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
def active_dust_add(ds,m,grid_of_sizes,nsizes,dustdens,specific_energy,refined=[False]):
#first, save the grid_of_sizes to the ds.paramteters so we can carry it around
ds.parameters['reg_grid_of_sizes'] = grid_of_sizes #named 'reg_grid_of_sizes'
#for empty cells, use the median size distribution
for isize in range(nsizes):
wzero = np.where(grid_of_sizes[:,isize] == 0)[0]
wnonzero = np.where(grid_of_sizes[:,isize] != 0)[0]
grid_of_sizes[wzero,isize] = np.median(grid_of_sizes[wnonzero,isize])
print(len(wzero)/len(wnonzero))
#now load the mapping between grain bin and filename for the lookup table
data = np.load(cfg.par.pd_source_dir+'active_dust/dust_files/binned_dust_sizes.npz')
grain_size_left_edge_array = data['grain_size_left_edge_array']
grain_size_right_edge_array = data['grain_size_right_edge_array']
dust_filenames = data['outfile_filenames']
nbins = len(grain_size_left_edge_array)
#find which sizes in the hydro simulation correspond to the
#pre-binned extinction law sizes from dust_file_writer.py
dust_file_to_grain_size_mapping_idx = []
x=np.linspace(cfg.par.otf_extinction_log_min_size,cfg.par.otf_extinction_log_max_size,nsizes)
for i in range(nbins):
dust_file_to_grain_size_mapping_idx.append(find_nearest(x,grain_size_left_edge_array[i]))
#set up the frac array that is nbins big. this is the
#fractional contribution of each dust file bin which is based
#on the total number of grains in the grid in that bin.
#frac =np.zeros([dustdens.shape[0],nbins])
dsf_grid = np.zeros([dustdens.shape[0],nbins])
frac_grid = np.zeros([dustdens.shape[0],nbins])
debug_nearest_extinction_curve = np.zeros([nbins])
if cfg.par.OTF_EXTINCTION_MRN_FORCE == True:
grid_sum = np.zeros(nbins)
#how DNSF was set up. not needed other than for testing
x=np.linspace(-4,0,41)
#load an example dust size function for testing against
dsf = np.loadtxt(cfg.par.pd_source_dir+'active_dust/mrn_dn.txt')#DNSF_example.txt')
#nbins = len(grain_size_left_edge_array)
for i in range(nbins):
#find the index bounds in x that we want to interpolate between
idx0 = find_nearest(x,grain_size_left_edge_array[i])
if x[idx0] > grain_size_left_edge_array[i]: idx0 -= 1
idx1 = idx0+1
dsf_interp = np.interp(grain_size_left_edge_array[i],[x[idx0],x[idx1]],[dsf[idx0],dsf[idx1]])
#this sets the fraction of each bin size we need (for the
#entire grid!)
dsf_grid[:,i] = dsf_interp
grid_sum[i] = np.sum(dsf_grid[:,i])
debug_nearest_extinction_curve[i] = dsf_interp
#set up the frac array that is nbins big. this is the
#fractional contribution of each dust file bin which is based
#on the total number of grains in the grid in that bin.
frac = grid_sum/np.sum(grid_sum)
#now we need to set the localized extinction law. we do
#this by comparing, fractionally, a given cell's number of
#grains in that bin to the maximum number of grains that
#the grid has in that bin.
for i in range(nbins):
frac_grid[:,i] = dsf_grid[:,i]/np.max(dsf_grid[:,i])*frac[i]
'''
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(x,dsf,label='dsf')
ax.plot(grain_size_left_edge_array,frac_grid[0,:],label='frac_grid')
ax.plot(grain_size_left_edge_array,grid_sum,label='grid_sum')
ax.plot(grain_size_left_edge_array,debug_nearest_extinction_curve,label='d_n_e_c')
ax.set_yscale('log')
plt.legend()
fig.savefig('junk.png',dpi=300)
import pdb
pdb.set_trace()
'''
#------------------------
else:
grid_sum = np.zeros(nbins)
#this sets the fraction of each bin size we need (for the
#entire grid!)
for i in range(nbins):
grid_sum[i] = np.sum(grid_of_sizes[:,dust_file_to_grain_size_mapping_idx[i]])
#set up the frac array that is nbins big. this is the
#fractional contribution of each dust file bin which is based
#on the total number of grains in the grid in that bin.
frac = grid_sum/np.sum(grid_sum)
#now we need to set the localized extinction law. we do
#this by comparing, fractionally, a given cell's number of
#grains in that bin to the maximum number of grains that
#the grid has in that bin.
