if x0_center_choose == 1:
x0_center = x0_center_pick
else:
if geomfile_type == "gfile":
midped, topped = find_pedestal(file_name=geomfile_name,
path_name='',
plot=False)
elif geomfile_type == "GENE_tracor":
midped = x0_from_para
x0_center = midped
print('mid pedestal is at r/a = ' + str(x0_center))
if geomfile_type == "gfile":
Lref, Bref, R_major, q0, shat0 = get_geom_pars(geomfile_name,
x0_center)
print("Lref=" + str(Lref))
print("x0_center=" + str(x0_center))
index_begin = np.argmin(abs(uni_rhot - x0_center + 2 * (1. - x0_center)))
te_u = te_u[index_begin:len(uni_rhot) - 1]
ne_u = ne_u[index_begin:len(uni_rhot) - 1]
vrot_u = vrot_u[index_begin:len(uni_rhot) - 1]
q = q[index_begin:len(uni_rhot) - 1]
tprime_e = tprime_e[index_begin:len(uni_rhot) - 1]
nprime_e = nprime_e[index_begin:len(uni_rhot) - 1]
uni_rhot = uni_rhot[index_begin:len(uni_rhot) - 1]
center_index = np.argmin(abs(uni_rhot - x0_center))
def Parameter_reader(profile_name, geomfile, q_scale, manual_ped, mid_ped0,
plot, output_csv):
n0 = 1.
mref = 2. # mass of ion in proton mass
rhot0, rhop0, te0, ti0, ne0, ni0, vrot0 = read_profile_file(
profile_type, profile_name, geomfile_name)
if geomfile_type == "gfile":
xgrid, q = read_geom_file(geomfile_type, geomfile_name, suffix)
elif geomfile_type == "GENE_tracor":
xgrid, q, Lref, Bref, x0_from_para = read_geom_file(
geomfile_type, geomfile_name, suffix)
q = q * q_scale
if geomfile_type == "GENE_tracor":
rhot0_range_min = np.argmin(abs(rhot0 - xgrid[0]))
rhot0_range_max = np.argmin(abs(rhot0 - xgrid[-1]))
rhot0 = rhot0[rhot0_range_min:rhot0_range_max]
rhop0 = rhop0[rhot0_range_min:rhot0_range_max]
te0 = te0[rhot0_range_min:rhot0_range_max]
ti0 = ti0[rhot0_range_min:rhot0_range_max]
ne0 = ne0[rhot0_range_min:rhot0_range_max]
ni0 = ni0[rhot0_range_min:rhot0_range_max]
vrot0 = vrot0[rhot0_range_min:rhot0_range_max]
uni_rhot = np.linspace(min(rhot0), max(rhot0), len(rhot0) * 10.)
te_u = interp(rhot0, te0, uni_rhot)
ne_u = interp(rhot0, ne0, uni_rhot)
ni_u = interp(rhot0, ni0, uni_rhot)
vrot_u = interp(rhot0, vrot0, uni_rhot)
q = interp(xgrid, q, uni_rhot)
tprime_e = -fd_d1_o4(te_u, uni_rhot) / te_u
nprime_e = -fd_d1_o4(ne_u, uni_rhot) / ne_u
qprime = fd_d1_o4(q, uni_rhot) / q
#center_index = np.argmax((tprime_e*te_u+nprime_e*ne_u)[0:int(len(tprime_e)*0.99)])
if manual_ped == 1:
x0_center = mid_ped0
else:
if geomfile_type == "gfile":
midped, topped = find_pedestal(file_name=geomfile_name,
path_name='',
plot=False)
elif geomfile_type == "GENE_tracor":
midped = x0_from_para
x0_center = midped
print('mid pedestal is at r/a = ' + str(x0_center))
if geomfile_type == "gfile":
Lref, Bref, R_major, q0, shat0 = get_geom_pars(geomfile_name,
x0_center)
print("Lref=" + str(Lref))
print("x0_center=" + str(x0_center))
index_begin = np.argmin(abs(uni_rhot - x0_center + 2 * (1. - x0_center)))
te_u = te_u[index_begin:len(uni_rhot) - 1]
ne_u = ne_u[index_begin:len(uni_rhot) - 1]
ni_u = ni_u[index_begin:len(uni_rhot) - 1]
vrot_u = vrot_u[index_begin:len(uni_rhot) - 1]
q = q[index_begin:len(uni_rhot) - 1]
tprime_e = tprime_e[index_begin:len(uni_rhot) - 1]
nprime_e = nprime_e[index_begin:len(uni_rhot) - 1]
qprime = qprime[index_begin:len(uni_rhot) - 1]
uni_rhot = uni_rhot[index_begin:len(uni_rhot) - 1]
Lt = 1. / tprime_e
Ln = 1. / nprime_e
Lq = 1. / qprime
center_index = np.argmin(abs(uni_rhot - x0_center))
q0 = q[center_index]
ne = ne_u / (10.**19.) # in 10^19 /m^3
ni = ni_u / (10.**19.) # in 10^19 /m^3
te = te_u / 1000. #in keV
m_SI = mref * 1.6726 * 10**(-27)
me_SI = 9.11 * 10**(-31)
c = 1.
