def eval_fsa_beq2(self, grid=None):
grid, rzp = self.get_gridrzp(grid)
self.dump_fsa_beq2 = self.output_nml.get('dump_fsa_beq2', False)
if self.dump_fsa_beq2:
if len(rzp.shape) == 2:
field = self.eval_symm('fsa_beq2', grid, dmode=1, eq=1)
self.fielddict['fsa_beq2'] = fc.Scalar(field, rzp, 1, True)
else:
field = self.eval.eval_field('fsa_beq2', grid, dmode=1, eq=1)
self.fielddict['fsa_beq2'] = fc.Scalar(field, rzp, 1, True)
else:
self.fielddict['fsa_beq2'] = 0.0
return None
def eval_diff(self, grid=None):
''' Get the diff shape scalars from eval nimrod
Some extralogic is needed to pull apart the different
diff shape scalar fields from the output of eval_field
note when ds_nqty>1
elecd_diff is 0
iso_visc_diff is 1
'''
diff_dmode = self.diff_dmode
grid, rzp = self.get_gridrzp(grid)
self.ds_nqty = self.equil_nml.get('ds_nqty', 1)
if len(rzp.shape) == 2:
field = self.eval_symm('d', rzp, dmode=diff_dmode, eq=1)
else:
field = self.eval.eval_field('d', grid, dmode=diff_dmode, eq=1)
diff_shape = []
ishape = list(field.shape)
ishape[0] = ishape[0] // self.ds_nqty
ifield = np.zeros(ishape)
for ii in range(self.ds_nqty):
ifield[0] = field[ii]
if diff_dmode > 0:
start = self.ds_nqty + ii * 3
end = start + 3
ifield[1:4] = field[start:end]
if diff_dmode > 1:
start = self.ds_nqty * 4 + ii * 6
end = start + 6
ifield[4:10] = field[start:end]
diff_shape.append(fc.Scalar(ifield, rzp, diff_dmode, True))
self.fielddict['diff_shape'] = diff_shape
return None
def eval_p(self, grid=None, fft=False):
grid, rzp = self.get_gridrzp(grid)
if self.peq or self.p0:
if len(rzp.shape) == 2:
field = self.eval_symm('p', rzp, dmode=self.pdmode, eq=1)
self.fielddict['peq'] = fc.Scalar(field, rzp, self.pdmode,
True)
else:
field = self.eval.eval_field('p',
grid,
dmode=self.pdmode,
eq=1)
self.fielddict['peq'] = fc.Scalar(field, rzp, self.pdmode,
True)
if self.p0:
if len(rzp.shape) == 2:
field2 = self.eval_symm('p', rzp, dmode=self.pdmode, eq=3)
field2 = field2 - field #sutract eq
self.fielddict['p0'] = fc.Scalar(field2, rzp, self.pdmode,
True)
else:
field2 = self.eval.eval_field('p',
grid,
dmode=self.pdmode,
eq=3)
field2 = field2 - field #subtract eq
self.fielddict['p0'] = fc.Scalar(field2, rzp, self.pdmode,
True)
if self.ppert:
field = self.eval.eval_field('p', grid, dmode=self.pdmode, eq=0)
if self.p0:
field = field - field2 #remove n=0 perturbation
self.fielddict['ppert'] = fc.Scalar(field, rzp, self.pdmode, True)
if fft:
self.fielddict['pfour'] = self.fft(field, type='s')
return None
def eval_neo_mask(self, grid=None):
grid, rzp = self.get_gridrzp(grid)
r0 = self.closure_nml.get('neo_axis_r', 0)
z0 = self.closure_nml.get('neo_axis_z', 0)
rbump = self.closure_nml.get('neo_bump_r0', 1.0)
shape = list(rzp.shape)
shape[0] = 4
fval = np.zeros(shape)
fval[0] = 1.0
r2 = (np.power(rzp[0] - r0, 2) + np.power(rzp[1] - z0, 2)) / rbump**2
if isinstance(r2, np.float):
if r2 < 1.0:
#check
bump = np.exp(1 - 1. / (1. - r2))
dbumpdr2 = -r2 / (1 - r2)**3
dr2dx = 2 * (rzp[0] - r0) / rbump**2
dr2dz = 2 * (rzp[1] - r0) / rbump**2
d2r2dxx = 2 / rbump**2
d2r2dyy = 2 / rbump**2
d2r2dxy = 0.0
#print(type(bump),type(dbumpdr2),type(dr2dx))
#print(dbumpdr2.shape)
fval[0] = 1.0 - bump
fval[1] = -bump * dbumpdr2 * dr2dx
fval[2] = -bump * dbumpdr2 * dr2dz
else:
result = np.where(r2 < 1.0)
for indicies in zip(*result):
#check
bump = np.exp(1 - 1. / (1. - r2[indicies]))
dbumpdr2 = -r2[indicies] / (1 - r2[indicies])**3
dr2dx = 2 * (rzp[(0, ) + indicies] - r0) / rbump**2
dr2dz = 2 * (rzp[(1, ) + indicies] - r0) / rbump**2
d2r2dxx = 2 / rbump**2
d2r2dyy = 2 / rbump**2
d2r2dxy = 0.0
#print(type(bump),type(dbumpdr2),type(dr2dx))
#print(dbumpdr2.shape)
fval[(0, ) + indicies] = 1.0 - bump
fval[(1, ) + indicies] = -bump * dbumpdr2 * dr2dx
fval[(2, ) + indicies] = -bump * dbumpdr2 * dr2dz
return fc.Scalar(fval, rzp, 1, True)