def setUp(self):
ad1 = atomic.element('carbon')
ad2 = atomic.element('li')
eq1 = atomic.CollRadEquilibrium(ad1)
eq2 = atomic.CollRadEquilibrium(ad2)
te = np.logspace(0, 3, 50)
ne = 1e19
self.y1 = eq1.ionisation_stage_distribution(te, ne)
self.y2 = eq2.ionisation_stage_distribution(te, ne)
def setUp(self):
self.ad = atomic.element('lithium')
self.temperature = np.logspace(0, 3, 50)
self.density = 1e19
self.times = np.logspace(-7, 0, 120)
self.times -= self.times[0]
self.rt = atomic.RateEquations(self.ad)
def setUp(self):
ad = atomic.element('li')
eq = atomic.CollRadEquilibrium(ad)
self.temperature = np.logspace(0, 3, 50)
self.electron_density = 1e19
# y is a FractionalAbundance object.
y = eq.ionisation_stage_distribution(self.temperature,
self.electron_density)
self.elc = atomic.ElectronCooling(y, neutral_fraction=1e-2)
def netauplot(netau):
""" Create a subplot for given ne * tau."""
taus = netau / densities
for i in range(len(densities)):
density = densities[i]
tau = taus[i]
for e in elementColors:
plotEps(atomic.element(e['el']),
density,
tau,
e['c'],
lw=linewidth[i])
# make the titles and set boundaries
netaustring = num2str(netau)
title1 = r'$n_e \tau = 10^{' + netaustring + '} \; [\mathrm{s} \; \mathrm{m}^{-3}]$'
plt.title(title1)
plt.ylabel(r'$\epsilon_{cool} \; [\mathrm{eV}]$')
plt.xlabel(r'$T_e \; [\mathrm{eV}]$')
plt.ylim(ymin=1e-1, ymax=1e5)
plt.xlim(xmin=0.4, xmax=2000)
# create the two legends
lh = []
for e in elementColors:
lh.append(
mlines.Line2D([], [],
color=e['c'],
label=atomic.element(e['el']).element))
elements_legend = plt.legend(handles=lh, loc=2)
ax = plt.gca().add_artist(elements_legend)
lh = []
for i in range(len(densities)):
d = num2str(densities[i])
tau = num2str(taus[i])
lab = r'$n_e = 10^{' + d + r'},\; \tau = 10^{' + tau + '}$'
lh.append(mlines.Line2D([], [], color='k', label=lab, lw=linewidth[i]))
plt.legend(handles=lh, loc=4)
plt.grid(True)
def test_diffusion(self):
"""Tests that the total number of particles stays constant at 1.0."""
ad = atomic.element('carbon')
temperature = np.logspace(0, 3, 100)
density = 1e19
tau = 1e-3
times = np.logspace(-7, 0, 120)
times -= times[0]
rt = atomic.RateEquationsWithDiffusion(ad)
yy = rt.solve(times, temperature, density, tau)
expected = np.ones(len(times))
result = [
np.sum(yy.abundances[i].y) / len(temperature)
for i in range(len(times))
]
np.testing.assert_array_almost_equal(expected, result)
def setUp(self):
"""This is more of an Integration Test than a unit test."""
