def test_vs_RADEX():
for filename,coll_ID,Tex_values,tau_nu_values in\
zip(filenames,collider_names,RADEX_Tex,RADEX_tau_nu):
lamda_filepath = os.path.join(here,filename)
params = zip(Ntot_values,width_v_values,Tkin_values,
coll_partner_densities_values,test_trans,Tex_values,tau_nu_values)
for Ntot,width_v,Tkin,collp_dens,trans_num,Tex,tau_nu in params:
coll_partner_densities = {coll_ID:collp_dens}
test_nebula = nebula.Nebula(
data_filepath=lamda_filepath,geometry=geometry,
ext_background=ext_background,Tkin=Tkin,
coll_partner_densities=coll_partner_densities,
Ntot=Ntot,line_profile=line_profile,width_v=width_v)
test_nebula.solve_radiative_transfer()
fluxes = test_nebula.observed_fluxes(
source_surface=surface,d_observer=distance)
#see radex_output_readme.txt or RADEX manual for explanation of the follwing formula
trans = test_nebula.emitting_molecule.rad_transitions[trans_num]
RADEX_intensity = (helpers.B_nu(nu=trans.nu0,T=Tex)- ext_background(trans.nu0))\
* (1-np.exp(-tau_nu))
RADEX_flux = np.pi*RADEX_intensity*trans.line_profile.width_nu
pyradex_flux = fluxes[trans_num]
print('RADEX flux: {:g}; pyradex flux: {:g}'.format(RADEX_flux,pyradex_flux))
pyradex_tau = test_nebula.tau_nu0[trans_num]
print('RADEX tau: {:g}; pyradex tau: {:g}'.format(tau_nu,pyradex_tau))
print('RADEX Tex: {:g} K; pyradex Tex: {:g} K'.format(Tex,test_nebula.Tex[trans_num]))
assert np.isclose(RADEX_flux,pyradex_flux,atol=0,rtol=0.7)
assert np.isclose(pyradex_tau,tau_nu,atol=0.01,rtol=0.1)
print('\n')
def compute_line_fluxes(self, solid_angle):
'''Compute the observed spectra and total fluxes for each line. This
requires that the radiative transfer has been solved. This method
computes the attributes obs_line_fluxes (total observed line fluxes in W/m2)
and obs_line_spectra (observed line spectra in W/m2/Hz).
Parameters:
---------------
solid_angle: float
the solid angle of the source in [rad2]
'''
self.obs_line_fluxes = []
self.obs_line_spectra = []
for i, line in enumerate(self.emitting_molecule.rad_transitions):
nu_array = line.line_profile.nu_array
x1 = self.level_pop[line.low.number]
x2 = self.level_pop[line.up.number]
source_function = helpers.B_nu(T=self.Tex[i], nu=nu_array)
tau_nu = line.tau_nu_array(N1=x1 * self.Ntot, N2=x2 * self.Ntot)
line_flux_nu = self.geometry.compute_flux_nu(
tau_nu=tau_nu,
source_function=source_function,
solid_angle=solid_angle)
self.obs_line_spectra.append(line_flux_nu) #W/m2/Hz
line_flux = np.trapz(line_flux_nu,
line.line_profile.nu_array) #[W/m2]
self.obs_line_fluxes.append(line_flux)
def test_compute_line_fluxes():
n = thin_LTE_test_nebula.Ntot / (2 * r)
tot_particles = n * 4 / 3 * r**3 * np.pi
up_level_particles = np.array([
tot_particles * expected_LTE_level_pop[trans.up.number]
for trans in thin_LTE_test_nebula.emitting_molecule.rad_transitions
])
A21 = [
trans.A21
for trans in thin_LTE_test_nebula.emitting_molecule.rad_transitions
]
Delta_E = [
trans.Delta_E
for trans in thin_LTE_test_nebula.emitting_molecule.rad_transitions
]
expected_LTE_fluxes_thin = up_level_particles * A21 * Delta_E / (
4 * np.pi * r**2)
assert np.allclose(thin_LTE_test_nebula.line_fluxes,
expected_LTE_fluxes_thin,
atol=0,
rtol=1e-2)
thick_LTE_test_nebula.solve_radiative_transfer()
#the higher transitions might not be optically thick, so just test the lowest transition:
lowest_trans = thick_LTE_test_nebula.emitting_molecule.rad_transitions[0]
expected_thick_LTE_flux_lowest_trans = np.pi*helpers.B_nu(nu=lowest_trans.nu0,T=Tkin)\
* lowest_trans.line_profile.width_nu
assert np.isclose(thick_LTE_test_nebula.line_fluxes[0],
expected_thick_LTE_flux_lowest_trans,
atol=0,
rtol=1e-2)
