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
import numpy as np
from omfit_classes.omfit_path import OMFITpath
from omfit_classes.omfit_ascii import OMFITascii
from omfit_classes.sortedDict import OMFITdataset
from omfit_classes.omfit_dir import OMFITdir
from omfit_classes.omfit_gacode import OMFITgacode
from omfit_classes.omfit_asciitable import OMFITasciitable
__all__ = [
'OMFITtglfEPinput',
'OMFITalphaInput',
'OMFITtglf_eig_spectrum',
'OMFITtglf_wavefunction',
'OMFITtglf_flux_spectrum',
'OMFITtglf_nete_crossphase_spectrum',
'OMFITtglf_potential_spectrum',
'OMFITtglf_fluct_spectrum',
'OMFITtglf_intensity_spectrum',
'OMFITtglf',
'OMFITtglf_nsts_crossphase_spectrum',
'sum_ky_spectrum',
]
def get_ky_spectrum(filename):
with open(filename, 'r') as f:
content = f.readlines()
content = ''.join(content[2:]).split()
ky = np.array(content, dtype=float)
return ky
class OMFITtglf_QL_flux_spectrum(OMFITdataset, OMFITascii):
'''
Parse the out.tglf.QL_flux_spectrum file
:param filename: Path to the out.tglf.QL_flux_spectrum file
'''
def __init__(self, filename, ky_file, field_labels, spec_labels, **kw):
OMFITascii.__init__(self, filename, **kw)
self.dynaLoad = True
OMFITdataset.__init__(self)
self.ky_file = self.OMFITproperties['ky_file'] = ky_file
self.field_labels = self.OMFITproperties['field_labels'] = field_labels
self.spec_labels = self.OMFITproperties['spec_labels'] = spec_labels
self.n_species = len(spec_labels)
self.n_fields = len(field_labels)
@dynaLoad
def load(self):
with open(self.filename, 'r') as f:
lines = f.readlines()
ntype, nspecies, nfield, nky, nmodes = list(map(int, lines[3].strip().split()))
with open(self.filename) as f:
content = f.read()
tmpdict = SortedDict()
ky = get_ky_spectrum(self.ky_file)
ql = []
for line in lines[6:]:
line = line.strip().split()
if any([x.startswith(("s", "m")) for x in line]):
continue
for x in line:
ql.append(float(x))
QLw = array(ql).reshape(nspecies, nfield, nmodes, nky, ntype)
types = ['particle', 'energy', 'toroidal stress', 'parallel stress', 'exchange']
self.labels = types # used in plot.
for i, t in enumerate(types):
QL_data_array = DataArray(
data=QLw[:, :, :, :, i],
dims=["species", 'field', 'mode_num', 'ky'],
coords={
"species": self.spec_labels,
"field": self.field_labels,
"mode_num": np.arange(nmodes) + 1,
"ky": ky,
},
)
self.update({t: QL_data_array})
def plot(self, fn=None):
'''
Plot the QL (flux) weights spectra
:param fn: A FigureNotebook instance
'''
ns = self.n_species
species = self.spec_labels
nf = self.n_fields
fields = self.field_labels
if fn is None:
from omfit_plot import FigureNotebook
tabbed = FigureNotebook(nfig=0, name='TGLF QL (flux) weight spectra')
else:
tabbed = fn
for k in self.labels:
fig = tabbed.add_figure(label=k)
fig.suptitle(k)
for s in range(ns):
for f in range(nf):
if s == 0 and f == 0:
ax0 = ax = axr = fig.use_subplot(ns, nf, s * nf + f + 1)
elif f == 0:
ax = axr = fig.use_subplot(ns, nf, s * nf + f + 1, sharex=ax0)
else:
ax = fig.use_subplot(ns, nf, s * nf + f + 1, sharex=ax0, sharey=axr)
if f == 0:
ax.set_ylabel('Species: %s' % (self.spec_labels[s]))
if s == 0:
ax.set_title([r'$\phi$', r'$B_{\perp}$', r'$B_{\parallel}$'][f])
# The actual data,
ax.plot(self['ky'], self[k].sel(species=species[s], field=fields[f], mode_num=1).data)
ax.set_xscale('log')
ax.axis('tight')
autofmt_sharexy(fig=fig)
fig.canvas.draw()
class OMFITtglf_QL_intensity_spectrum(OMFITdataset, OMFITascii):
'''
Parse the out.tglf.QL_intensity_spectrum file
:param filename: Path to the out.tglf.QL_flux_spectrum file
'''
def __init__(self, filename, ky_file, n_species, spec_labels, **kw):
OMFITascii.__init__(self, filename, **kw)
self.dynaLoad = True
OMFITdataset.__init__(self)
self.ky_file = self.OMFITproperties['ky_file'] = ky_file
self.n_species = self.OMFITproperties['n_species'] = n_species
self.spec_labels = self.OMFITproperties['spec_labels'] = spec_labels
@dynaLoad
def load(self):
with open(self.filename, 'r') as f:
lines = f.readlines()
ntype, nspecies, nky, nmodes = list(map(int, lines[4].strip().split()))
with open(self.filename) as f:
content = f.read()
tmpdict = SortedDict()
ky = get_ky_spectrum(self.ky_file)
ql = []
for line in lines[7:]:
line = line.strip().split()
if any([x.startswith(("s", "m")) for x in line]):
continue
for x in line:
ql.append(float(x))
QLw = array(ql).reshape(nspecies, nmodes, nky, ntype)
types = ['density', 'temperature', 'U', 'Q']
for i, t in enumerate(types):
QL_data_array = DataArray(
data=QLw[:, :, :, i],
dims=["species", 'mode_num', 'ky'],
coords={
"species": self.spec_labels,
"mode_num": np.arange(nmodes) + 1,
"ky": ky,
},
)
self.update({t: QL_data_array})
[docs]class OMFITtglf_eig_spectrum(OMFITdataset, OMFITascii):
def __init__(self, filename, ky_file, nmodes, **kw):
OMFITascii.__init__(self, filename, **kw)
self.dynaLoad = True
OMFITdataset.__init__(self)
self.ky_file = self.OMFITproperties['ky_file'] = ky_file
self.nmodes = self.OMFITproperties['nmodes'] = nmodes
[docs] @dynaLoad
def load(self):
nmodes = self.nmodes
with open(self.filename, 'r') as f:
content = f.readlines()
content = ''.join(content[2:]).split()
ky = get_ky_spectrum(self.ky_file)
gamma = []
freq = []
for k in range(self.nmodes):
gamma.append(np.array(content[2 * k :: nmodes * 2], dtype=float))
freq.append(np.array(content[2 * k + 1 :: nmodes * 2], dtype=float))
gamma = np.array(gamma)
freq = np.array(freq)
gamma = DataArray(gamma, dims=('mode_num', 'ky'), coords={'ky': ky, 'mode_num': np.arange(nmodes) + 1})
freq = DataArray(freq, dims=('mode_num', 'ky'), coords={'ky': ky, 'mode_num': np.arange(nmodes) + 1})
self.update({'gamma': gamma, 'freq': freq})
[docs] def plot(self, axs=None):
'''
Plot the eigenvalue spectrum as growth rate and frequency
:param axs: A length 2 sequence of matplotlib axes (growth rate, frequency)
'''
if axs is None:
fig, axs = pyplot.subplots(2, 1, num=pyplot.gcf().number, squeeze=True, sharex=True)
else:
fig = axs[0].get_figure()
for k in range(self.nmodes):
axs[0].plot(self['ky'], self['gamma(%d)' % (k + 1)], label='Growth rate $\\gamma_{%d}$' % (k + 1))
axs[1].plot(self['ky'], self['freq(%d)' % (k + 1)], label='Frequency $\\omega_{%d}$' % (k + 1))
axs[0].set_ylabel('Growth rate $\\gamma$')
with warnings.catch_warnings():
# We don't care if matplotlib has an underflow while drawing
warnings.filterwarnings('ignore', category=RuntimeWarning, message='underflow*')
axs[0].set_yscale('symlog', linthresh=0.01)
axs[1].set_yscale('symlog', linthresh=0.01)
axs[1].set_ylabel('Frequency $\\omega$')
axs[1].axhline(0, color='gray', ls='--')
axs[1].set_xlabel('$k_\\perp \\rho$')
axs[0].set_xscale('log')
fig.canvas.draw()
@dynaLoad
def __getitem__(self, key):
if key in self:
return OMFITdataset.__getitem__(self, key)
if key.startswith('gamma') and '(' in key:
ind = int(key.split('(')[1].split(')')[0])
return self['gamma'].sel(mode_num=ind).values
if key.startswith('freq') and '(' in key:
ind = int(key.split('(')[1].split(')')[0])
return self['freq'].sel(mode_num=ind).values
raise KeyError(key)
[docs]class OMFITtglf_wavefunction(OMFITascii, SortedDict):
def __init__(self, filename, **kw):
OMFITascii.__init__(self, filename, **kw)
SortedDict.__init__(self)
self.dynaLoad = True
[docs] @dynaLoad
def load(self):
with open(self.filename, 'r') as f:
lines = f.readlines()
nmodes, nfields, npoints = list(map(int, lines[0].split()))
self.nmodes = nmodes
self.nfields = nfields
keys = lines[1].split()
self.headers = keys[1:]
content = ''.join(lines[2:]).split()
self['theta'] = np.array(content[0 :: nmodes * nfields * 2 + 1], dtype=float)
for ik, k in enumerate(keys[1:]):
for n in range(nmodes):
self['%s(%d)' % (k, n + 1)] = np.array(content[2 * n * nfields + 1 + ik :: nmodes * nfields * 2 + 1], dtype=float)
[docs] def plot(self):
def pi_mult_formatter(x, pos):
if x == 0:
return '0'
if x == np.pi:
return r'$\pi$'
if x == -np.pi:
return r'$-\pi$'
return r'$%d\pi$' % (int(round(x / np.pi, 0)))
fig, axs = pyplot.subplots(self.nfields, self.nmodes, num=pyplot.gcf().number, sharex=True, sharey='row', squeeze=False)
for n in range(self.nmodes):
for ki, k in enumerate(self.headers):
axs[int(ki // 2)][n].plot(self['theta'], self['%s(%d)' % (k, n + 1)], ls=['-', '--'][ki % 2])
if n == 0:
axs[int(ki // 2)][n].set_ylabel(k.split('(')[1].split(')')[0])
if ki == len(self.headers) - 1:
axs[int(ki // 2)][n].set_xlabel(r'$\theta$')
axs[int(ki // 2)][n].xaxis.set_major_locator(MultipleLocator(np.pi))
axs[int(ki // 2)][n].xaxis.set_major_formatter(FuncFormatter(pi_mult_formatter))
fig.canvas.draw()
[docs]class OMFITtglf_flux_spectrum(OMFITdataset, OMFITascii):
'''
Parse the out.tglf.sum_flux_spectrum file and provide a convenient means for
plotting it.
:param filename: Path to the out.tglf.sum_flux_spectrum file
:param n_species: Number of species
:param n_fields: Number of fields
'''
def __init__(self, filename, ky_file, field_labels, spec_labels, **kw):
OMFITascii.__init__(self, filename, **kw)
self.dynaLoad = True
OMFITdataset.__init__(self)
self.ky_file = self.OMFITproperties['ky_file'] = ky_file
self.spec_labels = self.OMFITproperties['spec_labels'] = spec_labels
self.field_labels = self.OMFITproperties['field_labels'] = field_labels
self.n_species = len(self.spec_labels)
self.n_fields = len(self.field_labels)
[docs] @dynaLoad
def load(self):
ns = self.n_species
nf = self.n_fields
with open(self.filename) as f:
content = f.read()
tmpdict = SortedDict()
ky = get_ky_spectrum(self.ky_file)
tmpdict['ky'] = np.array(ky, dtype=float)
for s in range(ns):
for f in range(nf):
ind1 = content.find('species =')
ind2 = content.find('species =', ind1 + 10)
data = content[ind1:ind2]
first_two = data.splitlines()[:2]
spec = int(first_two[0].split()[2])
field = int(first_two[0].split()[5])
spec_label = self.spec_labels[s]
labels = [k.strip().replace('flux', '').strip() for k in first_two[1].split(',')]
keys = [k + '_field_%d_spec_%s' % (field, spec_label) for k in labels]
labels = labels[:]
data = ''.join(data.splitlines()[2:]).split()
for ki, k in enumerate(keys):
tmpdict[k] = np.array(data[ki :: len(keys)], dtype=float)
content = content[content.find('species =', 10) :]
ky = tmpdict['ky']
tmparray = {}
for label in labels:
tmparray['_'.join(label.split())] = DataArray(
np.zeros((ns, nf, len(ky))),
dims=('species', 'field', 'ky'),
coords={'species': self.spec_labels, 'field': self.field_labels, 'ky': ky},
)
for k, v in list(tmpdict.items()):
if k == 'ky':
continue
quant, field = k.split('_field_', 2)
quant = '_'.join(quant.split())
field, spec_label = field.split('_spec_')
spec_ind = self.spec_labels.index(spec_label)
field_ind = int(field) - 1
tmparray[quant][spec_ind, field_ind, :] = v
self.update(tmparray)
self._initialized = False
self.labels = labels
self._initialized = True
[docs] @dynaLoad
def plot(self, fn=None):
'''
Plot the flux spectra
:param fn: A FigureNotebook instance
'''
ns = self.n_species
nf = self.n_fields
if fn is None:
from omfit_plot import FigureNotebook
tabbed = FigureNotebook(nfig=0, name='TGLF Flux Spectra')
else:
tabbed = fn
for k in self.labels:
fig = tabbed.add_figure(label=k)
fig.suptitle(k)
for s in range(ns):
for f in range(nf):
if s == 0 and f == 0:
ax0 = ax = axr = fig.use_subplot(ns, nf, s * nf + f + 1)
elif f == 0:
ax = axr = fig.use_subplot(ns, nf, s * nf + f + 1, sharex=ax0)
else:
ax = fig.use_subplot(ns, nf, s * nf + f + 1, sharex=ax0, sharey=axr)
if f == 0:
ax.set_ylabel('Species: %s' % (self.spec_labels[s]))
if s == 0:
ax.set_title([r'$\phi$', r'$B_{\perp}$', r'$B_{\parallel}$'][f])
ax.plot(self['ky'], self[k + '_field_%d_spec_%s' % (f + 1, self.spec_labels[s])])
ax.set_xscale('log')
ax.axis('tight')
autofmt_sharexy(fig=fig)
fig.canvas.draw()
@dynaLoad
def __getitem__(self, key):
if key in self:
return OMFITdataset.__getitem__(self, key)
try:
quant, field = key.split('_field_', 2)
quant = '_'.join(quant.split())
field, spec_label = field.split('_spec_')
spec_ind = self.spec_labels.index(spec_label)
field_ind = int(field) - 1
return self[quant].isel(field=field_ind, species=spec_ind)
except Exception as _excp:
print(_excp)
raise KeyError(k)
[docs]class OMFITtglf_nete_crossphase_spectrum(OMFITdataset, OMFITascii):
'''
Parse the out.tglf.nete_crossphase_spectrum file and provide a convenient means for
plotting it.
