
# because only region within infall radius is calculated by IDL program, need to project it to the original grid
rho_env_tsc = np.zeros([len(rc), len(thetac)])
for irc in range(len(rc)):
if rc[irc] in rc_idl:
rho_env_tsc[irc,:] = rho_env_tsc_idl[np.where(rc_idl == rc[irc]),:]
# extrapolate for the NaN values at the outer radius, usually at radius beyond the infall radius
# using r^-2 profile at radius greater than infall radius
# and map the 2d strcuture onto 3d grid
def poly(x, y, x0, deg=2):
import numpy as np
p = np.polyfit(x, y, deg)
y0 = 0
for i in range(0, len(p)):
y0 = y0 + p[i]*x0**(len(p)-i-1)
return y0
# rho_env_copy = np.array(rho_env_tsc)
# if max(rc) > R_inf:
# ind_infall = np.where(rc <= R_inf)[0][-1]
# print ind_infall
# for ithetac in range(0, len(thetac)):
# # rho_dum = np.log10(rho_env_copy[(rc > R_inf) & (np.isnan(rho_env_copy[:,ithetac]) == False),ithetac])
# # rc_dum = np.log10(rc[(rc > R_inf) & (np.isnan(rho_env_copy[:,ithetac]) == False)])
# # rc_dum_nan = np.log10(rc[(rc > R_inf) & (np.isnan(rho_env_copy[:,ithetac]) == True)])
# # # print rc_dum
# # for i in range(0, len(rc_dum_nan)):
# # rho_extrapol = poly(rc_dum, rho_dum, rc_dum_nan[i])
# # rho_env_copy[(np.log10(rc) == rc_dum_nan[i]),ithetac] = 10**rho_extrapol
# #
# for i in range(ind_infall, len(rc)):
# rho_env_copy[i, ithetac] = 10**(np.log10(rho_env_copy[ind_infall, ithetac]) - 2*(np.log10(rc[i]/rc[ind_infall])))
# rho_env2d = rho_env_copy
# rho_env = np.empty((nx,ny,nz))
# for i in range(0, nz):
# rho_env[:,:,i] = rho_env2d
# map TSC solution from IDL to actual 2-D grid
rho_env_tsc2d = np.empty((nx,ny))
if max(ri) > R_inf:
ind_infall = np.where(rc <= R_inf)[0][-1]
for i in range(0, len(rc)):
if i <= ind_infall:
rho_env_tsc2d[i,:] = rho_env_tsc[i,:]
else:
rho_env_tsc2d[i,:] = 10**(np.log10(rho_env_tsc[ind_infall,:]) - 2*(np.log10(rc[i]/rc[ind_infall])))
else:
rho_env_tsc2d = rho_env_tsc
# map it to 3-D grid
rho_env = np.empty((nx,ny,nz))
for i in range(0, nz):
rho_env[:,:,i] = rho_env_tsc2d
if dyn_cav == True:
print 'Calculate the cavity properties using the criteria that swept-up mass = outflowed mass'
# using swept-up mass = flow mass to derive the edge of the extended flat density region
v_outflow = 1e2 * 1e5
rho_cav_edge = outflow_inner_edge(np.copy(rho_env), (ri,thetai,phii),M_env_dot,v_outflow,theta_cav, R_env_min)
dict_params['rho_cav_edge'] = rho_cav_edge
# assume gas-to-dust ratio = 100
rho_cav_center = 0.01 * 0.1*M_env_dot*rho_cav_edge/v_outflow/2 / (2*np.pi/3*rho_cav_edge**3*(1-np.cos(np.radians(theta_cav))))
dict_params['rho_cav_center'] = rho_cav_center
print 'inner edge is %5f AU and density is %e g/cm3' % (rho_cav_edge/AU, rho_cav_center)
# create the array of density of disk and the whole structure
#
rho_disk = np.zeros([len(rc),len(thetac),len(phic)])
rho = np.zeros([len(rc),len(thetac),len(phic)])
# Calculate the disk scale height by the normalization of h100
def f(w,z,beta,rstar,h100):
f = 2*PI*w*(1-np.sqrt(rstar/w))*(rstar/w)**(beta+1)*np.exp(-0.5*(z/(w**beta*h100/100**beta))**2)
return f
# The function for calculating the normalization of disk using the total disk mass
#
rho_0 = M_disk/(nquad(f,[[R_disk_min,R_disk_max],[-R_env_max,R_env_max]], args=(beta,rstar,h100)))[0]
i = 0
j = 0
if 'rho_cav_center' in locals() == False:
rho_cav_center = 5.27e-18 # 1.6e-17 # 5.27e-18
print 'Use 5.27e-18 as the default value for cavity center'
if 'rho_cav_edge' in locals() == False:
rho_cav_edge = 40*AU
print 'Use 40 AU as the default value for size of the inner region'
discont = 1
for ir in range(0,len(rc)):
for itheta in range(0,len(thetac)):
for iphi in range(0,len(phic)):
if rc[ir] > R_env_min:
# Envelope profile
w = abs(rc[ir]*np.cos(np.pi/2 - thetac[itheta]))
z = rc[ir]*np.sin(np.pi/2 - thetac[itheta])
if ellipsoid == False:
z_cav = c0*abs(w)**1.5
if z_cav == 0:
z_cav = R_env_max
cav_con = abs(z) > abs(z_cav)
else:
# condition for the outer ellipsoid
cav_con = (2*(w/b_out)**2 + ((abs(z)-z_out)/a_out)**2) < 1
if cav_con:
# open cavity
if ellipsoid == False:
if rho_cav_edge == 0:
rho_cav_edge = R_env_min
if (rc[ir] <= rho_cav_edge) & (rc[ir] >= R_env_min):
rho_env[ir,itheta,iphi] = g2d * rho_cav_center#*((rc[ir]/AU)**2)
else:
rho_env[ir,itheta,iphi] = g2d * rho_cav_center*discont*(rho_cav_edge/rc[ir])**power
i += 1
else:
# condition for the inner ellipsoid
if (2*(w/b_in)**2 + ((abs(z)-z_in)/a_in)**2) > 1:
rho_env[ir,itheta,iphi] = rho_cav_out
else:
rho_env[ir,itheta,iphi] = rho_cav_in
i +=1
# Disk profile
if ((w >= R_disk_min) and (w <= R_disk_max)) == True:
h = ((w/(100*AU))**beta)*h100
rho_disk[ir,itheta,iphi] = rho_0*(1-np.sqrt(rstar/w))*(rstar/w)**(beta+1)*np.exp(-0.5*(z/h)**2)
# Combine envelope and disk
rho[ir,itheta,iphi] = rho_disk[ir,itheta,iphi] + rho_env[ir,itheta,iphi]
else:
rho[ir,itheta,iphi] = 1e-40
# add the dust mass into the total count
cell_mass = rho[ir, itheta, iphi] * (1/3.)*(ri[ir+1]**3 - ri[ir]**3) * (phii[iphi+1]-phii[iphi]) * -(np.cos(thetai[itheta+1])-np.cos(thetai[itheta]))
total_mass = total_mass + cell_mass
# rho_env = rho_env + 1e-40
# rho_disk = rho_disk + 1e-40
# rho = rho + 1e-40
# apply gas-to-dust ratio of 100
rho_dust = rho/g2d
total_mass_dust = total_mass/MS/g2d
print 'Total dust mass = %f Solar mass' % total_mass_dust
if record == True:
# Record the input and calculated parameters
params = dict_params.copy()
params.update({'d_sub': d_sub/AU, 'M_env_dot': M_env_dot/MS*yr, 'R_inf': R_inf/AU, 'R_cen': R_cen/AU, 'mstar': mstar/MS, 'M_tot_gas': total_mass/MS})
record_hyperion(params,record_dir)
if plot == True:
# rc setting
# mat.rcParams['text.usetex'] = True
# mat.rcParams['font.family'] = 'serif'
# mat.rcParams['font.serif'] = 'Times'
# mat.rcParams['font.sans-serif'] = 'Computer Modern Sans serif'
# Plot the azimuthal averaged density
fig = plt.figure(figsize=(8,6))
ax_env = fig.add_subplot(111,projection='polar')
# take the weighted average
# rho2d is the 2-D projection of gas density
rho2d = np.sum(rho**2,axis=2)/np.sum(rho,axis=2)
zmin = 1e-22/mmw/mh
cmap = plt.cm.CMRmap
rho2d_exp = np.hstack((rho2d,rho2d,rho2d[:,0:1]))
thetac_exp = np.hstack((thetac-PI/2, thetac+PI/2, thetac[0]-PI/2))
# plot the gas density
img_env = ax_env.pcolormesh(thetac_exp,rc/AU,rho2d_exp/mmw/mh,cmap=cmap,norm=LogNorm(vmin=zmin,vmax=1e9)) # np.nanmax(rho2d_exp/mmw/mh)
ax_env.set_xlabel(r'$\rm{Polar\,angle\,(Degree)}$',fontsize=20)
ax_env.set_ylabel(r'$\rm{Radius\,(AU)}$',fontsize=20)
ax_env.tick_params(labelsize=20)
ax_env.set_yticks(np.arange(0,R_env_max/AU,R_env_max/AU/5))
# ax_env.set_ylim([0,10000])
ax_env.set_xticklabels([r'$\rm{90^{\circ}}$',r'$\rm{45^{\circ}}$',r'$\rm{0^{\circ}}$',r'$\rm{-45^{\circ}}$',\
r'$\rm{-90^{\circ}}$',r'$\rm{-135^{\circ}}$',r'$\rm{180^{\circ}}$',r'$\rm{135^{\circ}}$'])
# fix the tick label font
ticks_font = mpl.font_manager.FontProperties(family='STIXGeneral',size=20)
for label in ax_env.get_yticklabels():
label.set_fontproperties(ticks_font)
ax_env.grid(True)
cb = fig.colorbar(img_env, pad=0.1)
cb.ax.set_ylabel(r'$\rm{Averaged\,Gas\,Density\,(cm^{-3})}$',fontsize=20)
cb.set_ticks([1e2,1e3,1e4,1e5,1e6,1e7,1e8,1e9])
cb.set_ticklabels([r'$\rm{10^{2}}$',r'$\rm{10^{3}}$',r'$\rm{10^{4}}$',r'$\rm{10^{5}}$',r'$\rm{10^{6}}$',\
r'$\rm{10^{7}}$',r'$\rm{10^{8}}$',r'$\rm{\geq 10^{9}}$'])
cb_obj = plt.getp(cb.ax.axes, 'yticklabels')
plt.setp(cb_obj,fontsize=20)
fig.savefig(outdir+outname+'_gas_density.png', format='png', dpi=300, bbox_inches='tight')
fig.clf()
# Plot the radial density profile
fig = plt.figure(figsize=(12,9))
ax = fig.add_subplot(111)
plot_grid = [0,49,99,149,199]
alpha = np.linspace(0.3,1.0,len(plot_grid))
for i in plot_grid:
rho_rad, = ax.plot(np.log10(rc/AU), np.log10(rho2d[:,i]/g2d/mmw/mh),'-',color='b',linewidth=2, markersize=3,alpha=alpha[plot_grid.index(i)])
tsc_only, = ax.plot(np.log10(rc/AU), np.log10(rho_env_tsc2d[:,i]/mmw/mh),'o',color='r',linewidth=2, markersize=3,alpha=alpha[plot_grid.index(i)])
rinf = ax.axvline(np.log10(R_inf/AU), linestyle='--', color='k', linewidth=1.5)
cen_r = ax.axvline(np.log10(R_cen/AU), linestyle=':', color='k', linewidth=1.5)
# sisslope, = ax.plot(np.log10(rc/AU), -2*np.log10(rc/AU)+A-(-2)*np.log10(plot_r_inf), linestyle='--', color='Orange', linewidth=1.5)
# gt_R_cen_slope, = ax.plot(np.log10(rc/AU), -1.5*np.log10(rc/AU)+B-(-1.5)*np.log10(plot_r_inf), linestyle='--', color='Orange', linewidth=1.5)
# lt_R_cen_slope, = ax.plot(np.log10(rc/AU), -0.5*np.log10(rc/AU)+A-(-0.5)*np.log10(plot_r_inf), linestyle='--', color='Orange', linewidth=1.5)
lg = plt.legend([rho_rad, tsc_only, rinf, cen_r],\
[r'$\rm{\rho_{dust}}$',r'$\rm{\rho_{tsc}}$',r'$\rm{infall\,radius}$',r'$\rm{centrifugal\,radius}$'],\
fontsize=20, numpoints=1)
ax.set_xlabel(r'$\rm{log(Radius)\,(AU)}$',fontsize=20)
ax.set_ylabel(r'$\rm{log(Gas \slash Dust\,Density)\,(cm^{-3})}$',fontsize=20)
[ax.spines[axis].set_linewidth(1.5) for axis in ['top','bottom','left','right']]
ax.minorticks_on()
ax.tick_params('both',labelsize=18,width=1.5,which='major',pad=15,length=5)
ax.tick_params('both',labelsize=18,width=1.5,which='minor',pad=15,length=2.5)
# fix the tick label font
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)
ax.set_ylim([0,15])
fig.gca().set_xlim(left=np.log10(0.05))
# ax.set_xlim([np.log10(0.8),np.log10(10000)])
# subplot shows the radial density profile along the midplane
ax_mid = plt.axes([0.2,0.2,0.2,0.2], frameon=True)
ax_mid.plot(np.log10(rc/AU), np.log10(rho2d[:,199]/g2d/mmw/mh),'o',color='b',linewidth=1, markersize=2)
ax_mid.plot(np.log10(rc/AU), np.log10(rho_env_tsc2d[:,199]/mmw/mh),'-',color='r',linewidth=1, markersize=2)
# ax_mid.set_ylim([0,10])
# ax_mid.set_xlim([np.log10(0.8),np.log10(10000)])
ax_mid.set_ylim([0,15])
fig.savefig(outdir+outname+'_gas_radial.pdf',format='pdf',dpi=300,bbox_inches='tight')
fig.clf()
# Insert the calculated grid and dust density profile into hyperion
m.set_spherical_polar_grid(ri, thetai, phii)
# temperary for comparing full TSC and infall-only TSC model
# import sys
# sys.path.append(os.path.expanduser('~')+'/programs/misc/')
# from tsc_comparison import tsc_com
# rho_tsc, rho_ulrich = tsc_com()
m.add_density_grid(rho_dust.T, d)
# m.add_density_grid(rho.T, outdir+'oh5.hdf5') # numpy read the array in reverse order
# Define the luminsoity source
source = m.add_spherical_source()
source.luminosity = (4*PI*rstar**2)*sigma*(tstar**4) # [ergs/s]
source.radius = rstar # [cm]
source.temperature = tstar # [K]
source.position = (0., 0., 0.)
print 'L_center = % 5.2f L_sun' % ((4*PI*rstar**2)*sigma*(tstar**4)/LS)
# # add an infrared source at the center
# L_IR = 0.04
# ir_source = m.add_spherical_source()
# ir_source.luminosity = L_IR*LS
# ir_source.radius = rstar # [cm]
# ir_source.temperature = 500 # [K] peak at 10 um
# ir_source.position = (0., 0., 0.)
# print 'Additional IR source, L_IR = %5.2f L_sun' % L_IR
# Setting up the wavelength for monochromatic radiative transfer
lambda0 = 0.1
lambda1 = 2.0
lambda2 = 50.0
lambda3 = 95.0
lambda4 = 200.0
lambda5 = 314.0
lambda6 = 1000.0
n01 = 10.0
n12 = 20.0
n23 = 50.0
lam01 = lambda0 * (lambda1/lambda0)**(np.arange(n01)/n01)
lam12 = lambda1 * (lambda2/lambda1)**(np.arange(n12)/n12)
lam23 = lambda2 * (lambda6/lambda2)**(np.arange(n23+1)/n23)
lam = np.concatenate([lam01,lam12,lam23])
nlam = len(lam)
# Create camera wavelength points
n12 = 70.0
n23 = 70.0
n34 = 70.0
n45 = 50.0
n56 = 50.0
lam12 = lambda1 * (lambda2/lambda1)**(np.arange(n12)/n12)
lam23 = lambda2 * (lambda3/lambda2)**(np.arange(n23)/n23)
lam34 = lambda3 * (lambda4/lambda3)**(np.arange(n34)/n34)
lam45 = lambda4 * (lambda5/lambda4)**(np.arange(n45)/n45)
lam56 = lambda5 * (lambda6/lambda5)**(np.arange(n56+1)/n56)
lam_cam = np.concatenate([lam12,lam23,lam34,lam45,lam56])
n_lam_cam = len(lam_cam)
# Radiative transfer setting
# number of photons for temp and image
lam_list = lam.tolist()
# print lam_list
m.set_raytracing(True)
# option of using more photons for imaging
if better_im == False:
im_photon = 1e6
else:
im_photon = 5e7
if mono == True:
# Monechromatic radiative transfer setting
m.set_monochromatic(True, wavelengths=lam_list)
m.set_n_photons(initial=1000000, imaging_sources=im_photon, imaging_dust=im_photon,raytracing_sources=1000000, raytracing_dust=1000000)
else:
# regular wavelength grid setting
m.set_n_photons(initial=1000000, imaging=im_photon,raytracing_sources=1000000, raytracing_dust=1000000)
# number of iteration to compute dust specific energy (temperature)
m.set_n_initial_iterations(20)
# m.set_convergence(True, percentile=95., absolute=1.5, relative=1.02)
m.set_convergence(True, percentile=dict_params['percentile'], absolute=dict_params['absolute'], relative=dict_params['relative'])
m.set_mrw(True) # Gamma = 1 by default
# m.set_forced_first_scattering(forced_first_scattering=True)
# Setting up images and SEDs
# SED setting
# Infinite aperture
syn_inf = m.add_peeled_images(image=False)
# use the index of wavelength array used by the monochromatic radiative transfer
if mono == False:
syn_inf.set_wavelength_range(1400, 2.0, 1400.0)
syn_inf.set_viewing_angles([dict_params['view_angle']], [0.0])
syn_inf.set_uncertainties(True)
syn_inf.set_output_bytes(8)
# aperture
# 7.2 in 10 um scaled by lambda / 10
# flatten beyond 20 um
# default aperture
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, 1300],\
'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, 101]}
# 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)
# name = np.arange(1,len(wl_aper)+1)
# aper = np.empty_like(wl_aper)
# for i in range(0, len(wl_aper)):
# if wl_aper[i] < 5:
# # aper[i] = 1.2 * 7
# aper[i] = 1.8 * 4
# elif (wl_aper[i] < 14) & (wl_aper[i] >=5):
# # aper[i] = 7.2 * wl_aper[i]/10.
# aper[i] = 1.8 * 4
# elif (wl_aper[i] >= 14) & (wl_aper[i] <40):
# # aper[i] = 7.2 * 2
# aper[i] = 5.1 * 4
# else:
# aper[i] = 24.5
# dict_peel_sed = {}
# for i in range(0, len(wl_aper)):
# aper_dum = aper[i]/2 * (1/3600.*np.pi/180.)*dstar*pc
# dict_peel_sed[str(name[i])] = m.add_peeled_images(image=False)
# # use the index of wavelength array used by the monochromatic radiative transfer
# if mono == False:
# # dict_peel_sed[str(name[i])].set_wavelength_range(1300, 2.0, 1300.0)
# dict_peel_sed[str(name[i])].set_wavelength_range(1000, 2.0, 1000.0)
# dict_peel_sed[str(name[i])].set_viewing_angles([dict_params['view_angle']], [0.0])
# # aperture should be given in cm
# dict_peel_sed[str(name[i])].set_aperture_range(1, aper_dum, aper_dum)
# dict_peel_sed[str(name[i])].set_uncertainties(True)
# dict_peel_sed[str(name[i])].set_output_bytes(8)
dict_peel_sed = {}
for i in range(0, len(aper_reduced)):
aper_dum = aper_reduced[i]/2 * (1/3600.*np.pi/180.)*dstar*pc
dict_peel_sed[str(index_reduced[i])] = m.add_peeled_images(image=False)
# use the index of wavelength array used by the monochromatic radiative transfer
if mono == False:
dict_peel_sed[str(index_reduced[i])].set_wavelength_range(1400, 2.0, 1400.0)
dict_peel_sed[str(index_reduced[i])].set_viewing_angles([dict_params['view_angle']], [0.0])
# aperture should be given in cm and its the radius of the aperture
dict_peel_sed[str(index_reduced[i])].set_aperture_range(1, aper_dum, aper_dum)
dict_peel_sed[str(index_reduced[i])].set_uncertainties(True)
dict_peel_sed[str(index_reduced[i])].set_output_bytes(8)
# image setting
syn_im = m.add_peeled_images(sed=False)
# use the index of wavelength array used by the monochromatic radiative transfer
if mono == False:
syn_im.set_wavelength_range(1400, 2.0, 1400.0)
# pixel number
syn_im.set_image_size(300, 300)
syn_im.set_image_limits(-R_env_max, R_env_max, -R_env_max, R_env_max)
syn_im.set_viewing_angles([dict_params['view_angle']], [0.0])
syn_im.set_uncertainties(True)
# output as 64-bit
syn_im.set_output_bytes(8)
# 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'
# Number of unique photons that passed through the cell
m.conf.output.output_n_photons = 'last'
m.write(outdir+outname+'.rtin')
if radmc == True:
# RADMC-3D still use a pre-defined aperture with lazy for-loop
aper = np.zeros([len(lam)])
ind = 0
for wl in lam:
if wl < 5:
aper[ind] = 8.4
elif wl >= 5 and wl < 14:
aper[ind] = 1.8 * 4
elif wl >= 14 and wl < 40:
aper[ind] = 5.1 * 4
else:
aper[ind] = 24.5
ind += 1
# Write the wavelength_micron.inp file
#
f_wave = open(outdir+'wavelength_micron.inp','w')
f_wave.write('%d \n' % int(nlam))
for ilam in range(0,nlam):
f_wave.write('%f \n' % lam[ilam])
f_wave.close()
# Write the camera_wavelength_micron.inp file
#
f_wave_cam = open(outdir+'camera_wavelength_micron.inp','w')
f_wave_cam.write('%d \n' % int(nlam))
for ilam in range(0,nlam):
f_wave_cam.write('%f \n' % lam[ilam])
f_wave_cam.close()
# Write the aperture_info.inp
#
f_aper = open(outdir+'aperture_info.inp','w')
f_aper.write('1 \n')
f_aper.write('%d \n' % int(nlam))
for iaper in range(0, len(aper)):
f_aper.write('%f \t %f \n' % (lam[iaper],aper[iaper]/2))
f_aper.close()
# Write the stars.inp file
#
f_star = open(outdir+'stars.inp','w')
f_star.write('2\n')
f_star.write('1 \t %d \n' % int(nlam))
f_star.write('\n')
f_star.write('%e \t %e \t %e \t %e \t %e \n' % (rstar*0.9999,mstar,0,0,0))
f_star.write('\n')
for ilam in range(0,nlam):
f_star.write('%f \n' % lam[ilam])
f_star.write('\n')
f_star.write('%f \n' % -tstar)
f_star.close()
# Write the grid file
#
f_grid = open(outdir+'amr_grid.inp','w')
f_grid.write('1\n') # iformat
f_grid.write('0\n') # AMR grid style (0=regular grid, no AMR)
f_grid.write('150\n') # Coordinate system coordsystem<100: Cartisian; 100<=coordsystem<200: Spherical; 200<=coordsystem<300: Cylindrical
f_grid.write('0\n') # gridinfo
f_grid.write('1 \t 1 \t 1 \n') # Include x,y,z coordinate
f_grid.write('%d \t %d \t %d \n' % (int(nx)-1,int(ny),int(nz))) # Size of the grid
[f_grid.write('%e \n' % ri[ir]) for ir in range(1,len(ri))]
[f_grid.write('%f \n' % thetai[itheta]) for itheta in range(0,len(thetai))]
[f_grid.write('%f \n' % phii[iphi]) for iphi in range(0,len(phii))]
f_grid.close()
# Write the density file
#
f_dust = open(outdir+'dust_density.inp','w')
f_dust.write('1 \n') # format number
f_dust.write('%d \n' % int((nx-1)*ny*nz)) # Nr of cells
f_dust.write('1 \n') # Nr of dust species
for iphi in range(0,len(phic)):
for itheta in range(0,len(thetac)):
for ir in range(1,len(rc)):
f_dust.write('%e \n' % rho_dust[ir,itheta,iphi])
f_dust.close()
# Write the dust opacity table
f_dustkappa = open(outdir+'dustkappa_oh5_extended.inp','w')
f_dustkappa.write('3 \n') # format index for including g-factor
f_dustkappa.write('%d \n' % len(dust['nu'])) # number of wavlength/frequency in the table
for i in range(len(dust['nu'])):
f_dustkappa.write('%f \t %f \t %f \t %f \n' % (c/dust['nu'][i]*1e4, dust['chi'][i], dust['chi'][i]*dust['albedo'][i]/(1-dust['albedo'][i]), dust['g'][i]))
f_dustkappa.close()
# Write the Dust opacity control file
#
f_opac = open(outdir+'dustopac.inp','w')
f_opac.write('2 Format number of this file\n')
f_opac.write('1 Nr of dust species\n')
f_opac.write('============================================================================\n')
f_opac.write('1 Way in which this dust species is read\n')
f_opac.write('0 0=Thermal grain\n')
# f_opac.write('klaus Extension of name of dustkappa_***.inp file\n')
f_opac.write('oh5_extended Extension of name of dustkappa_***.inp file\n')
f_opac.write('----------------------------------------------------------------------------\n')
f_opac.close()
# In[112]:
# Write the radmc3d.inp control file
#
f_control = open(outdir+'radmc3d.inp','w')
f_control.write('nphot = %d \n' % 100000)
f_control.write('scattering_mode_max = 2\n')
f_control.write('camera_min_drr = 0.1\n')
f_control.write('camera_min_dangle = 0.1\n')
f_control.write('camera_spher_cavity_relres = 0.1\n')
f_control.write('istar_sphere = 1\n')
f_control.write('modified_random_walk = 1\n')
f_control.close()
return m
# from input_reader import input_reader_table
# from pprint import pprint
# filename = '/Users/yaolun/programs/misc/hyperion/test_input.txt'
# params = input_reader_table(filename)
# pprint(params[0])
# indir = '/Users/yaolun/test/'
# outdir = '/Users/yaolun/test/'
# dust_file = '/Users/yaolun/programs/misc/oh5_hyperion.txt'
# # dust_file = '/Users/yaolun/Copy/dust_model/Ormel2011/hyperion/(ic-sil,gra)3opc.txt'
# # fix_params = {'R_min': 0.14}
# fix_params = {}
# setup_model(indir,outdir,'model_test',params[0],dust_file,plot=True,record=False,\
# idl=False,radmc=False,fix_params=fix_params,ellipsoid=False)
import numpy as np from hyperion.model import Model from hyperion.util.constants import c m = Model() m.set_cartesian_grid([-1.,0, 1], [-1., 1.], [-1., 1]) density = np.zeros(m.grid.shape) density[:,:,0] = 1. vx = np.ones(m.grid.shape) * -1e8 vy = np.zeros(m.grid.shape) vz = np.zeros(m.grid.shape) m.add_density_grid(density, 'kmh_lite.hdf5', velocity=(vx, vy, vz)) # narrow emission line spectrum at 1 micron wav = np.array([0.9999, 1.0001]) fnu = np.array([1., 1.]) nu = c / (wav * 1.e-4) s = m.add_spherical_source() s.position = 0.5, 0., 0. s.velocity = -1e8, 0., 0. s.spectrum = nu[::-1], fnu[::-1] s.luminosity = 1 s.radius = 0.1 # Set up images
import random
random.seed('hyperion') # ensure that random numbers are the same every time
import numpy as np
from hyperion.model import Model
from hyperion.util.constants import pc, lsun
# Define cell walls
x = np.linspace(-10., 10., 101) * pc
y = np.linspace(-10., 10., 101) * pc
z = np.linspace(-10., 10., 101) * pc
# Initialize model and set up density grid
m = Model()
m.set_cartesian_grid(x, y, z)
m.add_density_grid(np.ones((100, 100, 100)) * 1.e-20, 'kmh_lite.hdf5')
# Generate random sources
for i in range(100):
s = m.add_point_source()
xs = random.uniform(-10., 10.) * pc
ys = random.uniform(-10., 10.) * pc
zs = random.uniform(-10., 10.) * pc
s.position = (xs, ys, zs)
s.luminosity = 10. ** random.uniform(0., 3.) * lsun
s.temperature = random.uniform(3000., 8000.)