#this block tests if we're in an octree or not (i.e., we
#could be in a voronoi mesh, in which case refined doesn't
#mean anything). this is necessary since for an octree we
#don't want to worry about the Trues
if np.sum(refined) > 0:
wFalse = np.where(np.asarray(refined) == 0)[0]
for i in range(nbins):
frac_grid[wFalse,i] = grid_of_sizes[:,dust_file_to_grain_size_mapping_idx[i]]/np.max(grid_of_sizes[:,dust_file_to_grain_size_mapping_idx[i]])*frac[i]
else:
#we take the fractioal grain size distribution
#from each size bin, and multiply it by the
#cells in each grid (weighted by the ratio of
#the logarithm of the actual number of grains
#in that bin in that cell to the log of the
#cell with the most grains in that bin).
for i in range(nbins):
frac_grid[:,i] = np.log10(grid_of_sizes[:,dust_file_to_grain_size_mapping_idx[i]])/np.max(np.log10(grid_of_sizes[:,dust_file_to_grain_size_mapping_idx[i]]))*frac[i]
#now add the dust grids to hyperion
for bin in range(nbins):
file = dust_filenames[bin]
d = SphericalDust(cfg.par.pd_source_dir+'active_dust/'+file)
m.add_density_grid(dustdens*frac_grid[:,bin],d,specific_energy=specific_energy)
#m.add_density_grid(dustdens*frac[bin],d,specific_energy=specific_energy)
#finally, save the grid_of_sizes and grain sizes to the ds.paramteters so we can carry it around
ds.parameters['reg_grid_of_sizes'] = grid_of_sizes #named 'reg_grid_of_sizes'
ds.parameters['grain_sizes_in_micron '] = 10.**(x)
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
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 extract_extinction_map(SO_cm, SO, dust):
'''
Extracting extinction map from radiative transfer calculation.
Parameters
----------
SO_cm : SyntheticImage
FluxCompensator object of cm observation
SO : SyntheticImage
FluxCompensator object of synthetic observation where fieldstars should be added to.
dust : str
Path and name of dust file.
Returns
-------
A_v : numpy.ndarray
Optical extinction map.
'''
# S0_cm needs to be at 1cm
w0 = SO_cm.wav[0]
if w0 > 10000. or w0 < 9999.:
raise Exception('WARNING: Dust extinction image is not at 1cm.')
# check if resolution is the same
if SO.resolution['rad'] != SO_cm.resolution['rad']:
raise Exception(
'WARNING: Extinction image at 1cm and image from pipeline do not have the same resolution.'
)
# check if units are correct
if SO_cm.units != 'ergs/cm^2/s/Hz':
raise Exception('WARNING: Units of SO_cm need to be ergs/cm^2/s/Hz.')
if isinstance(dust, str):
# load dust properties from hyperion
from hyperion.dust import SphericalDust
d = SphericalDust(dust)
kappa = d.optical_properties.kappa
chi = d.optical_properties.chi
wav = d.optical_properties.wav
else:
# load dust_kappa, dust_chi & dust_wav
# from tuple dust={kappa, chi, wav}
kappa = dust['kappa']
chi = dust['chi']
wav = dust['wav']
if wav[0] < wav[1]:
kappa = kappa[::-1]
chi = chi[::-1]
wav = wav[::-1]
# extrapolate kappa at 1cm and chi at all wavelengths of SO
kappa_cm = np.interp(w0, wav[::-1], kappa[::-1]) # cm^2/g
chi_wav = np.interp(SO.wav, wav[::-1], chi[::-1]) # cm^2/g
# constants in cgs
T = 10.
pi = 3.141592653589793
h = 6.626068e-27
k = 1.3806503e-16
c = 29979245800.0
# Plank function and Jnu at 1cm with T=10K
nu = c / (w0 * 1e-4)
Bnu = 2 * h * nu**3 / c**2 * (np.exp(h * nu / k / T) - 1)**(-1)
Jnu = Bnu * kappa_cm
# surface density * chi = tau
# surface density = surface brightnes / Jnu
sr = SO_cm.resolution['rad']**2
tau = SO_cm.val / Jnu / sr * chi_wav
# extinction at 1 cm
A_lam = 1.086 * tau
# convert to A_v
print 'CAUTION: Extinction law from Kim et al. is used.'
