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()
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)
ax.text(0.02, 0.95, label, transform=ax.transAxes, va='top')
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:
t_normalized = np.logspace(-4, 0, 500)
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/pec96#c_pju*.dat')
#tp = tp.filter_energy(2e1, 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)
ax.text(0.02, 0.95, label, transform=ax.transAxes, va='top')