qref = 1.6 * 10**(-19)
#refes to GENE manual
coll_c = 2.3031 * 10**(-5) * Lref * ne / (te)**2 * (
24 - np.log(np.sqrt(ne * 10**13) / (te * 1000)))
coll_ei = 4 * (ni / ne) * coll_c * np.sqrt(
te * 1000. * qref / me_SI) / Lref
nuei = coll_ei
beta = 403. * 10**(-5) * ne * te / Bref**2.
nref = ne_u[center_index]
te_mid = te_u[center_index]
Tref = te_u[center_index] * qref
cref = np.sqrt(Tref / m_SI)
Omegaref = qref * Bref / m_SI / c
rhoref = cref / Omegaref
kymin = n0 * q0 * rhoref / (Lref * x0_center)
kyGENE = kymin * (q / q0) * np.sqrt(te_u / te_mid) * (
x0_center / uni_rhot) #Add the effect of the q varying
#from mtm_doppler
omMTM = kyGENE * (tprime_e + nprime_e)
gyroFreq = 9.79E3 / np.sqrt(mref) * np.sqrt(te_u) / Lref
mtmFreq = omMTM * gyroFreq / (2. * np.pi * 1000.)
omegaDoppler = abs(vrot_u * n0 / (2. * np.pi * 1000.))
omega = mtmFreq + omegaDoppler
omega_n_GENE = kyGENE * (nprime_e) #in cs/a
omega_n = omega_n_GENE * gyroFreq / (2. * np.pi * 1000.) #in kHz
coll_ei = coll_ei / (1000.) #in kHz
shat = Ln / Lq
eta = Ln / Lt
ky = kyGENE
nu = (coll_ei) / (omega_n)
mean_rho, xstar = omega_gaussian_fit(uni_rhot, mtmFreq, rhoref, Lref, plot)
if plot == True:
plt.clf()
plt.xlabel('r/a')
plt.ylabel('eta')
plt.plot(uni_rhot, eta, label='eta')
plt.show()
plt.clf()
#plt.title('mode number finder')
plt.xlabel('r/a')
plt.ylabel('omega*(Lab), kHz')
plt.plot(uni_rhot, omega, label='omega*(Lab)')
plt.show()
plt.clf()
plt.xlabel('r/a')
plt.ylabel('eta')
plt.plot(uni_rhot, eta, label='eta')
plt.show()
plt.clf()
plt.xlabel('r/a')
plt.ylabel('shat')
plt.plot(uni_rhot, shat, label='shat')
plt.show()
plt.clf()
plt.xlabel('r/a')
plt.ylabel('beta')
plt.plot(uni_rhot, beta, label='beta')
plt.show()
plt.clf()
plt.xlabel('r/a')
plt.ylabel('ky rhoi')
plt.plot(uni_rhot, ky, label='ky')
plt.show()
if output_csv == True:
with open('profile_output.csv', 'w') as csvfile:
data = csv.writer(csvfile, delimiter=',')
data.writerow([
'x/a', 'nu_ei(kHz)', 'omega*n(kHz)', 'omega* plasma(kHz)',
'Doppler shift(kHz)', 'nu/omega*n', 'eta', 'shat', 'beta',
'ky rhoi(for n=1)'
])
for i in range(len(uni_rhot)):
data.writerow([
uni_rhot[i], coll_ei[i], omega_n[i], mtmFreq[i],
omegaDoppler[i], nu[i], eta[i], shat[i], beta[i], ky[i]
])
csvfile.close()
return uni_rhot, nu, eta, shat, beta, ky, q, mtmFreq, omegaDoppler, omega_n, omega_n_GENE, xstar, Lref, rhoref
def Parameter_reader(path, profile_name, geomfile, q_scale, manual_ped,
mid_ped0, plot, output_csv):
n0 = 1.