# Specify that the specific energy and density are needed
m.conf.output.output_specific_energy = 'last'
m.conf.output.output_density = 'last'
def setup_model(indir,
outdir,
model=False,
denser_wall=False,
plot=False,
low_res=False,
flat=True,
scale=1.0):
import numpy as np
import astropy.constants as const
import scipy as sci
import matplotlib.pyplot as plt
import matplotlib as mat
import os
from matplotlib.colors import LogNorm
from scipy.optimize import fsolve
from scipy.integrate import nquad
from envelope_func import func
from hyperion.model import Model
# Constants setup
c = const.c.cgs.value
AU = 1.49598e13 # Astronomical Unit [cm]
pc = 3.08572e18 # Parsec [cm]
MS = 1.98892e33 # Solar mass [g]
LS = 3.8525e33 # Solar luminosity [erg/s]
RS = 6.96e10 # Solar radius [cm]
G = 6.67259e-8 # Gravitational constant [cm3/g/s^2]
yr = 60 * 60 * 24 * 365 # Years in seconds
PI = np.pi # PI constant
sigma = const.sigma_sb.cgs.value # Stefan-Boltzmann constant
m = Model()
# Create dust properties
# Hyperion needs nu, albedo, chi, g, p_lin_max
from hyperion.dust import HenyeyGreensteinDust
# Read in the dust opacity table used by RADMC-3D
dust_radmc = dict()
[dust_radmc['wl'], dust_radmc['abs'], dust_radmc['scat'],
dust_radmc['g']] = np.genfromtxt('dustkappa_oh5_extended.inp',
skip_header=2).T
# opacity per mass of dust?
dust_hy = dict()
dust_hy['nu'] = c / dust_radmc['wl'] * 1e4
ind = np.argsort(dust_hy['nu'])
dust_hy['nu'] = dust_hy['nu'][ind]
dust_hy['albedo'] = (dust_radmc['scat'] /
(dust_radmc['abs'] + dust_radmc['scat']))[ind]
dust_hy['chi'] = (dust_radmc['abs'] + dust_radmc['scat'])[ind]
dust_hy['g'] = dust_radmc['g'][ind]
dust_hy['p_lin_max'] = 0 * dust_radmc['wl'][ind] # assume no polarization
d = HenyeyGreensteinDust(dust_hy['nu'], dust_hy['albedo'], dust_hy['chi'],
dust_hy['g'], dust_hy['p_lin_max'])
# dust sublimation does not occur
# d.set_sublimation_temperature(None)
d.write(outdir + 'oh5.hdf5')
d.plot(outdir + 'oh5.png')
# Grids and Density
# Calculation inherited from the script used for RADMC-3D
# Parameters setup
# Import the model parameters from another file
#
params = np.genfromtxt(indir + '/params.dat', dtype=None)
tstar = params[0][1]
mstar = params[1][1] * MS
rstar = params[2][1] * RS
M_env_dot = params[3][1] * MS / yr
M_disk_dot = params[4][1] * MS / yr
R_env_max = params[5][1] * AU
R_env_min = params[6][1] * AU
theta_cav = params[7][1]
R_disk_max = params[8][1] * AU
R_disk_min = params[9][1] * AU
R_cen = R_disk_max
M_disk = params[10][1] * MS
beta = params[11][1]
h100 = params[12][1] * AU
rho_cav = params[13][1]
if denser_wall == True:
wall = params[14][1] * AU
rho_wall = params[15][1]
rho_cav_center = params[16][1]
rho_cav_edge = params[17][1] * AU
# Grid Parameters
nx = 300L
if low_res == True:
nx = 100L
ny = 400L
nz = 50L
[nx, ny, nz] = [scale * nx, scale * ny, scale * nz]
# nx = 20
# ny = 40
# nz = 5
# Model Parameters
#
rin = rstar
rout = R_env_max
rcen = R_cen
# Star Parameters
#
mstar = mstar
rstar = rstar * 0.9999
tstar = tstar
pstar = [0., 0., 0.]
# Make the Coordinates
#
ri = rin * (rout / rin)**(np.arange(nx + 1).astype(dtype='float') /
float(nx))
ri = np.hstack((0.0, ri))
thetai = PI * np.arange(ny + 1).astype(dtype='float') / float(ny)
phii = PI * 2.0 * np.arange(nz + 1).astype(dtype='float') / float(nz)
# Keep the constant cell size in r-direction
#
if flat == True:
ri_cellsize = ri[1:-1] - ri[0:-2]
ind = np.where(
ri_cellsize / AU > 100.0)[0][0] # The largest cell size is 100 AU
ri = np.hstack(
(ri[0:ind],
ri[ind] + np.arange(np.ceil(
(rout - ri[ind]) / 100 / AU)) * 100 * AU))
nxx = nx
nx = len(ri) - 1
# Assign the coordinates of the center of cell as its coordinates.
#
rc = 0.5 * (ri[0:nx] + ri[1:nx + 1])
thetac = 0.5 * (thetai[0:ny] + thetai[1:ny + 1])
phic = 0.5 * (phii[0:nz] + phii[1:nz + 1])
# phic = 0.5*( phii[0:nz-1] + phii[1:nz] )
# Make the dust density model
# Make the density profile of the envelope
#
print 'Calculating the dust density profile...'
if theta_cav != 0:
c0 = R_env_max**(-0.5) * np.sqrt(1 / np.sin(np.radians(theta_cav))**3 -
1 / np.sin(np.radians(theta_cav)))
else:
c0 = 0
rho_env = np.zeros([len(rc), len(thetac), len(phic)])
rho_disk = np.zeros([len(rc), len(thetac), len(phic)])
rho = np.zeros([len(rc), len(thetac), len(phic)])
def f(w, z, beta, rstar, h100):
f = 2 * PI * w * (1 - np.sqrt(rstar / w)) * (rstar / w)**(
beta + 1) * np.exp(-0.5 * (z / (w**beta * h100 / 100**beta))**2)
return f
rho_0 = M_disk / (nquad(
f, [[R_disk_min, R_disk_max], [-R_env_max, R_env_max]],
args=(beta, rstar, h100)))[0]
i = 0
j = 0
if 'rho_cav_center' in locals() == False:
rho_cav_center = 5.27e-18 # 1.6e-17 # 5.27e-18
print 'Use 5.27e-18 as the default value for cavity center'
if 'rho_cav_edge' in locals() == False:
rho_cav_edge = 40 * AU
print 'Use 40 AU as the default value for size of the inner region'
discont = 1
if denser_wall == False:
for ir in range(0, len(rc)):
for itheta in range(0, len(thetac)):
for iphi in range(0, len(phic)):
if rc[ir] > R_env_min:
# Envelope profile
w = abs(rc[ir] * np.cos(np.pi / 2 - thetac[itheta]))
z = rc[ir] * np.sin(np.pi / 2 - thetac[itheta])
z_cav = c0 * abs(w)**1.5
if z_cav == 0:
z_cav = R_env_max
if abs(z) > abs(z_cav):
# rho_env[ir,itheta,iphi] = rho_cav
# Modification for using density gradient in the cavity
if rc[ir] <= rho_cav_edge:
rho_env[
ir, itheta,
iphi] = rho_cav_center #*((rc[ir]/AU)**2)
else:
rho_env[ir, itheta,
iphi] = rho_cav_center * discont * (
rho_cav_edge / rc[ir])**2
i += 1
else:
j += 1
mu = abs(np.cos(thetac[itheta]))
mu_o = np.abs(
fsolve(func, [0.5, 0.5, 0.5],
args=(rc[ir], rcen, mu))[0])
rho_env[ir, itheta, iphi] = M_env_dot / (
4 * PI * (G * mstar * rcen**3)**0.5
) * (rc[ir] / rcen)**(-3. / 2) * (1 + mu / mu_o)**(
-0.5) * (mu / mu_o +
2 * mu_o**2 * rcen / rc[ir])**(-1)
# Disk profile
if ((w >= R_disk_min) and (w <= R_disk_max)) == True:
h = ((w / (100 * AU))**beta) * h100
rho_disk[ir, itheta, iphi] = rho_0 * (1 - np.sqrt(
rstar / w)) * (rstar / w)**(beta + 1) * np.exp(
-0.5 * (z / h)**2)
# Combine envelope and disk
rho[ir, itheta,
iphi] = rho_disk[ir, itheta,
iphi] + rho_env[ir, itheta, iphi]
# # testing the effect of new solver
# # Envelope profile
# w = abs(rc[ir]*np.cos(np.pi/2 - thetac[itheta]))
# z = rc[ir]*np.sin(np.pi/2 - thetac[itheta])
# z_cav = c0*abs(w)**1.5
# if z_cav == 0:
# z_cav = R_env_max
# if abs(z) > abs(z_cav):
# # rho_env[ir,itheta,iphi] = rho_cav
# # Modification for using density gradient in the cavity
# if rc[ir] <= rho_cav_edge:
# rho_env[ir,itheta,iphi] = rho_cav_center#*((rc[ir]/AU)**2)
# else:
# rho_env[ir,itheta,iphi] = rho_cav_center*discont*(rho_cav_edge/rc[ir])**2
# i += 1
# else:
# j += 1
# mu = abs(np.cos(thetac[itheta]))
# # Implement new root finding algorithm
# roots = np.roots(np.array([1.0, 0.0, rc[ir]/rcen-1.0, -mu*rc[ir]/rcen]))
# if len(roots[roots.imag == 0]) == 1:
# if (abs(roots[roots.imag == 0]) - 1.0) <= 0.0:
# mu_o_dum = roots[roots.imag == 0]
# else:
# mu_o_dum = -0.5
# print 'Problem with cubic solving, cos(theta) = ', mu_o_dum
# print 'parameters are ', np.array([1.0, 0.0, rc[ir]/rcen-1.0, -mu*rc[ir]/rcen])
# else:
# mu_o_dum = -0.5
# for imu in range(0, len(roots)):
# if roots[imu]*mu >= 0.0:
# if (abs((abs(roots[imu]) - 1.0)) <= 1e-5):
# mu_o_dum = 1.0 * np.sign(mu)
# else:
# mu_o_dum = roots[imu]
# if mu_o_dum == -0.5:
# print 'Problem with cubic solving, roots are: ', roots
# mu_o = mu_o_dum.real
# rho_env[ir,itheta,iphi] = M_env_dot/(4*PI*(G*mstar*rcen**3)**0.5)*(rc[ir]/rcen)**(-3./2)*(1+mu/mu_o)**(-0.5)*(mu/mu_o+2*mu_o**2*rcen/rc[ir])**(-1)
# # Disk profile
# if ((w >= R_disk_min) and (w <= R_disk_max)) == True:
# h = ((w/(100*AU))**beta)*h100
# rho_disk[ir,itheta,iphi] = rho_0*(1-np.sqrt(rstar/w))*(rstar/w)**(beta+1)*np.exp(-0.5*(z/h)**2)
# # Combine envelope and disk
# rho[ir,itheta,iphi] = rho_disk[ir,itheta,iphi] + rho_env[ir,itheta,iphi]
else:
rho[ir, itheta, iphi] = 1e-30
rho_env = rho_env + 1e-40
rho_disk = rho_disk + 1e-40
rho = rho + 1e-40
else:
for ir in range(0, len(rc)):
for itheta in range(0, len(thetac)):
for iphi in range(0, len(phic)):
# Envelope profile
w = abs(rc[ir] * np.cos(thetac[itheta]))
z = rc[ir] * np.sin(thetac[itheta])
z_cav = c * abs(w)**1.5
z_cav_wall = c * abs(w - wall)**1.5
if z_cav == 0:
z_cav = R_env_max
if abs(z) > abs(z_cav):
# rho_env[ir,itheta,iphi] = rho_cav
# Modification for using density gradient in the cavity
if rc[ir] <= 20 * AU:
rho_env[ir, itheta,
iphi] = rho_cav_center * ((rc[ir] / AU)**2)
else:
rho_env[ir, itheta,
iphi] = rho_cav_center * discont * (
20 * AU / rc[ir])**2
i += 1
elif (abs(z) > abs(z_cav_wall)) and (abs(z) < abs(z_cav)):
rho_env[ir, itheta, iphi] = rho_wall
else:
j += 1
mu = abs(np.cos(thetac[itheta]))
mu_o = np.abs(
fsolve(func, [0.5, 0.5, 0.5],
args=(rc[ir], rcen, mu))[0])
rho_env[ir, itheta, iphi] = M_env_dot / (
4 * PI * (G * mstar * rcen**3)**0.5) * (
rc[ir] / rcen)**(-3. / 2) * (1 + mu / mu_o)**(
-0.5) * (mu / mu_o +
2 * mu_o**2 * rcen / rc[ir])**(-1)
# Disk profile
if ((w >= R_disk_min) and (w <= R_disk_max)) == True:
h = ((w / (100 * AU))**beta) * h100
rho_disk[ir, itheta,
iphi] = rho_0 * (1 - np.sqrt(rstar / w)) * (
rstar / w)**(beta + 1) * np.exp(
-0.5 * (z / h)**2)
# Combine envelope and disk
rho[ir, itheta,
iphi] = rho_disk[ir, itheta,
iphi] + rho_env[ir, itheta, iphi]
rho_env = rho_env + 1e-40
rho_disk = rho_disk + 1e-40
rho = rho + 1e-40
# Insert the calculated grid and dust density profile into hyperion
m.set_spherical_polar_grid(ri, thetai, phii)
m.add_density_grid(rho.T, outdir +
'oh5.hdf5') # numpy read the array in reverse order
# Define the luminsoity source
source = m.add_spherical_source()
source.luminosity = (4 * PI * rstar**2) * sigma * (tstar**4) # [ergs/s]
source.radius = rstar # [cm]
source.temperature = tstar # [K]
source.position = (0., 0., 0.)
print 'L_center = % 5.2f L_sun' % ((4 * PI * rstar**2) * sigma *
(tstar**4) / LS)
# Setting up images and SEDs
image = m.add_peeled_images()
image.set_wavelength_range(300, 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=1000000,
imaging=1000000,
raytracing_sources=1000000,
raytracing_dust=1000000)
# number of iteration to compute dust specific energy (temperature)
m.set_n_initial_iterations(5)
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'
# Number of unique photons that passed through the cell
m.conf.output.output_n_photons = 'last'
m.write(outdir + 'old_setup2.rtin')
def setup_model(cli):
#
# Hyperion setup:
#
model = Model()
if(cli.mode == "temperature"):
#
# Dust properties:
#
dust_properties = SphericalDust('dust_integrated_full_scattering.hdf5')
#
# Write dust properties:
#
dust_properties.write('dust_properties.hdf5')
dust_properties.plot('dust_properties.png')
#
# Grid setup:
#
grid_wmin = 0
grid_wmax = 5.0*pc # 4.0*pc
grid_zmin = 0.0*pc
grid_zmax = 10.0*pc
grid_pmin = 0
grid_pmax = 2*pi
grid_dx = cli.resolution*pc
grid_dw = grid_dx # uniform resolution
grid_dz = grid_dx # uniform resolution
grid_dp = grid_dx # resolution at filament location at r = 1 pc
grid_Nw = int((grid_wmax - grid_wmin) / grid_dw)
grid_Nz = int((grid_zmax - grid_zmin) / grid_dz)
grid_Np = int(2*pi * 1.0*pc / grid_dp)
if(cli.verbose):
print("Grid setup:")
print(" Grid resolution =",cli.resolution, "pc.")
print(" grid_Nw =",grid_Nw)
print(" grid_Nz =",grid_Nz)
print(" grid_Np =",grid_Np)
#grid_w = np.logspace(np.log10(grid_wmin), np.log10(grid_wmax), grid_Nw)
#grid_w = np.hstack([0., grid_w]) # add innermost cell interface at w=0
grid_w = np.linspace(grid_wmin, grid_wmax, grid_Nw+1)
grid_z = np.linspace(grid_zmin, grid_zmax, grid_Nz+1)
grid_p = np.linspace(grid_pmin, grid_pmax, grid_Np+1)
model.set_cylindrical_polar_grid(grid_w, grid_z, grid_p)
#
# Dust density setup:
#
RC = 0.1*pc
nC = 6.6580e+03 # in cm^-3
nC *= cli.opticaldepth # the optical depth at 1 micron
nC *= m_h # in g cm^-3
nC /= 100.0 # converts from gas to dust density
rho = np.zeros(model.grid.shape)
#
# n(r) = nC / [ 1.0 + (r/RC)**2.0 ]
# x = -sin(2.0×pi×t) pc, y = +cos(2.0×pi×t) pc, z = 10.0×t pc, t = [0.0, 1.0]
# => t = m.grid.gz / (10*pc)
# => phi(t) = mod(360*t+270, 360)
#
for k in range(0, grid_Np):
for j in range(0, grid_Nz):
for i in range(0, grid_Nw):
t = model.grid.gz[k,j,i] / (10*pc)
if(cli.filament == "linear"):
filament_center_x = 0
filament_center_y = 0
elif(cli.filament == "spiraling"):
filament_center_x = - math.sin(2*pi*t)*pc
filament_center_y = + math.cos(2*pi*t)*pc
spherical_grid_r = model.grid.gw[k,j,i]
spherical_grid_phi = model.grid.gp[k,j,i]
cartesian_grid_x = spherical_grid_r * math.cos(spherical_grid_phi)
cartesian_grid_y = spherical_grid_r * math.sin(spherical_grid_phi)
rsquared = (
(cartesian_grid_x - filament_center_x)**2
+
(cartesian_grid_y - filament_center_y)**2
)
rho[k,j,i] = nC / (1.0 + (rsquared / (RC*RC)))
if rsquared**0.5 > 3*pc:
rho[k,j,i] = 0
rho[model.grid.gw > grid_wmax] = 0
rho[model.grid.gz < grid_zmin] = 0
rho[model.grid.gz > grid_zmax] = 0
model.add_density_grid(rho, 'dust_properties.hdf5')
#
# Check optical depth through the filament:
#
# (y,z = 0, 2.5 pc goes through the filament center in all setups)
#
# Determine index of closest grid cell to z = 2.5 pc:
#
dz_last = 2*abs(grid_zmax-grid_zmin)
for j in range(0, grid_Nz):
dz = abs(model.grid.gz[0,j,0] - 2.5*pc)
if(dz > dz_last):
j=j-1
break
else:
dz_last = dz
#
# Opacity at 1.0 micron (per gram dust):
#
chi = dust_properties.optical_properties.interp_chi_wav(1.0)
tau_max = 0
for k in range(0, grid_Np):
tau = 0
for i in range(0, grid_Nw):
dr = model.grid.widths[0,k,j,i]
dtau = dr * rho[k,j,i] * chi
tau += dtau
tau_max = max(tau_max, tau)
if(cli.filament == "linear"):
tau_max *= 2
dev = 100 * abs(cli.opticaldepth - tau_max) / cli.opticaldepth
if(cli.verbose):
print("Check:")
print(" Numerical integration of the optical depth through the filament center yields tau = ", tau_max)
print(" This corresponds to a deviation to the chosen setup value of", dev, "percent")
#
# Source:
#
if(cli.sources == "external"):
nu, jnu = np.loadtxt('bg_intensity_modified.txt', unpack=True)
source_R = 5*pc
source = model.add_external_spherical_source()
source.peeloff = False
source.position = (0, 0, 5.0*pc) # in a Cartesian frame
source.radius = source_R
source.spectrum = (nu, jnu)
#source_MeanIntensity_J = <integrate bg_intensity.txt>
#source_Area = 4.0 * pi * source_R*source_R
source.luminosity = 8237.0*lsun #source_Area * pi * source_MeanIntensity_J
elif(cli.sources == "stellar"):
source = model.add_point_source()
source.luminosity = 3.839e35 # in ergs s^-1
source.temperature = 10000.0 # in K
if(cli.filament == "linear"):
source.position = (3.0*pc, 0, 5.0*pc)
elif(cli.filament == "spiraling"):
source.position = (0 , 0, 3.0*pc)
#
# To compute total photon numbers:
#
grid_N = grid_Nw * grid_Nz * grid_Np
if(cli.verbose):
print("Radiation setup:")
print(" photons_temperature / cell =", cli.photons_temperature)
print(" photons_temperature total =", grid_N * cli.photons_temperature)
file = filename(cli, "temperature")
file += ".rtin"
else:
file = filename(cli, "temperature")
file += ".rtout"
try:
with open(file):
if(cli.verbose):
print("Using the specific energy distribution from file", file)
model.use_geometry(file)
model.use_quantities(file, only_initial=False, copy=False)
model.use_sources(file)
except IOError:
print("ERROR: File '", file, "' cannot be found. \nERROR: This file, containing the specific energy density, has to be computed first via calling hyperion.")
exit(2)
#
# To compute total photon numbers:
#
grid_Nw = len(model.grid.gw[0,0,:])
grid_Nz = len(model.grid.gw[0,:,0])
grid_Np = len(model.grid.gw[:,0,0])
grid_N = grid_Nw * grid_Nz * grid_Np
if(cli.verbose):
print("Grid setup:")
print(" grid_Nw =",grid_Nw)
print(" grid_Nz =",grid_Nz)
print(" grid_Np =",grid_Np)
print("Radiation setup:")
print(" photons_temperature / cell =", cli.photons_temperature)
print(" photons_temperature total =", grid_N * cli.photons_temperature)
print(" photons_raytracing / cell =", cli.photons_raytracing)
print(" photons_raytracing total =", grid_N * cli.photons_raytracing)
print(" photons_imaging / cell =", cli.photons_imaging)
print(" photons_imaging total =", grid_N * cli.photons_imaging)
file = filename(cli, "")
file += ".rtin"
##
## Temperature, Images, and SEDs:
##
if(cli.mode == "temperature"):
model.set_raytracing(True)
model.set_n_photons(
initial = grid_N * cli.photons_temperature,
raytracing_sources = grid_N * cli.photons_raytracing,
raytracing_dust = grid_N * cli.photons_raytracing,
imaging = grid_N * cli.photons_imaging
)
elif(cli.mode == "images"):
model.set_n_initial_iterations(0)
model.set_raytracing(True)
model.set_monochromatic(True, wavelengths=[100.0, 500.0, 0.55, 2.2])
model.set_n_photons(
raytracing_sources = grid_N * cli.photons_raytracing,
raytracing_dust = grid_N * cli.photons_raytracing,
imaging_sources = grid_N * cli.photons_imaging,
imaging_dust = grid_N * cli.photons_imaging
)
# group = 0
image1x = model.add_peeled_images(sed=False, image=True)
image1x.set_image_size(300, 300)
image1x.set_image_limits(-5*pc, +5*pc, 0, 10*pc)
image1x.set_viewing_angles([90],[0]) # along the x-direction
image1x.set_uncertainties(True)
image1x.set_output_bytes(8)
image1x.set_track_origin('basic')
# group = 1
image1y = model.add_peeled_images(sed=False, image=True)
image1y.set_image_size(300, 300)
image1y.set_image_limits(-5*pc, +5*pc, 0, 10*pc)
image1y.set_viewing_angles([90],[90]) # along the y-direction
image1y.set_uncertainties(True)
image1y.set_output_bytes(8)
image1y.set_track_origin('basic')
# group = 2
image1z = model.add_peeled_images(sed=False, image=True)
image1z.set_image_size(300, 300)
image1z.set_image_limits(-5*pc, +5*pc, -5*pc, +5*pc)
image1z.set_viewing_angles([0],[0]) # along the z-direction
image1z.set_uncertainties(True)
image1z.set_output_bytes(8)
image1z.set_track_origin('basic')
elif(cli.mode == "sed"):
model.set_n_initial_iterations(0)
model.set_raytracing(True)
model.set_n_photons(
raytracing_sources = grid_N * cli.photons_raytracing,
raytracing_dust = grid_N * cli.photons_raytracing,
imaging = grid_N * cli.photons_imaging
)
# group = 0
sed1 = model.add_peeled_images(sed=True, image=False)
sed1.set_wavelength_range(250, 0.01, 2000.0)
sed1.set_viewing_angles([90],[0]) # along the x-direction
sed1.set_peeloff_origin((0, 0, 2.5*pc))
sed1.set_aperture_range(1, 0.3*pc, 0.3*pc)
sed1.set_uncertainties(True)
sed1.set_output_bytes(8)
sed1.set_track_origin('basic')
# group = 1
sed2 = model.add_peeled_images(sed=True, image=False)
sed2.set_wavelength_range(250, 0.01, 2000.0)
sed2.set_viewing_angles([90],[0]) # along the x-direction
sed2.set_peeloff_origin((0, 0, 5.0*pc))
sed2.set_aperture_range(1, 0.3*pc, 0.3*pc)
sed2.set_uncertainties(True)
sed2.set_output_bytes(8)
sed2.set_track_origin('basic')
# group = 2
sed3 = model.add_peeled_images(sed=True, image=False)
sed3.set_wavelength_range(250, 0.01, 2000.0)
sed3.set_viewing_angles([90],[0]) # along the x-direction
sed3.set_peeloff_origin((0, 0, 7.5*pc))
sed3.set_aperture_range(1, 0.3*pc, 0.3*pc)
sed3.set_uncertainties(True)
sed3.set_output_bytes(8)
sed3.set_track_origin('basic')
##
## Write model for hyperion runs:
##
model.conf.output.output_density = 'last'
model.conf.output.output_specific_energy = 'last'
model.conf.output.output_n_photons = 'last'
model.write(file)
if(cli.verbose):
print("The input file for hyperion was written to", file)
from hyperion.model import Model from hyperion.util.constants import pc, lsun # Initialize model m = Model() # Set up 64x64x64 cartesian grid w = np.linspace(-pc, pc, 64) m.set_cartesian_grid(w, w, w) # Add density grid with constant density and add a higher density cube inside to # cause a shadow. density = np.ones(m.grid.shape) * 1e-21 density[26:38, 26:38, 26:38] = 1.e-18 m.add_density_grid(density, 'kmh_lite.hdf5') # Add a point source in the center s = m.add_point_source() s.position = (0.4 * pc, 0., 0.) s.luminosity = 1000 * lsun s.temperature = 6000. # Add multi-wavelength image for a single viewing angle image = m.add_peeled_images(sed=False, image=True) image.set_wavelength_range(1, 190., 210.) image.set_viewing_angles(np.repeat(45., 36), np.linspace(5., 355., 36)) image.set_image_size(400, 400) image.set_image_limits(-1.5 * pc, 1.5 * pc, -1.5 * pc, 1.5 * pc) # Set runtime parameters. We turn off scattering for the imaging since it is not
def setup_model(cli):
lsun_TRUST = 3.839e33
#
# Hyperion setup:
#
model = Model()
if(cli.mode == "temperature"):
#
# Dust properties:
#
dust_properties = SphericalDust('dust_integrated_full_scattering.hdf5')
#
# Write dust properties:
#
dust_properties.write('dust_properties.hdf5')
dust_properties.plot('dust_properties.png')
#
# Specify galaxy setup:
#
hR = 4000.0*pc # [cm]
Rmax = 5.0*hR # [cm]
hz_oldstars = 350.0*pc # [cm]
hz_youngstars = 200.0*pc # [cm]
hz_dust = 200.0*pc # [cm]
zmax_oldstars = 5.0*hz_oldstars # [cm]
zmax_youngstars = 5.0*hz_youngstars # [cm]
zmax_dust = 5.0*hz_dust # [cm]
zmax = zmax_oldstars # [cm]
reff = 1600.0*pc # [cm]
n = 3.0
q = 0.6
bn = 2.0*n - 1.0/3.0 + 4.0/405.0/n + 46.0/25515.0/n/n + 131.0/1148175.0/n/n/n
temperature_oldstars = 3500.0 # [K]
temperature_youngstars = 10000.0 # [K]
temperature_bulge = 3500.0 # [K]
luminosity_oldstars = 4.0e+10*lsun_TRUST # [ergs/s]
luminosity_youngstars = 1.0e+10*lsun_TRUST # [ergs/s]
luminosity_bulge = 3.0e+10*lsun_TRUST # [ergs/s]
w_oldstars = 0.25
w_youngstars = 0.75
w_dust = 0.75
phi0_oldstars = 0.0
phi0_youngstars = 20.0 * pi/180.0
phi0_dust = 20.0 * pi/180.0
modes = 2
pitchangle = 20.0 * pi/180.0
#
# Grid setup:
#
grid_wmin = 0.0
grid_wmax = Rmax
grid_zmin = -zmax
grid_zmax = +zmax
grid_pmin = 0.0
grid_pmax = 2.0*pi
grid_dx = cli.resolution*pc
grid_dw = grid_dx # uniform resolution
grid_dz = grid_dx # uniform resolution
grid_dp = grid_dx # resolution at characteristic radial disk spatial scale hR = 4000.0 pc
grid_Nw = int((grid_wmax - grid_wmin) / grid_dw) + 1
grid_Nz = int((grid_zmax - grid_zmin) / grid_dz) + 1
if(cli.case == 1):
grid_Np = 1
if(cli.case == 2):
grid_Np = int((grid_pmax - grid_pmin) * hR / grid_dp)
if(cli.verbose):
print("Grid setup:")
print(" Grid resolution =",cli.resolution, "pc.")