:param filename: Path to the out.tglf.nete_crossphase_spectrum file
:param nmodes: Number of modes computed by TGLF and used in the
'''
def __init__(self, filename, ky_file, nmodes, **kw):
OMFITascii.__init__(self, filename, **kw)
self.dynaLoad = True
OMFITdataset.__init__(self)
self.nmodes = self.OMFITproperties['nmodes'] = nmodes
self.ky_file = self.OMFITproperties['ky_file'] = ky_file
[docs] @dynaLoad
def load(self):
nmodes = self.nmodes
with open(self.filename, 'r') as f:
content = f.readlines()
content = ''.join(content[2:]).split()
ky = get_ky_spectrum(self.ky_file)
tmparray = []
for k in range(self.nmodes):
tmparray.append(np.array(content[k::nmodes], dtype=float))
self.update({'nete': DataArray(np.array(tmparray), dims=('mode_num', 'ky'), coords={'mode_num': np.arange(nmodes) + 1, 'ky': ky})})
[docs] def plot(self, ax=None):
'''
Plot the nete crossphase spectrum
:param ax: A matplotlib axes instance
'''
if ax is None:
fig, ax = pyplot.subplots(1, 1, squeeze=True)
ky = self['ky']
nmodes = self.nmodes
for k in range(nmodes):
ax.plot(ky, self['nete_crossphase_spectrum(%d)' % (k + 1)] / np.pi, label='Mode %d' % (k + 1))
ax.legend(loc='best')
ax.set_xscale('log')
ax.set_xlabel(r'$k_y$')
ax.set_ylabel(r'$\delta n_e$ $\delta T_e$ crossphase')
ax.set_title(r'$\Theta_{\delta n_e\delta T_e}$')
ax.axis('tight')
ax.set_yticks(np.linspace(-np.pi / 2.0, np.pi / 2.0, 5), [r'$-\pi/2$', r'$\pi/4$', '0', r'$\pi/4$', r'$\pi/2$'])
fmt = pyplot.ScalarFormatter()
fmt.set_scientific(False)
ax.xaxis.set_major_formatter(fmt)
@dynaLoad
def __getitem__(self, key):
if key in self:
return OMFITdataset.__getitem__(self, key)
if 'nete_crossphase_spectrum(' in key:
ind = int(key.split('(')[1].split(')')[0])
return self['nete'].sel(mode_num=ind)
raise KeyError(key)
[docs]class OMFITtglf_nsts_crossphase_spectrum(OMFITdataset, OMFITascii):
'''
Parse the out.tglf.nsts_crossphase_spectrum file and provide a convenient means for
plotting it.
:param filename: Path to the out.tglf.nsts_crossphase_spectrum file
:param nmodes: Number of modes computed by TGLF and used in the
'''
def __init__(self, filename, ky_file, nmodes, spec_labels, **kw):
OMFITascii.__init__(self, filename, **kw)
self.dynaLoad = True
OMFITdataset.__init__(self)
self.ky_file = self.OMFITproperties['ky_file'] = ky_file
self.spec_labels = self.OMFITproperties['spec_labels'] = spec_labels
self.nmodes = self.OMFITproperties['nmodes'] = nmodes
[docs] @dynaLoad
def load(self):
with open(self.filename, 'r') as f:
content = f.readlines() # Remove the main header
header = content.pop(0)
nspec = int(header.strip().split()[-2])
nmodes = self.nmodes
for ci, c in enumerate(content):
if ci == 0:
continue
if c.strip().startswith('species'):
nk = ci - 2 # Two header lines
break
nsts = np.zeros((nspec, nmodes, nk), dtype=float)
for si in range(nspec):
block = ' '.join(content[(2 + nk) * si + 2 : (2 + nk) * (si + 1)]).split()
if si == 0:
ky = get_ky_spectrum(self.ky_file)
for mi in range(nmodes):
nsts[si, mi, :] = np.array(block[mi::nmodes], dtype=float)
self.update(
{
'nsts': DataArray(
np.array(nsts),
dims=('species', 'mode_num', 'ky'),
coords={'species': self.spec_labels, 'mode_num': np.arange(nmodes) + 1, 'ky': ky},
)
}
)
[docs] def plot(self, axs=None):
'''
Plot the nsts crossphase spectrum
:param axs: A sequence of matplotlib axes instances of length len(self['species'])
'''
ky = self['ky']
nmodes = self.nmodes
nspec = len(self['species'])
if axs is None or len(np.atleast1d(axs)) != nspec:
fig, axs = pyplot.subplots(nspec, 1, squeeze=True, sharex=True)
fmt = pyplot.ScalarFormatter()
fmt.set_scientific(False)
for si, spec in enumerate(self['species']):
ax = axs[si]
ax.set_ylabel(f'Species: {spec.data}')
ax.xaxis.set_major_formatter(fmt)
ax.set_yticks(np.linspace(-1, 1, 5))
ax.set_yticklabels([r'$-\pi/2$', r'$\pi/4$', '0', r'$\pi/4$', r'$\pi/2$'])
for mi in range(nmodes):
ax.plot(ky, self['nsts'].isel(species=si, mode_num=mi) / np.pi, label='Mode %d' % (mi + 1))
ax.set_ylim([-1.1, 1.1])
ax.legend(loc='best')
ax.set_xscale('log')
ax.set_xlabel(r'$k_y$')
axs[0].set_title(r'$\Theta_{\delta n_s\delta T_s}$')
ax.axis('tight')
[docs]class OMFITtglf_potential_spectrum(OMFITdataset, OMFITascii):
'''
Parse the potential fluctuation spectrum in out.tglf.potential_spectrum and
provide a convenient means for plotting it.
:param filename: Path to the out.tglf.potential_spectrum file
'''
def __init__(self, filename, ky_file, nmodes, **kw):
OMFITascii.__init__(self, filename, **kw)
self.dynaLoad = True
OMFITdataset.__init__(self)
self.ky_file = self.OMFITproperties['ky_file'] = ky_file
self.nmodes = self.OMFITproperties['nmodes'] = nmodes
[docs] @dynaLoad
def load(self):
with open(self.filename, 'r') as f:
lines = f.readlines()
nmodes = self.nmodes
ky = get_ky_spectrum(self.ky_file)
description = lines[0]
columns = [x.strip() for x in lines[1].split(',')]
nc = len(columns)
content = ''.join(lines[6:]).split()
tmpdict = {}
tmpdict['ky'] = np.array(ky, dtype=float)
for ik, k in enumerate(columns):
tmp = []
for nm in range(nmodes):
tmp.append(np.array(content[ik + nm * nc :: nmodes * nc], dtype=float))
tmpdict[k] = tmp
for k, v in list(tmpdict.items()):
if k == 'ky':
continue
self[k] = xarray.DataArray(v, dims=('mode_num', 'ky'), coords={'ky': tmpdict['ky'], 'mode_num': np.arange(nmodes) + 1})
[docs] @dynaLoad
def plot(self, fn=None):
'''
Plot the fields
:param fn: A FigureNotebook instance
'''
from omfit_plot import FigureNotebook
if fn is None:
fn = FigureNotebook('Field spectra')
nmodes = self.nmodes
fmt = pyplot.ScalarFormatter()
fmt.set_scientific(False)
for k in self.data_vars:
for nm in range(nmodes):
if not np.any(self[k] != 0):
continue
fig, ax = fn.subplots(label=k + ', mode = ' + str(nm + 1), squeeze=True)
ax.plot(self['ky'], self[k][nm])
ax.set_xscale('log')
ax.axis('tight')
ax.xaxis.set_major_formatter(fmt)
ax.set_xlabel('$k_y$')
[docs]class OMFITtglf_fluct_spectrum(OMFITdataset, OMFITascii):
'''
Parse the {density,temperature} fluctuation spectrum in
out.tglf.{density,temperature}_spectrum and provide a convenient means for
plotting it.
:param filename: Path to the out.tglf.{density,temperature}_spectrum file
:param ns: Number of species
:param label: Type of fluctuations ('density' or 'temperature')
'''
pretty_names = {'density': r'\delta n ', 'temperature': r'\delta T '}
def __init__(self, filename, ky_file, ns, label, spec_labels, **kw):
OMFITascii.__init__(self, filename, **kw)
self.dynaLoad = True
OMFITdataset.__init__(self)
self.ky_file = self.OMFITproperties['ky_file'] = ky_file
self.ns = self.OMFITproperties['ns'] = ns
self.label = self.OMFITproperties['label'] = label
self.spec_labels = self.OMFITproperties['spec_labels'] = spec_labels
[docs] @dynaLoad
def load(self):
ns = self.ns
with open(self.filename, 'r') as f:
content = f.readlines()
content = ''.join(content[2:]).split()
ky = get_ky_spectrum(self.ky_file)
tmplist = []
for s in range(ns):
tmplist.append(np.array(content[s::ns], dtype=float))
self[self.label] = xarray.DataArray(np.array(tmplist), dims=('species', 'ky'), coords={'species': self.spec_labels, 'ky': ky})
[docs] @dynaLoad
def plot(self, axs=None):
'''
Plot the fluctuation spectrum
:param axs: A list of matplotlib axes of length self.ns
'''
from matplotlib import pyplot
from matplotlib.ticker import ScalarFormatter
if axs is None:
fig, axs = pyplot.subplots(self.ns, 1, sharex=True, sharey=True)
else:
if len(axs) != self.ns:
raise ValueError('Must pass length %s list of axes' % self.ns)
fig = axs[0].get_figure()
ns = self.ns
lab_base = self.pretty_names[self.label]
for s in range(ns):
ax = axs[s]
ax.plot(self['ky'], self[self.label + '_spec_%d' % (s + 1)], label=r'$%s_{%s}$' % (lab_base, self.spec_labels[s]))
ax.legend()
ax.set_xscale('log')
ax.axis('tight')
fmt = pyplot.ScalarFormatter()
fmt.set_scientific(False)
ax.xaxis.set_major_formatter(fmt)
ax.set_xlabel(r'$k_y$')
try:
autofmt_sharexy(fig=fig)
except NameError:
pass
def __getitem__(self, key):
if key in self:
return OMFITdataset.__getitem__(self, key)
if key.startswith(self.label + '_spec_'):
spec_ind = int(key.split('_')[-1]) - 1
return self[self.label].isel(species=spec_ind)
raise KeyError(key)
[docs]class OMFITtglf_intensity_spectrum(OMFITdataset, OMFITascii):
'''
Parse the intensity fluctuation spectrum in
out.tglf.{density,temperature}_spectrum and provide a convenient means for
plotting it.
:param filename: Path to the out.tglf.{density,temperature}_spectrum file
:param ns: Number of species
:param label: Type of fluctuations ('density' or 'temperature')
'''
def __init__(self, filename, ky_file, nmodes, ns, spec_labels, **kw):
OMFITascii.__init__(self, filename, **kw)
self.dynaLoad = True
OMFITdataset.__init__(self)
self.ky_file = self.OMFITproperties['ky_file'] = ky_file
self.nmodes = self.OMFITproperties['nmodes'] = nmodes
self.ns = self.OMFITproperties['ns'] = ns
self.spec_labels = self.OMFITproperties['spec_labels'] = spec_labels
[docs] @dynaLoad
def load(self):
ns = self.ns
nmodes = self.nmodes
ky = get_ky_spectrum(self.ky_file)
nky = len(ky)
with open(self.filename, 'r') as f:
dens, temp, vpara, enpara = np.loadtxt(f, skiprows=4, unpack=True)
dims = ("species", "ky", "mode_num")
coords = dict(species=self.spec_labels, ky=ky, mode_num=np.arange(nmodes) + 1)
density = dens.reshape(ns, nky, nmodes)
self['density'] = xarray.DataArray(density, dims=dims, coords=coords)
temperature = temp.reshape(ns, nky, nmodes)
self['temperature'] = xarray.DataArray(temperature, dims=dims, coords=coords)
parallel_vel = vpara.reshape(ns, nky, nmodes)
self['parallel_velocity'] = xarray.DataArray(parallel_vel, dims=dims, coords=coords)
parallel_energy = enpara.reshape(ns, nky, nmodes)
self['parallel_energy'] = xarray.DataArray(parallel_energy, dims=dims, coords=coords)
[docs]class OMFITtglf(OMFITdir):
'''
The purpose of this class is to be able to store all results from a given
TGLF run in its native format, but parsing the important parts into the tree
:param filename: Path to TGLF run
'''
def __init__(self, filename, **kw):
printd('Calling OMFITtglf init', topic='OMFITtglf')
OMFITdir.__init__(self, filename, **kw)
printd('print self.keys():', list(self.keys()), topic='OMFITtglf')
# remove 'OMFIT_run_command.sh'
if 'OMFIT_run_command.sh' in self:
del self['OMFIT_run_command.sh']
# convert OMFITpath to OMFITascii
for item in list(self.keys()):
if isinstance(self[item], OMFITpath):
self[item].__class__ = OMFITascii
# rename input.tglf.gen to input.tglf
if 'input.tglf.gen' in self:
input_tglf = self['input.tglf'] = OMFITgacode(self['input.tglf.gen'].filename, noCopyToCWD=True)
del self['input.tglf.gen']
# robust, consistent species labeling,
ns = self['input.tglf']['NS']
from omfit_classes.utils_math import element_symbol
spec_labels = []
lump_counter = 1
for i in range(1, ns + 1):
charge = self['input.tglf'][f'ZS_{i}']
mass = self['input.tglf'][f'MASS_{i}']
try:
l = element_symbol(charge, round(mass, 1) * 2).lower()
except ValueError:
l = f"lump{lump_counter}"
lump_counter += 1
spec_labels += [l]
self.spec_labels = spec_labels
# consistent field labels,
field_labels = ['phi']
if self['input.tglf']['USE_BPER']:
field_labels += ['B_perp']
if self['input.tglf']['USE_BPAR']:
field_labels += ['B_parallel']