def test_CMB_background():
test_nu = np.logspace(np.log10(1), np.log10(1000), 20) * constants.giga
test_z = (0, 2)
for z in test_z:
CMB = helpers.generate_CMB_background(z=z)
assert np.all(
CMB(test_nu) == helpers.B_nu(nu=test_nu, T=2.73 * (1 + z)))
def compute_line_fluxes(self):
'''Compute the flux for each line. This requires that the radiative transfer
has been solved'''
self.line_fluxes = []
for i, line in enumerate(self.emitting_molecule.rad_transitions):
nu_array = line.line_profile.nu_array
x1 = self.level_pop[line.low.number]
x2 = self.level_pop[line.up.number]
source_function = helpers.B_nu(T=self.Tex[i], nu=nu_array)
tau_nu = line.tau_nu_array(N1=x1 * self.Ntot, N2=x2 * self.Ntot)
line_flux_nu = self.geometry.compute_flux_nu(
tau_nu=tau_nu, source_function=source_function)
line_flux = np.trapz(line_flux_nu,
line.line_profile.nu_array) #[W/m2]
self.line_fluxes.append(line_flux)
def test_compute_line_fluxes():
for geo,neb in nebulae.items():
if 'RADEX' in geo or 'slab' in geo:
continue
Ntot = neb['thin_LTE'].Ntot
if 'sphere' in geo:
volume = 4/3*r**3*np.pi
n = Ntot/(2*r)
elif 'slab' in geo:
volume = slab_surface*slab_depth
n = Ntot/slab_depth
tot_particles = n*volume
thin_LTE_rad_transitions = neb['thin_LTE'].emitting_molecule.rad_transitions
up_level_particles = np.array(
[tot_particles*expected_LTE_level_pop[trans.up.number]
for trans in thin_LTE_rad_transitions])
A21 = np.array([trans.A21 for trans in thin_LTE_rad_transitions])
Delta_E = np.array([trans.Delta_E for trans in thin_LTE_rad_transitions])
energy_flux = up_level_particles*A21*Delta_E #W
if 'sphere' in geo:
expected_LTE_fluxes_thin = energy_flux/(4*np.pi*d**2)
elif 'slab' in geo:
#not isotropic, so I can't do the same thing as for sphere
expected_LTE_fluxes_thin = energy_flux/(4*np.pi*slab_surface)\
*get_solid_angle(geo)
line_profile_nu0 = np.array([trans.line_profile.phi_nu(trans.nu0) for
trans in thin_LTE_rad_transitions])
expected_LTE_fluxes_nu_thin = expected_LTE_fluxes_thin*line_profile_nu0
assert np.allclose(neb['thin_LTE'].obs_line_fluxes,expected_LTE_fluxes_thin,
atol=0,rtol=1e-2)
line_fluxes_nu_thin = [np.max(fluxes_nu) for fluxes_nu in
neb['thin_LTE'].obs_line_spectra]
assert np.allclose(line_fluxes_nu_thin,expected_LTE_fluxes_nu_thin,
atol=0,rtol=1e-2)
#the higher transitions might not be optically thick, so just test the
#lowest transition:
lowest_trans = neb['thick_LTE'].emitting_molecule.rad_transitions[0]
expected_thick_LTE_flux_nu = helpers.B_nu(nu=lowest_trans.nu0,T=Tkin)\
*get_solid_angle(geo)
expected_thick_LTE_flux = expected_thick_LTE_flux_nu\
* lowest_trans.line_profile.width_nu
assert np.isclose(neb['thick_LTE'].obs_line_fluxes[0],
expected_thick_LTE_flux,atol=0,rtol=1e-2)
def test_vs_RADEX():
for filename, coll_ID in zip(filenames, collider_names):
specie = filename[:-4]
lamda_filepath = os.path.join(here, filename)
params = zip(Ntot_values, width_v_values, Tkin_values,
coll_partner_densities_values, test_trans)
for i, (Ntot, width_v, Tkin, collp_dens,
trans_num) in enumerate(params):
coll_partner_densities = {coll_ID: collp_dens}
for geo in geometries:
Omega = get_solid_angle(geo)
print('looking at {:s}, {:s} (case {:d})'.format(
specie, geo, i))
test_nebula = nebula.Nebula(
datafilepath=lamda_filepath,
geometry=geo,
ext_background=ext_background,
Tkin=Tkin,
coll_partner_densities=coll_partner_densities,
Ntot=Ntot,
line_profile=line_profile,
width_v=width_v)
test_nebula.solve_radiative_transfer()
test_nebula.compute_line_fluxes(solid_angle=Omega)
trans = test_nebula.emitting_molecule.rad_transitions[
trans_num]
if 'sphere' in geo:
radex = RADEX['sphere']