print(" grid_Nw =",grid_Nw)
print(" grid_Nz =",grid_Nz)
print(" grid_Np =",grid_Np)
#grid_w = np.logspace(np.log10(grid_wmin), np.log10(grid_wmax), grid_Nw)
#grid_w = np.hstack([0., grid_w]) # add innermost cell interface at w=0
grid_w = np.linspace(grid_wmin, grid_wmax, grid_Nw+1)
grid_z = np.linspace(grid_zmin, grid_zmax, grid_Nz+1)
grid_p = np.linspace(grid_pmin, grid_pmax, grid_Np+1)
model.set_cylindrical_polar_grid(grid_w, grid_z, grid_p)
#
# Dust density and sources setup:
#
rho_oldstars = np.zeros(model.grid.shape)
rho_youngstars = np.zeros(model.grid.shape)
rho_bulge = np.zeros(model.grid.shape)
rho_dust = np.zeros(model.grid.shape)
for k in range(0, grid_Np):
for j in range(0, grid_Nz):
for i in range(0, grid_Nw):
R = model.grid.gw[k,j,i]
z = model.grid.gz[k,j,i]
m = math.sqrt(R*R + z*z/q/q)
rho_dust[k,j,i] = math.exp(- R/hR -abs(z)/hz_dust )
rho_oldstars[k,j,i] = math.exp(- R/hR -abs(z)/hz_oldstars )
rho_youngstars[k,j,i] = math.exp(- R/hR -abs(z)/hz_youngstars)
rho_bulge[k,j,i] = math.pow(m/reff, 0.5/n - 1.0) * math.exp(- bn * math.pow(m/reff, 1.0/n))
if(cli.case == 2):
phi = model.grid.gp[k,j,i]
perturb = math.sin(modes * (math.log(R/hR) / math.tan(pitchangle) - (phi - phi0_dust)))
rho_dust[k,j,i] *= (1.0 + w_dust * perturb)
perturb = math.sin(modes * (math.log(R/hR) / math.tan(pitchangle) - (phi - phi0_oldstars)))
rho_oldstars[k,j,i] *= (1.0 + w_oldstars * perturb)
perturb = math.sin(modes * (math.log(R/hR) / math.tan(pitchangle) - (phi - phi0_youngstars)))
rho_youngstars[k,j,i] *= (1.0 + w_youngstars * perturb)
rho_dust[model.grid.gw > grid_wmax] = 0
rho_dust[model.grid.gz < grid_zmin] = 0
rho_dust[model.grid.gz > grid_zmax] = 0
kappa_ref = dust_properties.optical_properties.interp_chi_wav(0.55693)
rho0 = cli.opticaldepth / (2.0 * hz_dust * kappa_ref)
rho_dust[:] *= rho0
model.add_density_grid(rho_dust, 'dust_properties.hdf5')
source_oldstars = model.add_map_source()
source_oldstars.luminosity = luminosity_oldstars
source_oldstars.temperature = temperature_oldstars
source_oldstars.map = rho_oldstars
source_youngstars = model.add_map_source()
source_youngstars.luminosity = luminosity_youngstars
source_youngstars.temperature = temperature_youngstars
source_youngstars.map = rho_youngstars
source_bulge = model.add_map_source()
source_bulge.luminosity = luminosity_bulge
source_bulge.temperature = temperature_bulge
source_bulge.map = rho_bulge
#
# Check face-on optical depth at 1.0 micron (per gram dust) through the dust disk:
#
tau = 0
k = 0
i = 0
for j in range(0, grid_Nz):
#print(model.grid.gz[k,j,i]/pc, rho_dust[k,j,i])
dz = model.grid.widths[1,k,j,i]
dtau = dz * rho_dust[k,j,i] * kappa_ref
tau += dtau
deviation = 100.0 * abs(cli.opticaldepth - tau) / cli.opticaldepth
if(cli.verbose):
print("Check optical depth of dust density setup:")
print(" kappa(0.55693 micron) = ", kappa_ref, "cm^2 g^-1")
print(" Numerical integration of the face-on optical depth at 0.55693 micron through the central dust disk yields tau = ", tau)
print(" This corresponds to a deviation to the chosen setup value of", deviation, "percent")
#
# Check central dust density:
#
rho_max = np.max(rho_dust)
if(cli.opticaldepth < 1.0):
rho_setup = 1.04366e-4 * msun/pc/pc/pc
if(cli.opticaldepth < 3.0):
rho_setup = 5.21829e-4 * msun/pc/pc/pc
else:
rho_setup = 2.60915e-3 * msun/pc/pc/pc
deviation = 100.0 * abs(rho_setup - rho_max) / rho_setup
if(cli.verbose):
print("Check value of central dust density:")
print(" rho_max = ", rho_max, "g cm^-3")
print(" This corresponds to a deviation to the chosen setup value of", deviation, "percent")
#
# To compute total photon numbers:
#
grid_N = grid_Nw * grid_Nz * grid_Np
if(cli.verbose):
print("Radiation setup:")
print(" photons_temperature / cell =", cli.photons_temperature)
print(" photons_temperature total =", grid_N * cli.photons_temperature)
file = filename(cli, "temperature")
file += ".rtin"
else:
file = filename(cli, "temperature")
file += ".rtout"
try:
with open(file):
if(cli.verbose):
print("Using the specific energy distribution from file", file)
model.use_geometry(file)
model.use_quantities(file, only_initial=False, copy=False)
model.use_sources(file)
except IOError:
print("ERROR: File '", file, "' cannot be found. \nERROR: This file, containing the specific energy density, has to be computed first via calling hyperion.")
exit(2)
#
# To compute total photon numbers:
#
grid_Nw = len(model.grid.gw[0,0,:])
grid_Nz = len(model.grid.gw[0,:,0])
grid_Np = len(model.grid.gw[:,0,0])
grid_N = grid_Nw * grid_Nz * grid_Np
if(cli.verbose):
print("Grid setup:")
print(" grid_Nw =",grid_Nw)
print(" grid_Nz =",grid_Nz)
print(" grid_Np =",grid_Np)
print("Radiation setup:")
print(" photons_temperature / cell =", cli.photons_temperature)
print(" photons_temperature total =", grid_N * cli.photons_temperature)
print(" photons_raytracing / cell =", cli.photons_raytracing)
print(" photons_raytracing total =", grid_N * cli.photons_raytracing)
print(" photons_imaging / cell =", cli.photons_imaging)
print(" photons_imaging total =", grid_N * cli.photons_imaging)
file = filename(cli, "")
file += ".rtin"
##
## Temperature, Images, and SEDs:
##
if(cli.mode == "temperature"):
model.set_raytracing(True)
model.set_n_photons(
initial = grid_N * cli.photons_temperature,
raytracing_sources = grid_N * cli.photons_raytracing,
raytracing_dust = grid_N * cli.photons_raytracing,
imaging = grid_N * cli.photons_imaging
)
elif(cli.mode == "images"):
model.set_n_initial_iterations(0)
model.set_raytracing(True)
# old setup: model.set_monochromatic(True, wavelengths=[0.4, 1.0, 10.0, 100.0, 500.0])
model.set_monochromatic(True, wavelengths=[0.45483, 1.2520, 26.114, 242.29])
model.set_n_photons(
raytracing_sources = grid_N * cli.photons_raytracing,
raytracing_dust = grid_N * cli.photons_raytracing,
imaging_sources = grid_N * cli.photons_imaging,
imaging_dust = grid_N * cli.photons_imaging
)
# group = 0
image1 = model.add_peeled_images(sed=False, image=True)
image1.set_image_size(501, 501)
image1.set_image_limits(-12500.0*pc, +12500.0*pc, -12500.0*pc, +12500.0*pc)
image1.set_viewing_angles([30],[0])
image1.set_uncertainties(True)
image1.set_output_bytes(8)
image1.set_track_origin('basic')
# group = 1
image2 = model.add_peeled_images(sed=False, image=True)
image2.set_image_size(501, 501)
image2.set_image_limits(-12500.0*pc, +12500.0*pc, -12500.0*pc, +12500.0*pc)
image2.set_viewing_angles([80],[90])
image2.set_uncertainties(True)
image2.set_output_bytes(8)
image2.set_track_origin('basic')
# group = 2
image3 = model.add_peeled_images(sed=False, image=True)
image3.set_image_size(501, 501)
image3.set_image_limits(-12500.0*pc, +12500.0*pc, -12500.0*pc, +12500.0*pc)
image3.set_viewing_angles([88],[0]) # mostly edge-on
image3.set_uncertainties(True)
image3.set_output_bytes(8)
image3.set_track_origin('basic')
elif(cli.mode == "seds"):
model.set_n_initial_iterations(0)
model.set_raytracing(True)
model.set_n_photons(
raytracing_sources = grid_N * cli.photons_raytracing,
raytracing_dust = grid_N * cli.photons_raytracing,
imaging = grid_N * cli.photons_imaging
)
# group = 0
sed1 = model.add_peeled_images(sed=True, image=False)
sed1.set_wavelength_range(47, 0.081333, 1106.56)
sed1.set_viewing_angles([30],[0])
sed1.set_peeloff_origin((0, 0, 0))
sed1.set_aperture_range(1, 25000.0*pc, 25000.0*pc)
sed1.set_uncertainties(True)
sed1.set_output_bytes(8)
sed1.set_track_origin('basic')
# group = 1
sed2 = model.add_peeled_images(sed=True, image=False)
sed2.set_wavelength_range(47, 0.081333, 1106.56)
sed2.set_viewing_angles([80],[0])
sed2.set_peeloff_origin((0, 0, 0))
sed2.set_aperture_range(1, 25000.0*pc, 25000.0*pc)
sed2.set_uncertainties(True)
sed2.set_output_bytes(8)
sed2.set_track_origin('basic')
# group = 2
sed3 = model.add_peeled_images(sed=True, image=False)
sed3.set_wavelength_range(47, 0.081333, 1106.56)
sed3.set_viewing_angles([88],[0])
sed3.set_peeloff_origin((0, 0, 0))
sed3.set_aperture_range(1, 25000.0*pc, 25000.0*pc)
sed3.set_uncertainties(True)
sed3.set_output_bytes(8)
sed3.set_track_origin('basic')
##
## Write model for hyperion runs:
##
model.conf.output.output_density = 'last'
model.conf.output.output_specific_energy = 'last'
model.conf.output.output_n_photons = 'last'
model.write(file)
if(cli.verbose):
print("The input file for hyperion was written to", file)
# the dust is directly behind, and the source is moving towards the dust, we # should see red-shifted emission from the source and blue-shifted scattered # light emission. import numpy as np from hyperion.model import Model from hyperion.util.constants import c m = Model() m.set_cartesian_grid([-1.0, 0, 1], [-1.0, 1.0], [-1.0, 1]) density = np.zeros(m.grid.shape) density[:, :, 0] = 1.0 m.add_density_grid(density, "kmh_lite.hdf5") # narrow emission line spectrum at 1 micron wav = np.array([0.9999, 1.0001]) fnu = np.array([1.0, 1.0]) nu = c / (wav * 1.0e-4) s = m.add_spherical_source() s.position = 0.5, 0.0, 0.0 s.velocity = -1e8, 0.0, 0.0 s.spectrum = nu[::-1], fnu[::-1] s.luminosity = 1 s.radius = 0.1 # Set up images
def setup_model(outdir,record_dir,outname,params,dust_file,tsc=True,idl=False,plot=False,\
low_res=True,flat=True,scale=1,radmc=False,mono=False,record=True,dstar=178.,\
aperture=None,dyn_cav=False,fix_params=None,alma=False,power=2,better_im=False,ellipsoid=False,\
TSC_dir='~/programs/misc/TSC/', IDL_path='/Applications/exelis/idl83/bin/idl',auto_disk=0.25):
"""
params = dictionary of the model parameters
alma keyword is obsoleted
outdir: The directory for storing Hyperion input files
record_dir: The directory contains "model_list.txt" for recording parameters
TSC_dir: Path the TSC-related IDL routines
IDL_path: The IDL executable
"""
import numpy as np
import astropy.constants as const
import scipy as sci
# to avoid X server error
import matplotlib as mpl
mpl.use('Agg')
#
import matplotlib.pyplot as plt
import os
from matplotlib.colors import LogNorm
from scipy.integrate import nquad
from hyperion.model import Model
from record_hyperion import record_hyperion
from outflow_inner_edge import outflow_inner_edge
from pprint import pprint
# import pdb
# pdb.set_trace()
# Constants setup
c = const.c.cgs.value
AU = 1.49598e13 # Astronomical Unit [cm]
pc = 3.08572e18 # Parsec [cm]
MS = 1.98892e33 # Solar mass [g]
LS = 3.8525e33 # Solar luminosity [erg/s]
RS = 6.96e10 # Solar radius [cm]
G = 6.67259e-8 # Gravitational constant [cm3/g/s^2]
yr = 60 * 60 * 24 * 365 # Years in seconds
PI = np.pi # PI constant
sigma = const.sigma_sb.cgs.value # Stefan-Boltzmann constant
mh = const.m_p.cgs.value + const.m_e.cgs.value
g2d = 100.
mmw = 2.37 # Kauffmann 2008
m = Model()
# Create dust properties
# Hyperion needs nu, albedo, chi, g, p_lin_max
from hyperion.dust import HenyeyGreensteinDust
# Read in the dust opacity table used by RADMC-3D
dust = dict()
# [dust_radmc['wl'], dust_radmc['abs'], dust_radmc['scat'], dust_radmc['g']] = np.genfromtxt(dust_file,skip_header=2).T
[dust['nu'], dust['albedo'], dust['chi'],
dust['g']] = np.genfromtxt(dust_file).T
# opacity per mass of dust?
# dust_hy = dict()
# dust_hy['nu'] = c/dust_radmc['wl']*1e4
# ind = np.argsort(dust_hy['nu'])
# dust_hy['nu'] = dust_hy['nu'][ind]
# dust_hy['albedo'] = (dust_radmc['scat']/(dust_radmc['abs']+dust_radmc['scat']))[ind]
# dust_hy['chi'] = (dust_radmc['abs']+dust_radmc['scat'])[ind]
# dust_hy['g'] = dust_radmc['g'][ind]
# dust_hy['p_lin_max'] = 0*dust_radmc['wl'][ind] # assume no polarization
# d = HenyeyGreensteinDust(dust_hy['nu'], dust_hy['albedo'], dust_hy['chi'], dust_hy['g'], dust_hy['p_lin_max'])
d = HenyeyGreensteinDust(dust['nu'], dust['albedo'], dust['chi'],
dust['g'], dust['g'] * 0)
# dust sublimation option
d.set_sublimation_temperature('slow', temperature=1600.0)
d.set_lte_emissivities(n_temp=3000, temp_min=0.1, temp_max=2000.)
# try to solve the freq. problem
d.optical_properties.extrapolate_nu(3.28e15, 4e15)
#
d.write(outdir + os.path.basename(dust_file).split('.')[0] + '.hdf5')
d.plot(outdir + os.path.basename(dust_file).split('.')[0] + '.png')
plt.clf()
# Grids and Density
# Calculation inherited from the script used for RADMC-3D
# Grid Parameters
nx = 300L
if low_res == True:
nx = 100L
ny = 400L
nz = 50L
[nx, ny, nz] = [int(scale * nx), int(scale * ny), int(scale * nz)]
# TSC model input setting
# params = np.genfromtxt(indir+'/tsc_params.dat', dtype=None)
dict_params = params # input_reader(params_file)
# TSC model parameter
cs = dict_params['Cs'] * 1e5
t = dict_params['age'] # year
omega = dict_params['Omega0']
# calculate related parameters
M_env_dot = 0.975 * cs**3 / G
mstar = M_env_dot * t * yr
R_cen = omega**2 * G**3 * mstar**3 / (16 * cs**8)
R_inf = cs * t * yr
# M_env_dot = dict_params['M_env_dot']*MS/yr
# R_cen = dict_params['R_cen']*AU
# R_inf = dict_params['R_inf']*AU
# protostar parameter
tstar = dict_params['tstar']
R_env_max = dict_params['R_env_max'] * AU
theta_cav = dict_params['theta_cav']
rho_cav_center = dict_params['rho_cav_center']
rho_cav_edge = dict_params['rho_cav_edge'] * AU
rstar = dict_params['rstar'] * RS
# Mostly fixed parameter
M_disk = dict_params['M_disk'] * MS
beta = dict_params['beta']
h100 = dict_params['h100'] * AU
rho_cav = dict_params['rho_cav']
# make M_disk varies with mstar, which is the mass of star+disk
if auto_disk != None:
if M_disk != 0:
print 'M_disk is reset to %4f of mstar (star+disk)' % auto_disk
M_disk = mstar * auto_disk
else:
print 'M_disk = 0 is found. M_disk is set to 0.'
# ellipsoid cavity parameter
if ellipsoid == True:
a_out = 130 * 178. * AU
b_out = 50 * 178. * AU
z_out = a_out
# a_in = 77.5 * 178. * AU
# b_in = 30 * 178. * AU
a_in = dict_params['a_in'] * 178. * AU
b_in = a_in / a_out * b_out
z_in = a_in
# rho_cav_out = 1e4 * mh
# rho_cav_in = 1e3 * mh
rho_cav_out = dict_params['rho_cav_out'] * mh
rho_cav_in = dict_params['rho_cav_in'] * mh
# Calculate the dust sublimation radius
T_sub = 1600
a = 1 #in micron
# realistic dust
# d_sub = 2.9388e7*(a/0.1)**-0.2 * (4*np.pi*rstar**2*sigma*tstar**4/LS)**0.5 / T_sub**3 *AU
# black body dust
d_sub = (LS / 16. / np.pi / sigma / AU**2 *
(4 * np.pi * rstar**2 * sigma * tstar**4 / LS) /
T_sub**4)**0.5 * AU
# use the dust sublimation radius as the inner radius of disk and envelope
R_disk_min = d_sub
R_env_min = d_sub
rin = rstar
rout = R_env_max
R_disk_max = R_cen
# Do the variable conversion
# cs = (G * M_env_dot / 0.975)**(1/3.) # cm/s
# t = R_inf / cs / yr # in year
# mstar = M_env_dot * t * yr
# omega = (R_cen * 16*cs**8 / (G**3 * mstar**3))**0.5
# print the variables for radmc3d
print 'Dust sublimation radius %6f AU' % (d_sub / AU)
print 'M_star %4f Solar mass' % (mstar / MS)
print 'Infall radius %4f AU' % (R_inf / AU)
# if there is any parameter found in fix_params, then fix them
if fix_params != None:
if 'R_min' in fix_params.keys():
R_disk_min = fix_params['R_min'] * AU
R_env_min = fix_params['R_min'] * AU
# Make the Coordinates
#
ri = rin * (rout / rin)**(np.arange(nx + 1).astype(dtype='float') /
float(nx))
ri = np.hstack((0.0, ri))
thetai = PI * np.arange(ny + 1).astype(dtype='float') / float(ny)
phii = PI * 2.0 * np.arange(nz + 1).astype(dtype='float') / float(nz)
# Keep the constant cell size in r-direction at large radii
#
if flat == True:
ri_cellsize = ri[1:-1] - ri[0:-2]
ind = np.where(
ri_cellsize / AU > 100.0)[0][0] # The largest cell size is 100 AU
ri = np.hstack(
(ri[0:ind],
ri[ind] + np.arange(np.ceil(
(rout - ri[ind]) / 100 / AU)) * 100 * AU))
nxx = nx
nx = len(ri) - 1
# Assign the coordinates of the center of cell as its coordinates.
#
rc = 0.5 * (ri[0:nx] + ri[1:nx + 1])
thetac = 0.5 * (thetai[0:ny] + thetai[1:ny + 1])
phic = 0.5 * (phii[0:nz] + phii[1:nz + 1])
# phic = 0.5*( phii[0:nz-1] + phii[1:nz] )
# Make the dust density model
# Make the density profile of the envelope
#
total_mass = 0
if tsc == False:
print 'Calculating the dust density profile with infall solution...'
if theta_cav != 0:
# c0 = R_env_max**(-0.5)*np.sqrt(1/np.sin(np.radians(theta_cav))**3-1/np.sin(np.radians(theta_cav)))
# using R = 10000 AU as the reference point
c0 = (10000. *
AU)**(-0.5) * np.sqrt(1 / np.sin(np.radians(theta_cav))**3 -
1 / np.sin(np.radians(theta_cav)))
else:
c0 = 0
rho_env = np.zeros([len(rc), len(thetac), len(phic)])
rho_disk = np.zeros([len(rc), len(thetac), len(phic)])
rho = np.zeros([len(rc), len(thetac), len(phic)])
if dyn_cav == True:
print 'WARNING: Calculation of interdependent cavity property has not implemented in infall-only solution!'
# Normalization for the total disk mass
def f(w, z, beta, rstar, h100):
f = 2 * PI * w * (1 - np.sqrt(rstar / w)) * (rstar / w)**(
beta + 1) * np.exp(-0.5 * (z /
(w**beta * h100 / 100**beta))**2)
return f
rho_0 = M_disk / (nquad(
f, [[R_disk_min, R_disk_max], [-R_env_max, R_env_max]],
args=(beta, rstar, h100)))[0]
i = 0
j = 0
if 'rho_cav_center' in locals() == False:
rho_cav_center = 5.27e-18 # 1.6e-17 # 5.27e-18
print 'Use 5.27e-18 as the default value for cavity center'
if 'rho_cav_edge' in locals() == False:
rho_cav_edge = 40 * AU
print 'Use 40 AU as the default value for size of the inner region'
discont = 1
for ir in range(0, len(rc)):
for itheta in range(0, len(thetac)):
for iphi in range(0, len(phic)):
if rc[ir] > R_env_min:
# Envelope profile
w = abs(rc[ir] * np.cos(np.pi / 2 - thetac[itheta]))
z = rc[ir] * np.sin(np.pi / 2 - thetac[itheta])
if ellipsoid == False:
z_cav = c0 * abs(w)**1.5
if z_cav == 0:
z_cav = R_env_max
cav_con = abs(z) > abs(z_cav)
else:
# condition for the outer ellipsoid
cav_con = (2 * (w / b_out)**2 +
((abs(z) - z_out) / a_out)**2) < 1
if cav_con:
# open cavity
if ellipsoid == False:
if rho_cav_edge == 0:
rho_cav_edge = R_env_min
if (rc[ir] <= rho_cav_edge) & (rc[ir] >=
R_env_min):
rho_env[
ir, itheta,
iphi] = g2d * rho_cav_center #*((rc[ir]/AU)**2)
else:
rho_env[
ir, itheta,
iphi] = g2d * rho_cav_center * discont * (
rho_cav_edge / rc[ir])**power
i += 1
else:
# condition for the inner ellipsoid
if (2 * (w / b_in)**2 +
((abs(z) - z_in) / a_in)**2) > 1:
rho_env[ir, itheta, iphi] = rho_cav_out
else:
rho_env[ir, itheta, iphi] = rho_cav_in
i += 1
else:
j += 1
mu = abs(np.cos(thetac[itheta]))
# Implement new root finding algorithm
roots = np.roots(
np.array([
1.0, 0.0, rc[ir] / R_cen - 1.0,
-mu * rc[ir] / R_cen
]))
if len(roots[roots.imag == 0]) == 1:
if (abs(roots[roots.imag == 0]) - 1.0) <= 0.0:
mu_o_dum = roots[roots.imag == 0]
else:
mu_o_dum = -0.5
print 'Problem with cubic solving, cos(theta) = ', mu_o_dum
print 'parameters are ', np.array([
1.0, 0.0, rc[ir] / R_cen - 1.0,
-mu * rc[ir] / R_cen
])
else:
mu_o_dum = -0.5
for imu in range(0, len(roots)):
if roots[imu] * mu >= 0.0:
if (abs(
(abs(roots[imu]) - 1.0)) <= 1e-5):
mu_o_dum = 1.0 * np.sign(mu)
else:
mu_o_dum = roots[imu]
if mu_o_dum == -0.5:
print 'Problem with cubic solving, roots are: ', roots
mu_o = mu_o_dum.real
rho_env[ir, itheta, iphi] = M_env_dot / (
4 * PI * (G * mstar * R_cen**3)**0.5) * (
rc[ir] / R_cen)**(-3. / 2) * (
1 + mu / mu_o)**(-0.5) * (
mu / mu_o +
2 * mu_o**2 * R_cen / rc[ir])**(-1)
# Disk profile
if ((w >= R_disk_min) and (w <= R_disk_max)) == True:
h = ((w / (100 * AU))**beta) * h100
rho_disk[ir, itheta, iphi] = rho_0 * (1 - np.sqrt(
rstar / w)) * (rstar / w)**(beta + 1) * np.exp(
-0.5 * (z / h)**2)
# Combine envelope and disk
rho[ir, itheta,
iphi] = rho_disk[ir, itheta,
iphi] + rho_env[ir, itheta, iphi]
else:
rho[ir, itheta, iphi] = 1e-30
# add the dust mass into the total count
cell_mass = rho[ir, itheta, iphi] * (1 / 3.) * (
ri[ir + 1]**3 -
ri[ir]**3) * (phii[iphi + 1] - phii[iphi]) * -(np.cos(
thetai[itheta + 1]) - np.cos(thetai[itheta]))
total_mass = total_mass + cell_mass
rho_env = rho_env + 1e-40
rho_disk = rho_disk + 1e-40
rho = rho + 1e-40
# TSC model
else:
print 'Calculating the dust density profile with TSC solution...'
if theta_cav != 0:
# c0 = R_env_max**(-0.5)*np.sqrt(1/np.sin(np.radians(theta_cav))**3-1/np.sin(np.radians(theta_cav)))
c0 = (1e4 *
AU)**(-0.5) * np.sqrt(1 / np.sin(np.radians(theta_cav))**3 -
1 / np.sin(np.radians(theta_cav)))
else:
c0 = 0
# If needed, calculate the TSC model via IDL
#
if idl == True:
print 'Using IDL to calculate the TSC model. Make sure you are running this on mechine with IDL.'
import pidly
# idl = pidly.IDL('/Applications/exelis/idl82/bin/idl')
idl = pidly.IDL(IDL_path)
idl('.r ' + TSC_dir + 'tsc.pro')
# idl.pro('tsc_run', outdir=outdir, grid=[nxx,ny,nz], time=t, c_s=cs, omega=omega, rstar=rstar, renv_min=R_env_min, renv_max=R_env_max)
# idl.pro('tsc_run', outdir=outdir, grid=[nxx,ny,nz], time=t, c_s=cs, omega=omega, rstar=rstar, renv_min=R_env_min, renv_max=min([R_inf,max(ri)])) # min([R_inf,max(ri)])
#
# only run TSC calculation within infall radius
# modify the rc array
rc_idl = rc[(rc < min([R_inf, max(ri)]))]
idl.pro(
'tsc_run',
outdir=outdir,
rc=rc_idl,
thetac=thetac,
time=t,
c_s=cs,
omega=omega,
renv_min=R_env_min
) #, rstar=rstar, renv_min=R_env_min, renv_max=min([R_inf,max(ri)])) # min([R_inf,max(ri)])
else:
print 'Read the pre-computed TSC model.'