self.field_labels = field_labels
# Note: as part of the parsing process, some entries under SELF are deleted.
# however we are not deleting the files these entries referred to,
# and because OMFITtglf inherits from OMFITdir these files will be carried
# through even if they are not visible in the OMFIT tree
if np.any([str(k).startswith('out') for k in self]):
self._parse_version()
self._parse_prec()
if not input_tglf['USE_TRANSPORT_MODEL']: # linear run
self['wavefunction'] = OMFITtglf_wavefunction(self['out.tglf.wavefunction'].filename, noCopyToCWD=True)
del self['out.tglf.wavefunction']
else:
if 'out.tglf.eigenvalue_spectrum' in self:
self['eigenvalue_spectrum'] = OMFITtglf_eig_spectrum(
self['out.tglf.eigenvalue_spectrum'].filename,
self['out.tglf.ky_spectrum'].filename,
nmodes=input_tglf['NMODES'],
noCopyToCWD=True,
)
del self['out.tglf.eigenvalue_spectrum']
try:
self._parse_potential_spectrum()
except Exception as _excp:
printe(_excp)
try:
self._parse_flucs_spectra()
except Exception as _excp:
printe(_excp)
try:
self._parse_flux_spectrum()
except Exception as _excp:
printe(_excp)
try:
self._parse_QL_flux_spectrum()
except Exception as _excp:
printe(_excp)
# try: # Not currently a TGLF output, leave for future compatibility.
# self._parse_QL_intensity_spectrum()
# except Exception as _excp:
# printe(_excp)
try:
self._parse_nete_crossphase_spectrum()
except Exception as _excp:
printe(_excp)
try:
self._parse_nsts_crossphase_spectrum()
except Exception as _excp:
printe(_excp)
try:
self._parse_gbflux()
except Exception as _excp:
printe(_excp)
try:
self._parse_run()
except Exception as _excp:
printe(_excp)
try:
self._parse_intensity_spectrum()
except Exception as _excp:
printe(_excp)
[docs] def plot(self):
for k in self:
if hasattr(self[k], 'plot'):
self[k].plot()
def __delitem__(self, key):
'''
Deleting an item only deletes it from the tree, not from the underlying
directory (which ``OMFITdir`` would do)
'''
super(OMFITdir, self).__delitem__(key)
def _parse_flucs_spectra(self):
'''
Parse the fluctuation spectra in out.tglf.{density,temperature}_spectrum
'''
ns = self['input.tglf']['NS']
result = SortedDict()
for field in ['density', 'temperature']:
if '%s_spectrum' % field in self:
continue
fn = 'out.tglf.%s_spectrum' % field
if fn not in self:
printe('No %s file to parse' % fn)
continue
self['%s_spectrum' % field] = OMFITtglf_fluct_spectrum(
self[fn].filename, self['out.tglf.ky_spectrum'].filename, ns, field, self.spec_labels, noCopyToCWD=True
)
del self[fn]
def _parse_potential_spectrum(self):
'''
Parse the potential fluctuation spectrum in out.tglf.potential_spectrum
'''
nm = self['input.tglf']['NMODES']
if 'potential_spectrum' in self:
return
for fn in ['out.tglf.potential_spectrum', 'out.tglf.field_spectrum']:
if fn not in self and fn == 'out.tglf.potential_spectrum':
continue
elif fn not in self and fn == 'out.tglf.field_spectrum':
printe('No %s file to parse' % fn)
return
self['potential_spectrum'] = OMFITtglf_potential_spectrum(
self[fn].filename, self['out.tglf.ky_spectrum'].filename, nmodes=nm, noCopyToCWD=True
)
del self[fn]
def _parse_version(self):
'''
Parse the version information in out.tglf.version
'''
with open(self['out.tglf.version'].filename, 'r') as f:
content = f.readlines()
self['GACODE_VERSION'], self['GACODE_PLATFORM'], self['TIMESTAMP'] = [x.strip() for x in content[:3]]
del self['out.tglf.version']
def _parse_prec(self):
'''
Parse the out.tglf.prec file (a single number)
'''
self['regression_prec'] = None
if 'out.tglf.prec' in self:
with open(self['out.tglf.prec'].filename, 'r') as f:
content = f.read()
self['regression_prec'] = float(content.strip())
del self['out.tglf.prec']
def _parse_flux_spectrum(self):
'''
Parse the flux spectrum from out.tglf.sum_flux_spectrum
'''
self['sum_flux_spectrum'] = OMFITtglf_flux_spectrum(
self['out.tglf.sum_flux_spectrum'].filename,
self['out.tglf.ky_spectrum'].filename,
self.field_labels,
self.spec_labels,
noCopyToCWD=True,
)
del self['out.tglf.sum_flux_spectrum']
def _parse_QL_flux_spectrum(self):
'''
Parse the QL weight spectrum from out.tglf.QL_flux_spectrum
'''
ns = self['input.tglf']['NS']
self['QL_flux_spectrum'] = OMFITtglf_QL_flux_spectrum(
self['out.tglf.QL_flux_spectrum'].filename,
self['out.tglf.ky_spectrum'].filename,
self.field_labels,
self.spec_labels,
noCopyToCWD=False,
)
# del self['out.tglf.QL_flux_spectrum'] # some workflows still parse the raw QL flux file
def _parse_QL_intensity_spectrum(self):
'''
Parse the QL weight spectrum from out.tglf.QL_intensity_spectrum
'''
ns = self['input.tglf']['NS']
self['QL_intensity_spectrum'] = OMFITtglf_QL_intensity_spectrum(
self['out.tglf.QL_intensity_spectrum'].filename,
self['out.tglf.ky_spectrum'].filename,
ns,
self.spec_labels,
noCopyToCWD=False,
)
def _parse_nete_crossphase_spectrum(self):
'''
Parse the cross phase spectrum from out.tglf.nete_crossphase_spectrum
'''
input_tglf = self['input.tglf']
self['nete_crossphase_spectrum'] = OMFITtglf_nete_crossphase_spectrum(
self['out.tglf.nete_crossphase_spectrum'].filename,
self['out.tglf.ky_spectrum'].filename,
nmodes=input_tglf['NMODES'],
noCopyToCWD=True,
)
del self['out.tglf.nete_crossphase_spectrum']
def _parse_nsts_crossphase_spectrum(self):
'''
Parse the cross phase spectrum from out.tglf.nsts_crossphase_spectrum
'''
input_tglf = self['input.tglf']
self['nsts_crossphase_spectrum'] = OMFITtglf_nsts_crossphase_spectrum(
self['out.tglf.nsts_crossphase_spectrum'].filename,
self['out.tglf.ky_spectrum'].filename,
input_tglf['NMODES'],
self.spec_labels,
noCopyToCWD=True,
)
del self['out.tglf.nsts_crossphase_spectrum']
def _parse_gbflux(self):
'''
Parse the flux from the out.tglf.gbflux file
'''
try:
with open(self['out.tglf.gbflux'].filename) as f:
content = f.read()
tmp = list(map(float, content.split()))
if 'std.tglf.gbflux' in self:
with open(self['std.tglf.gbflux'].filename) as f:
content = f.read()
tmp = uarray(tmp, list(map(float, content.split())))
tmp = tolist(np.reshape(tmp, (4, -1)))
species = ['elec'] + ['ion' + str(k) for k in range(1, len(tmp[0]))]
tmp.insert(0, species)
printd('Right before out.tglf.run creation', topic='OMFITtglf')
result = SortedDict()
result['header'] = ''
result['columns'] = ['.', 'Gam/Gam_GB', 'Q/Q_GB', 'Pi/Pi_GB', 'S/S_GB']
result['data'] = torecarray(tmp, result['columns'])
if 'out.tglf.gbflux' in self:
del self['out.tglf.gbflux']
if 'std.tglf.gbflux' in self:
del self['std.tglf.gbflux']
except ValueError:
result = OMFITasciitable(self.filename + '/out.tglf.gbflux', noCopyToCWD=True)
self['gbflux'] = result
def _parse_run(self):
'''
Parse the flux from the out.tglf.run file
data should be same as gbflux file, but also includes local MHD stability
'''
with open(self['out.tglf.run'].filename) as f:
lines = f.readlines()
results = SortedDict()
lines = [line[:-2] for line in lines] # Remove '\n' from end of lines
lines = [line.split() for line in lines]
for line in lines:
if 'D(R)' in line:
for i, item in enumerate(line):
try:
results[line[i - 2]] = float(item)
except Exception:
pass
elif 'Gam' in line[0]:
results['.'] = line
elif 'elec' in line[0] or 'ion' in line[0]:
line_name = line[0]
line_split = [float(l) for l in line[1:]]
results[line_name] = line_split
self['run'] = results
del self['out.tglf.run']
def _parse_intensity_spectrum(self):
input_tglf = self['input.tglf']
self['intensity_spectrum'] = OMFITtglf_intensity_spectrum(
self.filename + '/out.tglf.intensity_spectrum',
self['out.tglf.ky_spectrum'].filename,
input_tglf['NMODES'],
input_tglf['NS'],
self.spec_labels,
)
del self['out.tglf.intensity_spectrum']
[docs] def saturation_rule(self, saturation_rule_name):
ky_spect = np.asarray(self['eigenvalue_spectrum']['ky'])
gammas = np.asarray(self['eigenvalue_spectrum']['gamma']).T
potential = self['potential_spectrum']['potential'].T.values
with open(self['out.tglf.QL_flux_spectrum'].filename, 'r') as f:
lines = f.readlines()
nky, nm, ns, nfield, ntype = list(map(int, lines[3].split()))
# QL weights
with open(self['out.tglf.QL_flux_spectrum'].filename, 'r') as f:
QL_data = np.loadtxt(f, skiprows=4, unpack=True).reshape(nky, nm, ns, nfield, ntype)
particle_QL = QL_data[:, :, :, :, 0]
energy_QL = QL_data[:, :, :, :, 1]
toroidal_stress_QL = QL_data[:, :, :, :, 2]
parallel_stress_QL = QL_data[:, :, :, :, 3]
exchange_QL = QL_data[:, :, :, :, 4]
with open(self['out.tglf.spectral_shift'].filename, 'r') as f:
kx0_e = np.loadtxt(f, skiprows=5, unpack=True)
with open(self['out.tglf.scalar_saturation_parameters'].filename, 'r') as f:
ave_p0, B_unit, R_unit, q_unit, SAT_geo0_out, kx_geo0_out = np.loadtxt(f, skiprows=5, unpack=True)
R_unit = np.ones((21, 2)) * R_unit
return sum_ky_spectrum(
sat_rule_in=saturation_rule_name,
ky_spect=ky_spect,
gp=gammas,
ave_p0=ave_p0,
R_unit=R_unit,
kx0_e=kx0_e,
potential=potential,
particle_QL=particle_QL,
energy_QL=energy_QL,
toroidal_stress_QL=toroidal_stress_QL,
parallel_stress_QL=parallel_stress_QL,
exchange_QL=exchange_QL,
etg_fact=1.25,
c0=32.48,
c1=0.534,
exp1=1.547,
cx_cy=0.56,
alpha_x=1.15,
**self['input.tglf'],
)
def intensity_desat(ky_spect, gradP, q, taus_2):
'''
Dummy Experimental SATuration rule
:param ky_spect: poloidal wave number [nk]
:param gradP: P_PRIME_LOC (pressure gradient - see tglf inputs: https://gacode.io/tglf/tglf_list.html)
:param q: absolute value of safety factor (i.e. Q_LOC)
:param taus_2: ratio of T_main_ion/ T_e
'''
intensity_desat = (0.0082 / abs(gradP) + 0.1 * log10(ky_spect)) / (ky_spect ** ((3.81 * q) / (q + taus_2)))
intensity_desat = np.c_[intensity_desat, intensity_desat]
return intensity_desat
def intensity_sat0(
ky_spect,
gp,
ave_p0,
R_unit,
kx0_e,
etg_fact,
c0,
c1,
exp1,
cx_cy,
alpha_x,
B_unit=1.0,
as_1=1.0,
zs_1=-1.0,
taus_1=1.0,
mass_2=1.0,
alpha_quench=0,
):
'''
SAT0 template for modifying the TGLF SAT0 intensity function from [Staebler et al., Nuclear Fusion, 2013], still needs to be normalized (see below)
nk --> number of elements in ky spectrum
nm --> number of modes
ns --> number of species
nf --> number of fields (1: electrostatic, 2: electromagnetic parallel, 3:electromagnetic perpendicular)
:param ky_spect: poloidal wave number [nk]
:param gp: growth rates [nk, nm]
:param ave_p0: scalar average pressure
:param R_unit: scalar normalized major radius
:param kx0_e: spectral shift of the radial wavenumber due to VEXB_SHEAR [nk]
:param etg_fact: scalar TGLF calibration coefficient [1.25]
:param c0: scalar TGLF calibration coefficient [32.48]
:param c1: scalar TGLF calibration coefficient [0.534]
:param exp1: scalar TGLF calibration coefficient [1.547]
:param cx_cy: scalar TGLF calibration coefficient [0.56] (from Eq.13)
:param alpha_x: scalar TGLF calibration coefficient [1.15] (from Eq.13)
:param B_unit: scalar normalized magnetic field (set to 1.0)
:param as_1: scalar ratio of electron density to itself (must be 1.0)
:param zs_1: scalar ratio of electron charge to the first ion charge (very likely to be -1.0)
:param taus_1: scalar ratio of electron temperature to itself (must be 1.0)