mref = 2. # mass of ion in proton mass
if profile_type == "ITERDB":
rhot0, rhop0, te0, ti0, ne0, ni0, nz0, vrot0 = read_profile_file(
profile_type, path + '/' + profile_name,
path + '/' + geomfile_name, suffix)
elif profile_type == "pfile":
rhot0, rhop0, te0, ti0, ne0, ni0, nz0, vrot0 = read_profile_file(
profile_type, path + '/' + profile_name,
path + '/' + geomfile_name, suffix)
if geomfile_type == "gfile":
xgrid, q, R_ref = read_geom_file(geomfile_type,
path + '/' + geomfile_name, suffix)
elif geomfile_type == "GENE_tracor":
xgrid, q, Lref, R_ref, Bref, x0_from_para = read_geom_file(
geomfile_type, path + '/' + geomfile_name, suffix)
q = q * q_scale
if geomfile_type == "GENE_tracor" and profile_type != "profile":
rhot0_range_min = np.argmin(abs(rhot0 - xgrid[0]))
rhot0_range_max = np.argmin(abs(rhot0 - xgrid[-1]))
rhot0 = rhot0[rhot0_range_min:rhot0_range_max]
rhop0 = rhop0[rhot0_range_min:rhot0_range_max]
te0 = te0[rhot0_range_min:rhot0_range_max]
ti0 = ti0[rhot0_range_min:rhot0_range_max]
ne0 = ne0[rhot0_range_min:rhot0_range_max]
ni0 = ni0[rhot0_range_min:rhot0_range_max]
nz0 = nz0[rhot0_range_min:rhot0_range_max]
vrot0 = vrot0[rhot0_range_min:rhot0_range_max]
uni_rhot = np.linspace(min(rhot0), max(rhot0), len(rhot0) * 10)
te_u = interp(rhot0, te0, uni_rhot)
ne_u = interp(rhot0, ne0, uni_rhot)
ni_u = interp(rhot0, ni0, uni_rhot)
print(str((len(rhot0), len(nz0), len(uni_rhot))))
nz_u = interp(rhot0, nz0, uni_rhot)
vrot_u = interp(rhot0, vrot0, uni_rhot)
q = interp(xgrid, q, uni_rhot)
tprime_e = -fd_d1_o4(te_u, uni_rhot) / te_u
nprime_e = -fd_d1_o4(ne_u, uni_rhot) / ne_u
qprime = fd_d1_o4(q, uni_rhot) / q
#center_index = np.argmax((tprime_e*te_u+nprime_e*ne_u)[0:int(len(tprime_e)*0.99)])
if manual_ped == 1:
x0_center = mid_ped0
else:
if geomfile_type == "gfile":
midped, topped = find_pedestal(file_name=path + '/' +
geomfile_name,
path_name='',
plot=False)
elif geomfile_type == "GENE_tracor":
midped = x0_from_para
x0_center = midped
print('mid pedestal is at r/a = ' + str(x0_center))
if geomfile_type == "gfile":
Lref, Bref, R_major, q0, shat0 = get_geom_pars(
path + '/' + geomfile_name, x0_center)
print("Lref=" + str(Lref))
print("x0_center=" + str(x0_center))
index_begin = np.argmin(abs(uni_rhot - x0_center + 2 * (1. - x0_center)))
te_u = te_u[index_begin:len(uni_rhot) - 1]
ne_u = ne_u[index_begin:len(uni_rhot) - 1]
ni_u = ni_u[index_begin:len(uni_rhot) - 1]
nz_u = nz_u[index_begin:len(uni_rhot) - 1]
