import scipy, sys, os
import time
from scipy.interpolate import interp1d
# Make sure that package home is added to sys.path
sys.path.append('../')
import aurora
# read in default Aurora namelist
namelist = aurora.default_nml.load_default_namelist()
kp = namelist['kin_profs']
# Use gfile and statefile in local directory:
examples_dir = os.path.dirname(os.path.abspath(__file__))
geqdsk = omfit_eqdsk.OMFITgeqdsk(examples_dir + '/example.gfile')
inputgacode = omfit_gapy.OMFITgacode(examples_dir + '/example.input.gacode')
# transform rho_phi (=sqrt toroidal flux) into rho_psi (=sqrt poloidal flux) and save kinetic profiles
rhop = kp['Te']['rhop'] = kp['ne']['rhop'] = np.sqrt(
inputgacode['polflux'] / inputgacode['polflux'][-1])
kp['ne']['vals'] = inputgacode['ne'][None, :] * 1e13 # 1e19 m^-3 --> cm^-3
kp['Te']['vals'] = inputgacode['Te'][None, :] * 1e3 # keV --> eV
# set impurity species and sources rate
imp = namelist['imp'] = 'Ar'
namelist['source_type'] = 'const'
namelist['Phi0'] = 1e24
# Setup aurora sim to efficiently setup atomic rates and profiles over radius
asim = aurora.core.aurora_sim(namelist, geqdsk=geqdsk)
def Helike_emiss_metrics(imp='Ca', cs_den=None, rhop=None,
plot_individual_contributions=False, axs = None):
''' Obtain R(Te) and G(ne) from ratios of w,z,x,y He-like lines for an ion
'''
# Use gfile and statefile in local directory:
geqdsk = omfit_eqdsk.OMFITgeqdsk('/home/sciortino/Aurora/examples/example.gfile')
inputgacode = omfit_gapy.OMFITgacode('/home/sciortino/Aurora/examples/example.input.gacode')
# save kinetic profiles on a rhop (sqrt of norm. pol. flux) grid
rhop_kp = np.sqrt(inputgacode['polflux']/inputgacode['polflux'][-1])
ne = inputgacode['ne']*1e13 # 1e19 m^-3 --> cm^-3
Te = inputgacode['Te']*1e3 # keV --> eV
# get charge state distributions from ionization equilibrium
atom_data = aurora.atomic.get_atom_data(imp,['scd','acd'])
logTe, fz, rates = aurora.get_frac_abundances(atom_data, ne, Te, rho=rhop_kp)
if cs_den is None:
# use ionization equilibrium fractional abundances as densities
cs_den = fz
rhop = rhop_kp
else:
# use provided charge state densities, given on rhop grid
if rhop is None:
raise ValueError('Which rhop grid were cs_dens arrays given on??')
ne = interp1d(rhop_kp, ne, bounds_error=False, fill_value='extrapolate')(rhop)
Te = interp1d(rhop_kp, Te, bounds_error=False, fill_value='extrapolate')(rhop)
# normalize cs_den to match He-like density on axis
cs_den /= cs_den[0,-3]/fz[0,-1]
imp_Z = cs_den.shape[1] -1
# limit to core/pedestal
ridx = np.argmin(np.abs(rhop - 0.99))
ne = ne[:ridx]
Te = Te[:ridx]
rhop = rhop[:ridx]
cs_den = cs_den[:ridx]
# get w,z,x,y rate components
out = compute_Helike_rates(imp_Z, ne, Te/1e3) # Te input must be keV
w_comps, z_comps, x_comps, y_comps = out
# wavelengths for each line:
w_lam = 3.1773e-10
z_lam = 3.2111e-10
x_lam = 3.1892e-10
y_lam = 3.1928e-10
# conversion to frequency
f_E_lam = lambda lam: h*c/(lam)
n_H = cs_den[:,-2]
n_He = cs_den[:,-3]
n_Li = cs_den[:,-4]
# compute line rates in phot/s/cm^3
nw = n_Li*w_comps[0] + n_He*w_comps[1] + n_H*w_comps[2]
nz= n_Li*z_comps[0] + n_He*z_comps[1] + n_H*z_comps[2]
nx = n_He*x_comps[0] + n_H*x_comps[1] + nz*x_comps[2]