:param mass_2: scalar ratio of first ion mass to itself (must be 1.0)
:param alpha_quench: scalar if alpha_quench==0 and any element in spectral shift array is greater than zero (versus equal zero), apply spectral shift routine
:return: intensity function [nk, nm]
'''
if as_1 != 1.0 or taus_1 != 1.0 or mass_2 != 1.0:
raise ValueError('as_1, taus_1, mass_2 must be equal to 1.0')
kp = np.c_[ky_spect, ky_spect] # add column makes two column array for dependence on two modes
# kx0_e = np.c_[kx0_e, kx0_e]
ks = kp * np.sqrt(taus_1 * mass_2)
pols = (ave_p0 / np.abs(as_1 * zs_1 * zs_1)) ** 2
R_unit[R_unit == 0] = np.amax(R_unit) # when EV solver does not converge, R_unit is 0.0, but should be nonzero and the same for all ky
wd0 = ks * np.sqrt(taus_1 / mass_2) / R_unit
gnet = gp / wd0
cond = (alpha_quench == 0) * (np.abs(kx0_e) > 0)
notcond = (alpha_quench != 0) + (np.abs(kx0_e) == 0)
# Calculate intensity_given using scalar TGLF calibration coefficients
cnorm = c0 * pols
cnorm = cnorm / ((ks**etg_fact) * (ks > 1) + 1 * (ks <= 1))
intensity_out = cnorm * (wd0**2) * (gnet**exp1 + c1 * gnet) / (kp**4)
intensity_out = intensity_out / (((1 + cx_cy * kx0_e**2) ** 2) * cond + 1 * notcond)
intensity_out = intensity_out / (((1 + (alpha_x * kx0_e) ** 4) ** 2) * cond + 1 * notcond)
intensity_out = intensity_out / B_unit**2
intensity_out = np.nan_to_num(intensity_out)
intensity_out[
intensity_out == 0
] = 10e10 # when EV solver did not converge, intensity and potential are zero; change zeros to large number before division
return intensity_out # Careful: This still needs to be normalized; next operation is intensity_out = intensity_out * potential(TGLF SAT0 output) / intensity_out(default SAT0 params)
# See example with default parameters below and regression test 'omfit/regression/test_tglf_satpy.py'
def get_zonal_mixing(
ky_mix,
gamma_mix,
**kw,
):
"""
:param ky_mix: poloidal wavenumber [nk]
:param gamma_mix: most unstable growth rates [nk]
:param **kw: keyword list in input.tglf
"""
nky = len(ky_mix)
gammamax1 = gamma_mix[0]
kymax1 = ky_mix[0]
testmax1 = gammamax1 / kymax1
jmax1 = 0
kymin = 0
testmax = 0.0
j1 = 0
kycut = 0.8 / kw["rho_ion"]
if kw["ALPHA_ZF"] < 0:
kymin = 0.173 * np.sqrt(2.0) / kw["rho_ion"]
if kw["SAT_RULE"] in [2, 3]:
kycut = kw["grad_r0_out"] * kycut
kymin = kw["grad_r0_out"] * kymin
for j in range(0, nky - 1):
if ky_mix[j] <= kycut and ky_mix[j + 1] >= kymin:
j1 = j
kymax1 = ky_mix[j]
testmax1 = gamma_mix[j] / kymax1
if testmax1 > testmax:
testmax = testmax1
jmax_mix = j
if testmax == 0.0:
jmax_mix = j1
kymax1 = ky_mix[jmax_mix]
gammamax1 = gamma_mix[jmax_mix]
if kymax1 < kymin:
kymax1 = kymin
gammamax1 = gamma_mix[0] + (gamma_mix[1] - gamma_mix[0]) * (kymin - ky_mix[0]) / (ky_mix[1] - ky_mix[0])
if jmax_mix > 0 and jmax_mix < j1:
jmax1 = jmax_mix
f0 = gamma_mix[jmax1 - 1] / ky_mix[jmax1 - 1]
f1 = gamma_mix[jmax1] / ky_mix[jmax1]
f2 = gamma_mix[jmax1 + 1] / ky_mix[jmax1 + 1]
deltaky = ky_mix[jmax1 + 1] - ky_mix[jmax1 - 1]
x1 = (ky_mix[jmax1] - ky_mix[jmax1 - 1]) / deltaky
a = f0
b = (f1 - f0 * (1 - x1 * x1) - f2 * x1 * x1) / (x1 - x1 * x1)
c = f2 - f0 - b
xmax = -b / (2.0 * c)
if ky_mix[jmax1 - 1] < kymin:
xmin = (kymin - ky_mix[jmax1 - 1]) / deltaky
else:
xmin = 0.0
if xmax >= 1.0:
kymax1 = ky_mix[jmax1 + 1]
gammamax1 = f2 * kymax1
elif xmax < xmin:
if xmin > 0.0:
kymax1 = kymin
gammamax1 = (a + b * xmin + c * xmin * xmin) * kymin
else:
kymax1 = ky_mix[jmax1 - 1]
gammamax1 = f0 * kymax1
else:
kymax1 = ky_mix[jmax1 - 1] + deltaky * xmax
gammamax1 = (a + b * xmax + c * xmax * xmax) * kymax1
vzf_mix = gammamax1 / kymax1
kymax_mix = kymax1
return vzf_mix, kymax_mix, jmax_mix
def get_sat_params(sat_rule_in, ky, gammas, mts=5.0, ms=128, small=0.00000001, **kw):
"""
This function calculates the scalar saturation parameters and spectral shift needed
for the TGLF saturation rules, dependent on changes to 'tglf_geometry.f90' by Gary Staebler
:mts: the number of points in the s-grid (flux surface contour)
:ms: number of points along the arclength
:ds: the arc length differential on a flux surface
:R(ms): the major radius on the s-grid
:Z(ms): the vertical coordinate on the s-grid
:Bp(ms): the poloidal magnetic field on the s-grid normalized to B_unit
:**kw: input.tglf
"""
drmajdx_loc = kw["DRMAJDX_LOC"]
drmindx_loc = kw["DRMINDX_LOC"]
kappa_loc = kw["KAPPA_LOC"]
s_kappa_loc = kw["S_KAPPA_LOC"]
rmin_loc = kw["RMIN_LOC"]
rmaj_loc = kw["RMAJ_LOC"]
zeta_loc = kw["ZETA_LOC"]
q_s = kw["Q_LOC"]
q_prime_s = kw["Q_PRIME_LOC"]
p_prime_s = kw["P_PRIME_LOC"]
delta_loc = kw["DELTA_LOC"]
s_delta_loc = kw["S_DELTA_LOC"]
s_zeta_loc = kw["S_ZETA_LOC"]
alpha_e_in = kw["ALPHA_E"]
vexb_shear = kw["VEXB_SHEAR"]
sign_IT = kw["SIGN_IT"]
units = kw["UNITS"]
mass_2 = kw["MASS_2"]
taus_2 = kw["TAUS_2"]
zs_2 = kw["ZS_2"]
zmaj_loc = 0.0
dzmajdx_loc = 0.0
norm_ave = 0.0
SAT_geo1_out = 0.0
SAT_geo2_out = 0.0
dlp = 0.0
R = np.zeros(ms + 1)
Z = np.zeros(ms + 1)
Bp = np.zeros(ms + 1)
Bt = np.zeros(ms + 1)
B = np.zeros(ms + 1)
b_geo = np.zeros(ms + 1)
qrat_geo = np.zeros(ms + 1)
sin_u = np.zeros(ms + 1)
s_p = np.zeros(ms + 1)
r_curv = np.zeros(ms + 1)
psi_x = np.zeros(ms + 1)
costheta_geo = np.zeros(ms + 1)
pi_2 = 2 * np.pi
if rmin_loc < 0.00001:
rmin_loc = 0.00001
vs_2 = np.sqrt(taus_2 / mass_2)
gamma_reference_kx0 = gammas[0, :]
# Miller geo
rmin_s = rmin_loc
Rmaj_s = rmaj_loc
# compute the arclength around the flux surface:
# initial values define dtheta
theta = 0.0
x_delta = np.arcsin(delta_loc)
arg_r = theta + x_delta * np.sin(theta)
darg_r = 1.0 + x_delta * np.cos(theta)
arg_z = theta + zeta_loc * np.sin(2.0 * theta)
darg_z = 1.0 + zeta_loc * 2.0 * np.cos(2.0 * theta)
r_t = -rmin_loc * np.sin(arg_r) * darg_r
z_t = kappa_loc * rmin_loc * np.cos(arg_z) * darg_z
l_t = np.sqrt(r_t**2 + z_t**2)
# scale dtheta by l_t to keep mts points in each ds interval of size pi_2/ms
dtheta = pi_2 / (mts * ms * l_t)
l_t1 = l_t
arclength = 0.0
while theta < pi_2:
theta = theta + dtheta
if theta > pi_2:
theta = theta - dtheta
dtheta = pi_2 - theta
theta = pi_2
arg_r = theta + x_delta * np.sin(theta)
darg_r = 1.0 + x_delta * np.cos(theta) # d(arg_r)/dtheta
r_t = -rmin_loc * np.sin(arg_r) * darg_r # dR/dtheta
arg_z = theta + zeta_loc * np.sin(2.0 * theta)
darg_z = 1.0 + zeta_loc * 2.0 * np.cos(2.0 * theta) # d(arg_z)/dtheta
z_t = kappa_loc * rmin_loc * np.cos(arg_z) * darg_z # dZ/dtheta
l_t = np.sqrt(r_t**2 + z_t**2) # dl/dtheta
arclength = arclength + 0.50 * (l_t + l_t1) * dtheta # arclength along flux surface in poloidal direction
l_t1 = l_t
# Find the theta points which map to an equally spaced s-grid of ms points along the arclength
# going clockwise from the outboard midplane around the flux surface
# by searching for the theta where dR**2 + dZ**2 >= ds**2 for a centered difference df=f(m+1)-f(m-1).
# This keeps the finite difference error of dR/ds, dZ/ds on the s-grid small
ds = arclength / ms
t_s = np.zeros(ms + 1)
t_s[ms] = -pi_2
# Make a first guess based on theta = 0.0
theta = 0.0
arg_r = theta + x_delta * np.sin(theta)
darg_r = 1.0 + x_delta * np.cos(theta)
arg_z = theta + zeta_loc * np.sin(2.0 * theta)
darg_z = 1.0 + zeta_loc * 2.0 * np.cos(2.0 * theta)
r_t = -rmin_loc * np.sin(arg_r) * darg_r
z_t = kappa_loc * rmin_loc * np.cos(arg_z) * darg_z
l_t = np.sqrt(r_t**2 + z_t**2)
dtheta = -ds / l_t
theta = dtheta
l_t1 = l_t
for m in range(1, int(ms / 2) + 1):
arg_r = theta + x_delta * np.sin(theta)
darg_r = 1.0 + x_delta * np.cos(theta)
arg_z = theta + zeta_loc * np.sin(2.0 * theta)
darg_z = 1.0 + zeta_loc * 2.0 * np.cos(2.0 * theta)
r_t = -rmin_loc * np.sin(arg_r) * darg_r
z_t = kappa_loc * rmin_loc * np.cos(arg_z) * darg_z
l_t = np.sqrt(r_t**2 + z_t**2)
dtheta = -ds / (0.5 * (l_t + l_t1))
t_s[m] = t_s[m - 1] + dtheta
theta = t_s[m] + dtheta
l_t1 = l_t
# distribute endpoint error over interior points
dtheta = (t_s[int(ms / 2)] - (-np.pi)) / (ms / 2)
for m in range(1, int(ms / 2) + 1):
t_s[m] = t_s[m] - (m) * dtheta
t_s[ms - m] = -pi_2 - t_s[m]
# Quinn additions,
B_unit_out = np.zeros(ms + 1)
grad_r_out = np.zeros(ms + 1)
for m in range(0, ms + 1):
theta = t_s[m]
arg_r = theta + x_delta * np.sin(theta)
darg_r = 1.0 + x_delta * np.cos(theta)
arg_z = theta + zeta_loc * np.sin(2.0 * theta)
darg_z = 1.0 + zeta_loc * 2.0 * np.cos(2.0 * theta)
R[m] = rmaj_loc + rmin_loc * np.cos(arg_r) # = R(theta)
Z[m] = zmaj_loc + kappa_loc * rmin_loc * np.sin(arg_z) # = Z(theta)
R_t = -rmin_loc * np.sin(arg_r) * darg_r # = dR/dtheta
Z_t = kappa_loc * rmin_loc * np.cos(arg_z) * darg_z # = dZ/dtheta
l_t = np.sqrt(R_t**2 + Z_t**2) # = dl/dtheta
R_r = (
drmajdx_loc + drmindx_loc * np.cos(arg_r) - np.sin(arg_r) * s_delta_loc * np.sin(theta) / np.sqrt(1.0 - delta_loc**2)
) # = dR/dr
Z_r = (
dzmajdx_loc
+ kappa_loc * np.sin(arg_z) * (drmindx_loc + s_kappa_loc)
+ kappa_loc * np.cos(arg_z) * s_zeta_loc * np.sin(2.0 * theta)
)
det = R_r * Z_t - R_t * Z_r # Jacobian
grad_r = abs(l_t / det)
if m == 0:
B_unit = 1.0 / grad_r # B_unit choosen to make qrat_geo(0)/b_geo(0)=1.0
if drmindx_loc == 1.0:
B_unit = 1.0 # Waltz-Miller convention
B_unit_out[m] = B_unit
grad_r_out[m] = grad_r
Bp[m] = (rmin_s / (q_s * R[m])) * grad_r * B_unit
p_prime_s = p_prime_s * B_unit
q_prime_s = q_prime_s / B_unit
psi_x[m] = R[m] * Bp[m]
delta_s = 12.0 * ds
ds2 = 12.0 * ds**2
for m in range(0, ms + 1):
m1 = (ms + m - 2) % ms
m2 = (ms + m - 1) % ms
m3 = (m + 1) % ms
m4 = (m + 2) % ms
R_s = (R[m1] - 8.0 * R[m2] + 8.0 * R[m3] - R[m4]) / delta_s
Z_s = (Z[m1] - 8.0 * Z[m2] + 8.0 * Z[m3] - Z[m4]) / delta_s
s_p[m] = np.sqrt(R_s**2 + Z_s**2)
R_ss = (-R[m1] + 16.0 * R[m2] - 30.0 * R[m] + 16.0 * R[m3] - R[m4]) / ds2
Z_ss = (-Z[m1] + 16.0 * Z[m2] - 30.0 * Z[m] + 16.0 * Z[m3] - Z[m4]) / ds2
r_curv[m] = (s_p[m] ** 3) / (R_s * Z_ss - Z_s * R_ss)
sin_u[m] = -Z_s / s_p[m]
# Compute f=R*Bt such that the eikonal S which solves
# B*Grad(S)=0 has the correct quasi-periodicity S(s+Ls)=S(s)-2*pi*q_s, where Ls = arclength
f = 0.0
for m in range(1, ms + 1):
f = f + 0.5 * ds * (s_p[m - 1] / (R[m - 1] * psi_x[m - 1]) + s_p[m] / (R[m] * psi_x[m]))
f = pi_2 * q_s / f
for m in range(0, ms + 1):
Bt[m] = f / R[m]
B[m] = np.sqrt(Bt[m] ** 2 + Bp[m] ** 2)
qrat_geo[m] = (rmin_s / R[m]) * (B[m] / Bp[m]) / q_s
b_geo[m] = B[m]
costheta_geo[m] = -Rmaj_s * (Bp[m] / (B[m]) ** 2) * (Bp[m] / r_curv[m] - (f**2 / (Bp[m] * (R[m]) ** 3)) * sin_u[m])
for m in range(1, ms + 1):
dlp = s_p[m] * ds * (0.5 / Bp[m] + 0.5 / Bp[m - 1])
norm_ave += dlp
SAT_geo1_out += dlp * ((b_geo[0] / b_geo[m - 1]) ** 4 + (b_geo[0] / b_geo[m]) ** 4) / 2.0
SAT_geo2_out += dlp * ((qrat_geo[0] / qrat_geo[m - 1]) ** 4 + (qrat_geo[0] / qrat_geo[m]) ** 4) / 2.0
SAT_geo1_out = SAT_geo1_out / norm_ave
SAT_geo2_out = SAT_geo2_out / norm_ave
if units == "GYRO" and sat_rule_in == 1:
SAT_geo1_out = 1.0
SAT_geo2_out = 1.0
R_unit = Rmaj_s * b_geo[0] / (qrat_geo[0] * costheta_geo[0])
B_geo0_out = b_geo[0]
Bt0_out = f / Rmaj_s
grad_r0_out = b_geo[0] / qrat_geo[0]
# Additional outputs for SAT2 G1(theta), Gq(theta)
theta_out = t_s # theta grid over which everything is calculated.