elif 'slab' in geo:
radex = RADEX['slab']
Tex = radex[specie]['Tex'][i]
tau_nu = radex[specie]['tau'][i]
#see radex_output_readme.txt or RADEX manual for explanation of the
#follwing formula
RADEX_intensity = (helpers.B_nu(nu=trans.nu0,T=Tex)
-ext_background(trans.nu0))\
* (1-np.exp(-tau_nu))
RADEX_flux = RADEX_intensity * trans.line_profile.width_nu * Omega
pyradex_flux = test_nebula.obs_line_fluxes[trans_num]
print('RADEX flux: {:g}; pyradex flux: {:g}'.format(
RADEX_flux, pyradex_flux))
pyradex_tau = test_nebula.tau_nu0[trans_num]
pyradex_Tex = test_nebula.Tex[trans_num]
print('RADEX tau: {:g}; pyradex tau: {:g}'.format(
tau_nu, pyradex_tau))
print('RADEX Tex: {:g} K; pyradex Tex: {:g} K'.format(
Tex, pyradex_Tex))
if 'RADEX' in geo:
atol_flux = 0
rtol_flux = 0.3
atol_tau = 0.1
rtol_tau = 0.1
atol_Tex = 1
rtol_Tex = 0.2
else:
atol_flux = 0
rtol_flux = 0.7
atol_tau = 0.1
rtol_tau = 0.5
atol_Tex = 1
rtol_Tex = 0.5
if pyradex_tau < 0:
assert tau_nu < 0
assert Tex < 0
assert pyradex_Tex < 0
else:
assert np.isclose(pyradex_tau,
tau_nu,
atol=atol_tau,
rtol=rtol_tau)
assert np.isclose(RADEX_flux,
pyradex_flux,
atol=atol_flux,
rtol=rtol_flux)
assert np.isclose(Tex,
pyradex_Tex,
atol=atol_Tex,
rtol=rtol_Tex)
print('\n')
print('\n')
test_molecule = molecule.Molecule.from_LAMDA_datafile(
data_filepath=lamda_filepath)
Tkin = 100
expected_LTE_level_pop = test_molecule.LTE_level_pop(Tkin)
coll_partner_densities_default = {
'para-H2': 1e1 / constants.centi**3,
'ortho-H2': 1e1 / constants.centi**3
}
coll_partner_densities_large = {
'para-H2': 1e10 / constants.centi**3,
'ortho-H2': 1e10 / constants.centi**3
}
coll_partner_densities_0 = {'para-H2': 0, 'ortho-H2': 0}
Jbar_lines_0 = np.zeros(test_molecule.n_rad_transitions)
Jbar_lines_B_nu = [
helpers.B_nu(nu=trans.nu0, T=Tkin)
for trans in test_molecule.rad_transitions
]
beta_lines_thin = np.ones(test_molecule.n_rad_transitions)
I_ext_lines_0 = np.zeros(test_molecule.n_rad_transitions)
def get_level_pops(coll_partner_densities, Jbar_lines, beta_lines,
I_ext_lines):
kwargs = {
'molecule': test_molecule,
'coll_partner_densities': coll_partner_densities,
'Tkin': Tkin
}
rate_equations_std = nebula.RateEquations(mode='std', **kwargs)
level_pop_std = rate_equations_std.solve(Jbar_lines=Jbar_lines)
from scipy import constants
import numpy as np
here = os.path.dirname(os.path.abspath(__file__))
lamda_filepath = os.path.join(here,'co.dat')
test_molecule = molecule.Molecule.from_LAMDA_datafile(datafilepath=lamda_filepath)
Tkin = 100
expected_LTE_level_pop = test_molecule.LTE_level_pop(Tkin)
coll_partner_densities_default = {'para-H2':1e1/constants.centi**3,
'ortho-H2':1e1/constants.centi**3}
coll_partner_densities_large = {'para-H2':1e10/constants.centi**3,
'ortho-H2':1e10/constants.centi**3}
coll_partner_densities_0 = {'para-H2':0,'ortho-H2':0}
Jbar_lines_0 = np.zeros(test_molecule.n_rad_transitions)
Jbar_lines_B_nu = [helpers.B_nu(nu=trans.nu0,T=Tkin) for trans in
test_molecule.rad_transitions]
beta_lines_thin = np.ones(test_molecule.n_rad_transitions)
I_ext_lines_0 = np.zeros(test_molecule.n_rad_transitions)
def get_level_pops(coll_partner_densities,Jbar_lines,beta_lines,I_ext_lines):
kwargs = {'molecule':test_molecule,
'coll_partner_densities':coll_partner_densities,'Tkin':Tkin}
rate_equations_std = nebula.RateEquations(mode='std',**kwargs)
level_pop_std = rate_equations_std.solve(Jbar_lines=Jbar_lines)
rate_equations_ALI = nebula.RateEquations(mode='ALI',**kwargs)
level_pop_ALI = rate_equations_ALI.solve(
beta_lines=beta_lines,I_ext_lines=I_ext_lines)
return level_pop_std,level_pop_ALI
def test_compute_level_populations_no_excitation():
