def test_that_two_level_analytic_pop_dens_ratios_are_computed_correctly():
#
# load the two level species data
#
species_data = DataLoader().load('two_level_1')
n_c = 1e6 * u.meter ** -3
t_rad = 1000.0 * u.Kelvin
#
# for each value in the temperature range
# - compute the population density ration numerically
# - compute the population density analytically
#
# plot the comaprision
#
ratio_numeric_vs_t_kin = []
ratio_analytic_vs_t_kin = []
t_kin_range = numpy.logspace(1.0, 10.0, 50) * u.Kelvin
for t_kin in t_kin_range:
pop_dens_eq_numeric = population_density_at_steady_state(
species_data,
t_kin,
t_rad,
n_c)
pop_dens_eq_ratio_numeric = pop_dens_eq_numeric[1] / pop_dens_eq_numeric[0]
ratio_numeric_vs_t_kin.append(pop_dens_eq_ratio_numeric)
pop_dens_eq_ratio_analytic = analytic.population_density_ratio_two_level(
species_data.energy_levels.data['g'],
species_data.energy_levels.data['E'],
species_data.k_dex_matrix_interpolator(t_kin)[1, 0],
species_data.a_matrix[1, 0],
n_c,
t_kin,
t_rad
)
ratio_analytic_vs_t_kin.append(pop_dens_eq_ratio_analytic)
print(t_kin)
ratio_numeric_vs_t_kin = numpy.array(ratio_numeric_vs_t_kin).flatten()
ratio_analytic_vs_t_kin = numpy.array(ratio_analytic_vs_t_kin).flatten()
relative_error = numpy.abs(
1.0 - numpy.array(ratio_numeric_vs_t_kin) / numpy.array(
ratio_analytic_vs_t_kin)
)
assert relative_error.max() < 1.2e-15
assert relative_error.std() < 5.0e-16
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 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)
import matplotlib.pyplot as plt
import numpy
from astropy import units as u
from frigus.cooling_function.fits import fit_glover
from frigus.population import (cooling_rate_at_steady_state,
population_density_at_steady_state)
from frigus.readers.dataset import DataLoader
import matplotlib
from matplotlib.ticker import ScalarFormatter
from decimal import Decimal
species_data = DataLoader().load('H2_lique')
species_data_wrathmall = DataLoader().load('H2_wrathmall')
plt.ion()
#fig, axs = plt.subplots(figsize=(8, 8))
# density of the colliding species, in m^3
nc_h_rng = [1.e6, 1.e7, 1.e8, 1.e9, 1.e10, 1.e11, 1.e12, 1.e13, 1.e14
] * u.meter**-3
t_rad = 0.0 * u.Kelvin
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)]
<seealso>
</seealso>
"""
from __future__ import print_function
import numpy
from astropy import units as u
import matplotlib.pyplot as plt
from frigus.readers.dataset import DataLoader
from frigus.population import population_density_at_steady_state
from frigus.solvers import analytic
#
# load the two level species data
#
species_data = DataLoader().load('two_level_1')
n_c = 1e6 * u.meter ** -3
t_kin = 100000.0 * u.Kelvin
t_rad = 100.0 * u.Kelvin
#
# for each value in the temperature range
# - compute the population density ration numerically
# - compute the population density analytically
#
# plot the comaprision
#
ratio_numeric_vs_t_kin = []
ratio_analytic_vs_t_kin = []
t_kin_range = numpy.logspace(1.0, 10.0, 50) * u.Kelvin
<seealso>
</seealso>
"""
from __future__ import print_function
import numpy
from astropy import units as u
import matplotlib.pyplot as plt
from scipy.integrate import ode
from frigus.population import (population_density_at_steady_state,
compute_transition_rate_matrix)
from frigus.readers.dataset import DataLoader
# load the species data
species_data = DataLoader().load('HD_lipovka')
# time range and timestep
t_0 = 0.0
t_f = 1e10
dt = 1e5
# the environment parameters
nc_h = 1e8 * u.meter**-3
t_kin = 2000.0 * u.Kelvin
t_rad = 2.73 * u.Kelvin
# the rates matrix (that is fixed if the environemnt params above are fixed)
# so it is computed once
m_matrix = compute_transition_rate_matrix(species_data, t_kin, t_rad, nc_h)
.. warning:: this results in negative population densities. fix it.
