def time_dependent_power(solution, times, ensemble_average=False):
element = solution.atomic_data.element
if ensemble_average:
solution = solution.ensemble_average()
title = element + r' ensemble averaged power'
else:
title = element + r' time dependent power'
ax = plt.gca()
for y in solution.select_times(times):
rad = atomic.Radiation(y)
ax.loglog(solution.temperature,
rad.specific_power['total'],
color='black',
ls='--')
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)
def Lz_radiated_power(rate_equations, taus, color):
linestyles = ['dashed', 'solid', 'dashed']
for i, tau in enumerate(taus):
print(tau * 1e3)
times = np.logspace(-7, np.log10(tau * 10), 20)
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])
#lines_abundance = ax.semilogy(r, y.y.T*100)
#ax.set_ylim(0.3, 400)
#yy.y_coronal.replot_colored(line, lines_abundance)
def normalized_gradient(x, y):
return -np.gradient(y)/np.gradient(x)/y
# fractional abundance, Zeff, Zmean
y_selected = y_bar.select_times(ne_tau)
for y in y_selected:
#ax3.semilogy(r, y.y[-1,:].T*100, color='black')
#lines = ax4.plot(r, y.effective_charge(impurity_fraction),
# color='black', ls='--')
rad = atomic.Radiation(y, impurity_fraction=impurity_fraction)
total_power = rad.power['total']
ax4.plot(r, total_power)
radiation_parameter = total_power / (impurity_fraction * density)
line, = ax5.plot(r, radiation_parameter)
rlte = normalized_gradient(r, temperature)
rlrad = normalized_gradient(r, total_power)
ax6.plot(r, rlrad)
ax6.plot(r, rlte, 'k--')
ax6.set_ylim(0,10)
#from matplotlib.ticker import FormatStrFormatter
#ax = ax3
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
def test_power(self):
rad = atomic.Radiation(self.y1)
power = rad.power
def test_get_neutral_density_default_zero(self):
rad = atomic.Radiation(self.y1)
expected = 0.
result = rad.get_neutral_density()
self.assertEqual(expected, result)
def test_get_neutral_density(self):
rad = atomic.Radiation(self.y1, neutral_fraction=1e-2)
expected = 1e17
result = rad.get_neutral_density()
self.assertEqual(expected, result)
def test_get_impurity_density_zero(self):
rad = atomic.Radiation(self.y1, impurity_fraction=0)
expected = 0
result = rad.get_impurity_density()
self.assertEqual(expected, result)
def test_get_impurity_density_default_1(self):
rad = atomic.Radiation(self.y1)
expected = 1e19
result = rad.get_impurity_density()
self.assertEqual(expected, result)
def test_get_impurity_density_finite2(self):
"""There are 10 times as many impurities as main ions."""
rad = atomic.Radiation(self.y1, impurity_fraction=10)
expected = 1e20
result = rad.get_impurity_density()
self.assertEqual(expected, result)
def test_get_impurity_density_finite(self):
rad = atomic.Radiation(self.y1, impurity_fraction=0.1)
expected = 1e18
result = rad.get_impurity_density()
self.assertEqual(expected, result)