Bt_out = B # total magnetic field matching theta_out grid.
# Compute spetral shift kx0_e
vexb_shear_s = vexb_shear * sign_IT
vexb_shear_kx0 = alpha_e_in * vexb_shear_s
kx0_factor = abs(b_geo[0] / qrat_geo[0] ** 2)
kx0_factor = 1.0 + 0.40 * (kx0_factor - 1.0) ** 2
kyi = ky * vs_2 * mass_2 / abs(zs_2)
wE = kx0_factor * np.array([min(x / 0.3, 1.0) for x in kyi]) * vexb_shear_kx0 / gamma_reference_kx0
kx0_e = -(0.36 * vexb_shear_kx0 / gamma_reference_kx0 + 0.38 * wE * np.tanh((0.69 * wE) ** 6))
if sat_rule_in == 1:
if units == "CGYRO":
wE = 0.0
kx0_factor = 1.0
kx0_e = -(0.53 * vexb_shear_kx0 / gamma_reference_kx0 + 0.25 * wE * np.tanh((0.69 * wE) ** 6))
elif sat_rule_in == 2 or sat_rule_in == 3:
kw["grad_r0_out"] = grad_r0_out
kw["SAT_RULE"] = sat_rule_in
if bool(kw["USE_AVE_ION_GRID"]):
indices = (is_ for is_ in range(2, kw['NS'] + 1) if (kw[f'ZS_{is_}'] * kw[f'AS_{is_}']) / abs(kw['AS_1'] * kw['ZS_1']) > 0.1)
charge = sum(kw[f'ZS_{is_}'] * kw[f'AS_{is_}'] for is_ in indices)
rho_ion = sum(
kw[f'ZS_{is_}'] * kw[f'AS_{is_}'] * (kw[f'MASS_{is_}'] * kw[f'TAUS_{is_}']) ** 0.5 / kw[f'ZS_{is_}'] for is_ in indices
)
rho_ion /= charge if charge != 0 else 1
else:
rho_ion = (kw['MASS_2'] * kw['TAUS_2']) ** 0.5 / kw['ZS_2']
kw['rho_ion'] = rho_ion
vzf_out, kymax_out, _ = get_zonal_mixing(ky, gamma_reference_kx0, **kw)
if abs(kymax_out * vzf_out * vexb_shear_kx0) > small:
kx0_e = -0.32 * ((ky / kymax_out) ** 0.3) * vexb_shear_kx0 / (ky * vzf_out)
else:
kx0_e = np.zeros(len(ky))
a0 = 1.3
if sat_rule_in == 1:
a0 = 1.45
elif sat_rule_in == 2 or sat_rule_in == 3:
a0 = 1.6
kx0_e = np.array([min(abs(x), a0) * x / abs(x) for x in kx0_e])
kx0_e[np.isnan(kx0_e)] = 0
return (
kx0_e,
SAT_geo1_out,
SAT_geo2_out,
R_unit,
Bt0_out,
B_geo0_out,
grad_r0_out,
theta_out,
Bt_out,
grad_r_out,
B_unit_out,
)
def mode_transition_function(x, y1, y2, x_ITG, x_TEM):
if x < x_ITG:
y = y1
elif x > x_TEM:
y = y2
else:
y = y1 * ((x_TEM - x) / (x_TEM - x_ITG)) + y2 * ((x - x_ITG) / (x_TEM - x_ITG))
return y
def linear_interpolation(x, y, x0):
i = 0
while x[i] < x0:
i += 1
y0 = ((y[i] - y[i - 1]) * x0 + (x[i] * y[i - 1] - x[i - 1] * y[i])) / (x[i] - x[i - 1])
return y0
def intensity_sat(
sat_rule_in,
ky_spect,
gp,
kx0_e,
nmodes,
QL_data,
expsub=2.0,
alpha_zf_in=1.0,
kx_geo0_out=1.0,
SAT_geo_out=1.0,
bz1=0.0,
bz2=0.0,
return_phi_params=False,
**kw,
):
"""
TGLF SAT1 from [Staebler et al., 2016, PoP], SAT2 from [Staebler et al., NF, 2021] and [Staebler et al., PPCF, 2021],
and SAT3 [Dudding et al., NF, 2022] takes both CGYRO and TGLF outputs as inputs
:param sat_rule_in: saturation rule [1, 2, 3]
:param ky_spect: poloidal wavenumber [nk]
:param gp: growth rates [nk, nm]
:param kx0_e: spectral shift of the radial wavenumber due to VEXB_SHEAR [nk]
:param nmodes_in: number of modes stored in quasi-linear weights [1, ..., 5]
:param QL_data: Quasi-linear weights [ky, nm, ns, nf, type (i.e. particle,energy,stress_tor,stress_para,exchange)]
:param expsub: scalar exponent in gammaeff calculation [2.0]
:param alpha_zf_in: scalar switch for the zonal flow coupling coefficient [1.0]
:param kx_geo_out: scalar switch for geometry [1.0]
:param SAT_geo_out: scalar switch for geoemtry [1.0]
:param bz1: scalar correction to zonal flow mixing term [0.0]
:param bz2: scalar correction to zonal flow mixing term [0.0]
:param return_phi_params: bool, option to return parameters for calculing the SAT1, SAT2 model for phi [False]
:param **kw: keyword list in input.tglf
"""
if bool(kw["USE_AVE_ION_GRID"]):
indices = (is_ for is_ in range(2, kw['NS'] + 1) if (kw[f'ZS_{is_}'] * kw[f'AS_{is_}']) / abs(kw['AS_1'] * kw['ZS_1']) > 0.1)
charge = sum(kw[f'ZS_{is_}'] * kw[f'AS_{is_}'] for is_ in indices)
rho_ion = sum(
kw[f'ZS_{is_}'] * kw[f'AS_{is_}'] * (kw[f'MASS_{is_}'] * kw[f'TAUS_{is_}']) ** 0.5 / kw[f'ZS_{is_}'] for is_ in indices
)
rho_ion /= charge if charge != 0 else 1
else:
rho_ion = (kw['MASS_2'] * kw['TAUS_2']) ** 0.5 / kw['ZS_2']
kw['rho_ion'] = rho_ion
nky = len(ky_spect)
if len(np.shape(gp)) > 1:
gammas1 = gp[:, 0] # SAT1 and SAT2 use the growth rates of the most unstable modes
else:
gammas1 = gp
gamma_net = np.zeros(nky)
if sat_rule_in == 1:
etg_streamer = 1.05
kyetg = etg_streamer / kw["rho_ion"]
measure = np.sqrt(kw["TAUS_1"] * kw["MASS_2"])
czf = abs(alpha_zf_in)
small = 1.0e-10
cz1 = 0.48 * czf
cz2 = 1.0 * czf
cky = 3.0
sqcky = np.sqrt(cky)
cnorm = 14.29
if sat_rule_in in [2, 3]:
kw["UNITS"] = "CGYRO"
units_in = kw["UNITS"]
else:
units_in = kw["UNITS"]
kycut = 0.8 / kw["rho_ion"]
# ITG/ETG-scale separation (for TEM scales see [Creely et al., PPCF, 2019])
vzf_out, kymax_out, jmax_out = get_zonal_mixing(ky_spect, gammas1, **kw)
if kw["RLNP_CUTOFF"] > 0.0:
ptot = 0
dlnpdr = 0
for i in range(1, kw["NS"] + 1, 1):
ptot += kw["AS_%s" % i] * kw["TAUS_%s" % i] # only kinetic species
dlnpdr += kw["AS_%s" % i] * kw["TAUS_%s" % i] * (kw["RLNS_%s" % i] + kw["RLTS_%s" % i])
dlnpdr = kw["RMAJ_LOC"] * dlnpdr / max(ptot, 0.01)
if dlnpdr >= kw["RLNP_CUTOFF"]:
dlnpdr = kw["RLNP_CUTOFF"]
if dlnpdr < 4.0:
dlnpdr = 4.0
else:
dlnpdr = 12.0
if sat_rule_in == 2 or sat_rule_in == 3:
# SAT2 fit for CGYRO linear modes NF 2021 paper
b0 = 0.76
b1 = 1.22
b2 = 3.74
if nmodes > 1:
b2 = 3.55
b3 = 1.0
d1 = (kw["Bt0_out"] / kw["B_geo0_out"]) ** 4 # PPCF paper 2020
d1 = d1 / kw["grad_r0_out"]
# WARNING: this is correct, but it's the reciprocal in the paper (typo in paper)
Gq = kw["B_geo0_out"] / kw["grad_r0_out"]
d2 = b3 / Gq**2
cnorm = b2 * (12.0 / dlnpdr)
kyetg = 1000.0 # does not impact SAT2
cky = 3.0
sqcky = np.sqrt(cky)
kycut = b0 * kymax_out
cz1 = 0.0
cz2 = 1.05 * czf
measure = 1.0 / kymax_out
if sat_rule_in == 3:
kmax = kymax_out
gmax = vzf_out * kymax_out
kmin = 0.685 * kmax
aoverb = -1.0 / (2 * kmin)
coverb = -0.751 * kmax
kT = 1.0 / kw["rho_ion"] # SAT3 used up to ky rho_av = 1.0, then SAT2
k0 = 0.6 * kmin
kP = 2.0 * kmin
c_1 = -2.42
x_ITG = 0.8
x_TEM = 1.0
Y_ITG = 3.3 * (gmax**2) / (kmax**5)
Y_TEM = 12.7 * (gmax**2) / (kmax**4)
scal = 0.82 # Q(SAT3 GA D) / (2 * QLA(ITG,Q) * Q(SAT2 GA D))
Ys = np.zeros(nmodes)
xs = np.zeros(nmodes)
for k in range(1, nmodes + 1):
sum_W_i = 0
# sum over ion species, requires electrons to be species 1
for is_ in range(2, np.shape(QL_data)[2] + 1):
sum_W_i += QL_data[:, k - 1, is_ - 1, 0, 1]
# check for singularities in weight ratio near kmax
i = 1
while ky_spect[i - 1] < kmax:
i += 1
if sum_W_i[i - 1] == 0.0 or sum_W_i[i - 2] == 0.0:
x = 0.5
else:
abs_W_ratio = np.abs(QL_data[:, k - 1, 0, 0, 1] / sum_W_i)
abs_W_ratio = np.nan_to_num(abs_W_ratio)
x = linear_interpolation(ky_spect, abs_W_ratio, kmax)
xs[k - 1] = x
Y = mode_transition_function(x, Y_ITG, Y_TEM, x_ITG, x_TEM)
Ys[k - 1] = Y
ax = 0.0
ay = 0.0
exp_ax = 1
if kw["ALPHA_QUENCH"] == 0.0:
if sat_rule_in == 1:
# spectral shift model parameters
ax = 1.15
ay = 0.56
exp_ax = 4
elif sat_rule_in == 2 or sat_rule_in == 3:
ax = 1.21
ay = 1.0
exp_ax = 2
units_in = "CGYRO"
for j in range(0, nky):
kx = kx0_e[j]
if sat_rule_in == 2 or sat_rule_in == 3:
ky0 = ky_spect[j]
if ky0 < kycut:
kx_width = kycut / kw["grad_r0_out"]
else:
kx_width = kycut / kw["grad_r0_out"] + b1 * (ky0 - kycut) * Gq
kx = kx * ky0 / kx_width
gamma_net[j] = gammas1[j] / (1.0 + abs(ax * kx) ** exp_ax)
if sat_rule_in == 1:
vzf_out, kymax_out, jmax_out = get_zonal_mixing(ky_spect, gamma_net, **kw)
else:
vzf_out_fp = vzf_out
vzf_out = vzf_out * gamma_net[jmax_out] / max(gammas1[jmax_out], small)
gammamax1 = vzf_out * kymax_out
kymax1 = kymax_out
jmax1 = jmax_out
vzf1 = vzf_out
# include zonal flow effects on growth rate model:
gamma_mix1 = np.zeros(nky)
gamma = np.zeros(nky)
for j in range(0, nky):
gamma0 = gamma_net[j]
ky0 = ky_spect[j]
if sat_rule_in == 1:
if ky0 < kymax1:
gamma[j] = max(gamma0 - cz1 * (kymax1 - ky0) * vzf1, 0.0)
else:
gamma[j] = cz2 * gammamax1 + max(gamma0 - cz2 * vzf1 * ky0, 0.0)
elif sat_rule_in == 2 or sat_rule_in == 3:
if ky0 < kymax1:
gamma[j] = gamma0
else:
gamma[j] = gammamax1 + max(gamma0 - cz2 * vzf1 * ky0, 0.0)
gamma_mix1[j] = gamma[j]
# Mix over ky>kymax with integration weight
mixnorm1 = np.zeros(nky)
for j in range(jmax1 + 2, nky):
gamma_ave = 0.0
mixnorm1 = ky_spect[j] * (
np.arctan(sqcky * (ky_spect[nky - 1] / ky_spect[j] - 1.0)) - np.arctan(sqcky * (ky_spect[jmax1 + 1] / ky_spect[j] - 1.0))
