def test_that_the_lique_cooling_function_is_computed_correctly():
t_rad = 0.0 * u.K
nc_h = 1e6 * u.m**-3
species_data = DataLoader().load('H2_lique')
t_array = u.Quantity([100.0, 500.0, 1000.0, 2000.0, 5000.0]) * u.K
cooling_rate_expected = u.Quantity([
1.09526153e-28, # T=100K
2.67540424e-26, # T=500K
6.64684189e-25, # T=1000K
8.35387575e-24, # T=2000K
1.00510330e-22 # T=5000K
]) * u.erg / u.s
cooling_rate_for_t_array = u.Quantity([
cooling_rate_at_steady_state(species_data, t, t_rad, nc_h)
for t in t_array
])
assert_allclose(cooling_rate_for_t_array.cgs.value,
cooling_rate_expected.cgs.value,
rtol=2e-6,
atol=0.0)
def test_that_the_lipovka_cooling_function_is_computed_correctly():
t_rad = 0.0 * u.K
nc_h = 1e6 * u.m**-3
species_data = DataLoader().load('HD_lipovka')
t_array = u.Quantity([100.0, 500.0, 1000.0, 1500.0, 2000.0]) * u.K
cooling_rate_expected = u.Quantity([
2.239044e-25, # T=100K
2.861033e-24, # T=500K
6.783145e-24, # T=1000K
1.108309e-23, # T=1500K
1.570218e-23 # T=2000K
]) * u.erg / u.s
cooling_rate_for_t_array = u.Quantity([
cooling_rate_at_steady_state(species_data, t, t_rad, nc_h)
for t in t_array
])
assert_allclose(cooling_rate_for_t_array.cgs.value,
cooling_rate_expected.cgs.value,
rtol=1e-6,
atol=0.0)
def compute(self):
"""
Compute the cooling function for the specified grid
:return: ndarray
"""
self._compute_mesh()
cooling_rate = numpy.zeros_like(self.n_grid.value).flatten()
for i, (n, t_kin, t_rad) in enumerate(
zip(self.n_grid.flat, self.t_kin_grid.flat,
self.t_rad_grid.flat)):
cooling_rate[i] = cooling_rate_at_steady_state(
self.species, t_kin, t_rad, n).cgs.value
cooling_rate_grid = cooling_rate.reshape(self.n_grid.shape)
self.cooling_function = cooling_rate_grid
return cooling_rate_grid
t_rng = numpy.logspace(2, 3.6, 10) * u.Kelvin
# T_rng = species_data.raw_data.collision_rates_T_range
plot_markers = [(2 + i // 2, 1 + i % 2, 0) for i in range(16)]
for nc_index, nc_h in enumerate(nc_h_rng):
lambda_vs_t_kin = []
lambda_vs_t_kin_wrathmall = []
pop_dens_vs_t_kin = []
for i, t_kin in enumerate(t_rng):
print(t_kin, nc_h)
lambda_vs_t_kin += [
cooling_rate_at_steady_state(species_data, t_kin, t_rad, nc_h)
]
pop_dens_vs_t_kin += [
population_density_at_steady_state(species_data, t_kin, t_rad,
nc_h)
]
lambda_vs_t_kin_wrathmall += [
cooling_rate_at_steady_state(species_data_wrathmall, t_kin, t_rad,
nc_h)
]
print(t_kin, pop_dens_vs_t_kin[i][0], pop_dens_vs_t_kin[i][2],
pop_dens_vs_t_kin[i][4], pop_dens_vs_t_kin[i][6],
pop_dens_vs_t_kin[i][8], pop_dens_vs_t_kin[i][10],
pop_dens_vs_t_kin[i][12], pop_dens_vs_t_kin[i][14],
fobj.write('#{:<12} {:<12} {:<12} {:<12}\n'.format(
u.m**-3, u.Kelvin, u.Kelvin, u.Joule / u.second
))
fig, axs = pylab.subplots(1)
# the data
for nc_H in numpy.logspace(6, 12, 7)*u.meter**-3:
for t_rad in numpy.array([0.0], 'f8') * u.Kelvin:
cooling_rate_vs_t_kin = []
for t_kin in species_data.raw_data.collision_rates_t_range:
cooling_rate = cooling_rate_at_steady_state(
species_data,
t_kin,
t_rad,
nc_H
)
fobj.write('{:e} {:e} {:e} {:e}\n'.format(
nc_H.value,
t_rad.value,
t_kin.value,
cooling_rate.to(u.Joule / u.second).value)
)
cooling_rate_vs_t_kin.append(
cooling_rate.to(u.Joule / u.second).value
)
pylab.loglog(