vrot_u = vrot_u[index_begin:len(uni_rhot) - 1]
q = q[index_begin:len(uni_rhot) - 1]
tprime_e = tprime_e[index_begin:len(uni_rhot) - 1]
nprime_e = nprime_e[index_begin:len(uni_rhot) - 1]
qprime = qprime[index_begin:len(uni_rhot) - 1]
uni_rhot = uni_rhot[index_begin:len(uni_rhot) - 1]
Lt = 1. / tprime_e
Ln = 1. / nprime_e
center_index = np.argmin(abs(uni_rhot - x0_center))
q0 = q[center_index]
ne = ne_u / (10.**19.) # in 10^19 /m^3
ni = ni_u / (10.**19.) # in 10^19 /m^3
nz = nz_u / (10.**19.) # in 10^19 /m^3
te = te_u / 1000. #in keV
m_SI = mref * 1.6726 * 10**(-27)
me_SI = 9.11 * 10**(-31)
c = 1.
qref = 1.6 * 10**(-19)
#refes to GENE manual
coll_c = 2.3031 * 10**(-5) * Lref * ne / (te)**2 * (
24 - np.log(np.sqrt(ne * 10**13) / (te * 1000)))
coll_ei = 4. * coll_c * np.sqrt(te * 1000. * qref / me_SI) / Lref
nuei = coll_ei
beta = 403. * 10**(-5) * ne * te / Bref**2.
nref = ne_u[center_index]
te_mid = te_u[center_index]
Tref = te_u[center_index] * qref
cref = np.sqrt(Tref / m_SI)
Omegaref = qref * Bref / m_SI / c
rhoref = cref / Omegaref
rhoref_temp = rhoref * np.sqrt(te_u / te_mid)
kymin = n0 * q0 * rhoref / (Lref * x0_center)
kyGENE = kymin * (q / q0) * np.sqrt(te_u / te_mid) * (
x0_center / uni_rhot) #Add the effect of the q varying
#from mtm_doppler
omMTM = kyGENE * (tprime_e + nprime_e)
gyroFreq = 9.79E3 / np.sqrt(mref) * np.sqrt(te_u) / Lref
mtmFreq = omMTM * gyroFreq / (2. * np.pi * 1000.)
omegaDoppler = abs(vrot_u * n0 / (2. * np.pi * 1000.))
omega = mtmFreq + omegaDoppler
global zeff
zeff = ((ni + Z**2 * nz) / ne)[center_index]
if zeff_manual != False:
zeff = zeff_manual
print('********zeff*********')
print('zeff=' + str(zeff))
print('********zeff*********')
omega_n_GENE = kyGENE * (nprime_e) #in cs/a
print("*******************")
print("*******************")
print(np.max(omega_n_GENE))
print("*******************")
print("*******************")
omega_n = omega_n_GENE * gyroFreq / (2. * np.pi * 1000.) #in kHz
#coll_ei=coll_ei/(1000.) #in kHz
coll_ei = coll_ei / (2. * np.pi * 1000.) #in kHz
Lq = 1. / (Lref / (R_ref * q) * qprime)
shat = Ln / Lq
eta = Ln / Lt
ky = kyGENE * np.sqrt(2.)
nu = (coll_ei) / (np.max(omega_n))
nuei = nu * omega_n_GENE / omega_n
if plot == True:
if profile_type == "ITERDB0":
plt.clf()
plt.plot(uni_rhot, nuei, label='nuei(cs/a)')
plt.plot(uni_rhot, nuei * 2. * np.pi, label='nuei(cs/a)*2 pi')
plt.plot(uni_rhot, nuei * zeff, label='nuei*zeff')
plt.legend()
plt.title('nuei')
plt.axvline(0.96, color='red', alpha=1.)
