try:
# framework is running
from .startup_choice import *
except ImportError as _excp:
# class is imported by itself
if (
'attempted relative import with no known parent package' in str(_excp)
or 'No module named \'omfit_classes\'' in str(_excp)
or "No module named '__main__.startup_choice'" in str(_excp)
):
from startup_choice import *
else:
raise
from omfit_classes.brainfusetf import btf_connect
from omfit_classes.brainfuse import activateNets
import numpy as np
from scipy import constants
__all__ = ['eped_nn_fann', 'eped_nn_tf', 'eped_nn']
[docs]def eped_nn_fann(a, betan, bt, delta, ip, kappa, m, neped, r, zeffped, z=1, zi=6, mi=12, solution=['H', 'superH'][0], model='multiroot'):
"""
Routine that returns results of the EPED1-NN model
:param a: scalar or array of minor radius [m]
:param betan: scalar or array of beta_n
:param bt: scalar or array of toroidal magnetic field [T]
:param delta: scalar or array of triangularity
:param ip: scalar or array of plasma current [MA]
:param kappa: scalar or array of elongation
:param m: scalar or array of ion mass (2 for D and 2.5 for DT)
:param neped: scalar or array of density at pedestal [1E19 m^-3]
:param r: scalar or array of major radius [m]
:param zeffped: scalar or array of effective ion charge at pedestal
:param z: scalar or array of ion charge (only used for calculating Te from pressure)
:param mi: scalar or array of impurity ion mass (only used for calculating Te from pressure)
:param Zi: scalar or array of impurity ion charge (only used for calculating Te from pressure)
:param solution: 'H' or 'superH'
:return: scalars or arrays of electron temperature in keV and pedestal width as fraction of psi
"""
nets = OMFITsrc + f'/../modules/EPED/EPEDNN/{model}/*.net'
dB = {}
dB['a'] = a
dB['betan'] = betan
dB['bt'] = bt
dB['delta'] = delta
dB['ip'] = ip
dB['kappa'] = kappa
dB['m'] = m
dB['neped'] = neped
dB['r'] = r
dB['zeffped'] = zeffped
for k in dB:
dB[k] = np.atleast_1d(dB[k])
out, sut, targets, nets, out_ = activateNets(nets, dB)
outputNames = nets[list(nets.keys())[0]].outputNames
output_name = 'OUT_p_E1_%d' % ['H', 'meta', 'superH'].index(solution)
z = np.atleast_1d(z)
zi = np.atleast_1d(zi)
mi = np.atleast_1d(mi)
nival = neped * (zeffped - z) / (zi**2 - zi)
nval = neped - zi * nival
nsum = neped + nval + nival
pped = out[:, outputNames.index(output_name)]
Te = pped * 1e6 / (nsum * 1e19) / constants.e / 1e3
wid = out[:, outputNames.index(output_name.replace('_p_', '_wid_'))]
if len(Te) == 1:
return Te[0], wid[0]
else:
return Te, wid
[docs]def eped_nn_tf(
a,
betan,
bt,
delta,
ip,
kappa,
m,
neped,
r,
zeffped,
z=1,
zi=6,
mi=12,
solution=['H', 'superH'][0],
diamag=['GH', 'G', 'H'][0],
model='eped1nn/models/EPED_mb_128_pow_norm_common_30x10.pb',
):
"""
Routine that returns results of the EPED1-NN model
:param a: scalar or array of minor radius [m]
:param betan: scalar or array of beta_n
:param bt: scalar or array of toroidal magnetic field [T]
:param delta: scalar or array of triangularity
:param ip: scalar or array of plasma current [MA]
:param kappa: scalar or array of elongation
:param m: scalar or array of ion mass (2 for D and 2.5 for DT)
:param neped: scalar or array of density at pedestal [1E19 m^-3]
:param r: scalar or array of major radius [m]
:param zeffped: scalar or array of effective ion charge at pedestal
:param z: scalar or array of ion charge (only used for calculating Te from pressure)
:param mi: scalar or array of impurity ion mass (only used for calculating Te from pressure)
:param Zi: scalar or array of impurity ion charge (only used for calculating Te from pressure)
:param solution: 'H' or 'superH'
:param diamag: diamagnetic stabilization model 'GH' or 'G' or 'H'
:param model: string to select the EPED1NN model
:return: scalars or arrays of electron temperature in keV and pedestal width as fraction of psi
"""
output_name = f"OUT_p_E1_dmag{diamag}_sol{['H','meta','superH'].index(solution)}"
a = np.atleast_1d(a)
betan = np.atleast_1d(betan)
bt = np.atleast_1d(bt)
delta = np.atleast_1d(delta)
ip = np.atleast_1d(ip)
kappa = np.atleast_1d(kappa)
m = np.atleast_1d(m)
neped = np.atleast_1d(neped)
r = np.atleast_1d(r)
zeffped = np.atleast_1d(zeffped)
input = [a, betan, bt, delta, ip, kappa, m, neped, r, zeffped]
input = np.atleast_2d(input).T
with btf_connect(path=model) as tf:
input_names, output_names = tf.info()
with btf_connect(path=model) as tf:
output_list = tf.run(input=input)
output = {}
for k, oname in enumerate(output_names):
output[oname] = output_list[:, k]
z = np.atleast_1d(z)
zi = np.atleast_1d(zi)
mi = np.atleast_1d(mi)
nival = neped * (zeffped - z) / (zi**2 - zi)
nval = neped - zi * nival
nsum = neped + nval + nival
pped = output[output_name]
Te = pped * 1e6 / (nsum * 1e19) / constants.e / 1e3
wid = output[output_name.replace('_p_', '_wid_')]
if len(Te) == 1:
return Te[0], wid[0]
else:
return Te, wid
eped_nn = eped_nn_fann
############################################
if '__main__' == __name__:
test_classes_main_header()
print(eped_nn_fann(0.5, 1.0, 2.5, 0.6, 1.8, 1.9, 2.0, 3.6, 1.7, 2.0))
print(
eped_nn_fann([0.5, 0.5], [1.0, 1.0], [2.5, 2.5], [0.6, 0.6], [1.8, 1.8], [1.9, 1.9], [2.0, 2.0], [3.6, 3.6], [1.7, 1.7], [2.0, 2.0])
)
# print(eped_nn_tf(0.5, 1.0, 2.5, 0.6, 1.8, 1.9, 2.0, 3.6, 1.7, 2.0))
# print(
# eped_nn_tf([0.5, 0.5], [1.0, 1.0], [2.5, 2.5], [0.6, 0.6], [1.8, 1.8], [1.9, 1.9], [2.0, 2.0], [3.6, 3.6], [1.7, 1.7], [2.0, 2.0])
# )