rc_idl = rc[(rc < min([R_inf, max(ri)]))]
# read in the exist file
rho_env_tsc_idl = np.genfromtxt(outdir + 'rhoenv.dat').T
# because only region within infall radius is calculated by IDL program, need to project it to the original grid
rho_env_tsc = np.zeros([len(rc), len(thetac)])
for irc in range(len(rc)):
if rc[irc] in rc_idl:
rho_env_tsc[irc, :] = rho_env_tsc_idl[np.where(
rc_idl == rc[irc]), :]
# extrapolate for the NaN values at the outer radius, usually at radius beyond the infall radius
# using r^-2 profile at radius greater than infall radius
# and map the 2d strcuture onto 3d grid
def poly(x, y, x0, deg=2):
import numpy as np
p = np.polyfit(x, y, deg)
y0 = 0
for i in range(0, len(p)):
y0 = y0 + p[i] * x0**(len(p) - i - 1)
return y0
# rho_env_copy = np.array(rho_env_tsc)
# if max(rc) > R_inf:
# ind_infall = np.where(rc <= R_inf)[0][-1]
# print ind_infall
# for ithetac in range(0, len(thetac)):
# # rho_dum = np.log10(rho_env_copy[(rc > R_inf) & (np.isnan(rho_env_copy[:,ithetac]) == False),ithetac])
# # rc_dum = np.log10(rc[(rc > R_inf) & (np.isnan(rho_env_copy[:,ithetac]) == False)])
# # rc_dum_nan = np.log10(rc[(rc > R_inf) & (np.isnan(rho_env_copy[:,ithetac]) == True)])
# # # print rc_dum
# # for i in range(0, len(rc_dum_nan)):
# # rho_extrapol = poly(rc_dum, rho_dum, rc_dum_nan[i])
# # rho_env_copy[(np.log10(rc) == rc_dum_nan[i]),ithetac] = 10**rho_extrapol
# #
# for i in range(ind_infall, len(rc)):
# rho_env_copy[i, ithetac] = 10**(np.log10(rho_env_copy[ind_infall, ithetac]) - 2*(np.log10(rc[i]/rc[ind_infall])))
# rho_env2d = rho_env_copy
# rho_env = np.empty((nx,ny,nz))
# for i in range(0, nz):
# rho_env[:,:,i] = rho_env2d
# map TSC solution from IDL to actual 2-D grid
rho_env_tsc2d = np.empty((nx, ny))
if max(ri) > R_inf:
ind_infall = np.where(rc <= R_inf)[0][-1]
for i in range(0, len(rc)):
if i <= ind_infall:
rho_env_tsc2d[i, :] = rho_env_tsc[i, :]
else:
rho_env_tsc2d[i, :] = 10**(
np.log10(rho_env_tsc[ind_infall, :]) - 2 *
(np.log10(rc[i] / rc[ind_infall])))
else:
rho_env_tsc2d = rho_env_tsc
# map it to 3-D grid
rho_env = np.empty((nx, ny, nz))
for i in range(0, nz):
rho_env[:, :, i] = rho_env_tsc2d
if dyn_cav == True:
print 'Calculate the cavity properties using the criteria that swept-up mass = outflowed mass'
# using swept-up mass = flow mass to derive the edge of the extended flat density region
v_outflow = 1e2 * 1e5
rho_cav_edge = outflow_inner_edge(np.copy(rho_env),
(ri, thetai, phii), M_env_dot,
v_outflow, theta_cav, R_env_min)
dict_params['rho_cav_edge'] = rho_cav_edge
# assume gas-to-dust ratio = 100
rho_cav_center = 0.01 * 0.1 * M_env_dot * rho_cav_edge / v_outflow / 2 / (
2 * np.pi / 3 * rho_cav_edge**3 *
(1 - np.cos(np.radians(theta_cav))))
dict_params['rho_cav_center'] = rho_cav_center
print 'inner edge is %5f AU and density is %e g/cm3' % (
rho_cav_edge / AU, rho_cav_center)
# create the array of density of disk and the whole structure
#
rho_disk = np.zeros([len(rc), len(thetac), len(phic)])
rho = np.zeros([len(rc), len(thetac), len(phic)])
# Calculate the disk scale height by the normalization of h100
def f(w, z, beta, rstar, h100):
f = 2 * PI * w * (1 - np.sqrt(rstar / w)) * (rstar / w)**(
beta + 1) * np.exp(-0.5 * (z /
(w**beta * h100 / 100**beta))**2)
return f
# The function for calculating the normalization of disk using the total disk mass
#
rho_0 = M_disk / (nquad(
f, [[R_disk_min, R_disk_max], [-R_env_max, R_env_max]],
args=(beta, rstar, h100)))[0]
i = 0
j = 0
if 'rho_cav_center' in locals() == False:
rho_cav_center = 5.27e-18 # 1.6e-17 # 5.27e-18
print 'Use 5.27e-18 as the default value for cavity center'
if 'rho_cav_edge' in locals() == False:
rho_cav_edge = 40 * AU
print 'Use 40 AU as the default value for size of the inner region'
discont = 1
for ir in range(0, len(rc)):
for itheta in range(0, len(thetac)):
for iphi in range(0, len(phic)):
if rc[ir] > R_env_min:
# Envelope profile
w = abs(rc[ir] * np.cos(np.pi / 2 - thetac[itheta]))
z = rc[ir] * np.sin(np.pi / 2 - thetac[itheta])
if ellipsoid == False:
z_cav = c0 * abs(w)**1.5
if z_cav == 0:
z_cav = R_env_max
cav_con = abs(z) > abs(z_cav)
else:
# condition for the outer ellipsoid
cav_con = (2 * (w / b_out)**2 +
((abs(z) - z_out) / a_out)**2) < 1
if cav_con:
# open cavity
if ellipsoid == False:
if rho_cav_edge == 0:
rho_cav_edge = R_env_min
if (rc[ir] <= rho_cav_edge) & (rc[ir] >=
R_env_min):
rho_env[
ir, itheta,
iphi] = g2d * rho_cav_center #*((rc[ir]/AU)**2)
else:
rho_env[
ir, itheta,
iphi] = g2d * rho_cav_center * discont * (
rho_cav_edge / rc[ir])**power
i += 1
else:
# condition for the inner ellipsoid
if (2 * (w / b_in)**2 +
((abs(z) - z_in) / a_in)**2) > 1:
rho_env[ir, itheta, iphi] = rho_cav_out
else:
rho_env[ir, itheta, iphi] = rho_cav_in
i += 1
# Disk profile
if ((w >= R_disk_min) and (w <= R_disk_max)) == True:
h = ((w / (100 * AU))**beta) * h100
rho_disk[ir, itheta, iphi] = rho_0 * (1 - np.sqrt(
rstar / w)) * (rstar / w)**(beta + 1) * np.exp(
-0.5 * (z / h)**2)
# Combine envelope and disk
rho[ir, itheta,
iphi] = rho_disk[ir, itheta,
iphi] + rho_env[ir, itheta, iphi]
else:
rho[ir, itheta, iphi] = 1e-40
# add the dust mass into the total count
cell_mass = rho[ir, itheta, iphi] * (1 / 3.) * (
ri[ir + 1]**3 -
ri[ir]**3) * (phii[iphi + 1] - phii[iphi]) * -(np.cos(
thetai[itheta + 1]) - np.cos(thetai[itheta]))
total_mass = total_mass + cell_mass
# rho_env = rho_env + 1e-40
# rho_disk = rho_disk + 1e-40
# rho = rho + 1e-40
# apply gas-to-dust ratio of 100
rho_dust = rho / g2d
total_mass_dust = total_mass / MS / g2d
print 'Total dust mass = %f Solar mass' % total_mass_dust
if record == True:
# Record the input and calculated parameters
params = dict_params.copy()
params.update({
'd_sub': d_sub / AU,
'M_env_dot': M_env_dot / MS * yr,
'R_inf': R_inf / AU,
'R_cen': R_cen / AU,
'mstar': mstar / MS,
'M_tot_gas': total_mass / MS
})
record_hyperion(params, record_dir)
if plot == True:
# rc setting
# mat.rcParams['text.usetex'] = True
# mat.rcParams['font.family'] = 'serif'
# mat.rcParams['font.serif'] = 'Times'
# mat.rcParams['font.sans-serif'] = 'Computer Modern Sans serif'
# Plot the azimuthal averaged density
fig = plt.figure(figsize=(8, 6))
ax_env = fig.add_subplot(111, projection='polar')
# take the weighted average
# rho2d is the 2-D projection of gas density
rho2d = np.sum(rho**2, axis=2) / np.sum(rho, axis=2)
zmin = 1e-22 / mmw / mh
cmap = plt.cm.CMRmap
rho2d_exp = np.hstack((rho2d, rho2d, rho2d[:, 0:1]))
thetac_exp = np.hstack(
(thetac - PI / 2, thetac + PI / 2, thetac[0] - PI / 2))
# plot the gas density
img_env = ax_env.pcolormesh(
thetac_exp,
rc / AU,
rho2d_exp / mmw / mh,
cmap=cmap,
norm=LogNorm(vmin=zmin, vmax=1e9)) # np.nanmax(rho2d_exp/mmw/mh)
ax_env.set_xlabel(r'$\rm{Polar\,angle\,(Degree)}$', fontsize=20)
ax_env.set_ylabel(r'$\rm{Radius\,(AU)}$', fontsize=20)
ax_env.tick_params(labelsize=20)
ax_env.set_yticks(np.arange(0, R_env_max / AU, R_env_max / AU / 5))
# ax_env.set_ylim([0,10000])
ax_env.set_xticklabels([r'$\rm{90^{\circ}}$',r'$\rm{45^{\circ}}$',r'$\rm{0^{\circ}}$',r'$\rm{-45^{\circ}}$',\
r'$\rm{-90^{\circ}}$',r'$\rm{-135^{\circ}}$',r'$\rm{180^{\circ}}$',r'$\rm{135^{\circ}}$'])
# fix the tick label font
ticks_font = mpl.font_manager.FontProperties(family='STIXGeneral',
size=20)
for label in ax_env.get_yticklabels():
label.set_fontproperties(ticks_font)
ax_env.grid(True)
cb = fig.colorbar(img_env, pad=0.1)
cb.ax.set_ylabel(r'$\rm{Averaged\,Gas\,Density\,(cm^{-3})}$',
fontsize=20)
cb.set_ticks([1e2, 1e3, 1e4, 1e5, 1e6, 1e7, 1e8, 1e9])
cb.set_ticklabels([r'$\rm{10^{2}}$',r'$\rm{10^{3}}$',r'$\rm{10^{4}}$',r'$\rm{10^{5}}$',r'$\rm{10^{6}}$',\
r'$\rm{10^{7}}$',r'$\rm{10^{8}}$',r'$\rm{\geq 10^{9}}$'])
cb_obj = plt.getp(cb.ax.axes, 'yticklabels')
plt.setp(cb_obj, fontsize=20)
fig.savefig(outdir + outname + '_gas_density.png',
format='png',
dpi=300,
bbox_inches='tight')
fig.clf()
# Plot the radial density profile
fig = plt.figure(figsize=(12, 9))
ax = fig.add_subplot(111)
plot_grid = [0, 49, 99, 149, 199]
alpha = np.linspace(0.3, 1.0, len(plot_grid))
for i in plot_grid:
rho_rad, = ax.plot(np.log10(rc / AU),
np.log10(rho2d[:, i] / g2d / mmw / mh),
'-',
color='b',
linewidth=2,
markersize=3,
alpha=alpha[plot_grid.index(i)])
tsc_only, = ax.plot(np.log10(rc / AU),
np.log10(rho_env_tsc2d[:, i] / mmw / mh),
'o',
color='r',
linewidth=2,
markersize=3,
alpha=alpha[plot_grid.index(i)])
rinf = ax.axvline(np.log10(R_inf / AU),
linestyle='--',
color='k',
linewidth=1.5)
cen_r = ax.axvline(np.log10(R_cen / AU),
linestyle=':',
color='k',
linewidth=1.5)
# sisslope, = ax.plot(np.log10(rc/AU), -2*np.log10(rc/AU)+A-(-2)*np.log10(plot_r_inf), linestyle='--', color='Orange', linewidth=1.5)
# gt_R_cen_slope, = ax.plot(np.log10(rc/AU), -1.5*np.log10(rc/AU)+B-(-1.5)*np.log10(plot_r_inf), linestyle='--', color='Orange', linewidth=1.5)
# lt_R_cen_slope, = ax.plot(np.log10(rc/AU), -0.5*np.log10(rc/AU)+A-(-0.5)*np.log10(plot_r_inf), linestyle='--', color='Orange', linewidth=1.5)
lg = plt.legend([rho_rad, tsc_only, rinf, cen_r],\
[r'$\rm{\rho_{dust}}$',r'$\rm{\rho_{tsc}}$',r'$\rm{infall\,radius}$',r'$\rm{centrifugal\,radius}$'],\
fontsize=20, numpoints=1)
ax.set_xlabel(r'$\rm{log(Radius)\,(AU)}$', fontsize=20)
ax.set_ylabel(r'$\rm{log(Gas \slash Dust\,Density)\,(cm^{-3})}$',
fontsize=20)
[
ax.spines[axis].set_linewidth(1.5)
for axis in ['top', 'bottom', 'left', 'right']
]
ax.minorticks_on()
ax.tick_params('both',
labelsize=18,
width=1.5,
which='major',
pad=15,
length=5)
ax.tick_params('both',
labelsize=18,
width=1.5,
which='minor',
pad=15,
length=2.5)
# fix the tick label font
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)
ax.set_ylim([0, 15])
fig.gca().set_xlim(left=np.log10(0.05))
# ax.set_xlim([np.log10(0.8),np.log10(10000)])
# subplot shows the radial density profile along the midplane
ax_mid = plt.axes([0.2, 0.2, 0.2, 0.2], frameon=True)
ax_mid.plot(np.log10(rc / AU),
np.log10(rho2d[:, 199] / g2d / mmw / mh),
'o',
color='b',
linewidth=1,
markersize=2)
ax_mid.plot(np.log10(rc / AU),
np.log10(rho_env_tsc2d[:, 199] / mmw / mh),
'-',
color='r',
linewidth=1,
markersize=2)
# ax_mid.set_ylim([0,10])
# ax_mid.set_xlim([np.log10(0.8),np.log10(10000)])
ax_mid.set_ylim([0, 15])
fig.savefig(outdir + outname + '_gas_radial.pdf',
format='pdf',
dpi=300,
bbox_inches='tight')
fig.clf()
# Insert the calculated grid and dust density profile into hyperion
m.set_spherical_polar_grid(ri, thetai, phii)
# temperary for comparing full TSC and infall-only TSC model
# import sys
# sys.path.append(os.path.expanduser('~')+'/programs/misc/')
# from tsc_comparison import tsc_com
# rho_tsc, rho_ulrich = tsc_com()
m.add_density_grid(rho_dust.T, d)
# m.add_density_grid(rho.T, outdir+'oh5.hdf5') # numpy read the array in reverse order
# Define the luminsoity source
source = m.add_spherical_source()
source.luminosity = (4 * PI * rstar**2) * sigma * (tstar**4) # [ergs/s]
source.radius = rstar # [cm]
source.temperature = tstar # [K]
source.position = (0., 0., 0.)
print 'L_center = % 5.2f L_sun' % ((4 * PI * rstar**2) * sigma *
(tstar**4) / LS)
# # add an infrared source at the center
# L_IR = 0.04
# ir_source = m.add_spherical_source()
# ir_source.luminosity = L_IR*LS
# ir_source.radius = rstar # [cm]
# ir_source.temperature = 500 # [K] peak at 10 um
# ir_source.position = (0., 0., 0.)
# print 'Additional IR source, L_IR = %5.2f L_sun' % L_IR
# Setting up the wavelength for monochromatic radiative transfer
lambda0 = 0.1
lambda1 = 2.0
lambda2 = 50.0
lambda3 = 95.0
lambda4 = 200.0
lambda5 = 314.0
lambda6 = 1000.0
n01 = 10.0
n12 = 20.0
n23 = 50.0
lam01 = lambda0 * (lambda1 / lambda0)**(np.arange(n01) / n01)
lam12 = lambda1 * (lambda2 / lambda1)**(np.arange(n12) / n12)
lam23 = lambda2 * (lambda6 / lambda2)**(np.arange(n23 + 1) / n23)
lam = np.concatenate([lam01, lam12, lam23])
nlam = len(lam)
# Create camera wavelength points
n12 = 70.0
n23 = 70.0
n34 = 70.0
n45 = 50.0
n56 = 50.0
lam12 = lambda1 * (lambda2 / lambda1)**(np.arange(n12) / n12)
lam23 = lambda2 * (lambda3 / lambda2)**(np.arange(n23) / n23)
lam34 = lambda3 * (lambda4 / lambda3)**(np.arange(n34) / n34)
lam45 = lambda4 * (lambda5 / lambda4)**(np.arange(n45) / n45)
lam56 = lambda5 * (lambda6 / lambda5)**(np.arange(n56 + 1) / n56)
lam_cam = np.concatenate([lam12, lam23, lam34, lam45, lam56])
n_lam_cam = len(lam_cam)
# Radiative transfer setting
# number of photons for temp and image
lam_list = lam.tolist()
# print lam_list
m.set_raytracing(True)
# option of using more photons for imaging
if better_im == False:
im_photon = 1e6
else:
im_photon = 5e7
if mono == True:
# Monechromatic radiative transfer setting
m.set_monochromatic(True, wavelengths=lam_list)
m.set_n_photons(initial=1000000,
imaging_sources=im_photon,
imaging_dust=im_photon,
raytracing_sources=1000000,
raytracing_dust=1000000)
else:
# regular wavelength grid setting
m.set_n_photons(initial=1000000,
imaging=im_photon,
raytracing_sources=1000000,
raytracing_dust=1000000)
# number of iteration to compute dust specific energy (temperature)
m.set_n_initial_iterations(20)
# m.set_convergence(True, percentile=95., absolute=1.5, relative=1.02)
m.set_convergence(True,
percentile=dict_params['percentile'],
absolute=dict_params['absolute'],
relative=dict_params['relative'])
m.set_mrw(True) # Gamma = 1 by default
# m.set_forced_first_scattering(forced_first_scattering=True)
# Setting up images and SEDs
# SED setting
# Infinite aperture
syn_inf = m.add_peeled_images(image=False)
# use the index of wavelength array used by the monochromatic radiative transfer
if mono == False:
syn_inf.set_wavelength_range(1400, 2.0, 1400.0)
syn_inf.set_viewing_angles([dict_params['view_angle']], [0.0])
syn_inf.set_uncertainties(True)
syn_inf.set_output_bytes(8)
# aperture
# 7.2 in 10 um scaled by lambda / 10
# flatten beyond 20 um
# default aperture
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, 1300],\
'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, 101]}
# 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)
# name = np.arange(1,len(wl_aper)+1)
# aper = np.empty_like(wl_aper)
# for i in range(0, len(wl_aper)):
# if wl_aper[i] < 5:
# # aper[i] = 1.2 * 7
# aper[i] = 1.8 * 4
# elif (wl_aper[i] < 14) & (wl_aper[i] >=5):
# # aper[i] = 7.2 * wl_aper[i]/10.
# aper[i] = 1.8 * 4
# elif (wl_aper[i] >= 14) & (wl_aper[i] <40):
# # aper[i] = 7.2 * 2
# aper[i] = 5.1 * 4
# else:
# aper[i] = 24.5
# dict_peel_sed = {}
# for i in range(0, len(wl_aper)):
# aper_dum = aper[i]/2 * (1/3600.*np.pi/180.)*dstar*pc
# dict_peel_sed[str(name[i])] = m.add_peeled_images(image=False)
# # use the index of wavelength array used by the monochromatic radiative transfer
# if mono == False:
# # dict_peel_sed[str(name[i])].set_wavelength_range(1300, 2.0, 1300.0)
# dict_peel_sed[str(name[i])].set_wavelength_range(1000, 2.0, 1000.0)
# dict_peel_sed[str(name[i])].set_viewing_angles([dict_params['view_angle']], [0.0])
# # aperture should be given in cm
# dict_peel_sed[str(name[i])].set_aperture_range(1, aper_dum, aper_dum)
# dict_peel_sed[str(name[i])].set_uncertainties(True)
# dict_peel_sed[str(name[i])].set_output_bytes(8)
dict_peel_sed = {}
for i in range(0, len(aper_reduced)):
aper_dum = aper_reduced[i] / 2 * (1 / 3600. * np.pi /
180.) * dstar * pc
dict_peel_sed[str(index_reduced[i])] = m.add_peeled_images(image=False)
# use the index of wavelength array used by the monochromatic radiative transfer
if mono == False:
dict_peel_sed[str(index_reduced[i])].set_wavelength_range(
1400, 2.0, 1400.0)
dict_peel_sed[str(index_reduced[i])].set_viewing_angles(
[dict_params['view_angle']], [0.0])
# aperture should be given in cm and its the radius of the aperture
dict_peel_sed[str(index_reduced[i])].set_aperture_range(
1, aper_dum, aper_dum)
dict_peel_sed[str(index_reduced[i])].set_uncertainties(True)
dict_peel_sed[str(index_reduced[i])].set_output_bytes(8)
# image setting
syn_im = m.add_peeled_images(sed=False)
# use the index of wavelength array used by the monochromatic radiative transfer
if mono == False:
syn_im.set_wavelength_range(1400, 2.0, 1400.0)
# pixel number
syn_im.set_image_size(300, 300)
syn_im.set_image_limits(-R_env_max, R_env_max, -R_env_max, R_env_max)
syn_im.set_viewing_angles([dict_params['view_angle']], [0.0])
syn_im.set_uncertainties(True)
# output as 64-bit
syn_im.set_output_bytes(8)
# 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'
# Number of unique photons that passed through the cell
m.conf.output.output_n_photons = 'last'
m.write(outdir + outname + '.rtin')
if radmc == True:
# RADMC-3D still use a pre-defined aperture with lazy for-loop
aper = np.zeros([len(lam)])
ind = 0
for wl in lam:
if wl < 5:
aper[ind] = 8.4
elif wl >= 5 and wl < 14:
aper[ind] = 1.8 * 4
elif wl >= 14 and wl < 40:
aper[ind] = 5.1 * 4
else:
aper[ind] = 24.5
ind += 1
# Write the wavelength_micron.inp file
#
f_wave = open(outdir + 'wavelength_micron.inp', 'w')
f_wave.write('%d \n' % int(nlam))
for ilam in range(0, nlam):
f_wave.write('%f \n' % lam[ilam])
f_wave.close()
# Write the camera_wavelength_micron.inp file
#
f_wave_cam = open(outdir + 'camera_wavelength_micron.inp', 'w')
f_wave_cam.write('%d \n' % int(nlam))
for ilam in range(0, nlam):
f_wave_cam.write('%f \n' % lam[ilam])
f_wave_cam.close()
# Write the aperture_info.inp
#
f_aper = open(outdir + 'aperture_info.inp', 'w')
f_aper.write('1 \n')
f_aper.write('%d \n' % int(nlam))
for iaper in range(0, len(aper)):
f_aper.write('%f \t %f \n' % (lam[iaper], aper[iaper] / 2))
f_aper.close()
# Write the stars.inp file
#
f_star = open(outdir + 'stars.inp', 'w')
f_star.write('2\n')
f_star.write('1 \t %d \n' % int(nlam))
f_star.write('\n')
f_star.write('%e \t %e \t %e \t %e \t %e \n' %
(rstar * 0.9999, mstar, 0, 0, 0))
f_star.write('\n')
for ilam in range(0, nlam):
f_star.write('%f \n' % lam[ilam])
f_star.write('\n')
f_star.write('%f \n' % -tstar)
f_star.close()
# Write the grid file
#
f_grid = open(outdir + 'amr_grid.inp', 'w')
f_grid.write('1\n') # iformat
f_grid.write('0\n') # AMR grid style (0=regular grid, no AMR)
f_grid.write(
'150\n'
) # Coordinate system coordsystem<100: Cartisian; 100<=coordsystem<200: Spherical; 200<=coordsystem<300: Cylindrical
f_grid.write('0\n') # gridinfo
f_grid.write('1 \t 1 \t 1 \n') # Include x,y,z coordinate
f_grid.write('%d \t %d \t %d \n' %
(int(nx) - 1, int(ny), int(nz))) # Size of the grid
[f_grid.write('%e \n' % ri[ir]) for ir in range(1, len(ri))]
[
f_grid.write('%f \n' % thetai[itheta])
for itheta in range(0, len(thetai))
]
[f_grid.write('%f \n' % phii[iphi]) for iphi in range(0, len(phii))]
f_grid.close()
# Write the density file
#
f_dust = open(outdir + 'dust_density.inp', 'w')
f_dust.write('1 \n') # format number
f_dust.write('%d \n' % int((nx - 1) * ny * nz)) # Nr of cells
f_dust.write('1 \n') # Nr of dust species
for iphi in range(0, len(phic)):
for itheta in range(0, len(thetac)):
for ir in range(1, len(rc)):
f_dust.write('%e \n' % rho_dust[ir, itheta, iphi])
f_dust.close()
# Write the dust opacity table
f_dustkappa = open(outdir + 'dustkappa_oh5_extended.inp', 'w')
f_dustkappa.write('3 \n') # format index for including g-factor
f_dustkappa.write(
'%d \n' %
len(dust['nu'])) # number of wavlength/frequency in the table
for i in range(len(dust['nu'])):
f_dustkappa.write('%f \t %f \t %f \t %f \n' %
(c / dust['nu'][i] * 1e4, dust['chi'][i],
dust['chi'][i] * dust['albedo'][i] /
(1 - dust['albedo'][i]), dust['g'][i]))
f_dustkappa.close()
# Write the Dust opacity control file
#
f_opac = open(outdir + 'dustopac.inp', 'w')
f_opac.write('2 Format number of this file\n')
f_opac.write('1 Nr of dust species\n')
f_opac.write(
'============================================================================\n'
)
f_opac.write(
'1 Way in which this dust species is read\n')
f_opac.write('0 0=Thermal grain\n')
# f_opac.write('klaus Extension of name of dustkappa_***.inp file\n')
f_opac.write(
'oh5_extended Extension of name of dustkappa_***.inp file\n')
f_opac.write(
'----------------------------------------------------------------------------\n'
)
f_opac.close()
# In[112]:
# Write the radmc3d.inp control file
#
f_control = open(outdir + 'radmc3d.inp', 'w')
f_control.write('nphot = %d \n' % 100000)
f_control.write('scattering_mode_max = 2\n')
f_control.write('camera_min_drr = 0.1\n')
f_control.write('camera_min_dangle = 0.1\n')
f_control.write('camera_spher_cavity_relres = 0.1\n')
f_control.write('istar_sphere = 1\n')
f_control.write('modified_random_walk = 1\n')
f_control.close()
return m
# from input_reader import input_reader_table
# from pprint import pprint
# filename = '/Users/yaolun/programs/misc/hyperion/test_input.txt'
# params = input_reader_table(filename)
# pprint(params[0])
# indir = '/Users/yaolun/test/'
# outdir = '/Users/yaolun/test/'
# dust_file = '/Users/yaolun/programs/misc/oh5_hyperion.txt'
# # dust_file = '/Users/yaolun/Copy/dust_model/Ormel2011/hyperion/(ic-sil,gra)3opc.txt'
# # fix_params = {'R_min': 0.14}
# fix_params = {}
# setup_model(indir,outdir,'model_test',params[0],dust_file,plot=True,record=False,\
# idl=False,radmc=False,fix_params=fix_params,ellipsoid=False)
def setup_model(indir,outdir,model=False,denser_wall=False,plot=False,low_res=False,flat=True,scale=1.0):
import numpy as np
import astropy.constants as const
import scipy as sci
import matplotlib.pyplot as plt
import matplotlib as mat
import os
from matplotlib.colors import LogNorm
from scipy.optimize import fsolve
from scipy.integrate import nquad
from envelope_func import func
from hyperion.model import Model
# Constants setup
c = const.c.cgs.value
AU = 1.49598e13 # Astronomical Unit [cm]
pc = 3.08572e18 # Parsec [cm]
MS = 1.98892e33 # Solar mass [g]
LS = 3.8525e33 # Solar luminosity [erg/s]
RS = 6.96e10 # Solar radius [cm]
G = 6.67259e-8 # Gravitational constant [cm3/g/s^2]
yr = 60*60*24*365 # Years in seconds
PI = np.pi # PI constant
sigma = const.sigma_sb.cgs.value # Stefan-Boltzmann constant
m = Model()
# Create dust properties
# Hyperion needs nu, albedo, chi, g, p_lin_max
from hyperion.dust import HenyeyGreensteinDust
# Read in the dust opacity table used by RADMC-3D
dust_radmc = dict()
[dust_radmc['wl'], dust_radmc['abs'], dust_radmc['scat'], dust_radmc['g']] = np.genfromtxt('dustkappa_oh5_extended.inp',skip_header=2).T
# opacity per mass of dust?
dust_hy = dict()
dust_hy['nu'] = c/dust_radmc['wl']*1e4
ind = np.argsort(dust_hy['nu'])
dust_hy['nu'] = dust_hy['nu'][ind]
dust_hy['albedo'] = (dust_radmc['scat']/(dust_radmc['abs']+dust_radmc['scat']))[ind]
dust_hy['chi'] = (dust_radmc['abs']+dust_radmc['scat'])[ind]
dust_hy['g'] = dust_radmc['g'][ind]
dust_hy['p_lin_max'] = 0*dust_radmc['wl'][ind] # assume no polarization
d = HenyeyGreensteinDust(dust_hy['nu'], dust_hy['albedo'], dust_hy['chi'], dust_hy['g'], dust_hy['p_lin_max'])
# dust sublimation does not occur
# d.set_sublimation_temperature(None)
d.write(outdir+'oh5.hdf5')
d.plot(outdir+'oh5.png')
# Grids and Density
# Calculation inherited from the script used for RADMC-3D
# Parameters setup
# Import the model parameters from another file
#
params = np.genfromtxt(indir+'/params.dat',dtype=None)
tstar = params[0][1]
mstar = params[1][1]*MS
rstar = params[2][1]*RS
M_env_dot = params[3][1]*MS/yr
M_disk_dot = params[4][1]*MS/yr
R_env_max = params[5][1]*AU
R_env_min = params[6][1]*AU
theta_cav = params[7][1]
R_disk_max = params[8][1]*AU
R_disk_min = params[9][1]*AU
R_cen = R_disk_max
M_disk = params[10][1]*MS
beta = params[11][1]
h100 = params[12][1]*AU
rho_cav = params[13][1]
if denser_wall == True:
wall = params[14][1]*AU
rho_wall = params[15][1]
rho_cav_center = params[16][1]
rho_cav_edge = params[17][1]*AU
# Grid Parameters
nx = 300L
if low_res == True:
nx = 100L
ny = 400L
nz = 50L
[nx, ny, nz] = [scale*nx, scale*ny, scale*nz]
# nx = 20
# ny = 40
# nz = 5
# Model Parameters
#
rin = rstar
rout = R_env_max
rcen = R_cen
# Star Parameters
#
mstar = mstar
rstar = rstar*0.9999
tstar = tstar
pstar = [0.,0.,0.]
