def __init__(self, *args, **kwargs):
if not self._cached():
super(SparseFunction, self).__init__(*args, **kwargs)
# Set up sparse point coordinates
coordinates = kwargs.get('coordinates',
kwargs.get('coordinates_data'))
if isinstance(coordinates, Function):
self._coordinates = coordinates
else:
dimensions = (self.indices[-1], Dimension(name='d'))
# Only retain the local data region
if coordinates is not None:
coordinates = np.array(coordinates)
self._coordinates = SubFunction(name='%s_coords' % self.name,
parent=self,
dtype=self.dtype,
dimensions=dimensions,
shape=(self.npoint,
self.grid.dim),
space_order=0,
initializer=coordinates,
distributor=self._distributor)
if self.npoint == 0:
# This is a corner case -- we might get here, for example, when
# running with MPI and some processes get 0-size arrays after
# domain decomposition. We "touch" the data anyway to avoid the
# case ``self._data is None``
self.coordinates.data
def __indices_setup__(cls, **kwargs):
dimensions = kwargs.get('dimensions')
if dimensions is not None:
return dimensions, dimensions
else:
dimensions = (Dimension(name='p_%s' % kwargs["name"]), )
return dimensions, dimensions
def __indices_setup__(cls, **kwargs):
dimensions = kwargs.get('dimensions')
if dimensions is not None:
return dimensions
else:
return (kwargs['grid'].time_dim,
Dimension(name='p_%s' % kwargs["name"]))
def __init__(self, **kwargs):
super(SubDomainSet, self).__init__()
if self.implicit_dimension is None:
n = Dimension(name='n')
self.implicit_dimension = n
self._n_domains = kwargs.get('N', 1)
self._bounds = kwargs.get('bounds', None)
def __init__(self, **kwargs):
super().__init__()
if self.implicit_dimension is None:
self.implicit_dimension = Dimension(name='n')
self._n_domains = kwargs.get('N', 1)
self._global_bounds = kwargs.get('bounds', None)
def __init__(self, *args, **kwargs):
if not self._cached():
super(PrecomputedSparseFunction, self).__init__(*args, **kwargs)
# Grid points per sparse point (2 in the case of bilinear and trilinear)
r = kwargs.get('r')
if not isinstance(r, int):
raise TypeError('Need `r` int argument')
if r <= 0:
raise ValueError('`r` must be > 0')
self.r = r
gridpoints = SubFunction(name="%s_gridpoints" % self.name,
dtype=np.int32,
dimensions=(self.indices[-1],
Dimension(name='d')),
shape=(self.npoint, self.grid.dim),
space_order=0,
parent=self)
gridpoints_data = kwargs.get('gridpoints', None)
assert (gridpoints_data is not None)
gridpoints.data[:] = gridpoints_data[:]
self._gridpoints = gridpoints
interpolation_coeffs = SubFunction(
name="%s_interpolation_coeffs" % self.name,
dimensions=(self.indices[-1], Dimension(name='d'),
Dimension(name='i')),
shape=(self.npoint, self.grid.dim, self.r),
dtype=self.dtype,
space_order=0,
parent=self)
coefficients_data = kwargs.get('interpolation_coeffs', None)
assert (coefficients_data is not None)
interpolation_coeffs.data[:] = coefficients_data[:]
self._interpolation_coeffs = interpolation_coeffs
warning(
"Ensure that the provided interpolation coefficient and grid point "
+
"values are computed on the final grid that will be used for other "
+ "computations.")
def __init__(self, obj, r, gridpoints_data, coefficients_data):
if not isinstance(r, int):
raise TypeError('Need `r` int argument')
if r <= 0:
raise ValueError('`r` must be > 0')
self.r = r
self.obj = obj
self._npoint = obj._npoint
gridpoints = SubFunction(name="%s_gridpoints" % self.obj.name,
dtype=np.int32,
dimensions=(self.obj.indices[-1],
Dimension(name='d')),
shape=(self._npoint, self.obj.grid.dim),
space_order=0,
parent=self)
assert (gridpoints_data is not None)
gridpoints.data[:] = gridpoints_data[:]
self.obj._gridpoints = gridpoints
interpolation_coeffs = SubFunction(
name="%s_interpolation_coeffs" % self.obj.name,
dimensions=(self.obj.indices[-1], Dimension(name='d'),
Dimension(name='i')),
shape=(self.obj.npoint, self.obj.grid.dim, self.r),
dtype=self.obj.dtype,
space_order=0,
parent=self)
assert (coefficients_data is not None)
interpolation_coeffs.data[:] = coefficients_data[:]
self.obj._interpolation_coeffs = interpolation_coeffs
warning(
"Ensure that the provided interpolation coefficient and grid point "
+
"values are computed on the final grid that will be used for other "
+ "computations.")
def __indices_setup__(cls, **kwargs):
dimensions = as_tuple(kwargs.get('dimensions'))
if not dimensions:
dimensions = (Dimension(name='p_%s' % kwargs["name"]), )
return dimensions, dimensions