"""
from __future__ import print_function
import matplotlib.pyplot as plt
import numpy
from astropy import units as u
from frigus.cooling_function.fits import fit_glover
from frigus.population import (cooling_rate_at_steady_state,
population_density_at_steady_state)
from frigus.readers.dataset import DataLoader
species_data = DataLoader().load('H2_wrathmall')
# density of the colliding species, in m^3
nc_h = 1e6 * u.meter ** -3
t_rad = 0.0 * u.Kelvin
lambda_vs_t_kin = []
pop_dens_vs_t_kin = []
t_rng = species_data.raw_data.collision_rates_t_range
for i, t_kin in enumerate(t_rng):
#print(t_kin)
pop_dens_vs_t_kin += [
population_density_at_steady_state(species_data, t_kin, t_rad, nc_h)
"""
compute the cooling function as a function of kinetic temperature and gas
density and plot them in 3D
"""
from __future__ import print_function
import numpy as np
from astropy import units as u
from frigus.readers.dataset import DataLoader
from frigus.cooling_function.grid import CoolingFunctionGrid
grid = CoolingFunctionGrid()
grid.set_species(DataLoader().load('HD_lipovka'))
grid.set_density(np.logspace(6, 14, 10) * u.m**-3)
grid.set_t_kin(np.logspace(2, 3.2, 10) * u.Kelvin)
grid.set_t_rad(0.0 * u.Kelvin)
grid.plot_3d(x='n', y='t_kin', show=True)
print('done')
from numpy import fabs, logspace, array
from astropy import units as u
from frigus.solvers.analytic import population_denisty_ratio_two_level_no_radiation
from frigus.population import population_density_at_steady_state
from frigus.readers.dataset import DataLoader
# density of the colliding species, in m^3
nc = 1e14 * u.meter**-3
# the kinetic temperature of the gas
t_kin = 3000.0 * u.Kelvin
t_rad = 30.0 * u.Kelvin
species_data = DataLoader().load('two_level_1')
t_kin_range = logspace(2.0, 6.0, 100) * u.K
x_exact = array([
population_denisty_ratio_two_level_no_radiation(species_data, T_kin, nc)
for T_kin in t_kin_range
], 'f8')
x_numerical = array([
population_density_at_steady_state(species_data, T_kin, 0.0, nc)
for T_kin in t_kin_range
], 'f8')[:, :, 0]
# plot the relative population densities as a function of temperatures
pylab.figure()
# -*- coding: utf-8 -*-
"""
Calculate equilibrium population of species and the cooling function using
the Lipovka data
"""
from __future__ import print_function
import numpy as np
import matplotlib.pyplot as plt
from astropy import units as u
from frigus.cooling_function.fits import fit_lipovka
from frigus.population import cooling_rate_at_steady_state
from frigus.readers.dataset import DataLoader
species_data = DataLoader().load('HeH2')
plt.ion()
fig, axs = plt.subplots(figsize=(8, 8))
# density of the colliding species, in m^3
nc_h_rng = [1.e6, 1.e7, 1.e8, 1.e9, 1.e10, 1.e11, 1.e12, 1.e13, 1.e14
] * u.meter**-3
t_rad = 0.0 * u.Kelvin
t_rng = np.logspace(2, 3.2, 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):
# -*- coding: utf-8 -*-
"""
Calculate equilibrium population of species and the cooling function using
the Lipovka data
"""
from __future__ import print_function
import numpy as np
import matplotlib.pyplot as plt
from astropy import units as u
from frigus.cooling_function.fits import fit_coppola
from frigus.population import cooling_rate_at_steady_state
from frigus.readers.dataset import DataLoader
species_data = DataLoader().load('H2_lique')
plt.ion()
fig, axs = plt.subplots(figsize=(8, 8))
# density of the colliding species, in m^3
nc_h_rng = [
1.e6, 1.e7, 1.e8, 1.e9, 1.e10, 1.e11, 1.e12, 1.e13, 1.e14
] * u.meter ** -3
t_rad = 0.0 * u.Kelvin
t_rng = np.logspace(2, 3.2, 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)]
Calculate equilibrium population of species and the cooling function using the
Lique data.
"""
from __future__ import print_function
import matplotlib.pyplot as plt
import numpy
from astropy import units as u
from frigus.cooling_function.fits import fit_glover
from frigus.population import (cooling_rate_at_steady_state,
population_density_at_steady_state)
from frigus.readers.dataset import DataLoader
species_data = DataLoader().load('H2_low_energy_levels')
# density of the colliding species, in m^3
nc_h = 1e6 * u.meter**-3
t_rad = 0.0 * u.Kelvin
lambda_vs_t_kin = []
pop_dens_vs_t_kin = []
t_rng = species_data.raw_data.collision_rates_t_range
for t_kin in t_rng:
print(t_kin)
pop_dens_vs_t_kin += [
population_density_at_steady_state(species_data, t_kin, t_rad, nc_h)
# -*- coding: utf-8 -*-
"""
Calculate equilibrium population of species and the cooling function using
the data by Benjamin Desrorreaux et al 2018
"""
from __future__ import print_function
import numpy as np
import matplotlib.pyplot as plt
from astropy import units as u
from frigus.cooling_function.fits import fit_lipovka, fit_flower
from frigus.population import cooling_rate_at_steady_state
from frigus.readers.dataset import DataLoader
species_data = DataLoader().load('HD_galileo_project')
# density of the colliding species, in m^3
#nc_h_rng = [
# 1.e6, 1.e7, 1.e8, 1.e9, 1.e10, 1.e11, 1.e12, 1.e13, 1.e14
# ] * u.meter ** -3
nc_h_rng = [1.e6] * u.meter**-3
t_rad = 0.0 * u.Kelvin
t_rng = np.logspace(1, 3.1, 30) * u.Kelvin
#T_rng = species_data.raw_data.collision_rates_T_range
#T_rng_old = species_data_old.raw_data.collision_rates_T_range
plot_markers = [(2 + i // 2, 1 + i % 2, 0) for i in range(16)]
fig, axs = plt.subplots(figsize=(10, 10))