def simulation_from_shot(shot):
time_bbox = db[shot]['time_bbox']
background_bbox = db[shot]['background_bbox']
equilibrium_time = sum(time_bbox)/2.0
print 'Creating LiqueEqulibirium...'
eq = strahl.LiuqeEquilibrium(shot, equilibrium_time)
print 'Creating DMPXData...'
dmpx_data = dmpx_from_shot(shot).select_time(*time_bbox)
dmpx_data = dmpx_data.remove_offset(*background_bbox)
print 'Creating ThomsonData...'
thomson_data = thomson.thomson_data(shot)
thomson_data = thomson_data.select_time(*time_bbox)
print 'Creating InversionData'
inversion = gti.inverted_data(shot).select_time(*time_bbox)
sim = StrahlSimulation(eq, dmpx_data, thomson_data, inversion)
sim.gf_parameters = db[shot]
return sim
gf = GradientFlux(s, influx_bbox, rho_pol=rho_pol)
return s, gf, epsilon
def save_profiles(r, D, v, limits=(0,1)):
mask = (limits[0] < r) & (r < limits[1]) & (D > 0)
np.savetxt('D_profile.txt', D[mask])
np.savetxt('r_profile.txt', r[mask])
np.savetxt('v_profile.txt', v[mask])
if __name__ == '__main__':
working_directory = './wk'
of = os.path.join(working_directory, 'result', 'Arstrahl_result.dat')
res = strahl.viz.read_results(of)
inversion = gti.inverted_data(42314).select_time(0.7, 1.0)
parameters = dict(
influx_bbox = (0.725, 0.75),
background_bbox = (0.70, 0.705),
)
s, gf, epsilon = from_strahl_result(inversion, res, parameters)
plt.figure(21); plt.clf()
s.check_time_derivatives()
plt.axvspan(*gf.time_bbox, color='black', alpha=0.2)
plt.draw()
plt.figure(22); plt.clf()
s.check_spatial_derivatives()
plt.draw()