wav_ext, k_lam = np.loadtxt(ROOT +
'../database/extinction/extinction_law.txt',
unpack=True)
k_v = np.interp(0.550, wav_ext, k_lam)
k = np.interp(1., wav_ext, k_lam)
A_v = A_lam * (k_v / k)
return A_v[:, :, 0]
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
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(parfile, output, imaging=True):
# Read in model parameters
par = read_parfile(parfile, nested=True)
# Find all dust files
dust_files = {}
for par_name in par:
if 'dust' in par[par_name]:
dust_file = par[par_name]['dust']
dust_files[dust_file] = SphericalDust(dust_file)
# Find dimensionality of problem:
if 'disk' in par:
ndim = 2
optimize = False
elif 'cavity' in par:
ndim = 2
optimize = False
elif 'envelope' in par and 'rc' in par['envelope']:
ndim = 2
optimize = False
else:
ndim = 1
optimize = True
# Set up model
m = AnalyticalYSOModel(output)
if not 'star' in par:
raise Exception("Cannot compute a model without a central source")
# Set radius and luminosity
m.star.radius = par['star']['radius'] * rsun
m.star.luminosity = 4. * pi * (par['star']['radius'] * rsun) ** 2. \
* sigma * par['star']['temperature'] ** 4.
# Interpolate and set spectrum
nu, fnu = interp_atmos(par['star']['temperature'])
m.star.spectrum = (nu, fnu)
subtract_from_ambient = []
if 'disk' in par:
# Add the flared disk component
disk = m.add_flared_disk()
# Basic parameters
disk.mass = par['disk']['mass'] * msun
disk.rmax = par['disk']['rmax'] * au
disk.p = par['disk']['p']
disk.beta = par['disk']['beta']
disk.h_0 = par['disk']['h100'] * au
disk.r_0 = 100. * au
# Set inner and outer walls to be spherical
disk.cylindrical_inner_rim = False
disk.cylindrical_outer_rim = False
# Set dust
disk.dust = dust_files[par['disk']['dust']]
# Inner radius
if 'rmin' in par['disk']:
disk.rmin = par['disk']['rmin'] * OptThinRadius(TSUB)
else:
disk.rmin = OptThinRadius(TSUB)
# Settling
if 'eta' in par['disk']:
raise Exception("Dust settling not implemented")
# Accretion luminosity
if 'lacc' in par['disk']:
raise Exception("Accretion luminosity not implemented")
# m.setup_magnetospheric_accretion(par['disk']['lacc'] * lsun,
# par['disk']['rtrunc'],
# par['star']['fspot'], disk)
subtract_from_ambient.append(disk)
if 'envelope' in par:
if 'rc' in par['envelope']: # Ulrich envelope
envelope = m.add_ulrich_envelope()
envelope.rho_0 = par['envelope']['rho_0']
envelope.rc = par['envelope']['rc'] * au
elif 'power' in par['envelope']: # Power-law envelope
envelope = m.add_power_law_envelope()
envelope.power = par['envelope']['power']
envelope.rho_0 = par['envelope']['rho_0']
envelope.r_0 = 1000. * au
# Set dust
envelope.dust = dust_files[par['envelope']['dust']]
# Inner radius
if 'rmin' in par['envelope']:
envelope.rmin = par['envelope']['rmin'] * OptThinRadius(TSUB)
else:
envelope.rmin = OptThinRadius(TSUB)
subtract_from_ambient.append(envelope)
if 'cavity' in par:
if not 'envelope' in par:
raise Exception("Can't have a bipolar cavity without an envelope")
# Add the bipolar cavity component
cavity = envelope.add_bipolar_cavity()
# Basic parameters
cavity.power = par['cavity']['power']
cavity.r_0 = 10000 * au
cavity.theta_0 = par['cavity']['theta_0']
cavity.rho_0 = par['cavity']['rho_0']
cavity.rho_exp = 0.