# Make the Coordinates
#
ri = rin * (rout/rin)**(np.arange(nx+1).astype(dtype='float')/float(nx))
ri = np.hstack((0.0, ri))
thetai = PI*np.arange(ny+1).astype(dtype='float')/float(ny)
phii = PI*2.0*np.arange(nz+1).astype(dtype='float')/float(nz)
# Keep the constant cell size in r-direction
#
if flat == True:
ri_cellsize = ri[1:-1]-ri[0:-2]
ind = np.where(ri_cellsize/AU > 100.0)[0][0] # The largest cell size is 100 AU
ri = np.hstack((ri[0:ind],ri[ind]+np.arange(np.ceil((rout-ri[ind])/100/AU))*100*AU))
nxx = nx
nx = len(ri)-1
# Assign the coordinates of the center of cell as its coordinates.
#
rc = 0.5*( ri[0:nx] + ri[1:nx+1] )
thetac = 0.5*( thetai[0:ny] + thetai[1:ny+1] )
phic = 0.5*( phii[0:nz] + phii[1:nz+1] )
# phic = 0.5*( phii[0:nz-1] + phii[1:nz] )
# Make the dust density model
# Make the density profile of the envelope
#
print 'Calculating the dust density profile...'
if theta_cav != 0:
c0 = R_env_max**(-0.5)*np.sqrt(1/np.sin(np.radians(theta_cav))**3-1/np.sin(np.radians(theta_cav)))
else:
c0 = 0
rho_env = np.zeros([len(rc),len(thetac),len(phic)])
rho_disk = np.zeros([len(rc),len(thetac),len(phic)])
rho = np.zeros([len(rc),len(thetac),len(phic)])
def f(w,z,beta,rstar,h100):
f = 2*PI*w*(1-np.sqrt(rstar/w))*(rstar/w)**(beta+1)*np.exp(-0.5*(z/(w**beta*h100/100**beta))**2)
return f
rho_0 = M_disk/(nquad(f,[[R_disk_min,R_disk_max],[-R_env_max,R_env_max]], args=(beta,rstar,h100)))[0]
i = 0
j = 0
if 'rho_cav_center' in locals() == False:
rho_cav_center = 5.27e-18 # 1.6e-17 # 5.27e-18
print 'Use 5.27e-18 as the default value for cavity center'
if 'rho_cav_edge' in locals() == False:
rho_cav_edge = 40*AU
print 'Use 40 AU as the default value for size of the inner region'
discont = 1
if denser_wall == False:
for ir in range(0,len(rc)):
for itheta in range(0,len(thetac)):
for iphi in range(0,len(phic)):
if rc[ir] > R_env_min:
# Envelope profile
w = abs(rc[ir]*np.cos(np.pi/2-thetac[itheta]))
z = rc[ir]*np.sin(np.pi/2-thetac[itheta])
z_cav = c0*abs(w)**1.5
if z_cav == 0:
z_cav = R_env_max
if abs(z) > abs(z_cav):
# rho_env[ir,itheta,iphi] = rho_cav
# Modification for using density gradient in the cavity
if rc[ir] <= rho_cav_edge:
rho_env[ir,itheta,iphi] = rho_cav_center#*((rc[ir]/AU)**2)
else:
rho_env[ir,itheta,iphi] = rho_cav_center*discont*(rho_cav_edge/rc[ir])**2
i += 1
else:
j += 1
mu = abs(np.cos(thetac[itheta]))
mu_o = np.abs(fsolve(func,[0.5,0.5,0.5],args=(rc[ir],rcen,mu))[0])
rho_env[ir,itheta,iphi] = M_env_dot/(4*PI*(G*mstar*rcen**3)**0.5)*(rc[ir]/rcen)**(-3./2)*(1+mu/mu_o)**(-0.5)*(mu/mu_o+2*mu_o**2*rcen/rc[ir])**(-1)
# Disk profile
if ((w >= R_disk_min) and (w <= R_disk_max)) == True:
h = ((w/(100*AU))**beta)*h100
rho_disk[ir,itheta,iphi] = rho_0*(1-np.sqrt(rstar/w))*(rstar/w)**(beta+1)*np.exp(-0.5*(z/h)**2)
# Combine envelope and disk
rho[ir,itheta,iphi] = rho_disk[ir,itheta,iphi] + rho_env[ir,itheta,iphi]
# # testing the effect of new solver
# # Envelope profile
# w = abs(rc[ir]*np.cos(np.pi/2 - thetac[itheta]))
# z = rc[ir]*np.sin(np.pi/2 - thetac[itheta])
# z_cav = c0*abs(w)**1.5
# if z_cav == 0:
# z_cav = R_env_max
# if abs(z) > abs(z_cav):
# # rho_env[ir,itheta,iphi] = rho_cav
# # Modification for using density gradient in the cavity
# if rc[ir] <= rho_cav_edge:
# rho_env[ir,itheta,iphi] = rho_cav_center#*((rc[ir]/AU)**2)
# else:
# rho_env[ir,itheta,iphi] = rho_cav_center*discont*(rho_cav_edge/rc[ir])**2
# i += 1
# else:
# j += 1
# mu = abs(np.cos(thetac[itheta]))
# # Implement new root finding algorithm
# roots = np.roots(np.array([1.0, 0.0, rc[ir]/rcen-1.0, -mu*rc[ir]/rcen]))
# if len(roots[roots.imag == 0]) == 1:
# if (abs(roots[roots.imag == 0]) - 1.0) <= 0.0:
# mu_o_dum = roots[roots.imag == 0]
# else:
# mu_o_dum = -0.5
# print 'Problem with cubic solving, cos(theta) = ', mu_o_dum
# print 'parameters are ', np.array([1.0, 0.0, rc[ir]/rcen-1.0, -mu*rc[ir]/rcen])
# else:
# mu_o_dum = -0.5
# for imu in range(0, len(roots)):
# if roots[imu]*mu >= 0.0:
# if (abs((abs(roots[imu]) - 1.0)) <= 1e-5):
# mu_o_dum = 1.0 * np.sign(mu)
# else:
# mu_o_dum = roots[imu]
# if mu_o_dum == -0.5:
# print 'Problem with cubic solving, roots are: ', roots
# mu_o = mu_o_dum.real
# rho_env[ir,itheta,iphi] = M_env_dot/(4*PI*(G*mstar*rcen**3)**0.5)*(rc[ir]/rcen)**(-3./2)*(1+mu/mu_o)**(-0.5)*(mu/mu_o+2*mu_o**2*rcen/rc[ir])**(-1)
# # Disk profile
# if ((w >= R_disk_min) and (w <= R_disk_max)) == True:
# h = ((w/(100*AU))**beta)*h100
# rho_disk[ir,itheta,iphi] = rho_0*(1-np.sqrt(rstar/w))*(rstar/w)**(beta+1)*np.exp(-0.5*(z/h)**2)
# # Combine envelope and disk
# rho[ir,itheta,iphi] = rho_disk[ir,itheta,iphi] + rho_env[ir,itheta,iphi]
else:
rho[ir,itheta,iphi] = 1e-30
rho_env = rho_env + 1e-40
rho_disk = rho_disk + 1e-40
rho = rho + 1e-40
else:
for ir in range(0,len(rc)):
for itheta in range(0,len(thetac)):
for iphi in range(0,len(phic)):
# Envelope profile
w = abs(rc[ir]*np.cos(thetac[itheta]))
z = rc[ir]*np.sin(thetac[itheta])
z_cav = c*abs(w)**1.5
z_cav_wall = c*abs(w-wall)**1.5
if z_cav == 0:
z_cav = R_env_max
if abs(z) > abs(z_cav):
# rho_env[ir,itheta,iphi] = rho_cav
# Modification for using density gradient in the cavity
if rc[ir] <= 20*AU:
rho_env[ir,itheta,iphi] = rho_cav_center*((rc[ir]/AU)**2)
else:
rho_env[ir,itheta,iphi] = rho_cav_center*discont*(20*AU/rc[ir])**2
i += 1
elif (abs(z) > abs(z_cav_wall)) and (abs(z) < abs(z_cav)):
rho_env[ir,itheta,iphi] = rho_wall
else:
j += 1
mu = abs(np.cos(thetac[itheta]))
mu_o = np.abs(fsolve(func,[0.5,0.5,0.5],args=(rc[ir],rcen,mu))[0])
rho_env[ir,itheta,iphi] = M_env_dot/(4*PI*(G*mstar*rcen**3)**0.5)*(rc[ir]/rcen)**(-3./2)*(1+mu/mu_o)**(-0.5)*(mu/mu_o+2*mu_o**2*rcen/rc[ir])**(-1)
# Disk profile
if ((w >= R_disk_min) and (w <= R_disk_max)) == True:
h = ((w/(100*AU))**beta)*h100
rho_disk[ir,itheta,iphi] = rho_0*(1-np.sqrt(rstar/w))*(rstar/w)**(beta+1)*np.exp(-0.5*(z/h)**2)
# Combine envelope and disk
rho[ir,itheta,iphi] = rho_disk[ir,itheta,iphi] + rho_env[ir,itheta,iphi]
rho_env = rho_env + 1e-40
rho_disk = rho_disk + 1e-40
rho = rho + 1e-40
# Insert the calculated grid and dust density profile into hyperion
m.set_spherical_polar_grid(ri, thetai, phii)
m.add_density_grid(rho.T, outdir+'oh5.hdf5') # numpy read the array in reverse order
# Define the luminsoity source
source = m.add_spherical_source()
source.luminosity = (4*PI*rstar**2)*sigma*(tstar**4) # [ergs/s]
source.radius = rstar # [cm]
source.temperature = tstar # [K]
source.position = (0., 0., 0.)
print 'L_center = % 5.2f L_sun' % ((4*PI*rstar**2)*sigma*(tstar**4)/LS)
# Setting up images and SEDs
image = m.add_peeled_images()
image.set_wavelength_range(300, 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=1000000, imaging=1000000, raytracing_sources=1000000, raytracing_dust=1000000)
# number of iteration to compute dust specific energy (temperature)
m.set_n_initial_iterations(5)
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'
# Number of unique photons that passed through the cell
m.conf.output.output_n_photons = 'last'
m.write(outdir+'old_setup2.rtin')
def setup_model(outdir,record_dir,outname,params,dust_file,tsc=True,idl=False,plot=False,\
low_res=True,flat=True,scale=1,radmc=False,mono=False,mono_wave=None,
record=True,dstar=200.,aperture=None,dyn_cav=False,fix_params=None,
power=2,better_im=False,ellipsoid=False,TSC_dir='~/programs/misc/TSC/',
IDL_path='/Applications/exelis/idl83/bin/idl',auto_disk=0.25,fast_plot=False,
image_only=False, tsc_com=False, ext_source=None):
"""
params = dictionary of the model parameters
'alma' keyword is obsoleted
outdir: The directory for storing Hyperion input files
record_dir: The directory contains "model_list.txt" for recording parameters
TSC_dir: Path the TSC-related IDL routines
IDL_path: The IDL executable
fast_plot: Do not plot the polar plot of the density because the rendering
takes quite a lot of time.
mono: monochromatic radiative transfer mode (need to specify the wavelength
or a list of wavelength with 'mono_wave')
image_only: only run for images
"""
import numpy as np
import astropy.constants as const
from astropy.io import ascii
import scipy as sci
# to avoid X server error
import matplotlib as mpl
mpl.use('Agg')
#
import matplotlib.pyplot as plt
import os
from matplotlib.colors import LogNorm
from scipy.integrate import nquad
from hyperion.model import Model
from record_hyperion import record_hyperion
from outflow_inner_edge import outflow_inner_edge
from pprint import pprint
# Constants setup
c = const.c.cgs.value
AU = const.au.cgs.value # Astronomical Unit [cm]
pc = const.pc.cgs.value # Parsec [cm]
MS = const.M_sun.cgs.value # Solar mass [g]
LS = const.L_sun.cgs.value # Solar luminosity [erg/s]
RS = const.R_sun.cgs.value # Solar radius [cm]
G = const.G.cgs.value # Gravitational constant [cm3/g/s^2]
yr = 60*60*24*365 # Years in seconds
PI = np.pi # PI constant
sigma = const.sigma_sb.cgs.value # Stefan-Boltzmann constant
mh = const.m_p.cgs.value + const.m_e.cgs.value
g2d = 100.
mmw = 2.37 # Kauffmann 2008
m = Model()
# min and max wavelength to compute (need to define them first for checking dust properties)
# !!!
wav_min = 2.0
wav_max = 1400.
wav_num = 1400
# Create dust properties
# Hyperion needs nu, albedo, chi, g, p_lin_max
from hyperion.dust import HenyeyGreensteinDust
# Read in the dust opacity table used by RADMC-3D
dust = dict()
[dust['nu'], dust['albedo'], dust['chi'], dust['g']] = np.genfromtxt(dust_file).T
d = HenyeyGreensteinDust(dust['nu'], dust['albedo'], dust['chi'], dust['g'], dust['g']*0)
# dust sublimation option
d.set_sublimation_temperature('slow', temperature=1600.0)
d.set_lte_emissivities(n_temp=3000,
temp_min=0.1,
temp_max=2000.)
# if the min and/or max wavelength fall out of range
if c/wav_min/1e-4 > dust['nu'].max():
d.optical_properties.extrapolate_nu(dust['nu'].min(), c/wav_min/1e-4)
print 'minimum wavelength is out of dust model. The dust model is extrapolated.'
if c/wav_max/1e-4 < dust['nu'].min():
d.optical_properties.extrapolate_nu(c/wav_max/1e-4, dust['nu'].max())
print 'maximum wavelength is out of dust model. The dust model is extrapolated.'
# try to solve the freq. problem
d.optical_properties.extrapolate_nu(3.28e15, 5e15)
#
d.write(outdir+os.path.basename(dust_file).split('.')[0]+'.hdf5')
d.plot(outdir+os.path.basename(dust_file).split('.')[0]+'.png')
plt.clf()
# Grids and Density
# Grid Parameters
nx = 300L
if low_res == True:
nx = 100L
ny = 400L
nz = 50L
[nx, ny, nz] = [int(scale*nx), int(scale*ny), int(scale*nz)]
# TSC model input setting
dict_params = params
# TSC model parameter
cs = dict_params['Cs']*1e5
t = dict_params['age'] # year
omega = dict_params['Omega0']
# calculate related parameters
M_env_dot = 0.975*cs**3/G
mstar = M_env_dot * t * yr
R_cen = omega**2 * G**3 * mstar**3 /(16*cs**8)
R_inf = cs * t * yr
# protostar parameter
tstar = dict_params['tstar']
R_env_max = dict_params['R_env_max']*AU
theta_cav = dict_params['theta_cav']
rho_cav_center = dict_params['rho_cav_center']
rho_cav_edge = dict_params['rho_cav_edge']*AU
rstar = dict_params['rstar']*RS
# Mostly fixed parameter
M_disk = dict_params['M_disk']*MS
beta = dict_params['beta']
h100 = dict_params['h100']*AU
rho_cav = dict_params['rho_cav']
# make M_disk varies with mstar, which is the mass of star+disk
if auto_disk != None:
if M_disk != 0:
print 'M_disk is reset to %4f of mstar (star+disk)' % auto_disk
M_disk = mstar * auto_disk
else:
print 'M_disk = 0 is found. M_disk is set to 0.'
# ellipsoid cavity parameter
if ellipsoid == True:
# the numbers are given in arcsec
a_out = 130 * dstar * AU
b_out = 50 * dstar * AU
z_out = a_out
a_in = dict_params['a_in'] * dstar * AU
b_in = a_in/a_out*b_out
z_in = a_in
rho_cav_out = dict_params['rho_cav_out'] * mh
rho_cav_in = dict_params['rho_cav_in'] * mh
# Calculate the dust sublimation radius
T_sub = 1600
a = 1 # in micron
# realistic dust
# d_sub = 2.9388e7*(a/0.1)**-0.2 * (4*np.pi*rstar**2*sigma*tstar**4/LS)**0.5 / T_sub**3 *AU
# black body dust
d_sub = (LS/16./np.pi/sigma/AU**2*(4*np.pi*rstar**2*sigma*tstar**4/LS)/T_sub**4)**0.5 *AU
# use the dust sublimation radius as the inner radius of disk and envelope
R_disk_min = d_sub
R_env_min = d_sub
rin = rstar
rout = R_env_max
R_disk_max = R_cen
# print the variables
print 'Dust sublimation radius %6f AU' % (d_sub/AU)
print 'M_star %4f Solar mass' % (mstar/MS)
print 'Infall radius %4f AU' % (R_inf / AU)
# if there is any parameter found in fix_params, then fix them
if fix_params != None:
if 'R_min' in fix_params.keys():
R_disk_min = fix_params['R_min']*AU
R_env_min = fix_params['R_min']*AU
# Make the Coordinates
#
# if ext_source != None:
# rout = R_env_max*1.1
ri = rin * (rout/rin)**(np.arange(nx+1).astype(dtype='float')/float(nx))
ri = np.hstack((0.0, ri))
thetai = PI*np.arange(ny+1).astype(dtype='float')/float(ny)
phii = PI*2.0*np.arange(nz+1).astype(dtype='float')/float(nz)
# Keep the constant cell size in r-direction at large radii
#
if flat == True:
ri_cellsize = ri[1:-1]-ri[0:-2]
ind = np.where(ri_cellsize/AU > 100.0)[0][0] # The largest cell size is 100 AU
ri = np.hstack((ri[0:ind],ri[ind]+np.arange(np.ceil((rout-ri[ind])/100/AU))*100*AU))
nxx = nx
nx = len(ri)-1
# Assign the coordinates of the center of cell as its coordinates.
#
rc = 0.5*( ri[0:nx] + ri[1:nx+1] )
thetac = 0.5*( thetai[0:ny] + thetai[1:ny+1] )
phic = 0.5*( phii[0:nz] + phii[1:nz+1] )
# for non-TSC model
if tsc_com:
import hyperion as hp
from hyperion.model import AnalyticalYSOModel
non_tsc = AnalyticalYSOModel()
# Define the luminsoity source
nt_source = non_tsc.add_spherical_source()
nt_source.luminosity = (4*PI*rstar**2)*sigma*(tstar**4) # [ergs/s]
nt_source.radius = rstar # [cm]
nt_source.temperature = tstar # [K]
nt_source.position = (0., 0., 0.)
nt_source.mass = mstar
# Envelope structure
#
nt_envelope = non_tsc.add_ulrich_envelope()
nt_envelope.mdot = M_env_dot # Infall rate
nt_envelope.rmin = rin # Inner radius
nt_envelope.rc = R_cen # Centrifugal radius
nt_envelope.rmax = R_env_max # Outer radius
nt_envelope.star = nt_source
nt_grid = hp.grid.SphericalPolarGrid(ri, thetai, phii)
rho_env_ulrich = nt_envelope.density(nt_grid).T
rho_env_ulrich2d = np.sum(rho_env_ulrich**2,axis=2)/np.sum(rho_env_ulrich,axis=2)
# Make the dust density model
# Make the density profile of the envelope
#
total_mass = 0
if tsc == False:
print 'Calculating the dust density profile with infall solution...'
if theta_cav != 0:
# using R = 10000 AU as the reference point
c0 = (10000.*AU)**(-0.5)*np.sqrt(1/np.sin(np.radians(theta_cav))**3-1/np.sin(np.radians(theta_cav)))
else:
c0 = 0
rho_env = np.zeros([len(rc),len(thetac),len(phic)])
rho_disk = np.zeros([len(rc),len(thetac),len(phic)])
rho = np.zeros([len(rc),len(thetac),len(phic)])
if dyn_cav == True:
print 'WARNING: Calculation of interdependent cavity property has not implemented in infall-only solution!'
# Normalization for the total disk mass
def f(w,z,beta,rstar,h100):
f = 2*PI*w*(1-np.sqrt(rstar/w))*(rstar/w)**(beta+1)*np.exp(-0.5*(z/(w**beta*h100/100**beta))**2)
return f
rho_0 = M_disk/(nquad(f,[[R_disk_min,R_disk_max],[-R_env_max,R_env_max]], args=(beta,rstar,h100)))[0]
i = 0
j = 0
if 'rho_cav_center' in locals() == False:
rho_cav_center = 5e-19
print 'Use 5.27e-18 as the default value for cavity center'
if 'rho_cav_edge' in locals() == False:
rho_cav_edge = 40*AU
print 'Use 40 AU as the default value for size of the inner region'
discont = 1
for ir in range(0,len(rc)):
for itheta in range(0,len(thetac)):
for iphi in range(0,len(phic)):
if rc[ir] > R_env_min:
# Envelope profile
w = abs(rc[ir]*np.cos(np.pi/2 - thetac[itheta]))
z = rc[ir]*np.sin(np.pi/2 - thetac[itheta])
if ellipsoid == False:
z_cav = c0*abs(w)**1.5
if z_cav == 0:
z_cav = R_env_max
cav_con = abs(z) > abs(z_cav)
if theta_cav == 90:
cav_con = True
else:
# condition for the outer ellipsoid
cav_con = (2*(w/b_out)**2 + ((abs(z)-z_out)/a_out)**2) < 1
if cav_con:
# open cavity
if ellipsoid == False:
if rho_cav_edge == 0:
rho_cav_edge = R_env_min
if (rc[ir] <= rho_cav_edge) & (rc[ir] >= R_env_min):
rho_env[ir,itheta,iphi] = g2d * rho_cav_center
else:
rho_env[ir,itheta,iphi] = g2d * rho_cav_center*discont*(rho_cav_edge/rc[ir])**power
i += 1
else:
# condition for the inner ellipsoid
if (2*(w/b_in)**2 + ((abs(z)-z_in)/a_in)**2) > 1:
rho_env[ir,itheta,iphi] = rho_cav_out
else:
rho_env[ir,itheta,iphi] = rho_cav_in
i +=1
else:
j += 1
mu = abs(np.cos(thetac[itheta]))
# Implement new root finding algorithm
roots = np.roots(np.array([1.0, 0.0, rc[ir]/R_cen-1.0, -mu*rc[ir]/R_cen]))
if len(roots[roots.imag == 0]) == 1:
if (abs(roots[roots.imag == 0]) - 1.0) <= 0.0:
mu_o_dum = roots[roots.imag == 0]
else:
mu_o_dum = -0.5
print 'Problem with cubic solving, cos(theta) = ', mu_o_dum
print 'parameters are ', np.array([1.0, 0.0, rc[ir]/R_cen-1.0, -mu*rc[ir]/R_cen])
else:
mu_o_dum = -0.5
for imu in range(0, len(roots)):
if roots[imu]*mu >= 0.0:
if (abs((abs(roots[imu]) - 1.0)) <= 1e-5):
mu_o_dum = 1.0 * np.sign(mu)
else:
mu_o_dum = roots[imu]
if mu_o_dum == -0.5:
print 'Problem with cubic solving, roots are: ', roots
mu_o = mu_o_dum.real
rho_env[ir,itheta,iphi] = M_env_dot/(4*PI*(G*mstar*R_cen**3)**0.5)*(rc[ir]/R_cen)**(-3./2)*(1+mu/mu_o)**(-0.5)*(mu/mu_o+2*mu_o**2*R_cen/rc[ir])**(-1)
# Disk profile
if ((w >= R_disk_min) and (w <= R_disk_max)) == True:
h = ((w/(100*AU))**beta)*h100
rho_disk[ir,itheta,iphi] = rho_0*(1-np.sqrt(rstar/w))*(rstar/w)**(beta+1)*np.exp(-0.5*(z/h)**2)
# Combine envelope and disk
rho[ir,itheta,iphi] = rho_disk[ir,itheta,iphi] + rho_env[ir,itheta,iphi]
else:
rho[ir,itheta,iphi] = 1e-30
# add the dust mass into the total count
cell_mass = rho[ir, itheta, iphi] * (1/3.)*(ri[ir+1]**3 - ri[ir]**3) * (phii[iphi+1]-phii[iphi]) * -(np.cos(thetai[itheta+1])-np.cos(thetai[itheta]))
total_mass = total_mass + cell_mass
rho_env = rho_env + 1e-40
rho_disk = rho_disk + 1e-40
rho = rho + 1e-40
# TSC model
else:
print 'Calculating the dust density profile with TSC solution...'
if theta_cav != 0:
c0 = (1e4*AU)**(-0.5)*np.sqrt(1/np.sin(np.radians(theta_cav))**3-1/np.sin(np.radians(theta_cav)))
else:
c0 = 0
# If needed, calculate the TSC model via IDL
#
if idl == True:
print 'Using IDL to calculate the TSC model. Make sure you are running this on mechine with IDL.'
import pidly
idl = pidly.IDL(IDL_path)
idl('.r '+TSC_dir+'tsc.pro')
idl('.r '+TSC_dir+'tsc_run.pro')
#
# only run TSC calculation within infall radius
# modify the rc array
ind_infall = np.where(rc >= R_inf)[0][0]
if max(ri) > R_inf:
rc_idl = rc[0:ind_infall+1]
else:
rc_idl = rc[rc < max(ri)]
idl.pro('tsc_run', indir=TSC_dir, outdir=outdir, rc=rc_idl, thetac=thetac, time=t,
c_s=cs, omega=omega, renv_min=R_env_min)
file_idl = 'rhoenv.dat'
else:
print 'Read the pre-computed TSC model.'
