def get_fargs_eval( self, diff_var = None, chunk_size = None, **kwargs ):
if diff_var is not None:
raise StopIteration
mat, par_v, par_s = self.get_args( **kwargs )
aps, vgs = par_s.get_approximation( self.get_current_group(),
'Volume' )
apv, vgv = par_v.get_approximation( self.get_current_group(),
'Volume' )
self.set_data_shape( aps, apv )
cache = self.get_cache( 'cauchy_strain', 0 )
strain = cache( 'strain', self.get_current_group(), 0,
state = par_v, get_vector = self.get_vector )
cache = self.get_cache( 'grad_scalar', 0 )
p_grad = cache( 'grad', self.get_current_group(), 0, state = par_s )
n_qp = self.data_shape_r[1]
mat = nm.asarray( mat, dtype = nm.float64 )
mat = fix_mat_shape( mat, n_qp )
return (strain, p_grad, mat, vgv), (chunk_size, 1, 1, 1), 0
def get_fargs_grad( self, diff_var = None, chunk_size = None, **kwargs ):
mat, virtual, state = self.get_args( **kwargs )
apr, vgr = virtual.get_approximation( self.get_current_group(),
'Volume' )
apc, vgc = state.get_approximation( self.get_current_group(),
'Volume' )
self.set_data_shape( apr, apc )
shape, mode = self.get_shape_grad( diff_var, chunk_size )
aux = nm.array( [0], ndmin = 4, dtype = nm.float64 )
if diff_var is None:
cache = self.get_cache( 'grad_scalar', 0 )
p_grad = cache( 'grad', self.get_current_group(), 0, state = state )
else:
p_grad = aux
n_qp = self.data_shape_r[1]
mat = nm.asarray( mat, dtype = nm.float64 )
mat = fix_mat_shape( mat, n_qp )
return (aux, p_grad, mat, vgr), shape, mode
def get_fargs_div( self, diff_var = None, chunk_size = None, **kwargs ):
mat, state, virtual = self.get_args( **kwargs )
apr, vgr = virtual.get_approximation( self.get_current_group(),
'Volume' )
apc, vgc = state.get_approximation( self.get_current_group(),
'Volume' )
self.set_data_shape( apr, apc )
shape, mode = self.get_shape_div( diff_var, chunk_size )
aux = nm.array( [0], ndmin = 4, dtype = nm.float64 )
if diff_var is None:
cache = self.get_cache( 'cauchy_strain', 0 )
strain = cache( 'strain', self.get_current_group(), 0,
state = state, get_vector = self.get_vector )
else:
strain = aux
n_qp = self.data_shape_r[1]
mat = nm.asarray( mat, dtype = nm.float64 )
mat = fix_mat_shape( mat, n_qp )
return (strain, aux, mat, vgc), shape, mode + 2
