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
from dust import setup_dust
if not os.path.exists("models"):
os.mkdir("models")
if not os.path.exists("models/indiv"):
os.mkdir("models/indiv")
seed = -69102
for model_name in ["sky_updarm_gaussian_hole_morepah"]:
for spectral_type in spectral_types:
print "Processing %s..." % spectral_type
m = Model("models/indiv/energy_%s_%s" % (model_name, spectral_type.replace(" ", "_")))
seed += 1
m.set_seed(seed)
m = setup_sources(m, model_name, sky_model[model_name][0], spectral_type=spectral_type)
m = setup_dust(m, **sky_model[model_name][1])
m.set_n_initial_iterations(3)
m.set_n_photons(initial=100000000, imaging=0)
m.set_enforce_energy_range(False)
m.write()
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 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