ind_infall = np.where(rc >= R_inf)[0][0]
if max(ri) > R_inf:
rc_idl = rc[0:ind_infall+1]
else:
rc_idl = rc[rc < max(ri)]
if idl != False:
file_idl = idl
# read in the exist file
rho_env_tsc_idl = np.genfromtxt(outdir+file_idl).T
# because only region within infall radius is calculated by IDL program,
# need to project it to the original grid
rho_env_tsc = np.zeros([len(rc), len(thetac)])
for irc in range(len(rc)):
if rc[irc] in rc_idl:
rho_env_tsc[irc,:] = rho_env_tsc_idl[np.squeeze(np.where(rc_idl == rc[irc])),:]
# extrapolate for the NaN values at the outer radius, usually at radius beyond the infall radius
# using r^-2 profile at radius greater than infall radius
# and map the 2d strcuture onto 3-D grid
# map TSC solution from IDL to actual 2-D grid
rho_env_tsc2d = np.empty((nx,ny))
if max(ri) > R_inf:
for i in range(0, len(rc)):
if i <= ind_infall:
rho_env_tsc2d[i,:] = rho_env_tsc[i,:]
else:
rho_env_tsc2d[i,:] = 10**(np.log10(rho_env_tsc[ind_infall,:]) - 2*(np.log10(rc[i]/rc[ind_infall])))
else:
rho_env_tsc2d = rho_env_tsc
# map it to 3-D grid
rho_env = np.empty((nx,ny,nz))
for i in range(0, nz):
rho_env[:,:,i] = rho_env_tsc2d
# typical no used. Just an approach I tried to make the size of the
# constant desnity region self-consistent with the outflow cavity.
if dyn_cav == True:
print 'Calculate the cavity properties using the criteria that swept-up mass = outflowed mass'
# using swept-up mass = flow mass to derive the edge of the extended flat density region
v_outflow = 1e2 * 1e5
rho_cav_edge = outflow_inner_edge(np.copy(rho_env), (ri,thetai,phii),M_env_dot,v_outflow,theta_cav, R_env_min)
dict_params['rho_cav_edge'] = rho_cav_edge
# assume gas-to-dust ratio = 100
rho_cav_center = 0.01 * 0.1*M_env_dot*rho_cav_edge/v_outflow/2 / (2*np.pi/3*rho_cav_edge**3*(1-np.cos(np.radians(theta_cav))))
dict_params['rho_cav_center'] = rho_cav_center
print 'inner edge is %5f AU and density is %e g/cm3' % (rho_cav_edge/AU, rho_cav_center)
# create the array of density of disk and the whole structure
#
rho_disk = np.zeros([len(rc),len(thetac),len(phic)])
rho = np.zeros([len(rc),len(thetac),len(phic)])
# non-TSC option
if tsc_com:
rho_ulrich = np.zeros([len(rc),len(thetac),len(phic)])
# Calculate the disk scale height by the normalization of h100
def f(w,z,beta,rstar,h100):
f = 2*PI*w*(1-np.sqrt(rstar/w))*(rstar/w)**(beta+1)*np.exp(-0.5*(z/(w**beta*h100/100**beta))**2)
return f
# The function for calculating the normalization of disk using the total disk mass
#
rho_0 = M_disk/(nquad(f,[[R_disk_min,R_disk_max],[-R_env_max,R_env_max]], args=(beta,rstar,h100)))[0]
i = 0
j = 0
# put in default outflow cavity setting if nothing is specified
if 'rho_cav_center' in locals() == False:
rho_cav_center = 5e-19
print 'Use 5e-19 as the default value for cavity center'
if 'rho_cav_edge' in locals() == False:
rho_cav_edge = 40*AU
print 'Use 40 AU as the default value for size of the inner region'
discont = 1
for ir in range(0,len(rc)):
for itheta in range(0,len(thetac)):
for iphi in range(0,len(phic)):
# for external heating option
if (rc[ir] > R_env_min):
# Envelope profile
w = abs(rc[ir]*np.cos(np.pi/2 - thetac[itheta]))
z = rc[ir]*np.sin(np.pi/2 - thetac[itheta])
if ellipsoid == False:
z_cav = c0*abs(w)**1.5
if z_cav == 0:
z_cav = R_env_max
cav_con = abs(z) > abs(z_cav)
else:
# condition for the outer ellipsoid
cav_con = (2*(w/b_out)**2 + ((abs(z)-z_out)/a_out)**2) < 1
if cav_con:
# open cavity
if ellipsoid == False:
if rho_cav_edge == 0:
rho_cav_edge = R_env_min
if (rc[ir] <= rho_cav_edge) & (rc[ir] >= R_env_min):
rho_env[ir,itheta,iphi] = g2d * rho_cav_center#*((rc[ir]/AU)**2)
if tsc_com:
rho_env_ulrich[ir,itheta,iphi] = rho_env[ir,itheta,iphi]
else:
rho_env[ir,itheta,iphi] = g2d * rho_cav_center*discont*(rho_cav_edge/rc[ir])**power
if tsc_com:
rho_env_ulrich[ir,itheta,iphi] = rho_env[ir,itheta,iphi]
i += 1
else:
# condition for the inner ellipsoid
if (2*(w/b_in)**2 + ((abs(z)-z_in)/a_in)**2) > 1:
rho_env[ir,itheta,iphi] = rho_cav_out
if tsc_com:
rho_env_ulrich[ir,itheta,iphi] = rho_env[ir,itheta,iphi]
else:
rho_env[ir,itheta,iphi] = rho_cav_in
if tsc_com:
rho_env_ulrich[ir,itheta,iphi] = rho_env[ir,itheta,iphi]
i +=1
# Disk profile
if ((w >= R_disk_min) and (w <= R_disk_max)) == True:
h = ((w/(100*AU))**beta)*h100
rho_disk[ir,itheta,iphi] = rho_0*(1-np.sqrt(rstar/w))*(rstar/w)**(beta+1)*np.exp(-0.5*(z/h)**2)
# Combine envelope and disk
rho[ir,itheta,iphi] = rho_disk[ir,itheta,iphi] + rho_env[ir,itheta,iphi]
if tsc_com:
rho_ulrich[ir,itheta,iphi] = rho_disk[ir,itheta,iphi] + rho_env_ulrich[ir,itheta,iphi]
else:
rho[ir,itheta,iphi] = 1e-40
if tsc_com:
rho[ir,itheta,iphi] = 1e-40
# add the dust mass into the total count
cell_mass = rho[ir, itheta, iphi] * (1/3.)*(ri[ir+1]**3 - ri[ir]**3) * (phii[iphi+1]-phii[iphi]) * -(np.cos(thetai[itheta+1])-np.cos(thetai[itheta]))
total_mass = total_mass + cell_mass
# apply gas-to-dust ratio of 100
rho_dust = rho/g2d
if tsc_com:
rho_ulrich_dust = rho_ulrich/g2d
total_mass_dust = total_mass/MS/g2d
print 'Total dust mass = %f Solar mass' % total_mass_dust
if record == True:
# Record the input and calculated parameters
params = dict_params.copy()
params.update({'d_sub': d_sub/AU, 'M_env_dot': M_env_dot/MS*yr, 'R_inf': R_inf/AU, 'R_cen': R_cen/AU, 'mstar': mstar/MS, 'M_tot_gas': total_mass/MS})
record_hyperion(params,record_dir)
if plot == True:
# rho2d is the 2-D projection of gas density
# take the weighted average
rho2d = np.sum(rho**2,axis=2)/np.sum(rho,axis=2)
if tsc_com:
rho2d = np.sum(rho_ulrich**2,axis=2)/np.sum(rho_ulrich,axis=2)
if fast_plot == False:
# Plot the azimuthal averaged density
fig = plt.figure(figsize=(8,6))
ax_env = fig.add_subplot(111,projection='polar')
zmin = 1e-22/mmw/mh
zmin = 1e-1
cmap = plt.cm.CMRmap
rho2d_exp = np.hstack((rho2d,rho2d,rho2d[:,0:1]))
thetac_exp = np.hstack((thetac-PI/2, thetac+PI/2, thetac[0]-PI/2))
# plot the gas density
img_env = ax_env.pcolormesh(thetac_exp,rc/AU,rho2d_exp/mmw/mh,cmap=cmap,norm=LogNorm(vmin=zmin,vmax=1e6)) # np.nanmax(rho2d_exp/mmw/mh)
ax_env.set_xlabel(r'$\rm{Polar\,angle\,(Degree)}$',fontsize=20)
ax_env.set_ylabel('',fontsize=20, labelpad=-140)
ax_env.tick_params(labelsize=18)
ax_env.set_yticks(np.hstack((np.arange(0,(int(R_env_max/AU/10000.)+1)*10000, 10000),R_env_max/AU)))
ax_env.set_xticklabels([r'$\rm{90^{\circ}}$',r'$\rm{45^{\circ}}$',r'$\rm{0^{\circ}}$',r'$\rm{-45^{\circ}}$',\
r'$\rm{-90^{\circ}}$',r'$\rm{-135^{\circ}}$',r'$\rm{180^{\circ}}$',r'$\rm{135^{\circ}}$'])
ax_env.set_yticklabels([])
# fix the tick label font
ticks_font = mpl.font_manager.FontProperties(family='STIXGeneral',size=20)
for label in ax_env.get_yticklabels():
label.set_fontproperties(ticks_font)
ax_env.grid(True, color='LightGray', linewidth=1)
cb = fig.colorbar(img_env, pad=0.1)
cb.ax.set_ylabel(r'$\rm{Averaged\,Gas\,Density\,(cm^{-3})}$',fontsize=20)
# cb.set_ticks([1e2,1e3,1e4,1e5,1e6,1e7,1e8,1e9])
# cb.set_ticklabels([r'$\rm{10^{2}}$',r'$\rm{10^{3}}$',r'$\rm{10^{4}}$',r'$\rm{10^{5}}$',r'$\rm{10^{6}}$',\
# r'$\rm{10^{7}}$',r'$\rm{10^{8}}$',r'$\rm{\geq 10^{9}}$'])
# lower density ticks
cb.set_ticks([1e-1,1e0,1e1,1e2,1e3,1e4,1e5,1e6])
cb.set_ticklabels([r'$\rm{10^{-1}}$',r'$\rm{10^{0}}$',r'$\rm{10^{1}}$',r'$\rm{10^{2}}$',r'$\rm{10^{3}}$',
r'$\rm{10^{4}}$',r'$\rm{10^{5}}$',r'$\rm{\geq 10^{6}}$'])
cb_obj = plt.getp(cb.ax.axes, 'yticklabels')
plt.setp(cb_obj,fontsize=20)
fig.savefig(outdir+outname+'_gas_density.png', format='png', dpi=300, bbox_inches='tight')
fig.clf()
# Plot the radial density profile
fig = plt.figure(figsize=(12,9))
ax = fig.add_subplot(111)
plot_grid = [0,49,99,149,199]
color_grid = ['#e41a1c','#377eb8','#4daf4a','#984ea3','#ff7f00']
label = [r'$\rm{\theta='+str(int(np.degrees(thetai[plot_grid[0]])))+'^{\circ}}$',\
r'$\rm{\theta='+str(int(np.degrees(thetai[plot_grid[1]])))+'^{\circ}}$',\
r'$\rm{\theta='+str(1+int(np.degrees(thetai[plot_grid[2]])))+'^{\circ}}$',\
r'$\rm{\theta='+str(int(np.degrees(thetai[plot_grid[3]])))+'^{\circ}}$',\
r'$\rm{\theta='+str(1+int(np.degrees(thetai[plot_grid[4]])))+'^{\circ}}$']
alpha = np.linspace(0.3,1.0,len(plot_grid))
for i in plot_grid:
ax.plot(np.log10(rc[rc > 0.14*AU]/AU), np.log10(rho2d[rc > 0.14*AU,i]/g2d/mmw/mh)+plot_grid[::-1].index(i)*-0.2,'-',color=color_grid[plot_grid.index(i)],mec='None',linewidth=2.5, \
markersize=3, label=label[plot_grid.index(i)]) # alpha=alpha[plot_grid.index(i)],
ax.axvline(np.log10(R_inf/AU), linestyle='--', color='k', linewidth=1.5, label=r'$\rm{infall\,radius}$')
ax.axvline(np.log10(R_cen/AU), linestyle=':', color='k', linewidth=1.5, label=r'$\rm{centrifugal\,radius}$')
lg = plt.legend(fontsize=20, numpoints=1, ncol=2, framealpha=0.7, loc='upper right')
ax.set_xlabel(r'$\rm{log(Radius)\,(AU)}$',fontsize=20)
ax.set_ylabel(r'$\rm{log(Dust\,Density)\,(cm^{-3})}$',fontsize=20)
[ax.spines[axis].set_linewidth(1.5) for axis in ['top','bottom','left','right']]
ax.minorticks_on()
ax.tick_params('both',labelsize=18,width=1.5,which='major',pad=15,length=5)
ax.tick_params('both',labelsize=18,width=1.5,which='minor',pad=15,length=2.5)
# fix the tick label font
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)
ax.set_ylim([0,11])
fig.gca().set_xlim(left=np.log10(0.05))
fig.savefig(outdir+outname+'_gas_radial.pdf',format='pdf',dpi=300,bbox_inches='tight')
fig.clf()
# Insert the calculated grid and dust density profile into hyperion
m.set_spherical_polar_grid(ri, thetai, phii)
m.add_density_grid(rho_dust.T, d)
# for non-TSC option
if tsc_com:
m.add_density_grid(rho_ulrich_dust.T, d)
# Define the luminsoity source
source = m.add_spherical_source()
source.luminosity = (4*PI*rstar**2)*sigma*(tstar**4) # [ergs/s]
source.radius = rstar # [cm]
source.temperature = tstar # [K]
source.position = (0., 0., 0.)
print 'L_center = % 5.2f L_sun' % ((4*PI*rstar**2)*sigma*(tstar**4)/LS)
# if ext_source != None:
# # add external heating - ISRF
# # use standard receipe from Hyperion doc
# isrf = ascii.read(ext_source, names=['wavelength', 'J_lambda'])
# isrf_nu = c/(isrf['wavelength']*1e-4)
# isrf_jnu = isrf['J_lambda']*isrf['wavelength']/isrf_nu
#
# if 'mmp83' in ext_source:
# FOUR_PI_JNU = 0.0217
# else:
# FOUR_PI_JNU = raw_input('What is the FOUR_PI_JNU value?')
#
# s_isrf = m.add_external_spherical_source()
# s_isrf.radius = R_env_max
# s_isrf.spectrum = (isrf_nu, isrf_jnu)
# s_isrf.luminosity = PI * R_env_max**2 * FOUR_PI_JNU
m.set_raytracing(True)
# option of using more photons for imaging
if better_im == False:
im_photon = 1e6
else:
im_photon = 5e7
if mono == True:
if (type(mono_wave) == int) or (type(mono_wave) == float) or (type(mono_wave) == str):
mono_wave = float(mono_wave)
mono_wave = [mono_wave]
# Monochromatic radiative transfer setting
m.set_monochromatic(True, wavelengths=mono_wave)
m.set_n_photons(initial=1e6, imaging_sources=im_photon,
imaging_dust=im_photon,raytracing_sources=1e6, raytracing_dust=1e6)
else:
# regular wavelength grid setting
m.set_n_photons(initial=1e6, imaging=im_photon,raytracing_sources=1e6,
raytracing_dust=1e6)
# number of iteration to compute dust specific energy (temperature)
m.set_n_initial_iterations(20)
m.set_convergence(True, percentile=dict_params['percentile'],
absolute=dict_params['absolute'],
relative=dict_params['relative'])
m.set_mrw(True) # Gamma = 1 by default
# Setting up images and SEDs
if not image_only:
# SED setting
# Infinite aperture
syn_inf = m.add_peeled_images(image=False)
# use the index of wavelength array used by the monochromatic radiative transfer
if mono == False:
syn_inf.set_wavelength_range(wav_num, wav_min, wav_max)
syn_inf.set_viewing_angles([dict_params['view_angle']], [0.0])
syn_inf.set_uncertainties(True)
syn_inf.set_output_bytes(8)
# aperture
# 7.2 in 10 um scaled by lambda / 10
# flatten beyond 20 um
# default aperture (should always specify a set of apertures)
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, 1300],\
'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, 101]}
# 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)
dict_peel_sed = {}
for i in range(0, len(aper_reduced)):
aper_dum = aper_reduced[i]/2 * (1/3600.*np.pi/180.)*dstar*pc
dict_peel_sed[str(index_reduced[i])] = m.add_peeled_images(image=False)
# use the index of wavelength array used by the monochromatic radiative transfer
if mono == False:
dict_peel_sed[str(index_reduced[i])].set_wavelength_range(wav_num, wav_min, wav_max)
dict_peel_sed[str(index_reduced[i])].set_viewing_angles([dict_params['view_angle']], [0.0])
# aperture should be given in cm and its the radius of the aperture
dict_peel_sed[str(index_reduced[i])].set_aperture_range(1, aper_dum, aper_dum)
dict_peel_sed[str(index_reduced[i])].set_uncertainties(True)
dict_peel_sed[str(index_reduced[i])].set_output_bytes(8)
# image setting
syn_im = m.add_peeled_images(sed=False)
# use the index of wavelength array used by the monochromatic radiative transfer
if mono == False:
syn_im.set_wavelength_range(wav_num, wav_min, wav_max)
# pixel number
# !!!
if not mono:
pix_num = 300
else:
pix_num = 8000
#
syn_im.set_image_size(pix_num, pix_num)
syn_im.set_image_limits(-R_env_max, R_env_max, -R_env_max, R_env_max)
syn_im.set_viewing_angles([dict_params['view_angle']], [0.0])
syn_im.set_uncertainties(True)
syn_im.set_output_bytes(8)
# 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'
# Number of unique photons that passed through the cell
m.conf.output.output_n_photons = 'last'
m.write(outdir+outname+'.rtin')
if radmc == True:
# RADMC-3D still use a pre-defined aperture with lazy for-loop
aper = np.zeros([len(lam)])
ind = 0
for wl in lam:
if wl < 5:
aper[ind] = 8.4
elif wl >= 5 and wl < 14:
aper[ind] = 1.8 * 4
elif wl >= 14 and wl < 40:
aper[ind] = 5.1 * 4
else:
aper[ind] = 24.5
ind += 1
# Write the wavelength_micron.inp file
#
f_wave = open(outdir+'wavelength_micron.inp','w')
f_wave.write('%d \n' % int(nlam))
for ilam in range(0,nlam):
f_wave.write('%f \n' % lam[ilam])
f_wave.close()
# Write the camera_wavelength_micron.inp file
#
f_wave_cam = open(outdir+'camera_wavelength_micron.inp','w')
f_wave_cam.write('%d \n' % int(nlam))
for ilam in range(0,nlam):
f_wave_cam.write('%f \n' % lam[ilam])
f_wave_cam.close()
# Write the aperture_info.inp
#
f_aper = open(outdir+'aperture_info.inp','w')
f_aper.write('1 \n')
f_aper.write('%d \n' % int(nlam))
for iaper in range(0, len(aper)):
f_aper.write('%f \t %f \n' % (lam[iaper],aper[iaper]/2))
f_aper.close()
# Write the stars.inp file
#
f_star = open(outdir+'stars.inp','w')
f_star.write('2\n')
f_star.write('1 \t %d \n' % int(nlam))
f_star.write('\n')
f_star.write('%e \t %e \t %e \t %e \t %e \n' % (rstar*0.9999,mstar,0,0,0))
f_star.write('\n')
for ilam in range(0,nlam):
f_star.write('%f \n' % lam[ilam])
f_star.write('\n')
f_star.write('%f \n' % -tstar)
f_star.close()
# Write the grid file
#
f_grid = open(outdir+'amr_grid.inp','w')
f_grid.write('1\n') # iformat
f_grid.write('0\n') # AMR grid style (0=regular grid, no AMR)
f_grid.write('150\n') # Coordinate system coordsystem<100: Cartisian; 100<=coordsystem<200: Spherical; 200<=coordsystem<300: Cylindrical
f_grid.write('0\n') # gridinfo
f_grid.write('1 \t 1 \t 1 \n') # Include x,y,z coordinate
f_grid.write('%d \t %d \t %d \n' % (int(nx)-1,int(ny),int(nz))) # Size of the grid
[f_grid.write('%e \n' % ri[ir]) for ir in range(1,len(ri))]
[f_grid.write('%f \n' % thetai[itheta]) for itheta in range(0,len(thetai))]
[f_grid.write('%f \n' % phii[iphi]) for iphi in range(0,len(phii))]
f_grid.close()
# Write the density file
#
f_dust = open(outdir+'dust_density.inp','w')
f_dust.write('1 \n') # format number
f_dust.write('%d \n' % int((nx-1)*ny*nz)) # Nr of cells
f_dust.write('1 \n') # Nr of dust species
for iphi in range(0,len(phic)):
for itheta in range(0,len(thetac)):
for ir in range(1,len(rc)):
f_dust.write('%e \n' % rho_dust[ir,itheta,iphi])
f_dust.close()
# Write the dust opacity table
f_dustkappa = open(outdir+'dustkappa_oh5_extended.inp','w')
f_dustkappa.write('3 \n') # format index for including g-factor
f_dustkappa.write('%d \n' % len(dust['nu'])) # number of wavlength/frequency in the table
for i in range(len(dust['nu'])):
f_dustkappa.write('%f \t %f \t %f \t %f \n' % (c/dust['nu'][i]*1e4, dust['chi'][i], dust['chi'][i]*dust['albedo'][i]/(1-dust['albedo'][i]), dust['g'][i]))
f_dustkappa.close()
# Write the Dust opacity control file
#
f_opac = open(outdir+'dustopac.inp','w')
f_opac.write('2 Format number of this file\n')
f_opac.write('1 Nr of dust species\n')
f_opac.write('============================================================================\n')
f_opac.write('1 Way in which this dust species is read\n')
f_opac.write('0 0=Thermal grain\n')
# f_opac.write('klaus Extension of name of dustkappa_***.inp file\n')
f_opac.write('oh5_extended Extension of name of dustkappa_***.inp file\n')
f_opac.write('----------------------------------------------------------------------------\n')
f_opac.close()
# Write the radmc3d.inp control file
#
f_control = open(outdir+'radmc3d.inp','w')
f_control.write('nphot = %d \n' % 100000)
f_control.write('scattering_mode_max = 2\n')
f_control.write('camera_min_drr = 0.1\n')
f_control.write('camera_min_dangle = 0.1\n')
f_control.write('camera_spher_cavity_relres = 0.1\n')
f_control.write('istar_sphere = 1\n')
f_control.write('modified_random_walk = 1\n')
f_control.close()
return m
def run_thermal_hyperion(self, nphot=1e6, mrw=False, pda=False, \
niterations=20, percentile=99., absolute=2.0, relative=1.02, \
max_interactions=1e8, mpi=False, nprocesses=None):
d = []
for i in range(len(self.grid.dust)):
d.append(IsotropicDust( \
self.grid.dust[i].nu[::-1].astype(numpy.float64), \
self.grid.dust[i].albedo[::-1].astype(numpy.float64), \
self.grid.dust[i].kext[::-1].astype(numpy.float64)))
m = HypModel()
if (self.grid.coordsystem == "cartesian"):
m.set_cartesian_grid(self.grid.w1*AU, self.grid.w2*AU, \
self.grid.w3*AU)
elif (self.grid.coordsystem == "cylindrical"):
m.set_cylindrical_polar_grid(self.grid.w1*AU, self.grid.w3*AU, \
self.grid.w2)
elif (self.grid.coordsystem == "spherical"):
m.set_spherical_polar_grid(self.grid.w1*AU, self.grid.w2, \
self.grid.w3)
for i in range(len(self.grid.density)):
if (self.grid.coordsystem == "cartesian"):
m.add_density_grid(numpy.transpose(self.grid.density[i], \
axes=(2,1,0)), d[i])
if (self.grid.coordsystem == "cylindrical"):
m.add_density_grid(numpy.transpose(self.grid.density[i], \
axes=(1,2,0)), d[i])
if (self.grid.coordsystem == "spherical"):
m.add_density_grid(numpy.transpose(self.grid.density[i], \
axes=(2,1,0)), d[i])
sources = []
for i in range(len(self.grid.stars)):
sources.append(m.add_spherical_source())
sources[i].luminosity = self.grid.stars[i].luminosity * L_sun
sources[i].radius = self.grid.stars[i].radius * R_sun
sources[i].temperature = self.grid.stars[i].temperature
m.set_mrw(mrw)
m.set_pda(pda)
m.set_max_interactions(max_interactions)
m.set_n_initial_iterations(niterations)
m.set_n_photons(initial=nphot, imaging=0)
m.set_convergence(True, percentile=percentile, absolute=absolute, \
relative=relative)
m.write("temp.rtin")
m.run("temp.rtout", mpi=mpi, n_processes=nprocesses)
n = ModelOutput("temp.rtout")
grid = n.get_quantities()
self.grid.temperature = []
temperature = grid.quantities['temperature']
for i in range(len(temperature)):
if (self.grid.coordsystem == "cartesian"):
self.grid.temperature.append(numpy.transpose(temperature[i], \
axes=(2,1,0)))
if (self.grid.coordsystem == "cylindrical"):
self.grid.temperature.append(numpy.transpose(temperature[i], \
axes=(2,0,1)))
if (self.grid.coordsystem == "spherical"):
self.grid.temperature.append(numpy.transpose(temperature[i], \
axes=(2,1,0)))
os.system("rm temp.rtin temp.rtout")
def sph_m_gen(fname,field_add):
refined,dustdens,fc1,fw1,pf,ad = yt_octree_generate(fname,field_add)
xmin = (fc1[:,0]-fw1[:,0]/2.).convert_to_units('cm') #in proper cm
xmax = (fc1[:,0]+fw1[:,0]/2.).convert_to_units('cm')
ymin = (fc1[:,1]-fw1[:,1]/2.).convert_to_units('cm')
ymax = (fc1[:,1]+fw1[:,1]/2.).convert_to_units('cm')
zmin = (fc1[:,2]-fw1[:,2]/2.).convert_to_units('cm')
zmax = (fc1[:,2]+fw1[:,2]/2.).convert_to_units('cm')
#dx,dy,dz are the edges of the parent grid
dx = (np.max(xmax)-np.min(xmin)).value
dy = (np.max(ymax)-np.min(ymin)).value
dz = (np.max(zmax)-np.min(zmin)).value
xcent = np.mean([np.min(xmin),np.max(xmax)]) #kpc
ycent = np.mean([np.min(ymin),np.max(ymax)])
zcent = np.mean([np.min(zmin),np.max(zmax)])
boost = np.array([xcent,ycent,zcent])
print ('[pd_front end] boost = ',boost)
#Tom Robitaille's conversion from z-first ordering (yt's default) to
#x-first ordering (the script should work both ways)
refined_array = np.array(refined)
refined_array = np.squeeze(refined_array)
order = find_order(refined_array)
refined_reordered = []
dustdens_reordered = np.zeros(len(order))
for i in range(len(order)):
refined_reordered.append(refined[order[i]])
dustdens_reordered[i] = dustdens[order[i]]
refined = refined_reordered
dustdens=dustdens_reordered
#hyperion octree stats
max_level = hos.hyperion_octree_stats(refined)
pto.test_octree(refined,max_level)
dump_cell_info(refined,fc1,fw1,xmin,xmax,ymin,ymax,zmin,zmax)
np.save('refined.npy',refined)
np.save('density.npy',dustdens)
#========================================================================
#Initialize Hyperion Model
#========================================================================
m = Model()
if cfg.par.FORCE_RANDOM_SEED == True: m.set_seed(cfg.par.seed)
print ('Setting Octree Grid with Parameters: ')
#m.set_octree_grid(xcent,ycent,zcent,
# dx,dy,dz,refined)
m.set_octree_grid(0,0,0,dx/2,dy/2,dz/2,refined)
#get CMB:
energy_density_absorbed=energy_density_absorbed_by_CMB()
specific_energy = np.repeat(energy_density_absorbed.value,dustdens.shape)
if cfg.par.PAH == True:
# load PAH fractions for usg, vsg, and big (grain sizes)
frac = cfg.par.PAH_frac
# Normalize to 1
total = np.sum(list(frac.values()))
frac = {k: v / total for k, v in frac.items()}
for size in frac.keys():
d = SphericalDust(cfg.par.dustdir+'%s.hdf5'%size)
if cfg.par.SUBLIMATION == True:
d.set_sublimation_temperature('fast',temperature=cfg.par.SUBLIMATION_TEMPERATURE)
#m.add_density_grid(dustdens * frac[size], cfg.par.dustdir+'%s.hdf5' % size)
m.add_density_grid(dustdens*frac[size],d,specific_energy=specific_energy)
m.set_enforce_energy_range(cfg.par.enforce_energy_range)
else:
d = SphericalDust(cfg.par.dustdir+cfg.par.dustfile)
if cfg.par.SUBLIMATION == True:
d.set_sublimation_temperature('fast',temperature=cfg.par.SUBLIMATION_TEMPERATURE)
m.add_density_grid(dustdens,d,specific_energy=specific_energy)
#m.add_density_grid(dustdens,cfg.par.dustdir+cfg.par.dustfile)
m.set_specific_energy_type('additional')
return m,xcent,ycent,zcent,dx,dy,dz,pf,boost
def run_thermal_hyperion(self, nphot=1e6, mrw=False, pda=False, \
niterations=20, percentile=99., absolute=2.0, relative=1.02, \
max_interactions=1e8, mpi=False, nprocesses=None):
d = []
for i in range(len(self.grid.dust)):
d.append(IsotropicDust( \
self.grid.dust[i].nu[::-1].astype(numpy.float64), \
self.grid.dust[i].albedo[::-1].astype(numpy.float64), \
self.grid.dust[i].kext[::-1].astype(numpy.float64)))
m = HypModel()
if (self.grid.coordsystem == "cartesian"):
m.set_cartesian_grid(self.grid.w1*AU, self.grid.w2*AU, \
self.grid.w3*AU)
elif (self.grid.coordsystem == "cylindrical"):
m.set_cylindrical_polar_grid(self.grid.w1*AU, self.grid.w3*AU, \
self.grid.w2)
elif (self.grid.coordsystem == "spherical"):
m.set_spherical_polar_grid(self.grid.w1*AU, self.grid.w2, \
self.grid.w3)
for i in range(len(self.grid.density)):
if (self.grid.coordsystem == "cartesian"):
m.add_density_grid(numpy.transpose(self.grid.density[i], \
axes=(2,1,0)), d[i])
if (self.grid.coordsystem == "cylindrical"):
m.add_density_grid(numpy.transpose(self.grid.density[i], \
axes=(1,2,0)), d[i])
if (self.grid.coordsystem == "spherical"):
m.add_density_grid(numpy.transpose(self.grid.density[i], \
axes=(2,1,0)), d[i])
sources = []
for i in range(len(self.grid.stars)):
sources.append(m.add_spherical_source())
sources[i].luminosity = self.grid.stars[i].luminosity * L_sun
sources[i].radius = self.grid.stars[i].radius * R_sun
sources[i].temperature = self.grid.stars[i].temperature
m.set_mrw(mrw)
m.set_pda(pda)
m.set_max_interactions(max_interactions)
m.set_n_initial_iterations(niterations)
m.set_n_photons(initial=nphot, imaging=0)
m.set_convergence(True, percentile=percentile, absolute=absolute, \
relative=relative)
m.write("temp.rtin")
m.run("temp.rtout", mpi=mpi, n_processes=nprocesses)
n = ModelOutput("temp.rtout")
grid = n.get_quantities()
self.grid.temperature = []
temperature = grid.quantities['temperature']
for i in range(len(temperature)):
if (self.grid.coordsystem == "cartesian"):
self.grid.temperature.append(numpy.transpose(temperature[i], \
axes=(2,1,0)))
if (self.grid.coordsystem == "cylindrical"):
self.grid.temperature.append(numpy.transpose(temperature[i], \
axes=(2,0,1)))
if (self.grid.coordsystem == "spherical"):
self.grid.temperature.append(numpy.transpose(temperature[i], \
axes=(2,1,0)))
os.system("rm temp.rtin temp.rtout")
ds.add_field(('gas', 'dust_density'), function=_dust_density, units='g/cm**3')
amr = AMRGrid.from_yt(ds, quantity_mapping={'density':('gas','dust_density')})
# Set up Hyperion model
import numpy as np
from hyperion.model import Model
from hyperion.util.constants import pc
m = Model()
m.set_amr_grid(amr)
m.add_density_grid(amr['density'], '/Users/desika//pd/hyperion-dust-0.1.0/dust_files/d03_3.1_6.0_A.hdf5')
m.set_minimum_temperature(10)
m.set_n_initial_iterations(0)
m.set_raytracing(True)
# Add a point source in the center
s = m.add_point_source()
s.position = (0, 0., 0.)
