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```{try_on_binder}
```
```{code-cell}
:tags: [remove-cell]
:load: myst_code_init.py
```
# Tutorial: data functions and interpolation
This is work in progress, still missing:
* visualization in 1d
* visualization in 3d?
```{code-cell}
# wurlitzer: display dune's output in the notebook
#%load_ext wurlitzer
%matplotlib notebook
import numpy as np
np.warnings.filterwarnings('ignore') # silence numpys warnings
```
## creating functions
We'll work mostly in 2d in this tutorial since scalar functions in 2d can be best visualized within the notebook.
Let's set up a 2d grid first, as seen in other tutorials and examples.
```{code-cell}
from dune.xt.grid import Dim, Cube, Simplex, make_cube_grid
from dune.xt.functions import ConstantFunction, ExpressionFunction, GridFunction as GF
d = 2
omega = ([0, 0], [1, 1])
grid = make_cube_grid(Dim(d), Simplex(),
lower_left=omega[0], upper_right=omega[1], num_elements=[1, 1])
print(f'grid has {grid.size(0)} elements, '
f'{grid.size(d - 1)} edges and {grid.size(d)} vertices')
```
Some examples of data functions include analytical functions, such as
$$\begin{align}
f: \mathbb{R}^2 &\to \mathbb{R} && &g: \mathbb{R}^2 &\to \mathbb{R}^{2\times 2}\\
x &\mapsto \alpha&& &x&\mapsto A
\end{align}$$
for a given number $\alpha \in \mathbb{R}$ and matrix $A \in \mathbb{R}^{2 \times 2}$, or
$$\begin{align}
h: \mathbb{R}^2 &\to \mathbb{R}\\
x &\mapsto \text{epx}(x_0\, x_1)
\end{align}$$
or discrete functions $v_h \in V_h$, where $V_h$ is some finite-dimensional discrete function space associated with a grid. Other examples include functions obtained by reading values from a file.
Let's create some of these functions.
### using the `ConstantFunction`
For constant functions.
```{code-cell}
from dune.xt.functions import ConstantFunction
alpha = 0.5
f = ConstantFunction(dim_domain=Dim(d), dim_range=Dim(1), value=[alpha], name='f')
# note that we have to provide [alpha],
# since scalar constant functions expect a vector of length 1
A = [[1, 0], [0, 1]]
g = ConstantFunction(dim_domain=Dim(d), dim_range=(Dim(d), Dim(d)), value=A, name='g')
```
### using the `ExpressionFunction`
For functions given by an expression, where we have to specify the polynomial order of the expression (or the approximation order for non-polynomial functions).
Note that if the name of the variable is `Foo`, the components `Foo[0]`, ... `Foo[d - 1]` are available to be used in the expression.
* We have functions which do not provide a gradient ...
```{code-cell}
from dune.xt.functions import ExpressionFunction
h = ExpressionFunction(
dim_domain=Dim(d), variable='x', order=10, expression='exp(x[0]*x[1])', name='h')
```
* ... and functions which provide a gradient, useful for analytical solutions to compare to and compute $H^1$ errors
```{code-cell}
h = ExpressionFunction(
dim_domain=Dim(d), variable='x', order=10, expression='exp(x[0]*x[1])',
gradient_expressions=['x[1]*exp(x[0]*x[1])', 'x[0]*exp(x[0]*x[1])'], name='h')
```
### discrete functions
These often result from a discretization scheme, see the [tutorial on continuous Finite Elements for the stationary heat equation](dune_gdt_tutorial_on_cg_fem_for_the_stationary_heat_equation.md).
```{code-cell}
from dune.gdt import DiscontinuousLagrangeSpace, DiscreteFunction
V_h = DiscontinuousLagrangeSpace(grid, order=1)
v_h = DiscreteFunction(V_h, name='v_h') # initialized with a zero DoF vector
print(v_h.dofs.vector.sup_norm())
```
## visualizing functions
### visualizing scalar functions in 2d
We can easily visualize functions mapping from $\mathbb{R}^2 \to \mathbb{R}$. Internally, this is achieved by writing a `.vtk` file to disk and displaying the file using [K3D](https://github.com/K3D-tools/K3D-jupyter).
```{code-cell}
from dune.xt.functions import visualize_function
_ = visualize_function(h, grid)
```
**Note**: since functions are always visualized as piecewise linears on each grid element, `dune-grid` supports to write functions on a virtually refined grid, which may be an improvement for higher order data functions. To see this, let us have a look at the rather coarse grid consisting of two simplices:
```{code-cell}
from dune.xt.grid import visualize_grid
_ = visualize_grid(grid)
```
The two grid elements are clearly visible in the above plot of $h$.