plt.show()
index = np.argmin(
abs(float(input("Enter location of interest:\n")) - uni_rhot))
print('zeff(x/r=' + str(uni_rhot[index]) + ')=' + str(zeff[index]))
#print('id(x/r='+str(rho)+')='+str(id[index]))
print('nuei*zeff(x/r=' + str(uni_rhot[index]) + ')=' +
str((nuei * zeff)[index]))
print('nuei*2 pi(x/r=' + str(uni_rhot[index]) + ')=' +
str((nuei * 2. * np.pi)[index]))
plt.clf()
plt.xlabel('r/a')
plt.ylabel('nuei(kHZ)')
plt.plot(uni_rhot, nu * np.max(omega_n), label='coll')
plt.show()
d = {'uni_rhot': uni_rhot, 'nu*np.max(omega_n)': nu * np.max(omega_n)}
df = pd.DataFrame(d, columns=['uni_rhot', 'nu*np.max(omega_n)'])
df.to_csv('0nu_ei_smooth.csv', index=False)
plt.clf()
#plt.title('mode number finder')
plt.xlabel('r/a')
plt.ylabel('omega*, kHz')
plt.plot(uni_rhot, mtmFreq, label='omega*p')
plt.plot(uni_rhot, omega_n, label='omega*n')
plt.axvline(uni_rhot[np.argmax(mtmFreq)],
color='red',
alpha=1.,
label='peak of omega*p')
plt.axvline(uni_rhot[np.argmax(omega_n)],
color='green',
alpha=1.,
label='peak of omega*n')
plt.plot(uni_rhot, rhoref_temp, color='purple', label='rhoref')
plt.legend()
plt.show()
print("rho i for peak of omega*p: " +
str(rhoref_temp[np.argmax(mtmFreq)]))
print("rho i for peak of omega*n: " +
str(rhoref_temp[np.argmax(omega_n)]))
plt.clf()
plt.xlabel('r/a')
plt.ylabel('eta')
plt.plot(uni_rhot, eta, label='eta')
plt.show()
plt.clf()
#plt.title('mode number finder')
plt.xlabel('r/a')
plt.ylabel('omega*(Lab), kHz')
plt.plot(uni_rhot, omega, label='omega*(Lab)')
plt.show()
plt.clf()
plt.xlabel('r/a')
plt.ylabel('eta')
plt.plot(uni_rhot, eta, label='eta')
plt.show()
plt.clf()
plt.xlabel('r/a')
plt.ylabel('shat')
plt.plot(uni_rhot, shat, label='shat')
plt.show()
plt.clf()
plt.xlabel('r/a')
plt.ylabel('beta')
plt.plot(uni_rhot, beta, label='beta')
plt.show()
plt.clf()
plt.xlabel('r/a')
plt.ylabel('ky rhoi')
plt.plot(uni_rhot, ky, label='ky')
plt.show()
mean_rho, xstar = omega_gaussian_fit(uni_rhot, mtmFreq,
rhoref * np.sqrt(2.), Lref, path,
profile_name, plot)
if abs(mean_rho) + abs(xstar) < 0.0001:
quit()
if output_csv == True:
with open('profile_output.csv', 'w') as csvfile:
data = csv.writer(csvfile, delimiter=',')
data.writerow([
'x/a', 'nu_ei(kHz)', 'omega*n(kHz)', 'omega* plasma(kHz)',
'Doppler shift(kHz)', 'nu/omega*n', 'eta', 'shat', 'beta',
'ky rhoi(for n=1)'
])
for i in range(len(uni_rhot)):
data.writerow([
uni_rhot[i], coll_ei[i], omega_n[i], mtmFreq[i],
omegaDoppler[i], nu[i], eta[i], shat[i], beta[i], ky[i]
])
csvfile.close()
return uni_rhot, nu, eta, shat, beta, ky, q, mtmFreq, omegaDoppler, omega_n, omega_n_GENE, xstar, Lref, R_ref, rhoref