self.ad = atomic.element('Li')
self.eq = atomic.CollRadEquilibrium(self.ad)
from ensemble_average import annotate_lines
def parabolic_profile(y0):
x = np.linspace(1., 0, 50)
y = 1 - x**2
y *= y0
return x, y
r, temperature = parabolic_profile(3e3)
r, density = parabolic_profile(1e19)
try:
ad
except NameError:
from atomic.pec import TransitionPool
ad = atomic.element('argon')
tp = TransitionPool.from_adf15('adas_data/pec/transport_llu#ar*.dat')
ad = tp.filter_energy(2e3, 20e3, 'eV').create_atomic_data(ad)
eq = atomic.CoronalEquilibrium(ad)
y = eq.ionisation_stage_distribution(temperature, density)
ne_tau = np.array([1e-1, 1e-2, 1e-3])
impurity_fraction = 0.05
texts = ['$10^{%d}$' % i for i in np.log10(ne_tau)]
try:
tau_ss
except NameError:
else:
colors = [''] * 3
for i, key in enumerate(['line_power', 'continuum_power', 'cx_power']):
if label:
label_str = key
else:
label_str = None
c= ad.coeffs[key](charge, te, ne)
ax.loglog(te, c, colors[i], label=label_str, **kwargs)
ax.set_ylabel('$P/n_\mathrm{e} n_\mathrm{I}\ [\mathrm{W m^3}]$')
if __name__ == '__main__':
ad = atomic.element('carbon')
tp = TransitionPool.from_adf15('adas_data/pec/pec96#c_pju*.dat')
#tp = tp.filter_energy(2e3, 20e3, 'eV')
ad_filtered = tp.create_atomic_data(ad)
te = np.logspace(0, 3)
f = plt.figure(1); f.clf()
for i, charge in enumerate([1, 2, 5]):
ax = f.add_subplot(3, 1, i+1)
plot_coeffs(ad, te, charge)
plot_coeffs(ad_filtered, te, charge, marker='x', ls='', label=None)
ax.set_ylim(1e-35, 1e-30)
label = '$\mathrm{%s}^{%d+}$' % (ad.element, charge)
annotate_lines(
['$10^{%d}$' % i for i in np.log10(times * solution.density)])
power_coronal = atomic.Radiation(
solution.y_coronal).specific_power['total']
ax.loglog(solution.temperature, power_coronal, color='black')
ax.set_title(title)
if __name__ == '__main__':
times = np.logspace(-7, 0, 100)
temperature = np.logspace(0, 3, 100)
density = 1e19
rt = atomic.RateEquations(atomic.element('carbon'))
y = rt.solve(times, temperature, density)
taus = np.array([1e14, 1e15, 1e16, 1e17, 1e18]) / density
plt.figure(1)
plt.clf()
time_dependent_z(y, taus)
plt.draw()
plt.figure(2)
plt.clf()
time_dependent_power(y, taus)
plt.draw()
plt.figure(3)
import numpy as np
import atomic
ad = atomic.element('carbon')
eq = atomic.CoronalEquilibrium(ad)
temperature = np.logspace(0, 3, 50)
electron_density = 1e19
y = eq.ionisation_stage_distribution(temperature, electron_density)
rad = atomic.Radiation(y, neutral_fraction=1e-2)
import matplotlib.pyplot as plt
plt.figure(10)
plt.clf()
customize = True
lines = rad.plot()
if customize:
plt.ylabel(r'$P/n_\mathrm{i} n_\mathrm{e}\ [\mathrm{W m^3}]$')
plt.ylim(ymin=1e-35)
# annotation
s = '$n_0/n_\mathrm{e}$\n'
if rad.neutral_fraction == 0:
s += '$0$'
else:
ne = rad.electron_density
import numpy as np
import matplotlib.pyplot as plt
import atomic
elements = ['C', 'Ne', 'Ar']
temperature_ranges = {
'C': np.logspace(0, 3, 300),
'Ne': np.logspace(0, 4, 300),
'Ar': np.logspace(0, 5, 300),
}
for element in elements:
ad = atomic.element(element)
coronal = atomic.CoronalEquilibrium(ad)
temperature = temperature_ranges.get(element, np.logspace(0, 3, 300))
y = coronal.ionisation_stage_distribution(temperature, density=1e19)
plt.figure()
y.plot_vs_temperature()
#plt.savefig('coronal_equilibrium_%s.pdf' % element)
plt.show()
import numpy as np
import matplotlib.pyplot as plt
import atomic
elements = ['C', 'Ne', 'Ar']
temperature_ranges = {
'C' : np.logspace(0,3, 300),
'Ne' : np.logspace(0,4, 300),
'Ar' : np.logspace(0,5, 300),
}
for element in elements:
ad = atomic.element(element)
coronal = atomic.CoronalEquilibrium(ad)
temperature = temperature_ranges.get(element, np.logspace(0, 3, 300))
y = coronal.ionisation_stage_distribution(temperature, density=1e19)
plt.figure();
y.plot_vs_temperature()
#plt.savefig('coronal_equilibrium_%s.pdf' % element)
plt.show()
def parabolic_profile(y0):