)
for i in range(jmax1 + 1, nky - 1):
ky_1 = ky_spect[i]
ky_2 = ky_spect[i + 1]
mix1 = ky_spect[j] * (np.arctan(sqcky * (ky_2 / ky_spect[j] - 1.0)) - np.arctan(sqcky * (ky_1 / ky_spect[j] - 1.0)))
delta = (gamma[i + 1] - gamma[i]) / (ky_2 - ky_1)
mix2 = ky_spect[j] * mix1 + (ky_spect[j] * ky_spect[j] / (2.0 * sqcky)) * (
np.log(cky * (ky_2 - ky_spect[j]) ** 2 + ky_spect[j] ** 2) - np.log(cky * (ky_1 - ky_spect[j]) ** 2 + ky_spect[j] ** 2)
)
gamma_ave = gamma_ave + (gamma[i] - ky_1 * delta) * mix1 + delta * mix2
gamma_mix1[j] = gamma_ave / mixnorm1
if sat_rule_in == 3:
gamma_fp = np.zeros_like(ky_spect) # Assuming ky_spect is a numpy array
gamma = np.zeros_like(ky_spect) # Assuming ky_spect is a numpy array
for j in range(1, nky + 1):
gamma0 = gammas1[j - 1]
ky0 = ky_spect[j - 1]
if ky0 < kymax1:
gamma[j - 1] = gamma0
else:
gamma[j - 1] = (gammamax1 * (vzf_out_fp / vzf_out)) + max(gamma0 - cz2 * vzf_out_fp * ky0, 0.0)
gamma_fp[j - 1] = gamma[j - 1]
# USE_MIX is true by default
for j in range(jmax1 + 3, nky + 1): # careful: I'm switching here to Fortran indexing, but found jmax1 using python indexing
gamma_ave = 0.0
ky0 = ky_spect[j - 1]
kx = kx0_e[j - 1]
mixnorm = ky0 * (np.arctan(sqcky * (ky_spect[nky - 1] / ky0 - 1.0)) - np.arctan(sqcky * (ky_spect[jmax1 + 1] / ky0 - 1.0)))
for i in range(jmax1 + 2, nky): # careful: I'm switching here to Fortran indexing, but found jmax1 using python indexing
ky1 = ky_spect[i - 1]
ky2 = ky_spect[i]
mix1 = ky0 * (np.arctan(sqcky * (ky2 / ky0 - 1.0)) - np.arctan(sqcky * (ky1 / ky0 - 1.0)))
delta = (gamma[i] - gamma[i - 1]) / (ky2 - ky1)
mix2 = ky0 * mix1 + (ky0 * ky0 / (2.0 * sqcky)) * (
np.log(cky * (ky2 - ky0) ** 2 + ky0**2) - np.log(cky * (ky1 - ky0) ** 2 + ky0**2)
)
gamma_ave += (gamma[i - 1] - ky1 * delta) * mix1 + delta * mix2
gamma_fp[j - 1] = gamma_ave / mixnorm
if sat_rule_in == 3:
if ky_spect[-1] >= kT:
dummy_interp = np.zeros_like(ky_spect)
k = 0
while ky_spect[k] < kT:
k += 1
for i in range(k - 1, k + 1):
gamma0 = gp[i, 0]
ky0 = ky_spect[i]
kx = kx0_e[i]
if ky0 < kycut:
kx_width = kycut / kw["grad_r0_out"]
sat_geo_factor = kw["SAT_geo0_out"] * d1 * kw["SAT_geo1_out"]
else:
kx_width = kycut / kw["grad_r0_out"] + b1 * (ky0 - kycut) * Gq
sat_geo_factor = kw["SAT_geo0_out"] * (d1 * kw["SAT_geo1_out"] * kycut + (ky0 - kycut) * d2 * kw["SAT_geo2_out"]) / ky0
kx = kx * ky0 / kx_width
gammaeff = 0.0
if gamma0 > small:
gammaeff = gamma_fp[i]
# potentials without multimode and ExB effects, added later
dummy_interp[i] = scal * measure * cnorm * (gammaeff / (kx_width * ky0)) ** 2
if units_in != "GYRO":
dummy_interp[i] = sat_geo_factor * dummy_interp[i]
YT = linear_interpolation(ky_spect, dummy_interp, kT)
YTs = np.array([YT] * nmodes)
else:
if aoverb * (kP**2) + kP + coverb - ((kP - kT) * (2 * aoverb * kP + 1)) == 0:
YTs = np.zeros(nmodes)
else:
YTs = np.zeros(nmodes)
for i in range(1, nmodes + 1):
YTs[i - 1] = Ys[i - 1] * (
((aoverb * (k0**2) + k0 + coverb) / (aoverb * (kP**2) + kP + coverb - ((kP - kT) * (2 * aoverb * kP + 1))))
** abs(c_1)
)
# preallocate [nky] arrays for phi_params
gammaeff_out = np.zeros((nky, nmodes))
sig_ratio_out = np.zeros((nky, nmodes)) # SAT3
kx_width_out = np.zeros(nky)
sat_geo_factor_out = np.zeros(nky)
# intensity
field_spectrum_out = np.zeros((nky, nmodes))
for j in range(0, nky):
gamma0 = gp[j, 0]
ky0 = ky_spect[j]
kx = kx0_e[j]
if sat_rule_in == 1:
sat_geo_factor = kw["SAT_geo0_out"]
kx_width = ky0
if sat_rule_in == 2 or sat_rule_in == 3:
if ky0 < kycut:
kx_width = kycut / kw["grad_r0_out"]
sat_geo_factor = kw["SAT_geo0_out"] * d1 * kw["SAT_geo1_out"]
else:
kx_width = kycut / kw["grad_r0_out"] + b1 * (ky0 - kycut) * Gq
sat_geo_factor = kw["SAT_geo0_out"] * (d1 * kw["SAT_geo1_out"] * kycut + (ky0 - kycut) * d2 * kw["SAT_geo2_out"]) / ky0
kx = kx * ky0 / kx_width
if sat_rule_in == 1 or sat_rule_in == 2:
for i in range(0, nmodes):
gammaeff = 0.0
if gamma0 > small:
gammaeff = gamma_mix1[j] * (gp[j, i] / gamma0) ** expsub
if ky0 > kyetg:
gammaeff = gammaeff * np.sqrt(ky0 / kyetg)
field_spectrum_out[j, i] = measure * cnorm * ((gammaeff / (kx_width * ky0)) / (1.0 + ay * kx**2)) ** 2
if units_in != "GYRO":
field_spectrum_out[j, i] = sat_geo_factor * field_spectrum_out[j, i]
# add these outputs
gammaeff_out[j, i] = gammaeff
kx_width_out[j] = kx_width
sat_geo_factor_out[j] = sat_geo_factor
elif sat_rule_in == 3:
# First part
if gamma_fp[j] == 0:
Fky = 0.0
else:
Fky = (gamma_mix1[j] / gamma_fp[j]) ** 2 / (1.0 + ay * (kx**2)) ** 2
for i in range(1, nmodes + 1):
field_spectrum_out[j, i - 1] = 0.0
gammaeff = 0.0
if gamma0 > small:
if ky0 <= kP: # initial quadratic
sig_ratio = (aoverb * (ky0**2) + ky0 + coverb) / (aoverb * (k0**2) + k0 + coverb)
field_spectrum_out[j, i - 1] = Ys[i - 1] * (sig_ratio**c_1) * Fky * (gp[j, i - 1] / gamma0) ** (2 * expsub)
elif ky0 <= kT: # connecting quadratic
if YTs[i - 1] == 0.0 or kP == kT:
field_spectrum_out[j, i - 1] = 0.0
else:
doversig0 = ((Ys[i - 1] / YTs[i - 1]) ** (1.0 / abs(c_1))) - (
(aoverb * (kP**2) + kP + coverb - ((kP - kT) * (2 * aoverb * kP + 1)))
/ (aoverb * (k0**2) + k0 + coverb)
)
doversig0 = doversig0 * (1.0 / ((kP - kT) ** 2))
eoversig0 = -2 * doversig0 * kP + ((2 * aoverb * kP + 1) / (aoverb * (k0**2) + k0 + coverb))
foversig0 = ((Ys[i - 1] / YTs[i - 1]) ** (1.0 / abs(c_1))) - eoversig0 * kT - doversig0 * (kT**2)
sig_ratio = doversig0 * (ky0**2) + eoversig0 * ky0 + foversig0
field_spectrum_out[j, i - 1] = Ys[i - 1] * (sig_ratio**c_1) * Fky * (gp[j, i - 1] / gamma0) ** (2 * expsub)
else: # SAT2 for electron scale
gammaeff = gamma_mix1[j] * (gp[j, i - 1] / gamma0) ** expsub
if ky0 > kyetg:
gammaeff = gammaeff * np.sqrt(ky0 / kyetg)
field_spectrum_out[j, i - 1] = scal * measure * cnorm * ((gammaeff / (kx_width * ky0)) / (1.0 + ay * kx**2)) ** 2
if units_in != "GYRO":
field_spectrum_out[j, i - 1] = sat_geo_factor * field_spectrum_out[j, i - 1]
# add these outputs
gammaeff_out[j, i - 1] = gammaeff
sig_ratio_out[j, i - 1] = sig_ratio
kx_width_out[j] = kx_width
sat_geo_factor_out[j] = sat_geo_factor
# SAT3 QLA part
QLA_P = 0.0
QLA_E = 0.0
if sat_rule_in == 3:
QLA_P = np.zeros(nmodes)
QLA_E = np.zeros(nmodes)
for k in range(1, nmodes + 1):
# factor of 2 included for real symmetry
QLA_P[k - 1] = 2 * mode_transition_function(xs[k - 1], 1.1, 0.6, x_ITG, x_TEM)
QLA_E[k - 1] = 2 * mode_transition_function(xs[k - 1], 0.75, 0.6, x_ITG, x_TEM)
QLA_O = 2 * 0.8
else:
QLA_P = 1.0
QLA_E = 1.0
QLA_O = 1.0
phinorm = field_spectrum_out
# so the normal behavior doesn't change,
if return_phi_params:
out = dict(
phinorm=phinorm,
kx_width=kx_width_out, # [nky] kx_model (kx rms width)
gammaeff=gammaeff_out, # [nky, nmodes] effective growthrate
kx0_e=kx0_e, # [nky] spectral shift in kx
ax=ax, # SAT1 (cx), SAT2 (alpha_x)
ay=ay, # SAT1 (cy)
exp_ax=exp_ax, # SAT2 (sigma_x)
sat_geo_factor=sat_geo_factor_out, # SAT2=G(theta)**2; SAT1=sat_geo0_out
)
if sat_rule_in == 2:
# add G(theta) params,
out.update(dict(d1=d1, d2=d2, kycut=kycut, b3=b3))
if sat_rule_in == 3:
out.update(dict(sig_ratio=sig_ratio_out, k0=k0))
else:
out = phinorm, QLA_P, QLA_E, QLA_O # SAT123 intensity and QLA params
return out
def reconstruct_kxky_phi(ky_grid, kx_grid, gammas, imode=0, theta_grid=None, make_plots=False, QL_data=None, **kw):
'''Reconstruct the TGLF spectral shift model for the saturated potential spectrum.
This function can be used for SAT1, SAT2, or SAT3 (experimental)
The SAT2 SS-model also contains the \theta dim, the kxky_phi output may be thetakxky_phi (if len(theta_grid) > 1)
:arg ky_grid: 1d np.array [nky], ky's from eigenvalue spectrum from TGLF run.
:arg kx_grid: 1d np.array [nkx], kx's over which to reconstruct the kxky_phi spectrum.
:arg gammas: 2d np.array [nky,nmodes], growthrates from eig.value spectrum from TGLF run.
:kwarg imode: int, index of mode to use for reconstruction.
:kwarg theta_grid: 1d np.array [ntheta], theta's over which to reconstruct (SAT2 only), None -> theta=0
:kwarg QL_data: 5d np.array [ky, modes, species, fields, 5 (type)], QL (flux) weights used in SAT3 only.
NOTE: type must be arranged: ["particle","energy","toroidal stress","parallel stress","exchange"]
:kwarg make_plots: bool, for debugging.