ny = n_He*y_comps[0] + n_H*y_comps[1] + nz*y_comps[2]
# convert rates to J/s/cm^3
w = f_E_lam(w_lam) * nw
z = f_E_lam(z_lam) * nz
x = f_E_lam(x_lam) * nx
y = f_E_lam(y_lam) * ny
# compute atomic plasma diagnostics only in the plasma core/pedestal
R_ne = z / (x+y)
G_Te = (z + x + y)/w
# plot each line emissivity
if axs is not None:
ax0,ax1 = axs[0]
ax2 = axs[1]
ax3 = axs[2]
ls='--'
else:
if plot_individual_contributions:
# make use of extra side space for labels
fig = plt.figure(figsize=(11,7))
ax0 = plt.subplot2grid((5,5),(0,0), rowspan = 5, colspan=4)
ax1 = plt.subplot2grid((5,5),(0,4), rowspan = 5, colspan=1, sharex=ax0)
else:
fig, ax0 = plt.subplots()
ax1=None #dummy
fig,ax2 = plt.subplots()
fig,ax3 = plt.subplots()
ls='-'
lw=None
# emissivity profiles
ax0.plot(rhop, w, c='b', label='w', ls=ls, lw=lw)
ax0.plot(rhop, z, c='r', label='z', ls=ls, lw=lw)
ax0.plot(rhop, x, c='g', label='x', ls=ls, lw=lw)
ax0.plot(rhop, y, c='m', label='y', ls=ls, lw=lw)
ax0.set_xlabel(r'$\rho_p$')
ax0.set_ylabel('IE emissivity [A.U.]')
if plot_individual_contributions:
ax0.plot(rhop, f_E_lam(w_lam) *n_Li*w_comps[0], ls='--', c='b', label='ionization')
ax0.plot(rhop, f_E_lam(w_lam) *n_He*w_comps[1], ls=':', c='b', label='excitation', lw=lw)
ax0.plot(rhop, f_E_lam(w_lam) *n_H*w_comps[2], ls='-.', c='b', label='recombination')
ax0.plot(rhop, f_E_lam(z_lam) *n_Li*z_comps[0], ls='--', c='r', label='ionization')
ax0.plot(rhop, f_E_lam(z_lam) *n_He*z_comps[1], ls=':', c='r', label='excitation', lw=lw)
ax0.plot(rhop, f_E_lam(z_lam) *n_H*z_comps[2], ls='-.', c='r', label='recombination')
ax0.plot(rhop, f_E_lam(x_lam) *n_He*x_comps[0], ls=':', c='g', label='excitation', lw=lw)
ax0.plot(rhop, f_E_lam(x_lam) *n_H*x_comps[1], ls='-.', c='g', label='recombination')
ax0.plot(rhop, f_E_lam(x_lam) *nz*x_comps[2], marker='*', c='g', label='z-prop')
ax0.plot(rhop, f_E_lam(y_lam) *n_He*y_comps[0], ls=':', c='m', label='excitation', lw=lw)
ax0.plot(rhop, f_E_lam(y_lam) *n_H*y_comps[1], ls='-.', c='m', label='recombination')
ax0.plot(rhop, f_E_lam(y_lam) *nz * y_comps[2], marker='*', c='m', label='z-prop')
if axs is None: # if axes were given, no need to re-plot labels
if plot_individual_contributions:
# basic labels for each line
ax1.plot([],[], c='b', label='w')
ax1.plot([],[], c='r', label='z')
ax1.plot([],[], c='g', label='x')
ax1.plot([],[], c='m', label='y')
# show labels for each contribution
ax1.plot([],[], c='w', label=' ') #empty to separate colors and line styles
ax1.plot([],[], ls='--', c='k', label='ionization', lw=lw)
ax1.plot([],[], ls=':', c='k', label='excitation', lw=lw)
ax1.plot([],[], ls='-.', c='k', label='recombination', lw=lw)
ax1.plot([],[], marker='*', c='k', label='z cascade', lw=lw)
leg = ax1.legend(loc='center left').set_draggable(True)
ax1.axis('off')
else:
leg = ax0.legend().set_draggable(True)
plt.tight_layout()
# plot line ratios to w
ax2.plot(rhop, w/w, label='w', ls=ls, c='b')
ax2.plot(rhop, z/w, label='z', ls=ls, c='r')
ax2.plot(rhop, x/w, label='x', ls=ls, c='g')
ax2.plot(rhop, y/w, label='y', ls=ls, c='m')
ax2.set_yscale('log')
if axs is None: # if axes were given, no need to re-plot labels
ax2.legend().set_draggable(True)
ax2.set_xlabel(r'$\rho_p$')
ax2.set_ylabel('Line ratios to w')