# Very important is that the cavity density should not be *larger* than
# the envelope density.
cavity.cap_to_envelope_density = True
# Set dust
cavity.dust = dust_files[par['cavity']['dust']]
subtract_from_ambient.append(cavity)
if 'ambient' in par:
# Add the ambient medium contribution
ambient = m.add_ambient_medium(subtract=subtract_from_ambient)
# Set the density, temperature, and dust properties
ambient.rho = par['ambient']['density']
ambient.temperature = par['ambient']['temperature']
ambient.dust = dust_files[par['ambient']['dust']]
# If there is an envelope, set the outer radius to where the
# optically thin temperature would transition to the ambient medium
# temperature
if 'envelope' in par:
# Find radius where the optically thin temperature drops to the
# ambient temperature. We can do this only if we've already set
# up all the sources of emission beforehand (which we have)
rmax_temp = OptThinRadius(ambient.temperature).evaluate(m.star, envelope.dust)
# Find radius where the envelope density drops to the ambient density
rmax_dens = envelope.outermost_radius(ambient.rho)
# If disk radius is larger than this, use that instead
if 'disk' in par:
if disk.rmax > rmax_dens:
rmax_dens = disk.rmax
# Pick the largest
if rmax_temp < rmax_dens:
print("Setting envelope outer radius to that where rho(r) = rho_amb")
envelope.rmax = rmax_dens
else:
print("Setting envelope outer radius to that where T_thin(r) = T_amb")
envelope.rmax = OptThinRadius(ambient.temperature)
ambient.rmax = envelope.rmax
else:
if 'disk' in par:
# Find radius where the optically thin temperature drops to the
# ambient temperature. We can do this only if we've already set
# up all the sources of emission beforehand (which we have)
rmax_temp = OptThinRadius(ambient.temperature).evaluate(m.star, ambient.dust)
# Find outer disk radius
rmax_dens = disk.rmax
# Pick the largest
if rmax_temp < rmax_dens:
print("Setting ambient outer radius to outer disk radius")
ambient.rmax = rmax_dens
else:
print("Setting ambient outer radius to that where T_thin(r) = T_amb")
ambient.rmax = OptThinRadius(ambient.temperature)
else:
ambient.rmax = OptThinRadius(ambient.temperature)
# The inner radius for the ambient medium should be the largest of
# the inner radii for the disk and envelope
if 'envelope' in par and 'rmin' in par['envelope']:
if 'disk' in par and 'rmin' in par['disk']:
ambient.rmin = max(par['disk']['rmin'], \
par['envelope']['rmin']) \
* OptThinRadius(TSUB)
else:
ambient.rmin = par['envelope']['rmin'] * OptThinRadius(TSUB)
elif 'disk' in par and 'rmin' in par['disk']:
ambient.rmin = par['disk']['rmin'] * OptThinRadius(TSUB)
else:
ambient.rmin = OptThinRadius(TSUB)
# The ambient medium needs to go out to sqrt(2.) times the envelope
# radius to make sure the slab is full (don't need to do sqrt(3)
# because we only need a cylinder along line of sight)
ambient.rmax *= np.sqrt(2.)
# Make sure that the temperature in the model is always at least
# the ambient temperature
m.set_minimum_temperature(ambient.temperature)
else:
# Make sure that the temperature in the model is always at least
# the CMB temperature
m.set_minimum_temperature(2.725)
if 'envelope' in par:
raise Exception("Can't have an envelope without an ambient medium")
# Use raytracing to improve s/n of thermal/source emission
m.set_raytracing(True)
# Use the modified random walk
m.set_mrw(True, gamma=2.)
# Use the partial diffusion approximation
m.set_pda(True)
# Improve s/n of scattering by forcing the first interaction
m.set_forced_first_scattering(True)
# Set up grid.
if ndim == 1:
m.set_spherical_polar_grid_auto(400, 1, 1)
else:
m.set_spherical_polar_grid_auto(400, 300, 1)
# Find the range of radii spanned by the grid
rmin, rmax = m.radial_range()
# Set up SEDs
image = m.add_peeled_images(sed=True, image=False)
image.set_wavelength_range(200, 0.01, 5000.)
if 'ambient' in par:
image.set_aperture_range(20, rmin, rmax / np.sqrt(2.))
else:
image.set_aperture_range(20, rmin, rmax)
image.set_output_bytes(8)
image.set_track_origin(True)
image.set_uncertainties(True)
image.set_stokes(True)
if ndim == 1:
# Viewing angle does not matter
image.set_viewing_angles([45.], [45.])