x = np.linspace(1.0, 0, 50)
y = 1 - x ** 2
y *= y0
return x, y
r, temperature = parabolic_profile(3e3)
r, density = parabolic_profile(1e19)
try:
ad
except NameError:
from atomic.pec import TransitionPool
ad = atomic.element("argon")
tp = TransitionPool.from_adf15("adas_data/pec/transport_llu#ar*.dat")
ad = tp.filter_energy(2e3, 20e3, "eV").create_atomic_data(ad)
eq = atomic.CoronalEquilibrium(ad)
y = eq.ionisation_stage_distribution(temperature, density)
ne_tau = np.array([1e-1, 1e-2, 1e-3])
impurity_fraction = 0.05
texts = ["$10^{%d}$" % i for i in np.log10(ne_tau)]
try:
tau_ss
except NameError:
def plotEps(ad, color):
rt = atomic.RateEquationsWithDiffusion(ad)
min_log_temp = ad.coeffs['ionisation'].log_temperature[0]
temperature = np.logspace(min_log_temp, np.log10(2000), 40)
yy = rt.solve(times, temperature, density, tau)
elc = atomic.ElectronCooling(yy.abundances[-1])
eps = elc.epsilon(tau)
y1 = eps['total']
lab = ad.element
line, = plt.loglog(temperature, y1, color, label=lab)
tau = 1e-5
density = 1e20
times = np.logspace(-7, np.log10(tau) + 1, 120)
for el, c in list(elementColors.items()):
print(el)
plotEps(atomic.element(el), c)
plt.title(r'$n_e = \; ' + str(density) + r'\; [m^{-3}]$, $ \tau = 1e' +
str(int(np.log10(tau))) +
'\;[s]$, dashed=rad only, solid=with ionization')
plt.ylabel(r'$\epsilon_{cool} \; [\mathrm{eV}]$')
plt.xlabel(r'$T_e \; [\mathrm{eV}]$')
plt.ylim(ymin=1e-0, ymax=1e5)
plt.xlim(xmin=0.4, xmax=2000)
plt.legend(loc='best')
plt.grid(True)
plt.show()
def setUp(self):
self.ad = atomic.element('Li')
ax.set_xlabel(r'$T_\mathrm{e}\ \mathrm{(eV)}$')
ax.set_ylabel(r'$P/n_\mathrm{i} n_\mathrm{e}\ [\mathrm{W m^3}]$')
annotate_lines(['$10^{%d}$' % i for i in np.log10(times * solution.density)])
power_coronal = atomic.Radiation(solution.y_coronal).specific_power['total']
ax.loglog(solution.temperature, power_coronal, color='black')
ax.set_title(title)
if __name__ == '__main__':
times = np.logspace(-7, 0, 100)
temperature = np.logspace(0, 3, 100)
density = 1e19
rt = atomic.RateEquations(atomic.element('carbon'))
y = rt.solve(times, temperature, density)
taus = np.array([ 1e14, 1e15, 1e16, 1e17, 1e18])/density
plt.figure(1); plt.clf()
time_dependent_z(y, taus)
plt.draw()
plt.figure(2); plt.clf()
time_dependent_power(y, taus)
plt.draw()
plt.figure(3); plt.clf()
time_dependent_z(y, taus, ensemble_average=True)
plt.draw()
import numpy as np
import matplotlib.pyplot as plt
import atomic
from ensemble_average import time_dependent_power
if __name__ == '__main__':
times = np.logspace(-7, 0, 50)
temperature = np.logspace(0, 3, 50)
density = 1e19
from atomic.pec import TransitionPool
ad = atomic.element('argon')
tp = TransitionPool.from_adf15('adas_data/pec/transport_llu#ar*.dat')
ad = tp.filter_energy(2e3, 20e3, 'eV').create_atomic_data(ad)
rt = atomic.RateEquations(ad)
y = rt.solve(times, temperature, density)
taus = np.array([ 1e14, 1e15, 1e16, 1e17, 1e18])/density
plt.figure(2); plt.clf()
time_dependent_power(y, taus)
plt.ylim(ymin=1e-35)
plt.draw()
plt.figure(3); plt.clf()
time_dependent_power(y, taus, ensemble_average=True)
plt.ylim(ymin=1e-35)
plt.draw()
y = rt.solve(times, temperature, density, tau)
rad = atomic.Radiation(y.abundances[-1])
plt.loglog(temperature,
rad.specific_power['total'],
color=color,
ls=linestyles[i])
if __name__ == '__main__':
temperature = np.logspace(0, np.log10(2e2), 20)
density = 1e20
taus = np.array([0.2, 0.5, 2]) * 1e-3
plt.figure(1)
plt.clf()
plt.xlim(xmin=1, xmax=2e2)
plt.ylim(ymin=1e-35, ymax=1e-30)
legend_handles = []
for elem, col in list(elementColors.items()):
rt = atomic.RateEquationsWithDiffusion(atomic.element(elem))
Lz_radiated_power(rt, taus, col)
legend_handles.append(
mlines.Line2D([], [], color=col, label=rt.atomic_data.element))
plt.xlabel(r'$T_e \; [\mathrm{eV}]$')
plt.ylabel(r'$L_z [\mathrm{W m^3}]$')
plt.legend(handles=legend_handles, loc='best')
plt.grid(True)
plt.show()
