def get_fargs_eval( self, diff_var = None, chunk_size = None, **kwargs ):
mat, par1, par2 = self.get_args( **kwargs )
ap, vg = par1.get_approximation( self.get_current_group(), 'Volume' )
self.set_data_shape( ap )
cache = self.get_cache( 'state_in_volume_qp', 0 )
vec1_qp = cache( 'state', self.get_current_group(), 0,
state = par1, get_vector = self.get_vector )
cache = self.get_cache( 'state_in_volume_qp', 1 )
vec2_qp = cache( 'state', self.get_current_group(), 0,
state = par2, get_vector = self.get_vector )
if mat.ndim == 1:
mat = mat[...,nm.newaxis]
n_el, n_qp, dim, n_ep = self.data_shape
cache = self.get_cache( 'mat_in_qp', 0 )
mat_qp = cache( 'matqp', self.get_current_group(), 0,
mat = mat, ap = ap,
assumed_shapes = [(1, n_qp, 1, 1),
(n_el, n_qp, 1, 1)],
mode_in = None )
mat_qp = fix_mat_qp_shape( mat_qp, chunk_size )
return (vec1_qp, vec2_qp, mat_qp, vg), (chunk_size, 1, 1, 1), 0
def get_fargs_weak( self, diff_var = None, chunk_size = None, **kwargs ):
mat, virtual, state = self.get_args( **kwargs )
ap, vg = virtual.get_approximation( self.get_current_group(), 'Volume' )
self.set_data_shape( ap )
shape, mode = self.get_shape( diff_var, chunk_size )
cache = self.get_cache( 'state_in_volume_qp', 0 )
vec_qp = cache( 'state', self.get_current_group(), 0,
state = state, get_vector = self.get_vector )
if mat.ndim == 1:
mat = mat[...,nm.newaxis]
n_el, n_qp, dim, n_ep = self.data_shape
cache = self.get_cache( 'mat_in_qp', 0 )
mat_qp = cache( 'matqp', self.get_current_group(), 0,
mat = mat, ap = ap,
assumed_shapes = [(1, n_qp, 1, 1),
(n_el, n_qp, 1, 1)],
mode_in = None )
mat_qp = fix_mat_qp_shape( mat_qp, chunk_size )
bf = ap.get_base( 'v', 0, self.integral_name )
return (vec_qp, bf, mat_qp, vg), shape, mode
def prepare_data( self, chunk_size = None, **kwargs ):
mat, par, mat_shape, mode = self.get_args( **kwargs )
ap, vg = par.get_approximation( self.get_current_group(), 'Volume' )
n_el, n_qp, dim, n_ep = ap.get_v_data_shape( self.integral_name )
cache = self.get_cache( 'mat_in_qp', 0 )
mat_qp = cache( 'matqp', self.get_current_group(), 0,
mat = mat, ap = ap,
assumed_shapes = [(1, n_qp) + mat_shape,
(n_el, n_qp) + mat_shape],
mode_in = mode )
mat_qp = fix_mat_qp_shape( mat_qp, chunk_size )
shape = (chunk_size, 1) + mat_qp.shape[2:]
return vg, mat_qp, shape
def mat_to_qp( self, mat, ap ):
n_el, n_qp, dim, n_ep = self.data_shape
mat = nm.asarray( mat )
if mat.ndim == 0:
mat = mat[nm.newaxis,nm.newaxis]
cache = self.get_cache( 'mat_in_qp', 0 )
mat_qp = cache( 'matqp', self.get_current_group(), 0,
mat = mat, ap = ap,
assumed_shapes = [(1, n_qp, 1, 1),
(n_el, n_qp, 1, 1)],
mode_in = None )
mat_qp = fix_mat_qp_shape( mat_qp, n_el )
return mat_qp