Contents

# Interpolation¶

Firedrake offers various ways to interpolate expressions onto fields
(`Function`

s). Interpolation is often used to set up
initial conditions and/or boundary conditions. The basic syntax for
interpolation is:

```
# create new function f on function space V
f = interpolate(expression, V)
# alternatively:
f = Function(V).interpolate(expression)
# setting the values of an existing function
f.interpolate(expression)
```

Note

Interpolation is supported for most, but not all, of the elements that Firedrake provides. In particular, higher-continuity elements such as Argyris and Hermite do not presently support interpolation.

The recommended way to specify the source expression is UFL. UFL produces clear error messages in case of syntax or type errors, yet UFL expressions have good run-time performance, since they are translated to C interpolation kernels using TSFC technology. Moreover, UFL offers a rich language for describing expressions, including:

The coordinates: in physical space as

`SpatialCoordinate`

, and in reference space as`ufl.geometry.CellCoordinate`

.Firedrake

`Function`

s, derivatives of`Function`

s, and`Constant`

s.Literal numbers, basic arithmetic operations, and also mathematical functions such as

`sin`

,`cos`

,`sqrt`

,`abs`

, etc.Conditional expressions using UFL

`conditional`

.Compound expressions involving any of the above.

Here is an example demonstrating some of these features:

```
# g is a vector-valued Function, e.g. on an H(div) function space
f = interpolate(sqrt(3.2 * div(g)), V)
```

This also works as expected when interpolating into a a space defined on the facets of the mesh:

```
# where trace is a trace space on the current mesh:
f = interpolate(expression, trace)
```

## Interpolator objects¶

Firedrake is also able to generate reusable `Interpolator`

objects which provide caching of the interpolation operation. The
following line creates an interpolator which will interpolate the
current value of expression into the space V:

```
interpolator = Interpolator(expression, V)
```

If expression does not contain a `TestFunction()`

then
the interpolation can be performed with:

```
f = interpolator.interpolate()
```

Alternatively, one can use the interpolator to set the value of an existing `Function`

:

```
f = Function(V)
interpolator.interpolate(output=f)
```

If expression does not contain a `TestFunction()`

then
the interpolator acts to interpolate `Function`

s in the
test space to those in the target space. For example:

```
w = TestFunction(W)
interpolator = Interpolator(w, V)
```

Here, interpolator acts as the interpolation matrix from the
`FunctionSpace()`

W into the
`FunctionSpace()`

V. Such that if f is a
`Function`

in W then interpolator(f) is its
interpolation into g. As before, the output parameter can be used
to write into an existing `Function`

. Passing the
transpose=True option to `interpolate()`

will
cause the transpose interpolation to occur. This is equivalent to the
multigrid restriction operation which interpolates assembled 1-forms
in the dual space to V to assembled 1-forms in the dual space to
W.

## Interpolation from external data¶

Unfortunately, UFL interpolation is not applicable if some of the
source data is not yet available as a Firedrake `Function`

or UFL expression. Here we describe a recipe for moving external to
Firedrake fields.

Let us assume that there is some function `mydata(X)`

which takes as
input an \(n \times d\) array, where \(n\) is the number of
points at which the data values are needed, and \(d\) is the
geometric dimension of the mesh. `mydata(X)`

shall return a
\(n\) long vector of the scalar values evaluated at the points
provided. (Assuming that the target `FunctionSpace`

is
scalar valued, although this recipe can be extended to vector or
tensor valued fields.) Presumably `mydata`

works by interpolating
the external data source, but the precise details are not relevant
now. In this case, interpolation into a target function space `V`

proceeds as follows:

```
# First, grab the mesh.
m = V.ufl_domain()
# Now make the VectorFunctionSpace corresponding to V.
W = VectorFunctionSpace(m, V.ufl_element())
# Next, interpolate the coordinates onto the nodes of W.
X = interpolate(m.coordinates, W)
# Make an output function.
f = Function(V)
# Use the external data function to interpolate the values of f.
f.dat.data[:] = mydata(X.dat.data_ro)
```

This will also work in parallel, as the interpolation will occur on
each process, and Firedrake will take care of the halo updates before
the next operation using `f`

.

## C string expressions¶

Warning

C string expressions were a FEniCS compatibility feature which has now been removed. Users should use UFL expressions instead. This section only remains to assist in the transition of existing code.

Here are a couple of old-style C string expressions, and their modern replacements.

```
# Expression:
f = interpolate(Expression("sin(x[0]*pi)"), V)
# UFL equivalent:
x = SpatialCoordinate(V.mesh())
f = interpolate(sin(x[0] * math.pi), V)
# Expression with a Constant parameter:
f = interpolate(Expression('sin(x[0]*t)', t=t), V)
# UFL equivalent:
x = SpatialCoordinate(V.mesh())
f = interpolate(sin(x[0] * t), V)
```

## Python expression classes¶

Warning

Python expression classes were a FEniCS compatibility feature which has now been removed. Users should use UFL expressions instead. This section only remains to assist in the transition of existing code.

Since Python `Expression`

classes expressions are
deprecated, below are a few examples on how to replace them with UFL
expressions:

```
# Python expression:
class MyExpression(Expression):
def eval(self, value, x):
value[:] = numpy.dot(x, x)
def value_shape(self):
return ()
f.interpolate(MyExpression())
# UFL equivalent:
x = SpatialCoordinate(f.function_space().mesh())
f.interpolate(dot(x, x))
```

## Generating Functions with randomised values¶

The `randomfunctiongen`

module wraps the external numpy package numpy.random,
which gives Firedrake users an easy access to many stochastically sound random number generators,
including `PCG64`

, `Philox`

, and `SFC64`

, which are parallel-safe.
All distribution methods defined in numpy.random,
are made available, and one can pass a `FunctionSpace`

to most of these methods
to generate a randomised `Function`

.

```
mesh = UnitSquareMesh(2,2)
V = FunctionSpace(mesh, "CG", 1)
# PCG64 random number generator
pcg = PCG64(seed=123456789)
rg = RandomGenerator(pcg)
# beta distribution
f_beta = rg.beta(V, 1.0, 2.0)
print(f_beta.dat.data)
# produces:
# [0.56462514 0.11585311 0.01247943 0.398984 0.19097059 0.5446709 0.1078666 0.2178807 0.64848515]
```