# set good tick frequency on log-scale for comparison (a bit ad-hoc..)
ax2.set_yticks([0.1,0.3, 1.0, 3.0, 6.0])
ax2.get_yaxis().set_major_formatter(mpl.ticker.ScalarFormatter())
plt.tight_layout()
# plot atomic plasma diagnostics
ax3.plot(rhop, R_ne, label=r'R($n_e$)', ls=ls, c='b')
ax3.plot(rhop, G_Te, label=r'G($T_e$)', ls=ls, c='r')
if axs is None:
leg = ax3.legend().set_draggable(True)
ax3.set_xlabel(r'$\rho_p$')
# set good tick frequency on log-scale for comparison (a bit ad-hoc..)
ax3.set_yticks([0.1,0.3, 1.0, 3.0, 6.0])
ax3.get_yaxis().set_major_formatter(mpl.ticker.ScalarFormatter())
plt.tight_layout()
return [ax0,ax1], ax2, ax3
'''
import numpy as np
import matplotlib.pyplot as plt
plt.ion()
import omfit_gapy
import scipy, sys, os
import time
from scipy.interpolate import interp1d
# Make sure that package home is added to sys.path
import sys
sys.path.append('../')
import aurora
# read in some kinetic profiles
inputgacode = omfit_gapy.OMFITgacode('example.input.gacode')
# transform rho_phi (=sqrt toroidal flux) into rho_psi (=sqrt poloidal flux) and save kinetic profiles
rhop = np.sqrt(inputgacode['polflux'] / inputgacode['polflux'][-1])
ne_vals = inputgacode['ne'] * 1e13 # 1e19 m^-3 --> cm^-3
Te_vals = inputgacode['Te'] * 1e3 # keV --> eV
# get charge state distributions from ionization equilibrium for Ca
atom_data = aurora.atomic.get_atom_data('Ca', ['scd', 'acd'])
# get fractional abundances on ne (cm^-3) and Te (eV) grid
logTe, fz, rates = aurora.atomic.get_frac_abundances(atom_data,
ne_vals,
Te_vals,
rho=rhop)
import numpy as np
import matplotlib.pyplot as plt
plt.ion()
import omfit_eqdsk, omfit_gapy
import sys
from scipy.interpolate import interp1d
import aurora
# read in default Aurora namelist
namelist = aurora.default_nml.load_default_namelist()
# Use gfile and statefile in local directory:
geqdsk = omfit_eqdsk.OMFITgeqdsk(
'/home/sciortino/Aurora/examples/example.gfile')
inputgacode = omfit_gapy.OMFITgacode(
'/home/sciortino/Aurora/examples/example.input.gacode')
# save kinetic profiles on a rhop (sqrt of norm. pol. flux) grid
kp = namelist['kin_profs']
kp['Te']['rhop'] = kp['ne']['rhop'] = np.sqrt(inputgacode['polflux'] /
inputgacode['polflux'][-1])
kp['ne']['vals'] = inputgacode['ne'] * 1e13 # 1e19 m^-3 --> cm^-3
kp['Te']['vals'] = inputgacode['Te'] * 1e3 # keV --> eV
# set impurity species and sources rate
imp = namelist['imp'] = 'Ar'
namelist['source_type'] = 'const'
namelist['source_rate'] = 2e20 # particles/s
# Now get aurora setup
asim = aurora.core.aurora_sim(namelist, geqdsk=geqdsk)