:kwarg **kw: input.tglf
'''
sat_rule_in = kw["SAT_RULE"]
nmodes = kw["NMODES"]
if sat_rule_in == 0:
printe("ERROR: (reconstruct_kxky_phi) cannot reconstruct kxky_phi for SAT0")
return
elif sat_rule_in == 3:
printw("WARN: (reconstruct_kxky_phi) SAT3 2D spectrum is underconstrained by design.")
if QL_data is None:
printe("ERROR: (reconstruct_kxky_phi) QL_data cannot be 'None' for SAT3")
return
if make_plots:
import matplotlib.pyplot as plt
if QL_data is None:
# Not used by SAT1, 2
# Must be shape: [ky, modes, species, fields, 5 (type)]
QL_data = np.zeros((len(ky_grid), nmodes, 3, 3, 5))
# call get_sat_params(),
# NOTE: output has been expanded to include theta_out, Bt_out, grad_r_out, and B_unit_out.
kx0_e, SAT_geo1_out, SAT_geo2_out, R_unit, Bt0_out, B_geo0_out, grad_r0_out, theta_out, Bt_out, grad_r_out, B_unit_out = get_sat_params(
sat_rule_in, ky_grid, gammas.T, **kw
)
sat_params = dict(
Bt0_out=Bt0_out,
B_geo0_out=B_geo0_out,
grad_r0_out=grad_r0_out,
SAT_geo0_out=1.0,
SAT_geo1_out=SAT_geo1_out,
SAT_geo2_out=SAT_geo2_out,
)
# call intensity_sat(),
# NOTE: the ouput is totally different when return_phi_params = True
phi_params = intensity_sat(sat_rule_in, ky_grid, gammas, kx0_e, nmodes, QL_data=QL_data, return_phi_params=True, **sat_params, **kw)
# --- --- --- SAT1 --- --- ---
# eq. 13 of [1] Staebler et al. "New Paradigm..." (2013)
# - <k_x> is given around eq. 14 of [1]. kx0_e = spectral_shift_out = <kx>/ky
# - NOTE: kx_width = ky
# - ay (="cx/cy") constant = 0.56
if sat_rule_in == 1:
ay = 0.56
phi2 = copy.deepcopy(phi_params["phinorm"][:, imode])
# remove the geo-factor (for SAT1 sat_geo_factor = 1.0)
phi2 /= phi_params["sat_geo_factor"]
# remove the influence of evaluating at kx=<kx>,
phi2 *= (1 + ay * kx0_e**2) ** 2
kx_grid = np.atleast_2d(kx_grid).T # broadcasting: [Nkx, 1]
kx_factor = 1 + ay * kx0_e**2 + (ay / ky_grid**2) * (kx_grid - kx0_e * ky_grid) ** 2
kxky_phi = np.sqrt(phi2) / kx_factor
# --- --- --- SAT2 --- --- ---
# eq. 34 of [1] Staebler et al. "Verification..." (2021).
# Start with the |dphi|^2 = phinorm.
# Remove the effect of evaluating at kx=kx0e and fs.averaging.
# Then we re-inject the \theta and kx dependance.
#
# Therefore we need G(\theta), k_x0, k_x^model
# - kx0_e (="k_x0/ky") calculated by get_sat_params.
# - kx_width (="k_x^model") calculated by intensity_sat (eq. 21 of [1])
# - G(theta) is calculated below, more complicated becase sat_geo_factor includes a fs.avg.
if sat_rule_in == 2:
phi2 = copy.deepcopy(phi_params["phinorm"][:, imode])
kx0 = kx0_e * ky_grid
# remove the applied geo-factor:
# NOTE: this factor is basically <G(\theta)^2>_theta
phi2 /= phi_params["sat_geo_factor"]
# remove the influence of evaluating at kx=kx0e,
phi2 *= (1 + (kx0 / phi_params["kx_width"]) ** 2) ** 2
kx_grid = np.atleast_2d(kx_grid).T # broadcasting: [Nkx, 1]
kx_factor = 1 + (kx0 / phi_params["kx_width"]) ** 2 + ((kx_grid - kx0) / phi_params["kx_width"]) ** 2
kxky_phi = np.sqrt(phi2) / kx_factor
# eqs. 16-20 of [1]
# See also [2] Staebler et al. "Geometry dependence..." (2021)
# Compute the geometry factor from scratch,
if theta_grid is None:
theta_grid = np.array([0.0])
ntheta = len(theta_grid)
nky = len(ky_grid)
# apply eq. 19, 20 of [1]
G1 = (B_geo0_out / Bt_out) ** 4 # = (B(0)/B(theta))**4
Gq = grad_r_out * B_unit_out / Bt_out # = |grad r|*B_unit/B(theta)
Gq = 1 / Gq # WARNING: There is a typo in [1], it's correct in [2]
G2 = (Gq[0] / Gq) ** 4 # = (Gq(0)/Gq(theta))**4
if make_plots:
fig, ax = plt.subplots(1, 1, num="G-Factors SAT2")
ax.plot(theta_out / np.pi, G1, label=r'$G_1(\theta)$')
ax.plot(theta_out / np.pi, Gq, label=r'$G_q(\theta)$')
ax.plot(theta_out / np.pi, G2, label=r'$G_2(\theta)$')
ax.axhline(1.0, ls='--', color='k') # both G1, G2 need to go to 1 at theta = 0.
ax.set_xlabel(r"$\theta/\pi$")
ax.legend()
# Interpolate G1, G2 onto the target theta grid,
from scipy.interpolate import interp1d
G1 = interp1d(theta_out, G1)(theta_grid)
G2 = interp1d(theta_out, G2)(theta_grid)
G_theta = np.zeros((ntheta, nky))
for i, ky0 in enumerate(ky_grid):
if ky0 < phi_params["kycut"]:
G_theta[:, i] = np.sqrt(phi_params["d1"] * G1)
else:
term1 = phi_params["d1"] * G1 * phi_params["kycut"]
term2 = phi_params["b3"] * phi_params["d2"] * G2 * (ky0 - phi_params["kycut"])
G_theta[:, i] = np.sqrt((term1 + term2) / ky0)
# Following broadcasting rules we have,
# G_theta: [ntheta,nky]
# kxky_phi: [nkx,nky]
# Make G_theta: [ntheta, 1, nky ] and kxky_phi [1, nkx, nky]
# The result will be [ntheta, nkx, nky] (or squeezed to [nkx, nky])
kxky_phi = np.atleast_2d(np.squeeze(kxky_phi[np.newaxis, :, :] * G_theta[:, np.newaxis, :]))
# --- --- --- SAT3 --- --- ---
# eq. 17 of Dudding et al. "A new quasilinear..." (2022).
# SAT3 is completely different, the spectral shift and absolute 2D peak are not directly modeled.
# Our approach is to FIX: <kx> and <|dphi|^2> at kx=<kx>, ky=k0
# Then we use a nonlinear equation solver (scipy.optimize.fsolve) to build the 2D phi(kx, ky) spectrum.
if sat_rule_in == 3:
# The 1D potential spectrum (summed over kx and fs-avg.)
phi2 = copy.deepcopy(phi_params["phinorm"][:, imode])
peak_phi2_k0 = 1.0 # = <|dphi|^2> at kx=<kx>, ky=k0 (Numerator of Dudding eq. 17 eval at ky=k0)
kx_shift = 0.0 # = <kx> In the future this can be kx0e
printw(f"WARN: (reconstruct_kxky_phi) fixing <kx>={kx_shift} and <|phi|^2>(kx=<kx>;ky=k0) = {peak_phi2_k0}")
# get the sig_ratio from the phi_params,
sig_ratio = phi_params["sig_ratio"][:, imode]
# given the selected value of peak_phi2_k0, we solve for sigma_ky=k0
from scipy.interpolate import interp1d
phi2_k0 = interp1d(ky_grid, phi2)(phi_params["k0"])
phi2_ratio = peak_phi2_k0 / phi2_k0
dkxi = 0.1
kxi = np.arange(-4, 4 + dkxi, dkxi)
def target_func(sigky0):
c1 = 1 / sigky0**2 * ((np.pi * sigky0 * phi2_ratio / dkxi) ** 2 - 2) # Equation 14 in Dudding.
return np.sum(1 + c1 * (kxi - kx_shift) ** 2 + (kxi - kx_shift) ** 4 / sigky0**4) - phi2_ratio
from scipy.optimize import fsolve
print(f"INFO: (reconstruct_kxky_phi) solving for sigma_ky=k0 using...")
print(f"\t k0 = {phi_params['k0']:.3f}")
print(f"\t phinorm(ky=k0) = {phi2_k0:.3f}")
print(f"\t init. guess = {min(sig_ratio):.3f}")
sigky0 = fsolve(target_func, min(sig_ratio), maxfev=1000)[0]
print(f"INFO: (reconstruct_kxky_phi) sigky0 = {sigky0}")
# With this one value of sigma_ky=k0 we get all the values of sigma_ky,
sigma_ky = sig_ratio * sigky0
kxi = np.atleast_2d(kxi).T # for broadcasting.
def target_func(peak_phi2):
c1 = 1 / sigma_ky**2 * ((np.pi * sigma_ky * peak_phi2 / phi2 / dkxi) ** 2 - 2)
return peak_phi2 * np.sum(1 / (1 + c1 * (kxi - kx_shift) ** 2 + (kxi - kx_shift) ** 4 / sigma_ky**4), axis=0) - phi2
# vectorized fsolve,
print(f"INFO: (reconstruct_kxky_phi) solving for phi2(kx=<kx>,ky) using...")
print(f"\t init. guess = phi2/Nky")
peak_phi2 = fsolve(target_func, phi2 / len(ky_grid), maxfev=1000)
# Now we can build the final function,
c1 = 1 / sigma_ky**2 * ((np.pi * sigma_ky * peak_phi2 / phi2 / dkxi) ** 2 - 2)
kx_grid = np.atleast_2d(kx_grid).T # [Nkx, 1]
kxky_phi = np.sqrt(peak_phi2 / (1 + c1 * (kx_grid - kx_shift) ** 2 + (kx_grid - kx_shift) ** 4 / sigma_ky**4))
# NORMALIZATION: the kxky_phi matrix has the GB-norm.
# to obtain "real" units it should be multiplied by: (rho_s,unit * Te) / (a * e)
if make_plots:
fig, ax = plt.subplots(1, 1, num=f"omfit_tglf.py: reconstruct_kxky_phi (mode_num={imode+1})")
if sat_rule_in == 1:
txt = ""
phinorm_txt = r"$\sqrt{\langle|\delta\phi|^2(k_{x0e},ky)\rangle_\theta}$"
ax.semilogx(ky_grid, kx0_e, 'g-', label=r'$k_{x,0}^e$')
elif sat_rule_in == 2:
theta0 = theta_grid[0] * 180 / np.pi
txt = rf"$|_{{\theta={theta0:.1f}^\circ}}$"
phinorm_txt = r"$\sqrt{\langle|\delta\phi|^2(k_{x0e},ky)\rangle_\theta}$"
ax.semilogx(ky_grid, phi_params["sat_geo_factor"], '--', color="orange", label=r"$\langle G^2(\theta)\rangle_\theta$")
ax.semilogx(ky_grid, G_theta[0, :], color='orange', label=rf'G($\theta={theta0:.1f}^\circ$)')
ax.semilogx(ky_grid, kx0_e, 'g-', label=r'$k_{x,0}^e$')
ax.semilogx(ky_grid, phi_params['kx_width'] / 10, 'r-', label=r'$k_{x,width}/10$')
elif sat_rule_in == 3:
txt = ""
phinorm_txt = r"$\sqrt{\langle|\delta\phi|^2(k_y)\rangle_{x,\theta}}$"
ax.semilogx(ky_grid, sigma_ky, color='orange', label=rf'$\sigma_{{ky}}$ | $\sigma_{{ky=k0}}$={sigky0:.2f}')
# Always plot the 1D potential spectrum output by TGLF (sqrt)
ax.semilogx(ky_grid, np.sqrt(phi_params["phinorm"][:, imode]), 'k-', label=phinorm_txt)
# Always plot the kx=0 slice,
ikx0 = np.argmin(abs(np.squeeze(kx_grid)))
if kxky_phi.ndim == 3:
ax.semilogx(ky_grid, kxky_phi[0, ikx0, :], 'b-', lw=3, label=r'$|\delta\phi|(0,k_y)$' + txt)
else:
ax.semilogx(ky_grid, kxky_phi[ikx0, :], 'b-', lw=3, label=r'$|\delta\phi|(0,k_y)$' + txt)
ax.set_xlabel(r"$k_y$")
ax.axhline(0, ls='--', color='k')
ax.legend()
return kxky_phi
def reconstruct_kxky_n(
ky_grid,
kx_grid,
gammas,
field_spectrum,
intensity_spectrum,
theta_grid=None,
ispecies=0,
imode=0,
sum_modes=False,
interp0=True,
make_plots=False,
**kw,
):
'''Reconstruct the fluctuating density spectrum: kxky_n using,
- the TGLF spectral shift model for the saturated potential spectrum (kxky_phi)
- the QL weights for the density field
Assumptions: the QL weights are independant of kx, theta (SAT2 only).
:arg ky_grid: (np.array) [nky], ky's from eigenvalue spectrum from TGLF run.
:arg kx_grid: (np.array) [nkx], kx's over which to reconstruct the kxky_phi spectrum.
Hint: use something like: kx_grid = np.linspace(-4, 4, 64)
:arg gammas: (np.array) [nky,nmodes], growthrates from eigenvvalue spectrum from TGLF run.
:arg field_spectrum: (OMFITtglf_potential_spectrum) object from TGLF Experimental_spectra
:arg intensity_spectrum: (OMFITtglf_intensity_spectrum) object from TGLF Experimental_spectra
:kwarg theta_grid: (None or np.array) of theta values in [-2pi, 0], if None theta=0 is used.
:kwarg ispecies: (int) species index for field, intensity spectra. electrons=0
:kwarg imode: (int) mode_num index for field, intensity spectra.
:kwarg sum_modes: (bool) sum over modes, if True the value of imode is irrelevant.
:kwarg interp0: (bool) option to interpolate over zeros in the middle of the potential spectrum.
:kwarg make_plots: (bool) for debugging.