s.luminosity = 1000 * lsun
s.temperature = 6000.
i = m.add_peeled_images(sed=False, image=True)
i.set_image_limits(-1 * pc, 1 * pc, -1 * pc, 1 * pc)
def arepo_m_gen(fname, field_add):
reg, ds, dustdens = arepo_vornoi_grid_generate(fname, field_add)
xcent = ds.quan(cfg.model.x_cent, 'code_length').to('cm') #proper cm
ycent = ds.quan(cfg.model.y_cent, 'code_length').to('cm')
zcent = ds.quan(cfg.model.z_cent, 'code_length').to('cm')
boost = np.array([xcent, ycent, zcent])
print('[arepo_tributary/vornoi_m_gen]: boost = ', boost)
#========================================================================
#Initialize Hyperion Model
#========================================================================
m = Model()
#because we boost the stars to a [0,0,0] coordinate center, we
#want to make sure our vornoi tesslation is created in the same manner.
particle_x = reg["gas", "coordinates"][:, 0].to('cm')
particle_y = reg["gas", "coordinates"][:, 1].to('cm')
particle_z = reg["gas", "coordinates"][:, 2].to('cm')
#just for the sake of symmetry, pass on a dx,dy,dz since it can be
#used optionally downstream in other functions.
dx = 2. * ds.quan(cfg.par.zoom_box_len, 'kpc').to('cm')
dy = 2. * ds.quan(cfg.par.zoom_box_len, 'kpc').to('cm')
dz = 2. * ds.quan(cfg.par.zoom_box_len, 'kpc').to('cm')
print('[arepo_tributary] boost = ', boost)
print('[arepo_tributary] xmin (pc)= ', (xcent - dx / 2.).to('pc'))
print('[arepo_tributary] xmax (pc)= ', (xcent + dx / 2.).to('pc'))
print('[arepo_tributary] ymin (pc)= ', (ycent - dy / 2.).to('pc'))
print('[arepo_tributary] ymax (pc)= ', (ycent + dy / 2.).to('pc'))
print('[arepo_tributary] zmin (pc)= ', (zcent - dz / 2.).to('pc'))
print('[arepo_tributary] zmax (pc)= ', (zcent + dz / 2.).to('pc'))
x_pos_boost = (particle_x - xcent).to('cm')
y_pos_boost = (particle_y - ycent).to('cm')
z_pos_boost = (particle_z - zcent).to('cm')
m.set_voronoi_grid(x_pos_boost.value, y_pos_boost.value, z_pos_boost.value)
#get CMB:
energy_density_absorbed = energy_density_absorbed_by_CMB()
specific_energy = np.repeat(energy_density_absorbed.value, dustdens.shape)
if cfg.par.otf_extinction == False:
if cfg.par.PAH == True:
# load PAH fractions for usg, vsg, and big (grain sizes)
frac = cfg.par.PAH_frac
# Normalize to 1
total = np.sum(list(frac.values()))
frac = {k: v / total for k, v in frac.items()}
for size in frac.keys():
d = SphericalDust(cfg.par.dustdir + '%s.hdf5' % size)
if cfg.par.SUBLIMATION == True:
d.set_sublimation_temperature(
'fast', temperature=cfg.par.SUBLIMATION_TEMPERATURE)
#m.add_density_grid(dustdens * frac[size], cfg.par.dustdir+'%s.hdf5' % size)
m.add_density_grid(dustdens * frac[size],
d,
specific_energy=specific_energy)
m.set_enforce_energy_range(cfg.par.enforce_energy_range)
else:
d = SphericalDust(cfg.par.dustdir + cfg.par.dustfile)
if cfg.par.SUBLIMATION == True:
d.set_sublimation_temperature(
'fast', temperature=cfg.par.SUBLIMATION_TEMPERATURE)
m.add_density_grid(dustdens, d, specific_energy=specific_energy)
#m.add_density_grid(dustdens,cfg.par.dustdir+cfg.par.dustfile)
else: #instead of using a constant extinction law across the
#entire galaxy, we'll compute it on a cell-by-cell bassis by
#using information about the grain size distribution from
#the simulation itself.
ad = ds.all_data()
nsizes = reg['PartType0', 'NumGrains'].shape[1]
try:
assert (np.sum(ad['PartType0', 'NumGrains']) > 0)
except AssertionError:
raise AssertionError(
"[arepo_tributary:] There are no dust grains in this simulation. This can sometimes happen in an early snapshot of a simulation where the dust has not yet had time to form."
)
grid_of_sizes = reg['PartType0', 'NumGrains']
active_dust_add(ds, m, grid_of_sizes, nsizes, dustdens,
specific_energy)
m.set_specific_energy_type('additional')
return m, xcent, ycent, zcent, dx.value, dy.value, dz.value, reg, ds, boost
def setup_model_shell(indir,outdir,outname,rin_shell=None,denser_wall=False,tsc=True,idl=False,plot=False,low_res=False,flat=True,scale=1.0):
import numpy as np
import astropy.constants as const
import scipy as sci
import matplotlib.pyplot as plt
import matplotlib as mat
import os
from matplotlib.colors import LogNorm
from scipy.optimize import fsolve
from scipy.optimize import newton
from scipy.integrate import nquad
from envelope_func import func
import hyperion as hp
from hyperion.model import Model
from plot_density import plot_density
# Constants setup
c = const.c.cgs.value
AU = 1.49598e13 # Astronomical Unit [cm]
pc = 3.08572e18 # Parsec [cm]
MS = 1.98892e33 # Solar mass [g]
LS = 3.8525e33 # Solar luminosity [erg/s]
RS = 6.96e10 # Solar radius [cm]
G = 6.67259e-8 # Gravitational constant [cm3/g/s^2]
yr = 60*60*24*365 # Years in seconds
PI = np.pi # PI constant
sigma = const.sigma_sb.cgs.value # Stefan-Boltzmann constant
m = Model()
# Create dust properties
# Hyperion needs nu, albedo, chi, g, p_lin_max
from hyperion.dust import HenyeyGreensteinDust
# Read in the dust opacity table used by RADMC-3D
dust_radmc = dict()
[dust_radmc['wl'], dust_radmc['abs'], dust_radmc['scat'], dust_radmc['g']] = np.genfromtxt('dustkappa_oh5_extended.inp',skip_header=2).T
# opacity per mass of dust?
dust_hy = dict()
dust_hy['nu'] = c/dust_radmc['wl']*1e4
ind = np.argsort(dust_hy['nu'])
dust_hy['nu'] = dust_hy['nu'][ind]
dust_hy['albedo'] = (dust_radmc['scat']/(dust_radmc['abs']+dust_radmc['scat']))[ind]
dust_hy['chi'] = (dust_radmc['abs']+dust_radmc['scat'])[ind]
dust_hy['g'] = dust_radmc['g'][ind]
dust_hy['p_lin_max'] = 0*dust_radmc['wl'][ind] # assume no polarization
d = HenyeyGreensteinDust(dust_hy['nu'], dust_hy['albedo'], dust_hy['chi'], dust_hy['g'], dust_hy['p_lin_max'])
# dust sublimation does not occur
# d.set_sublimation_temperature(None)
d.write(outdir+'oh5.hdf5')
d.plot(outdir+'oh5.png')
# Grids and Density
# Calculation inherited from the script used for RADMC-3D
# Grid Parameters
nx = 300L
if low_res == True:
nx = 100L
ny = 400L
nz = 50L
[nx, ny, nz] = [scale*nx, scale*ny, scale*nz]
if tsc == False:
# Parameters setup
# Import the model parameters from another file
#
params = np.genfromtxt(indir+'/params.dat',dtype=None)
tstar = params[0][1]
mstar = params[1][1]*MS
rstar = params[2][1]*RS
M_env_dot = params[3][1]*MS/yr
M_disk_dot = params[4][1]*MS/yr
R_env_max = params[5][1]*AU
R_env_min = params[6][1]*AU
theta_cav = params[7][1]
R_disk_max = params[8][1]*AU
R_disk_min = params[9][1]*AU
R_cen = R_disk_max
M_disk = params[10][1]*MS
beta = params[11][1]
h100 = params[12][1]*AU
rho_cav = params[13][1]
if denser_wall == True:
wall = params[14][1]*AU
rho_wall = params[15][1]
rho_cav_center = params[16][1]
rho_cav_edge = params[17][1]*AU
# Model Parameters
#
rin = rstar
rout = R_env_max
rcen = R_cen
# Star Parameters
#
mstar = mstar
rstar = rstar*0.9999
tstar = tstar
pstar = [0.,0.,0.]
else:
# TSC model input setting
params = np.genfromtxt(indir+'/tsc_params.dat', dtype=None)
# TSC model parameter
M_env_dot = params[0][1]*MS/yr
R_cen = params[1][1]*AU
R_inf = params[2][1]*AU
# protostar parameter
tstar = params[3][1]
R_env_max = params[4][1]*AU
theta_cav = params[5][1]
rho_cav_center = params[6][1]
rho_cav_edge = params[7][1]*AU
rstar = params[8][1]*RS
# Calculate the dust sublimation radius
T_sub = 2000
a = 1 #in micron
d_sub = (306.86*(a/0.1)**-0.4 * (4*np.pi*rstar**2*sigma*tstar**4/LS) / T_sub)**0.5 *AU
# use the dust sublimation radius as the inner radius of disk and envelope
R_disk_min = d_sub
R_env_min = d_sub
rin = rstar
rout = R_env_max
R_disk_max = R_cen
# mostly fixed parameter
M_disk = 0.5*MS
beta = 1.093
h100 = 8.123*AU
rho_cav = 1e-21
# Do the variable conversion
cs = (G * M_env_dot / 0.975)**(1/3.) # cm/s
t = R_inf / cs / yr # in year
mstar = M_env_dot * t * yr
omega = (R_cen * 16*cs**8 / (G**3 * mstar**3))**0.5
# Make the Coordinates
#
ri = rin * (rout/rin)**(np.arange(nx+1).astype(dtype='float')/float(nx))
ri = np.hstack((0.0, ri))
thetai = PI*np.arange(ny+1).astype(dtype='float')/float(ny)
phii = PI*2.0*np.arange(nz+1).astype(dtype='float')/float(nz)
# Keep the constant cell size in r-direction
#
if flat == True:
ri_cellsize = ri[1:-1]-ri[0:-2]
ind = np.where(ri_cellsize/AU > 100.0)[0][0] # The largest cell size is 100 AU
ri = np.hstack((ri[0:ind],ri[ind]+np.arange(np.ceil((rout-ri[ind])/100/AU))*100*AU))
nxx = nx
nx = len(ri)-1
# Assign the coordinates of the center of cell as its coordinates.
#
rc = 0.5*( ri[0:nx] + ri[1:nx+1] )
thetac = 0.5*( thetai[0:ny] + thetai[1:ny+1] )
phic = 0.5*( phii[0:nz] + phii[1:nz+1] )
# phic = 0.5*( phii[0:nz-1] + phii[1:nz] )
# Make the dust density model
# Make the density profile of the envelope
#
if rin_shell == None:
rin_shell = 0.3*R_env_max
if tsc == False:
print 'Calculating the dust density profile with infall solution...'
rho_env = np.zeros([len(rc),len(thetac),len(phic)])
rho = np.zeros([len(rc),len(thetac),len(phic)])
for ir in range(0,len(rc)):
for itheta in range(0,len(thetac)):
for iphi in range(0,len(phic)):
if rc[ir] > rin_shell:
# Envelope profile
mu = abs(np.cos(PI/2 - thetac[itheta]))
# Implement new root finding algorithm
roots = np.roots(np.array([1.0, 0.0, rc[ir]/rcen-1.0, -mu*rc[ir]/rcen]))
if len(roots[roots.imag == 0]) == 1:
if (abs(roots[roots.imag == 0]) - 1.0) <= 0.0:
mu_o_dum = roots[roots.imag == 0]
else:
mu_o_dum = -0.5
print 'Problem with cubic solving, cos(theta) = ', mu_o_dum
print 'parameters are ', np.array([1.0, 0.0, rc[ir]/rcen-1.0, -mu*rc[ir]/rcen])
else:
mu_o_dum = -0.5
for imu in range(0, len(roots)):
if roots[imu]*mu >= 0.0:
if (abs((abs(roots[imu]) - 1.0)) <= 1e-5):
mu_o_dum = 1.0 * np.sign(mu)
else:
mu_o_dum = roots[imu]
if mu_o_dum == -0.5:
print 'Problem with cubic solving, roots are: ', roots
mu_o = mu_o_dum.real
rho_env[ir,itheta,iphi] = M_env_dot/(4*PI*(G*mstar*rcen**3)**0.5)*(rc[ir]/rcen)**(-3./2)*(1+mu/mu_o)**(-0.5)*(mu/mu_o+2*mu_o**2*rcen/rc[ir])**(-1)
rho[ir,itheta,iphi] = rho_env[ir,itheta,iphi]
else:
rho[ir,itheta,iphi] = 1e-25
rho_env = rho_env + 1e-40
rho = rho + 1e-40
# TSC model
else:
print 'Calculating the dust density profile with TSC solution...'
# If needed, calculate the TSC model via IDL
#
if idl == True:
print 'Using IDL to calculate the TSC model. Make sure you are running this on mechine with IDL.'
import pidly
idl = pidly.IDL('/Applications/exelis/idl82/bin/idl')
idl('.r ~/programs/misc/TSC/tsc.pro')
idl.pro('tsc_run', outdir=outdir, grid=[nxx,ny,nz], time=t, c_s=cs, omega=omega, rstar=rstar, renv_min=R_env_min, renv_max=R_env_max)
else:
print 'Read the pre-computed TSC model.'
# read in the exist file
rho_env_tsc = np.genfromtxt(outdir+'rhoenv.dat').T
# extrapolate for the NaN values at the outer radius, usually at radius beyond the infall radius
# map the 2d strcuture onto 3d grid
def poly(x, y, x0, deg=1):
import numpy as np
p = np.polyfit(x, y, deg)
y0 = 0
for i in range(0, len(p)):
y0 = y0 + p[i]*x0**(len(p)-i-1)
return y0
rho_env_copy = np.array(rho_env_tsc)
for ithetac in range(0, len(thetac)):
rho_dum = np.log10(rho_env_copy[(rc > 1.1*R_inf) & (np.isnan(rho_env_copy[:,ithetac]) == False),ithetac])
rc_dum = np.log10(rc[(rc > 1.1*R_inf) & (np.isnan(rho_env_copy[:,ithetac]) == False)])
rc_dum_nan = np.log10(rc[(rc > 1.1*R_inf) & (np.isnan(rho_env_copy[:,ithetac]) == True)])
for i in range(0, len(rc_dum_nan)):
rho_extrapol = poly(rc_dum, rho_dum, rc_dum_nan[i])
rho_env_copy[(np.log10(rc) == rc_dum_nan[i]),ithetac] = 10**rho_extrapol
rho_env2d = rho_env_copy
rho_env = np.empty((nx,ny,nz))
for i in range(0, nz):
rho_env[:,:,i] = rho_env2d
# create the array of density of disk and the whole structure
#
rho = np.zeros([len(rc),len(thetac),len(phic)])
# The function for calculating the normalization of disk using the total disk mass
#
for ir in range(0,len(rc)):
for itheta in range(0,len(thetac)):
for iphi in range(0,len(phic)):
if rc[ir] > rin_shell:
# Envelope profile
rho[ir,itheta,iphi] = rho_env[ir,itheta,iphi]
else:
rho[ir,itheta,iphi] = 1e-25
rho_env = rho_env + 1e-40
rho = rho + 1e-40
# Call function to plot the density
plot_density(rho, rc, thetac,'/Users/yaolun/bhr71/hyperion/', plotname='shell')
# Insert the calculated grid and dust density profile into hyperion
m.set_spherical_polar_grid(ri, thetai, phii)
m.add_density_grid(rho.T, outdir+'oh5.hdf5') # numpy read the array in reverse order
# Define the luminsoity source
source = m.add_spherical_source()
source.luminosity = (4*PI*rstar**2)*sigma*(tstar**4) # [ergs/s]
source.radius = rstar # [cm]
source.temperature = tstar # [K]
source.position = (0., 0., 0.)
print 'L_center = % 5.2f L_sun' % ((4*PI*rstar**2)*sigma*(tstar**4)/LS)
# Setting up the wavelength for monochromatic radiative transfer
lambda0 = 0.1
lambda1 = 2.0
lambda2 = 50.0
lambda3 = 95.0
lambda4 = 200.0
lambda5 = 314.0
lambda6 = 670.0
n01 = 10.0
n12 = 20.0
n23 = (lambda3-lambda2)/0.02
n34 = (lambda4-lambda3)/0.03
n45 = (lambda5-lambda4)/0.1
n56 = (lambda6-lambda5)/0.1
lam01 = lambda0 * (lambda1/lambda0)**(np.arange(n01)/n01)
lam12 = lambda1 * (lambda2/lambda1)**(np.arange(n12)/n12)
lam23 = lambda2 * (lambda3/lambda2)**(np.arange(n23)/n23)
lam34 = lambda3 * (lambda4/lambda3)**(np.arange(n34)/n34)
lam45 = lambda4 * (lambda5/lambda4)**(np.arange(n45)/n45)
lam56 = lambda5 * (lambda6/lambda5)**(np.arange(n56+1)/n56)
lam = np.concatenate([lam01,lam12,lam23,lam34,lam45,lam56])
nlam = len(lam)
# Create camera wavelength points
n12 = 70.0
n23 = 70.0
n34 = 70.0
n45 = 50.0
n56 = 50.0
lam12 = lambda1 * (lambda2/lambda1)**(np.arange(n12)/n12)
lam23 = lambda2 * (lambda3/lambda2)**(np.arange(n23)/n23)
lam34 = lambda3 * (lambda4/lambda3)**(np.arange(n34)/n34)
lam45 = lambda4 * (lambda5/lambda4)**(np.arange(n45)/n45)
lam56 = lambda5 * (lambda6/lambda5)**(np.arange(n56+1)/n56)
lam_cam = np.concatenate([lam12,lam23,lam34,lam45,lam56])
n_lam_cam = len(lam_cam)
# Radiative transfer setting
# number of photons for temp and image
m.set_raytracing(True)
m.set_monochromatic(True, wavelengths=[3.6, 4.5, 5.8, 8.0, 24, 70, 100, 160, 250, 350, 500])
m.set_n_photons(initial=1000000, imaging_sources=1000000, imaging_dust=1000000,raytracing_sources=1000000, raytracing_dust=1000000)
# imaging=100000, raytracing_sources=100000, raytracing_dust=100000
# number of iteration to compute dust specific energy (temperature)
m.set_n_initial_iterations(5)
m.set_convergence(True, percentile=99., absolute=1.5, relative=1.02)
m.set_mrw(True) # Gamma = 1 by default
# m.set_forced_first_scattering(forced_first_scattering=True)
# Setting up images and SEDs
image = m.add_peeled_images()
# image.set_wavelength_range(300, 2.0, 670.0)
# use the index of wavelength array used by the monochromatic radiative transfer
image.set_wavelength_index_range(2,12)
# 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)
# 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'
# Number of unique photons that passed through the cell
m.conf.output.output_n_photons = 'last'
m.write(outdir+outname+'.rtin')
def setup_model(outdir, record_dir, outname, params, dust_file, wav_range, aperture,
tsc=True, idl=False, plot=False, low_res=True, max_rCell=100,
scale=1, radmc=False, mono_wave=None, norecord=False,
dstar=200., dyn_cav=False, fix_params=None,
power=2, mc_photons=1e6, im_photons=1e6, ellipsoid=False,
TSC_dir='~/programs/misc/TSC/',
IDL_path='/Applications/exelis/idl83/bin/idl', auto_disk=0.25,
fast_plot=False, image_only=False, ulrich=False):
"""
params = dictionary of the model parameters
'alma' keyword is obsoleted
outdir: The directory for storing Hyperion input files
record_dir: The directory contains "model_list.txt" for recording parameters
TSC_dir: Path the TSC-related IDL routines
IDL_path: The IDL executable
fast_plot: Do not plot the polar plot of the density because the rendering
takes quite a lot of time.
mono: monochromatic radiative transfer mode (need to specify the wavelength
or a list of wavelength with 'mono_wave')
image_only: only run for images
"""
import numpy as np
import astropy.constants as const
import scipy as sci
# to avoid X server error
import matplotlib as mpl
mpl.use('Agg')
#
import matplotlib.pyplot as plt
import os
from matplotlib.colors import LogNorm
from scipy.integrate import nquad
from hyperion.model import Model
from record_hyperion import record_hyperion
from pprint import pprint
# Constants setup
c = const.c.cgs.value
AU = const.au.cgs.value # Astronomical Unit [cm]
pc = const.pc.cgs.value # Parsec [cm]
MS = const.M_sun.cgs.value # Solar mass [g]
LS = const.L_sun.cgs.value # Solar luminosity [erg/s]
RS = const.R_sun.cgs.value # Solar radius [cm]
G = const.G.cgs.value # Gravitational constant [cm3/g/s^2]
yr = 60*60*24*365 # Years in seconds
PI = np.pi # PI constant
sigma = const.sigma_sb.cgs.value # Stefan-Boltzmann constant
mh = const.m_p.cgs.value + const.m_e.cgs.value
g2d = 100.
mmw = 2.37 # Kauffmann 2008
m = Model()
# min and max wavelength to compute (need to define them first for checking dust properties)
wav_min, wav_max, wav_num = wav_range
# Create dust properties
# Hyperion needs nu, albedo, chi, g, p_lin_max
from hyperion.dust import HenyeyGreensteinDust
dust = dict()
[dust['nu'], dust['albedo'], dust['chi'], dust['g']] = np.genfromtxt(dust_file).T
d = HenyeyGreensteinDust(dust['nu'], dust['albedo'], dust['chi'], dust['g'], dust['g']*0)
# dust sublimation option
# dust sublimation temperture specified here
T_sub = 1600.0
d.set_sublimation_temperature('slow', temperature=T_sub)
d.set_lte_emissivities(n_temp=3000,
temp_min=0.1,
temp_max=2000.)
# if the min and/or max wavelength fall out of range
if c/wav_min/1e-4 > dust['nu'].max():
d.optical_properties.extrapolate_nu(dust['nu'].min(), c/wav_min/1e-4)
print('minimum wavelength is out of dust model. The dust model is extrapolated.')
if c/wav_max/1e-4 < dust['nu'].min():
d.optical_properties.extrapolate_nu(c/wav_max/1e-4, dust['nu'].max())
print('maximum wavelength is out of dust model. The dust model is extrapolated.')
# try to solve the freq. problem
d.optical_properties.extrapolate_nu(3.28e15, 5e15)
#
d.write(outdir+os.path.basename(dust_file).split('.')[0]+'.hdf5')
d.plot(outdir+os.path.basename(dust_file).split('.')[0]+'.png')
plt.clf()
# Grids and Density
# Grid Parameters
nx = 300L
if low_res == True:
nx = 100L
ny = 400L
nz = 50L
[nx, ny, nz] = [int(scale*nx), int(scale*ny), int(scale*nz)]
# TSC model input setting
dict_params = params
# TSC model parameter
cs = dict_params['Cs']*1e5
t = dict_params['age'] # year
omega = dict_params['Omega0']
# calculate related parameters
M_env_dot = 0.975*cs**3/G
mstar = M_env_dot * t * yr
R_cen = omega**2 * G**3 * mstar**3 /(16*cs**8)
R_inf = cs * t * yr
# protostar parameter
tstar = dict_params['tstar']
R_env_max = dict_params['R_env_max']*AU
theta_cav = dict_params['theta_cav']
rho_cav_center = dict_params['rho_cav_center']
rho_cav_edge = dict_params['rho_cav_edge']*AU
rstar = dict_params['rstar']*RS
# Mostly fixed parameter
M_disk = dict_params['M_disk']*MS
beta = dict_params['beta']
h100 = dict_params['h100']*AU
rho_cav = dict_params['rho_cav']
# make M_disk varies with mstar, which is the mass of star+disk
if auto_disk != None:
if M_disk != 0:
print('M_disk is reset to %4f of mstar (star+disk)' % auto_disk)
M_disk = mstar * auto_disk
else:
print('M_disk = 0 is found. M_disk is set to 0.')