else:
# Use stratified random sampling to ensure that all models
# contain a viewing angle in each bin, but also ensure we have a
# continuum of viewing angles over all models
xi = np.random.uniform(0., 90./float(NVIEW), NVIEW)
theta = xi + np.linspace(0., 90. * (1. - 1./float(NVIEW)), NVIEW)
image.set_viewing_angles(theta, np.repeat(45., NVIEW))
if 'ambient' in par: # take a slab to avoid spherical geometrical effects
w = ambient.rmax / np.sqrt(2.)
image.set_depth(-w, w)
else: # don't need to take a slab, as no ambient material or envelope
image.set_depth(-np.inf, np.inf)
# Set number of photons
if imaging:
n_imaging=1e6
n_raytracing_sources=10000
n_raytracing_dust=1e6
else:
n_imaging=0
n_raytracing_sources=0
n_raytracing_dust=0
if ndim == 1:
m.set_n_photons(initial=100, imaging=n_imaging,
raytracing_sources=n_raytracing_sources,
raytracing_dust=n_raytracing_dust)
else:
m.set_n_photons(initial=1000000, imaging=n_imaging,
raytracing_sources=n_raytracing_sources,
raytracing_dust=n_raytracing_dust)
# Set physical array output to 32-bit
m.set_output_bytes(4)
# Set maximum of 10^8 interactions per photon
m.set_max_interactions(1e8)
# Only request certain arrays to be output
m.conf.output.output_density = 'none'
m.conf.output.output_specific_energy = 'last'
m.conf.output.output_n_photons = 'none'
m.conf.output.output_density_diff = 'last'
# Set number of temperature iterations and convergence criterion
m.set_n_initial_iterations(10)
m.set_convergence(True, percentile=99.0, absolute=2.0, relative=1.1)
# Don't copy the full input into the output files
m.set_copy_input(False)
# Check whether the model is very optically thick
mf = m.to_model()
if 'envelope' in par:
from hyperion.model.helpers import tau_to_radius
surface = tau_to_radius(mf, tau=1., wav=5e3)
rtau = np.min(surface)
if rtau > rmin and optimize:
log.warn("tau_5mm > 1 for all (theta,phi) values - truncating "
"inner envelope from {0:.3f}au to {1:.3f}au".format(mf.grid.r_wall[1] / au, rtau / au))
for item in mf.grid['density']:
item.array[mf.grid.gr < rtau] = 0.
# Write out file
mf.write(copy=False, absolute_paths=False,
physics_dtype=np.float32, wall_dtype=float)
# * system size = 10x10x10 pc
# * system coordinates (x,y,z min/max) = -5 to +5 pc
# * slab z extent = -2 to -5 pc
# * slab xy extend = -5 pc to 5 pc
# * z optical depth @0.55um in slab = 0.1, 1, 20
# * optical depth outside slab = 0
x = np.linspace(-5 * pc, 5 * pc, 100)
y = np.linspace(-5 * pc, 5 * pc, 100)
z = np.hstack([np.linspace(-5 * pc, -2 * pc, 100), 5 * pc])
m.set_cartesian_grid(x, y, z)
# Grain Properties:
d = SphericalDust('integrated_hg_scattering.hdf5')
chi_v = d.optical_properties.interp_chi_wav(0.55)
# Determine density in slab
rho0 = tau_v / (3 * pc * chi_v)
# Set up density grid
density = np.ones(m.grid.shape) * rho0
density[-1,:,:] = 0.
m.add_density_grid(density, d)
# Set up illuminating source:
s = m.add_point_source()
s.position = (0., 0., 4 * pc)
s.temperature = 10000.0
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
from hyperion.dust import SphericalDust
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from astropy import units as u
from astropy import constants as constants
dustfile = '/ufrc/narayanan/desika.narayanan/pd/hyperion-dust/dust_files/d03_3.1_6.0_A.hdf5'
dust = SphericalDust(dustfile)
mw_df_nu = dust.optical_properties.nu
mw_df_chi = dust.optical_properties.chi
mw_df_lam = (constants.c/(mw_df_nu*u.Hz)).to(u.micron)
x = 1./mw_df_lam
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(x,mw_df_chi)
ax.set_xlim([1,10])
ax.set_xlabel('x')
ax.set_ylabel(r'$\chi$')
fig.savefig('extinction.png',dpi=300)