:kwarg **kw: input.tglf
'''
if interp0:
from scipy.interpolate import interp1d
if sum_modes:
nmodes = kw["NMODES"]
mode_inds = range(nmodes)
else:
nmodes = 1
mode_inds = [imode]
# To safeguard overwriting when masking.
field_spectrum = copy.deepcopy(field_spectrum)
intensity_spectrum = copy.deepcopy(intensity_spectrum)
if make_plots:
import matplotlib.pyplot as plt
fig, ax = plt.subplots(1, nmodes, num="omfit_tglf.py: reconstruct_kxky_n", sharex=True, sharey=True, squeeze=False)
ax = ax.flatten()
sum_kxky_n2 = 0.0
for im in mode_inds:
kxky_phi = reconstruct_kxky_phi(ky_grid, kx_grid, gammas, imode=im, theta_grid=theta_grid, make_plots=make_plots, **kw)
# QL Weights for the density field are backed-out of other TGLF outputs,
phi_bar_out = field_spectrum["potential"].isel(mode_num=im).data # (nky,)
n_intensity = intensity_spectrum["density"].isel(mode_num=im, species=ispecies).data # (nky,)
# n_weights is a reconstruction of the TGLF variable "N_weight"
# "N_weight" is calculated in: tglf_LS.f90: subroutine get_QL_weights
# Formally, for each ky, mode, and species we compute:
# N_weight[is] = SUM(j->N_BASIS) |dn[is,j]|^2 ! dn comes from the TGLF eigenvector.
# N_weight /= phi_norm ! phi_norm = SUM(j->N_BASIS) |dphi[j]|^2
# cf. eq. 4 of Staebler et al. "Verification..." (2021).
# TGLF does not write N_weight to a file (yet)... but it does write,
# intensity_spectrum_out(1, is, iky, imode) = phi2_bar*N_weight
# field_spectrum_out(2, iky, imode) = phi2_bar = phi_bar_out
# To avoid div-by-zero,
bad = phi_bar_out == 0
if any(bad):
printw(f"WARN: (reconstruct_kxky_n) zeros found in potential_spectrum for ky = {ky_grid[bad]}")
if interp0:
printw("WARN: (reconstruct_kxky_n) interpolating over zeros in potential_spectrum")
phi_bar_out = interp1d(
ky_grid[~bad],
phi_bar_out[~bad],
bounds_error=False,
fill_value=np.nan, # if there are zeros at the boundary we make them NaNs (then set to zero in n_weights)
)(ky_grid)
else:
# fill with NaN,
phi_bar_out[bad] = np.nan * phi_bar_out[bad]
bad = n_intensity == 0
if any(bad):
printw(f"WARN: (reconstruct_kxky_n) zeros found in intensity_spectrum for ky = {ky_grid[bad]}")
if interp0:
printw("WARN: (reconstruct_kxky_n) interpolating over zeros in intensity_spectrum")
n_intensity = interp1d(
ky_grid[~bad],
n_intensity[~bad],
bounds_error=False,
fill_value=np.nan, # if there are zeros at the boundary we make them NaNs (then set to zero in n_weights)
)(ky_grid)
else:
# fill with NaN,
n_intensity[bad] = np.nan * n_intensity[bad]
n_weights = n_intensity / phi_bar_out # (nky,)
# send NaN's to zero,
n_weights = np.nan_to_num(n_weights)
# Work with the square of the density spectrum until the end,
kxky_n2 = n_weights * np.square(kxky_phi) # (nky,) * ([ntheta], nkx, nky) --> ([ntheta], nkx, nky)
# NORMALIZATION: this kxky_n matrix has the GB-norm.
# to obtain "real" units it should be multiplied by: ne*(rho_s,unit/a)
sum_kxky_n2 += kxky_n2
if make_plots:
ikx = np.argmin(abs(kx_grid)) # kx = 0
ax[im].semilogx(ky_grid, phi_bar_out, 'b-', label="potential_spectrum") # <|dphi|^2>
ax[im].semilogx(ky_grid, n_intensity, 'g-', label="intensity_spectrum") # <|dn|^2>
ax[im].semilogx(ky_grid, n_weights, 'k-', label="QL n-weights") # <|dn|^2>/<|dphi|^2>
ax[im].axhline(1.0, ls='--', color='k')
if kxky_n2.ndim == 3:
itheta = 0
txt = fr"$|_{{\theta={theta_grid[itheta]*180/np.pi:.1f}^\circ}}$"
ax[im].semilogx(ky_grid, kxky_n2[itheta, ikx, :], 'r-', label=r"$|\delta n|^2$" + txt)
else:
ax[im].semilogx(ky_grid, kxky_n2[ikx, :], 'r-', label=r"$|\delta n|^2$")
ax[im].set_xlabel(r"$k_y$")
ax[im].set_title(f"mode_num={im+1}")
if make_plots:
ax[0].legend()
# Note that we sum the squares of the modes, this is also done by TGLF when it creates the "density spectrum" object.
kxky_n = np.sqrt(sum_kxky_n2)
return kxky_n
def reconstruct_kxky_T(
ky_grid, kx_grid, gammas, field_spectrum, intensity_spectrum, ispecies=-1, imode=0, theta_grid=None, make_plots=False, **kw
):
'''Reconstruct the 2D fluctuating temperature spectrum: kxky_T using,
- the TGLF spectral shift model for the saturated potential spectrum (kxky_phi)
- the QL phase shifts of the temperature field
This function is an extension of the method above (reconstruct_kxky_phi).
See the :arg, :kwarg definitions above.
:arg field_spectrum: OMFITtglf_potential_spectrum object.
:arg intensity_spectrum: OMFITtglf_intensity_spectrum object.
:kwarg ispecies: int, used to select the proper field for QL weights.
'''
kxky_phi = reconstruct_kxky_phi(ky_grid, kx_grid, gammas, imode=imode, theta_grid=theta_grid, make_plots=make_plots, **kw)
# The TGLF output files: "out.tglf.intensity_spectrum", and "out.tglf.field_spectrum"
# are needed to calculate the QL weights.
phi_bar_out = field_spectrum["potential"].data[imode, :] # (nky,)
T_intensity = intensity_spectrum["temperature"].data[imode, :, ispecies] # (nky,)
# T_weights is a reconstruction of the TGLF variable "T_weight"
# "T_weight" is calculated in: tglf_LS.f90: subroutine get_QL_weights
# Formally, for each ky, mode, and species we compute:
# T_weight[is] = SUM(j->N_BASIS) |dtemp[is,j]|^2 ! wherein, dtemp = dpress - dn
# T_weight /= phi_norm ! phi_norm = SUM(j->N_BASIS) |dphi[j]|^2
# cf. eq. 4 of Staebler et al. "Verification..." (2021).
# TGLF does not write T_weight to a file... but it does write,
# intensity_spectrum_out(2, is, iky, imode) = phi2_bar*T_weight
# field_spectrum_out(2, iky, imode) = phi2_bar = phi_bar_out
# To avoid div-by-zero,
bad = phi_bar_out == 0
if any(bad):
printw("! zeros found in potential_spectrum - interpolate/extrapolate")
from scipy.interpolate import interp1d
phi_bar_out = interp1d(ky_grid[~bad], phi_bar_out[~bad], bounds_error=False, fill_value='extrapolate')(ky_grid)
T_weights = T_intensity / phi_bar_out # (nky,)
# ASSUME: QL weights are independent of kx, theta
# Take sqrt() to get "T" instead of T^2.
kxky_T = np.sqrt(T_weights) * kxky_phi # (nky,) * (nkx, nky, [ntheta]) --> (nkx, nky, [ntheta])
# NORMALIZATION: this kxky_T matrix has the GB-norm.
# to obtain "real" units it should be multiplied by: (rho_s,unit * Te) / a
if make_plots:
fig, ax = plt.subplots(1, 1, num="reconstruct_kxky_T")
ax.semilogx(ky_grid, phi_bar_out, label='potential_spectrum')
ax.semilogx(ky_grid, T_intensity, label='intensity_spectrum')
ax.semilogx(ky_grid, T_weights, 'k-', lw=2, label='QL T-weights')
ax.axhline(1.0, ls='--', color='k') # N_weights = 1 --> no phase shift.
ax.legend()
return kxky_T
def flux_integrals(
NM,
NS,
NF,
i,
ky,
dky0,
dky1,
particle,
energy,
toroidal_stress,
parallel_stress,
exchange,
particle_flux_out,
energy_flux_out,
stress_tor_out,
stress_par_out,
exchange_out,
q_low_out,
taus_1=1.0,
mass_2=1.0,
):
'''
Compute the flux integrals
'''
for nm in range(NM):
for ns in range(NS):
for j in range(NF):
particle_flux_out[nm][ns][j] += dky0 * (0 if i == 0 else particle[i - 1][nm][ns][j]) + dky1 * particle[i][nm][ns][j]
energy_flux_out[nm][ns][j] += dky0 * (0 if i == 0 else energy[i - 1][nm][ns][j]) + dky1 * energy[i][nm][ns][j]
stress_tor_out[nm][ns][j] += (
dky0 * (0 if i == 0 else toroidal_stress[i - 1][nm][ns][j]) + dky1 * toroidal_stress[i][nm][ns][j]
)
stress_par_out[nm][ns][j] += (
dky0 * (0 if i == 0 else parallel_stress[i - 1][nm][ns][j]) + dky1 * parallel_stress[i][nm][ns][j]
)
exchange_out[nm][ns][j] += dky0 * (0 if i == 0 else exchange[i - 1][nm][ns][j]) + dky1 * exchange[i][nm][ns][j]
if ky * taus_1 * mass_2 <= 1:
q_low_out[nm][ns] = energy_flux_out[nm][ns][0] + energy_flux_out[nm][ns][1]
return particle_flux_out, energy_flux_out, stress_tor_out, stress_par_out, exchange_out, q_low_out
[docs]def sum_ky_spectrum(
sat_rule_in,
ky_spect,
gp,
ave_p0,
R_unit,
kx0_e,
potential,
particle_QL,
energy_QL,
toroidal_stress_QL,
parallel_stress_QL,
exchange_QL,
etg_fact=1.25,
c0=32.48,
c1=0.534,
exp1=1.547,
cx_cy=0.56,
alpha_x=1.15,
**kw,
):
'''
Perform the sum over ky spectrum
The inputs to this function should be already weighted by the intensity function
nk --> number of elements in ky spectrum
nm --> number of modes
ns --> number of species
nf --> number of fields (1: electrostatic, 2: electromagnetic parallel, 3:electromagnetic perpendicular)
:param sat_rule_in:
:param ky_spect: k_y spectrum [nk]
:param gp: growth rates [nk, nm]
:param ave_p0: scalar average pressure
:param R_unit: scalar normalized major radius
:param kx0_e: spectral shift of the radial wavenumber due to VEXB_SHEAR [nk]
:param potential: input potential fluctuation spectrum [nk, nm]
:param particle_QL: input particle fluctuation spectrum [nk, nm, ns, nf]
:param energy_QL: input energy fluctuation spectrum [nk, nm, ns, nf]
:param toroidal_stress_QL: input toroidal_stress fluctuation spectrum [nk, nm, ns, nf]
:param parallel_stress_QL: input parallel_stress fluctuation spectrum [nk, nm, ns, nf]
:param exchange_QL: input exchange fluctuation spectrum [nk, nm, ns, nf]
:param etg_fact: scalar TGLF SAT0 calibration coefficient [1.25]
:param c0: scalar TGLF SAT0 calibration coefficient [32.48]
:param c1: scalar TGLF SAT0 calibration coefficient [0.534]
:param exp1: scalar TGLF SAT0 calibration coefficient [1.547]
:param cx_cy: scalar TGLF SAT0 calibration coefficient [0.56] (from TGLF 2008 POP Eq.13)
:param alpha_x: scalar TGLF SAT0 calibration coefficient [1.15] (from TGLF 2008 POP Eq.13)
:param \**kw: any additional argument should follow the naming convention of the TGLF_inputs
:return: dictionary with summations over ky spectrum:
* particle_flux_integral: [nm, ns, nf]
* energy_flux_integral: [nm, ns, nf]
* toroidal_stresses_integral: [nm, ns, nf]
* parallel_stresses_integral: [nm, ns, nf]
* exchange_flux_integral: [nm, ns, nf]
'''
phi_bar_sum_out = 0
NM = len(energy_QL[0, :, 0, 0]) # get the number of modes
NS = len(energy_QL[0, 0, :, 0]) # get the number of species
NF = len(energy_QL[0, 0, 0, :]) # get the number of fields
particle_flux_out = np.zeros((NM, NS, NF))
energy_flux_out = np.zeros((NM, NS, NF))
stress_tor_out = np.zeros((NM, NS, NF))
stress_par_out = np.zeros((NM, NS, NF))
exchange_out = np.zeros((NM, NS, NF))
q_low_out = np.zeros((NM, NS))
QLA_P = 1
QLA_E = 1
QLA_O = 1
QL_data = np.stack([particle_QL, energy_QL, toroidal_stress_QL, parallel_stress_QL, exchange_QL], axis=4)
# Multiply QL weights with desired intensity
if sat_rule_in in [0.0, 0, 'SAT0']:
intensity_factor = (
intensity_sat0(ky_spect, gp, ave_p0, R_unit, kx0_e, etg_fact, c0, c1, exp1, cx_cy, alpha_x)
* potential
/ intensity_sat0(ky_spect, gp, ave_p0, R_unit, kx0_e, 1.25, 32.48, 0.534, 1.547, 0.56, 1.15)
)
elif sat_rule_in in [1.0, 1, 'SAT1', 2.0, 2, 'SAT2', 3.0, 3, 'SAT3']:
intensity_factor, QLA_P, QLA_E, QLA_O = intensity_sat(sat_rule_in, ky_spect, gp, kx0_e, NM, QL_data, **kw)
elif sat_rule_in in [-1.0, -1, 'DESAT']:
intensity_factor = (
intensity_desat(ky_spect, kw['P_PRIME_LOC'], kw['Q_LOC'], kw['TAUS_2'])
* potential
/ intensity_sat0(ky_spect, gp, ave_p0, R_unit, kx0_e, 1.25, 32.48, 0.534, 1.547, 0.56, 1.15)
)
else:
raise ValueError("sat_rule_in must be [0.0, 0, 'SAT0'] or [1.0, 1, 'SAT1']")
shapes = [item.shape for item in [particle_QL, energy_QL, toroidal_stress_QL, parallel_stress_QL, exchange_QL] if item is not None][0]
particle = np.zeros(shapes)
energy = np.zeros(shapes)
toroidal_stress = np.zeros(shapes)
parallel_stress = np.zeros(shapes)
exchange = np.zeros(shapes)
for i in range(NS): # iterate over the species
for j in range(NF): # iterate over the fields
if particle_QL is not None:
particle[:, :, i, j] = particle_QL[:, :, i, j] * intensity_factor * QLA_P
if energy_QL is not None:
energy[:, :, i, j] = energy_QL[:, :, i, j] * intensity_factor * QLA_E
if toroidal_stress_QL is not None:
toroidal_stress[:, :, i, j] = toroidal_stress_QL[:, :, i, j] * intensity_factor * QLA_O
if parallel_stress_QL is not None:
parallel_stress[:, :, i, j] = parallel_stress_QL[:, :, i, j] * intensity_factor * QLA_O
if exchange_QL is not None:
exchange[:, :, i, j] = exchange_QL[:, :, i, j] * intensity_factor * QLA_O
dky0 = 0
ky0 = 0
for i in range(len(ky_spect)):
ky = ky_spect[i]
ky1 = ky
if i == 0:
dky1 = ky1
else:
dky = np.log(ky1 / ky0) / (ky1 - ky0)
dky1 = ky1 * (1.0 - ky0 * dky)
dky0 = ky0 * (ky1 * dky - 1.0)
particle_flux_out, energy_flux_out, stress_tor_out, stress_par_out, exchange_out, q_low_out = flux_integrals(
NM,
NS,
NF,
i,
ky,
dky0,
dky1,
particle,
energy,
toroidal_stress,
parallel_stress,
exchange,
particle_flux_out,
energy_flux_out,
stress_tor_out,
stress_par_out,
exchange_out,
q_low_out,
)
ky0 = ky1
results = {
"particle_flux_integral": particle_flux_out,
"energy_flux_integral": energy_flux_out,
"toroidal_stresses_integral": stress_tor_out,
"parallel_stresses_integral": stress_par_out,
"exchange_flux_integral": exchange_out,
}
return results