# ellipsoid cavity parameter
if ellipsoid == True:
print('Use ellipsoid cavity (experimental)')
# the numbers are given in arcsec
a_out = 130 * dstar * AU
b_out = 50 * dstar * AU
z_out = a_out
a_in = dict_params['a_in'] * dstar * AU
b_in = a_in/a_out*b_out
z_in = a_in
rho_cav_out = dict_params['rho_cav_out'] * mh
rho_cav_in = dict_params['rho_cav_in'] * mh
# Calculate the dust sublimation radius
# dust sublimation temperature specified when setting up the dust properties
# realistic dust
# a = 1 # in micron
# d_sub = 2.9388e7*(a/0.1)**-0.2 * (4*np.pi*rstar**2*sigma*tstar**4/LS)**0.5 / T_sub**3 *AU
# black body dust
d_sub = (LS/16./np.pi/sigma/AU**2*(4*np.pi*rstar**2*sigma*tstar**4/LS)/T_sub**4)**0.5 *AU
# use the dust sublimation radius as the inner radius of disk and envelope
R_disk_min = d_sub
R_env_min = d_sub
rin = rstar
rout = R_env_max
R_disk_max = R_cen
# print the variables
print('Dust sublimation radius %6f AU' % (d_sub/AU))
print('M_star %4f Solar mass' % (mstar/MS))
print('Infall radius %4f AU' % (R_inf / AU))
# if there is any parameter found in fix_params, then fix them
if fix_params != None:
if 'R_min' in fix_params.keys():
R_disk_min = fix_params['R_min']*AU
R_env_min = fix_params['R_min']*AU
# Make the Coordinates
#
ri = rin * (rout/rin)**(np.arange(nx+1).astype(dtype='float')/float(nx))
ri = np.hstack((0.0, ri))
thetai = PI*np.arange(ny+1).astype(dtype='float')/float(ny)
phii = PI*2.0*np.arange(nz+1).astype(dtype='float')/float(nz)
# Keep the constant cell size in r-direction at large radii
#
if max_rCell != None:
ri_cellsize = ri[1:-1]-ri[0:-2]
ind = np.where(ri_cellsize/AU > max_rCell)[0][0] # The largest cell size is 100 AU
ri = np.hstack((ri[0:ind],
ri[ind]+np.arange(np.ceil((rout-ri[ind])/max_rCell/AU))*max_rCell*AU))
nxx = nx
nx = len(ri)-1
# Assign the coordinates of the center of cell as its coordinates.
#
rc = 0.5*( ri[0:nx] + ri[1:nx+1] )
thetac = 0.5*( thetai[0:ny] + thetai[1:ny+1] )
phic = 0.5*( phii[0:nz] + phii[1:nz+1] )
# for non-TSC model
if ulrich:
import hyperion as hp
from hyperion.model import AnalyticalYSOModel
non_tsc = AnalyticalYSOModel()
# Define the luminsoity source
nt_source = non_tsc.add_spherical_source()
nt_source.luminosity = (4*PI*rstar**2)*sigma*(tstar**4) # [ergs/s]
nt_source.radius = rstar # [cm]
nt_source.temperature = tstar # [K]
nt_source.position = (0., 0., 0.)
nt_source.mass = mstar
# Envelope structure
#
nt_envelope = non_tsc.add_ulrich_envelope()
nt_envelope.mdot = M_env_dot # Infall rate
nt_envelope.rmin = rin # Inner radius
nt_envelope.rc = R_cen # Centrifugal radius
nt_envelope.rmax = R_env_max # Outer radius
nt_envelope.star = nt_source
nt_grid = hp.grid.SphericalPolarGrid(ri, thetai, phii)
rho_env_ulrich = nt_envelope.density(nt_grid).T
rho_env_ulrich2d = np.sum(rho_env_ulrich**2, axis=2)/np.sum(rho_env_ulrich, axis=2)
# Make the dust density model
#
# total mass counter
total_mass = 0
# normalization constant for cavity shape
if theta_cav != 0:
# using R = 10000 AU as the reference point
c0 = (10000.*AU)**(-0.5)*\
np.sqrt(1/np.sin(np.radians(theta_cav))**3-1/np.sin(np.radians(theta_cav)))
else:
c0 = 0
# empty density grid to be filled later
rho = np.zeros([len(rc), len(thetac), len(phic)])
# Normalization for the total disk mass
def f(w, z, beta, rstar, h100):
f = 2*PI*w*(1-np.sqrt(rstar/w))*(rstar/w)**(beta+1)*np.exp(-0.5*(z/(w**beta*h100/100**beta))**2)
return f
rho_0 = M_disk/(nquad(f,[[R_disk_min,R_disk_max],[-R_env_max,R_env_max]], args=(beta,rstar,h100)))[0]
# TODO: review
if dyn_cav == True:
if not tsc:
print('WARNING: Calculation of interdependent cavity property has not implemented in infall-only solution!')
else:
from outflow_inner_edge import outflow_inner_edge
# typical no used. Just an approach I tried to make the size of the
# constant desnity region self-consistent with the outflow cavity.
print 'Calculate the cavity properties using the criteria that swept-up mass = outflowed mass'
# using swept-up mass = flow mass to derive the edge of the extended flat density region
v_outflow = 1e2 * 1e5
rho_cav_edge = outflow_inner_edge(np.copy(rho_env), (ri,thetai,phii),M_env_dot,v_outflow,theta_cav, R_env_min)
dict_params['rho_cav_edge'] = rho_cav_edge
# assume gas-to-dust ratio = 100
rho_cav_center = 0.01 * 0.1*M_env_dot*rho_cav_edge/v_outflow/2 / (2*np.pi/3*rho_cav_edge**3*(1-np.cos(np.radians(theta_cav))))
dict_params['rho_cav_center'] = rho_cav_center
print 'inner edge is %5f AU and density is %e g/cm3' % (rho_cav_edge/AU, rho_cav_center)
# default setting for the density profile in cavity
if 'rho_cav_center' in locals() == False:
rho_cav_center = 5e-19
print('Use 5e-19 as the default value for cavity center')
if 'rho_cav_edge' in locals() == False:
rho_cav_edge = 40*AU
print('Use 40 AU as the default value for size of the inner region')
# discontinuity factor inside and outside of cavity inner edge
discont = 1
# determine the edge of constant region in the cavity
if rho_cav_edge == 0:
rho_cav_edge = R_env_min
if not tsc:
print('Calculating the dust density profile with infall solution...')
for ir in range(0,len(rc)):
for itheta in range(0,len(thetac)):
for iphi in range(0,len(phic)):
if rc[ir] > R_env_min:
# related coordinates
w = abs(rc[ir]*np.cos(np.pi/2 - thetac[itheta]))
z = rc[ir]*np.sin(np.pi/2 - thetac[itheta])
# Disk profile or envelope/cavity
if ((w >= R_disk_min) and (w <= R_disk_max)):
h = ((w/(100*AU))**beta)*h100
rho_dum = rho_0*(1-np.sqrt(rstar/w))*(rstar/w)**(beta+1)*np.exp(-0.5*(z/h)**2)
else:
# determine whether the current cell is in the cavity
if ellipsoid == False:
z_cav = c0*abs(w)**1.5
if z_cav == 0:
z_cav = R_env_max
cav_con = abs(z) > abs(z_cav)
if theta_cav == 90:
cav_con = True
else:
# condition for the outer ellipsoid
cav_con = (2*(w/b_out)**2 + ((abs(z)-z_out)/a_out)**2) < 1
# cavity density
if cav_con:
# open cavity
if ellipsoid == False:
if (rc[ir] <= rho_cav_edge) & (rc[ir] >= R_env_min):
rho_dum = g2d * rho_cav_center
else:
rho_dum = g2d * rho_cav_center*discont*(rho_cav_edge/rc[ir])**power
else:
# condition for the inner ellipsoid
if (2*(w/b_in)**2 + ((abs(z)-z_in)/a_in)**2) > 1:
rho_dum = rho_cav_out
else:
rho_dum = rho_cav_in
# envelope density
else:
mu = abs(np.cos(thetac[itheta]))
# Implement new root finding algorithm
roots = np.roots(np.array([1.0, 0.0, rc[ir]/R_cen-1.0, -mu*rc[ir]/R_cen]))
if len(roots[roots.imag == 0]) == 1:
if (abs(roots[roots.imag == 0]) - 1.0) <= 0.0:
mu_o_dum = roots[roots.imag == 0]
else:
mu_o_dum = -0.5
print('Problem with cubic solving, cos(theta) = ', mu_o_dum)
print('parameters are ', np.array([1.0, 0.0, rc[ir]/R_cen-1.0, -mu*rc[ir]/R_cen]))
else:
mu_o_dum = -0.5
for imu in range(0, len(roots)):
if roots[imu]*mu >= 0.0:
if (abs((abs(roots[imu]) - 1.0)) <= 1e-5):
mu_o_dum = 1.0 * np.sign(mu)
else:
mu_o_dum = roots[imu]
if mu_o_dum == -0.5:
print('Problem with cubic solving, roots are: ', roots)
mu_o = mu_o_dum.real
rho_dum = M_env_dot/(4*PI*(G*mstar*R_cen**3)**0.5)*(rc[ir]/R_cen)**(-3./2)*(1+mu/mu_o)**(-0.5)*(mu/mu_o+2*mu_o**2*R_cen/rc[ir])**(-1)
rho[ir,itheta,iphi] = rho_dum
else:
rho[ir,itheta,iphi] = 1e-30
# add the dust mass into the total count
cell_mass = rho[ir, itheta, iphi] * (1/3.)*(ri[ir+1]**3 - ri[ir]**3) * (phii[iphi+1]-phii[iphi]) * -(np.cos(thetai[itheta+1])-np.cos(thetai[itheta]))
total_mass = total_mass + cell_mass
# TSC model
else:
print('Calculating the dust density profile with TSC solution...')
# If needed, calculate the TSC model via IDL
#
if idl == True:
print('Using IDL to calculate the TSC model. Make sure you are running this on mechine with IDL.')
import pidly
idl = pidly.IDL(IDL_path)
idl('.r '+TSC_dir+'tsc.pro')
idl('.r '+TSC_dir+'tsc_run.pro')
#
# only run TSC calculation within infall radius
# modify the rc array
ind_infall = np.where(rc >= R_inf)[0][0]
if max(ri) > R_inf:
rc_idl = rc[0:ind_infall+1]
else:
rc_idl = rc[rc < max(ri)]
idl.pro('tsc_run', indir=TSC_dir, outdir=outdir, rc=rc_idl, thetac=thetac, time=t,
c_s=cs, omega=omega, renv_min=R_env_min)
file_idl = 'rhoenv.dat'
else:
print('Read the pre-computed TSC model.')
ind_infall = np.where(rc >= R_inf)[0][0]
if max(ri) > R_inf:
rc_idl = rc[0:ind_infall+1]
else:
rc_idl = rc[rc < max(ri)]
if idl != False:
file_idl = idl
# read in the exist file
rho_env_tsc_idl = np.genfromtxt(outdir+file_idl).T
# because only region within infall radius is calculated by IDL program,
# need to project it to the original grid
rho_env_tsc = np.zeros([len(rc), len(thetac)])
for irc in range(len(rc)):
if rc[irc] in rc_idl:
rho_env_tsc[irc,:] = rho_env_tsc_idl[np.squeeze(np.where(rc_idl == rc[irc])),:]
# extrapolate for the NaN values at the outer radius, usually at radius beyond the infall radius
# using r^-2 profile at radius greater than infall radius
# and map the 2d strcuture onto 3-D grid
# map TSC solution from IDL to actual 2-D grid
rho_env_tsc2d = np.empty((nx,ny))
if max(ri) > R_inf:
for i in range(0, len(rc)):
if i <= ind_infall:
rho_env_tsc2d[i,:] = rho_env_tsc[i,:]
else:
rho_env_tsc2d[i,:] = 10**(np.log10(rho_env_tsc[ind_infall,:]) - 2*(np.log10(rc[i]/rc[ind_infall])))
else:
rho_env_tsc2d = rho_env_tsc
# map it to 3-D grid
rho_env = np.repeat(rho_env_tsc2d[:,:,np.newaxis], nz, axis=2)
for ir in range(0,len(rc)):
for itheta in range(0,len(thetac)):
for iphi in range(0,len(phic)):
if rc[ir] > R_env_min:
# related coordinates
w = abs(rc[ir]*np.cos(np.pi/2 - thetac[itheta]))
z = rc[ir]*np.sin(np.pi/2 - thetac[itheta])
# initialize dummer rho for disk and cavity
rho_dum = 0
# Disk profile
if ((w >= R_disk_min) and (w <= R_disk_max)) == True:
h = ((w/(100*AU))**beta)*h100
rho_dum = rho_0*(1-np.sqrt(rstar/w))*(rstar/w)**(beta+1)*np.exp(-0.5*(z/h)**2)
else:
# determine whether the current cell is in the cavity
if ellipsoid == False:
z_cav = c0*abs(w)**1.5
if z_cav == 0:
z_cav = R_env_max
cav_con = abs(z) > abs(z_cav)
else:
# condition for the outer ellipsoid
cav_con = (2*(w/b_out)**2 + ((abs(z)-z_out)/a_out)**2) < 1
if cav_con:
# open cavity
if ellipsoid == False:
if (rc[ir] <= rho_cav_edge) & (rc[ir] >= R_env_min):
rho_dum = g2d * rho_cav_center
else:
rho_dum = g2d * rho_cav_center*discont*(rho_cav_edge/rc[ir])**power
else:
# condition for the inner ellipsoid
if (2*(w/b_in)**2 + ((abs(z)-z_in)/a_in)**2) > 1:
rho_dum = rho_cav_out
else:
rho_dum = rho_cav_in
rho[ir, itheta, iphi] = rho_env[ir, itheta, iphi] + rho_dum
else:
rho[ir,itheta,iphi] = 1e-40
# add the dust mass into the total count
cell_mass = rho[ir, itheta, iphi] * (1/3.)*(ri[ir+1]**3 - ri[ir]**3) * (phii[iphi+1]-phii[iphi]) * -(np.cos(thetai[itheta+1])-np.cos(thetai[itheta]))
total_mass = total_mass + cell_mass
# apply gas-to-dust ratio of 100
rho_dust = rho/g2d
total_mass_dust = total_mass/MS/g2d
print('Total dust mass = %f Solar mass' % total_mass_dust)
# Insert the calculated grid and dust density profile into hyperion
m.set_spherical_polar_grid(ri, thetai, phii)
m.add_density_grid(rho_dust.T, d)
# Define the luminsoity source
source = m.add_spherical_source()
source.luminosity = (4*PI*rstar**2)*sigma*(tstar**4) # [ergs/s]
source.radius = rstar # [cm]
source.temperature = tstar # [K]
source.position = (0., 0., 0.)
print('L_center = % 5.2f L_sun' % ((4*PI*rstar**2)*sigma*(tstar**4)/LS))
# radiative transfer settigs
m.set_raytracing(True)
# determine the number of photons for imaging
# the case of monochromatic
if mono_wave != None:
if (type(mono_wave) == int) or (type(mono_wave) == float) or (type(mono_wave) == str):
mono_wave = float(mono_wave)
mono_wave = [mono_wave]
# Monochromatic radiative transfer setting
m.set_monochromatic(True, wavelengths=mono_wave)
m.set_n_photons(initial=mc_photons, imaging_sources=im_photon,
imaging_dust=im_photon, raytracing_sources=im_photon,
raytracing_dust=im_photon)
# regular SED
else:
m.set_n_photons(initial=mc_photons, imaging=im_photon * wav_num,
raytracing_sources=im_photon,
raytracing_dust=im_photon)
# number of iteration to compute dust specific energy (temperature)
m.set_n_initial_iterations(20)
m.set_convergence(True, percentile=dict_params['percentile'],
absolute=dict_params['absolute'],
relative=dict_params['relative'])
m.set_mrw(True) # Gamma = 1 by default
# Setting up images and SEDs
if not image_only:
# SED setting
# Infinite aperture
syn_inf = m.add_peeled_images(image=False)
# use the index of wavelength array used by the monochromatic radiative transfer
if mono_wave == None:
syn_inf.set_wavelength_range(wav_num, wav_min, wav_max)
syn_inf.set_viewing_angles([dict_params['view_angle']], [0.0])
syn_inf.set_uncertainties(True)
syn_inf.set_output_bytes(8)
# aperture
# 7.2 in 10 um scaled by lambda / 10
# flatten beyond 20 um
# default aperture (should always specify a set of apertures)
# 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)
dict_peel_sed = {}
for i in range(0, len(aper_reduced)):
aper_dum = aper_reduced[i]/2 * (1/3600.*np.pi/180.)*dstar*pc
dict_peel_sed[str(index_reduced[i])] = m.add_peeled_images(image=False)
# use the index of wavelength array used by the monochromatic radiative transfer
if mono == False:
dict_peel_sed[str(index_reduced[i])].set_wavelength_range(wav_num, wav_min, wav_max)
dict_peel_sed[str(index_reduced[i])].set_viewing_angles([dict_params['view_angle']], [0.0])
# aperture should be given in cm and its the radius of the aperture
dict_peel_sed[str(index_reduced[i])].set_aperture_range(1, aper_dum, aper_dum)
dict_peel_sed[str(index_reduced[i])].set_uncertainties(True)
dict_peel_sed[str(index_reduced[i])].set_output_bytes(8)
# image setting
syn_im = m.add_peeled_images(sed=False)
# use the index of wavelength array used by the monochromatic radiative transfer
if mono_wave == None:
syn_im.set_wavelength_range(wav_num, wav_min, wav_max)
pix_num = 300
else:
pix_num = 8000
#
syn_im.set_image_size(pix_num, pix_num)
syn_im.set_image_limits(-R_env_max, R_env_max, -R_env_max, R_env_max)
syn_im.set_viewing_angles([dict_params['view_angle']], [0.0])
syn_im.set_uncertainties(True)
syn_im.set_output_bytes(8)
# 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'
# Number of unique photons that passed through the cell
m.conf.output.output_n_photons = 'last'
m.write(outdir+outname+'.rtin')
if plot:
# rho2d is the 2-D projection of gas density
# take the weighted average
rho2d = np.sum(rho**2, axis=2)/np.sum(rho, axis=2)
if fast_plot == False:
# Plot the azimuthal averaged density
fig = plt.figure(figsize=(8,6))
ax_env = fig.add_subplot(111, projection='polar')
# zmin = 1e-22/mmw/mh
zmin = 1e-1
cmap = plt.cm.CMRmap
rho2d_exp = np.hstack((rho2d, rho2d, rho2d[:,0:1]))
thetac_exp = np.hstack((thetac-PI/2, thetac+PI/2, thetac[0]-PI/2))
# plot the gas density
img_env = ax_env.pcolormesh(thetac_exp, rc/AU, rho2d_exp/mmw/mh,
cmap=cmap,
norm=LogNorm(vmin=zmin,vmax=1e6))
ax_env.set_xlabel(r'$\rm{Polar\,angle\,(Degree)}$',fontsize=20)
ax_env.set_ylabel('', fontsize=20, labelpad=-140)
ax_env.tick_params(labelsize=18)
ax_env.set_yticks(np.hstack((np.arange(0,(int(R_env_max/AU/10000.)+1)*10000, 10000),R_env_max/AU)))
ax_env.set_xticklabels([r'$\rm{90^{\circ}}$',r'$\rm{45^{\circ}}$',r'$\rm{0^{\circ}}$',r'$\rm{-45^{\circ}}$',\
r'$\rm{-90^{\circ}}$',r'$\rm{-135^{\circ}}$',r'$\rm{180^{\circ}}$',r'$\rm{135^{\circ}}$'])
ax_env.set_yticklabels([])
# fix the tick label font
ticks_font = mpl.font_manager.FontProperties(family='STIXGeneral',size=20)
for label in ax_env.get_yticklabels():
label.set_fontproperties(ticks_font)
ax_env.grid(True, color='LightGray', linewidth=1.5)
cb = fig.colorbar(img_env, pad=0.1)
cb.ax.set_ylabel(r'$\rm{Averaged\,Gas\,Density\,(cm^{-3})}$',fontsize=20)
cb.set_ticks([1e-1,1e0,1e1,1e2,1e3,1e4,1e5,1e6])
cb.set_ticklabels([r'$\rm{10^{-1}}$',r'$\rm{10^{0}}$',r'$\rm{10^{1}}$',r'$\rm{10^{2}}$',r'$\rm{10^{3}}$',
r'$\rm{10^{4}}$',r'$\rm{10^{5}}$',r'$\rm{\geq 10^{6}}$'])
cb_obj = plt.getp(cb.ax.axes, 'yticklabels')
plt.setp(cb_obj, fontsize=20)
fig.savefig(outdir+outname+'_gas_density.png', format='png', dpi=300, bbox_inches='tight')
fig.clf()
# Plot the radial density profile
fig = plt.figure(figsize=(12,9))
ax = fig.add_subplot(111)
plot_grid = [0, 49, 99, 149, 199]
color_grid = ['#e41a1c', '#377eb8', '#4daf4a', '#984ea3', '#ff7f00']
label = [r'$\rm{\theta='+str(int(np.degrees(thetai[plot_grid[0]])))+'^{\circ}}$',
r'$\rm{\theta='+str(int(np.degrees(thetai[plot_grid[1]])))+'^{\circ}}$',
r'$\rm{\theta='+str(1+int(np.degrees(thetai[plot_grid[2]])))+'^{\circ}}$',
r'$\rm{\theta='+str(int(np.degrees(thetai[plot_grid[3]])))+'^{\circ}}$',
r'$\rm{\theta='+str(1+int(np.degrees(thetai[plot_grid[4]])))+'^{\circ}}$']
alpha = np.linspace(0.3, 1.0, len(plot_grid))
for i in plot_grid:
ax.plot(np.log10(rc[rc > 0.14*AU]/AU), np.log10(rho2d[rc > 0.14*AU,i]/g2d/mmw/mh)+plot_grid[::-1].index(i)*-0.2,'-',color=color_grid[plot_grid.index(i)],mec='None',linewidth=2.5, \
markersize=3, label=label[plot_grid.index(i)])
ax.axvline(np.log10(R_inf/AU), linestyle='--', color='k', linewidth=1.5, label=r'$\rm{infall\,radius}$')
ax.axvline(np.log10(R_cen/AU), linestyle=':', color='k', linewidth=1.5, label=r'$\rm{centrifugal\,radius}$')
lg = plt.legend(fontsize=20, numpoints=1, ncol=2, framealpha=0.7, loc='upper right')
ax.set_xlabel(r'$\rm{log(Radius)\,(AU)}$', fontsize=20)
ax.set_ylabel(r'$\rm{log(Dust\,Density)\,(cm^{-3})}$', fontsize=20)
[ax.spines[axis].set_linewidth(1.5) for axis in ['top','bottom','left','right']]
ax.minorticks_on()
ax.tick_params('both', labelsize=18, width=1.5, which='major', pad=15, length=5)
ax.tick_params('both', labelsize=18, width=1.5, which='minor', pad=15, length=2.5)
# fix the tick label font
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)
ax.set_ylim([0,11])
fig.gca().set_xlim(left=np.log10(0.05))
fig.savefig(outdir+outname+'_gas_radial.pdf',format='pdf',dpi=300,bbox_inches='tight')
fig.clf()
# Record the input and calculated parameters
if not norecord == True:
params = dict_params.copy()
params.update({'d_sub': d_sub/AU,
'M_env_dot': M_env_dot/MS*yr,
'R_inf': R_inf/AU,
'R_cen': R_cen/AU,
'mstar': mstar/MS,
'M_tot_gas': total_mass/MS})
record_hyperion(params,record_dir)
return m
import numpy as np
from hyperion.model import Model
from hyperion.util.constants import pc, lsun
# Initialize model
m = Model()
# Set one-cell cartesian grid
w = np.linspace(-pc, pc, 32)
m.set_cartesian_grid(w, w, w)
# Add density grid with constant density
m.add_density_grid(np.ones(m.grid.shape) * 4.e-20, 'kmh_lite.hdf5')
# Add a point source in the center
s = m.add_point_source()
s.luminosity = 1000 * lsun
s.temperature = 6000.
# Add 10 SEDs for different viewing angles
image = m.add_peeled_images(sed=True, image=False)
image.set_wavelength_range(250, 0.01, 5000.)
image.set_viewing_angles(np.linspace(0., 90., 10), np.repeat(20., 10))
image.set_track_origin('basic')
# Add multi-wavelength image for a single viewing angle
image = m.add_peeled_images(sed=False, image=True)
image.set_wavelength_range(30, 1., 1000.)
image.set_viewing_angles([30.], [20.])
image.set_image_size(200, 200)
def __init__(self, par, **kwargs):
#Set up dust model for Hyperion
# Initalize the model
m = Model()
#Set central source position
m.add_spherical_source(luminosity = par.get('lsource',1.0*lsun),
radius = par.get('rsource',1.0*rsun),
mass = par.get('msource',1.0*msun),
temperature = par.get('tsource',5784.0),
position = par.get('position',(0.0,0.0,0.0))
# Use raytracing to improve s/n of thermal/source emission
m.set_raytracing(par.get('raytracing',True))
# Use the modified random walk
m.set_mrw(par.get('modrndwlk',True), gamma=par.get('mrw_gamma',2))
# Set up spatial grid
x = np.linspace(-1.5*par.get('rmax',100.*u.au), 1.5*par.get('rmax',100.*u.au), par.get('ngrid',257))
y = np.linspace(-1.5*par.get('rmax',100.*u.au), 1.5*par.get('rmax',100.*u.au), par.get('ngrid',257))
z = np.linspace(-1.5*par.get('rmax',100.*u.au), 1.5*par.get('rmax',100.*u.au), par.get('ngrid',257))
m.set_cartesian_grid(x,y,z)
print("Spatial grid set up.")
#Set up density grid
rho0 = par.get('rho0',1.5e-19)
alpha_in = par.get('alpha_in',-5.)
alpha_out = par.get('alpha_out',5.)
scaleheight= par.get('scaleheight',0.1)
rmin = par.get('rmin',70.*u.au)
rmax = par.get('rmax',100.*u.au)
rmid = (rmax + rmin) / 2
rr = np.sqrt(m.grid.gx ** 2 + m.grid.gy ** 2 + m.grid.gz ** 2)
#define density grid
density = np.zeros(m.grid.shape)
density = rho0 * ( (rr/rmid)**(2.*alpha_in) + (rr/rmid)**(2.*alpha_out) )**(-0.5) * np.exp(-((abs(m.grid.gz)/rr)**2/scaleheight**2)/2.0)
m.add_density_grid(density, d)
print("Density grid set up.")
# Set up SED for 10 viewing angles
sed = m.add_peeled_images(sed=par.get('api_sed',True), image=par.get('api_img',False))
sed.set_viewing_angles(np.linspace(0., 90., 10), np.repeat(45., 10))
sed.set_wavelength_range(par.get('nl',101), par.get('lmin',0.1), par.get('lmax',1000.))
sed.set_track_origin('basic')
# Set number of photons
m.set_n_photons(initial=par.get('nph_initial',1e4), imaging=par.get('nph_imging',1e5),
raytracing_sources=par.get('nph_rtsrcs',1e5), raytracing_dust=par.get('nph_rtdust',1e5))
# Set number of temperature iterations
m.set_n_initial_iterations(par.get('niter',5))
# Write out file
m.write('HyperionRT_sed.rtin')
print("Hyperion RT model created.")
def __call__(self, *args, **kwargs):
m.run('HyperionRT_sed.rtout', mpi=True,n_processes=6,overwrite=True)
print("Hyperion RT model executed.")
m = ModelOutput('HyperionRT_sed.rtout')
self.modelFlux = sed.value
def lnprior(self, theta, **kwargs):
if self.flatprior:
if (self.lims[0,0] < theta[0] < self.lims[0,1]) and \
(self.lims[1,0] < theta[1] < self.lims[1,1]) and \
(self.lims[2,0] < theta[2] < self.lims[2,1]) and \
(self.lims[3,0] < theta[3] < self.lims[3,1]) and \
np.sum(10**theta[4:]) <= 1. and np.all(theta[4:] < 0.):
return 0
else:
return -np.inf
else:
raise NotImplementedError()
def __str__(self, **kwargs):
raise NotImplementedError()
def __repr__(self, **kwargs):
raise NotImplementedError()
def inputFile(self, **kwargs):
#Create a dictionary with which to setup and run Hyperion RT models
#Dust parameters
par = {'dust':'"astrosilicate"',
'format':2,
'size':0.5,
'amin':0.5,
'amax':1000.,
'na':101,
'nang':91,
'nanx':11,
'nchem':1,
'gtd':0,
'lmin':0.1,
'lmax':1000.0,
'nl':101,
'massfrac':1.0,
'rho0':1.5e-19,
'optconst':'"silicate_d03.lnk"',
'disttype':'power',
'q':3.5,
#Source parameters
'lsource': 1.0*u.lsun,
'rsource': 1.0*u.rsun,
'msource': 1.0*u.msun,
'tsource': 5784.,
'position':[0.0,0.0,0.0],
#Disc parameters
'rmin': 70.*u.au,
'rmax': 100.*u.au,
'alpha_in': -5.,
'alpha_out': 5.,
'scaleheight': 0.1,
#RT parameters
'niter':5,
'nph_initial':1e4,
'nph_imging':1e5,
'nph_rtsrcs':1e5,
'nph_rtdust':1e5,
#Peel photons to get images
'api_sed':True,
'api_img':False,
}
return par
#convenience function to plot the SED of the Hyperion RT output
def plot_sed(par):
#Set up figure
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
#Read in Hyperion model output
try:
m = ModelOutput('HyperionRT_sed.rtout')
#Total SED
sed = m.get_sed(inclination=0, aperture=-1, distance=par['distance'],
component='total',units='Jy')
ax.loglog(sed.wav, sed.val, color='black', lw=3, alpha=0.5)
# Direct stellar photons
sed = m.get_sed(inclination=0, aperture=-1, distance=par['distance'],
component='source_emit',units='Jy')
ax.loglog(sed.wav, sed.val, color='blue')
# Scattered stellar photons
sed = m.get_sed(inclination=0, aperture=-1, distance=par['distance'],
component='source_scat',units='Jy')
ax.loglog(sed.wav, sed.val, color='teal')
# Direct dust photons
sed = m.get_sed(inclination=0, aperture=-1, distance=par['distance'],
component='dust_emit',units='Jy')
ax.loglog(sed.wav, sed.val, color='red')
# Scattered dust photons
sed = m.get_sed(inclination=0, aperture=-1, distance=par['distance'],
component='dust_scat',units='Jy')
ax.loglog(sed.wav, sed.val, color='orange')
ax.set_xlabel(r'$\lambda$ [$\mu$m]')
ax.set_ylabel(r'Flux [Jy]')
ax.set_xlim(par.get('lmin',0.1), par.get('lmax',1000.))
ax.set_ylim(1e-6*np.max(sed.value),1.1*np.max(sed.value))
fig.savefig('HyperionRT_sed_plot_components.png')
plt.close(fig)
except IOError:
print("No Hyperion RT output found, SED not plotted.")
#convenience function to write dust parameter file '<dust>.params' for Hyperion BHDust calculator (separate program)
def write_bhmie_file(par):
f=open(par.get('dust','astrosilicate')+'.params','w')
f.write('"'+par['dust']+'_'+str(par['size'])+'"'+'\n')
f.write(str(par['format'])+'\n')
f.write(str(par['amin'])+'\n')
f.write(str(par['amax'])+'\n')
f.write(str(par['na'])+'\n')
f.write(str(par['nang'])+'\n')
f.write(str(par['nanx'])+'\n')
f.write(str(par['nchem'])+'\n')
f.write(str(par['gtd'])+'\n')
f.write(str(par['lmin'])+' '+str(par['lmax'])+' '+str(par['nl'])+'\n')
f.write(''+'\n')
f.write(str(par['massfrac'])+'\n')
f.write(str(par['rho0'])+'\n')
f.write(str(par['optconst'])+'\n')
f.write(str(par['disttype'])+'\n')
f.write(str(par['amin'])+' '+str(par['amax'])+' '+str(par['q'])+'\n')
f.close()
print("BHMie dust input file created.")
import subprocess
subprocess.run(['bhmie',param_file])
print("BHMie dust output file created")