By enabling subsampling, we obtain a much smoother plot, where the underlying virtually refined grid is barely visible.
```{code-cell}
_ = visualize_function(h, grid, subsampling=True)
```
Subsampling may thus be a means to obtain pretty pictures, but it can also be misleading.
### visualizing functions in other dimensions
Functions mapping from and to other dimensions can still be written to disk to be viewed with external viewers such as [Paraview](https://www.paraview.org/).
Therefore, they need to be wrapped as a `GridFunction`, as explained in the next section.
## The `GridFunction`
No matter the type of data function, we want to be able to use all of these in a discretization scheme without changing the discretization code (e.g., we should be able to pass them all to one of the `discretize_...` functions in `discretize_elliptic_cg.py`).
As explained in [the tutorial on continuous Finite Elements for the stationary heat equation](dune_gdt_tutorial_on_cg_fem_for_the_stationary_heat_equation.md), these functions thus need to be localizable w.r.t. a grid.
While the discrete function $v_h$ is already associated with a grid (namely the `grid` used to create `V_h`), the analytical functions $f$, $g$ and $h$ are not, which poses some problems when writing generic discretization schemes.
Exactly for this purpose, `dune.xt.functions` contains a `GridFunction`, which is a quite powerful means to wrap all kinds of things into a function associated with a grid.
For instance, we can wrap any existing function.
```{code-cell}
from dune.xt.functions import GridFunction
f_grid = GridFunction(grid, f)
g_grid = GridFunction(grid, g)
h_grid = GridFunction(grid, h)
```
In addition, the `GridFunction` directly supports the creation of constant functions.
```{code-cell}
f_grid = GridFunction(grid, alpha, name='f')
g_grid = GridFunction(grid, A, name='g')
```
Another powerful means to obtain a function is to pass a lambda expression, the first argument of which (`x`) is the point in physical space to evaluate at.
```{code-cell}
h_grid_lambda = GF(grid,
order=10, evaluate_lambda=lambda x, _: [np.exp(x[0]*x[1])],
dim_range=Dim(1), name='h')
```
**Note** that using a Python lambda might not yield be the most efficient code, but it is a great way to quickly implement data functions.
### visualizing grid functions in all dimension
To continue the visualization example from above, we can also write any grid function or discrete function to disk.
```{code-cell}
f_grid.visualize(grid, 'f') # writes f.vtu
```
```{code-cell}
!ls -l f.vtu
```
Note that the grid function `f_grid` is associated with the *type* of the grid `grid`, but not with the actual object `grid`.
The function can thus be localized w.r.t. all grids of the same type as `grid`.
For visualization, for instance, we still need to pass an actual `grid` object with respect to which the visualization is to be carried out.
The discrete function, on the other hand, is defined w.r.t. an actual grid object (namely the one used to construct the corresponding discrete function space `V_h`).
```{code-cell}
v_h.visualize('v_h') # writes v_h.vtu
```
```{code-cell}
!ls -l v_h.vtu
```
## interpolation
It is often desirable to interpolate data functions in a given discrete function space.
### using `dune-gdt`s interpolation
For most discrete function space, the `default_interpolation` will do the right thing, e.g. perform an $L^2$- or Lagrange-Interpolation. There are more specialized interpolations for special cases, e.g. for Raviart-Thomas spaces.
```{code-cell}
from dune.gdt import (
default_interpolation,
visualize_function, # can also visualize discrete functions
)
h_h = default_interpolation(h_grid, V_h)
_ = visualize_function(h_h, subsampling=False)
```
### using vectorized Python code
For Lagrangian discrete function spaces the interpolation can be performed by point evaluation.
We can extract the correct Lagrange-points from the discrete function space and wrap them into a `numpy`-array (without copying them). We can then perform the usual vectorized `numpy` operations and store the result in the DoF-vector of a discrete function (by again wrapping the vector without a copy into a `numpy`-array).
```{code-cell}
interpolation_points = np.array(V_h.interpolation_points(), copy=False)
h_h_numpy = DiscreteFunction(V_h)
h_h_numpy_vec = np.array(h_h_numpy.dofs.vector, copy=False)
h_h_numpy_vec[:] = np.exp(interpolation_points[..., 0]*interpolation_points[..., 1])
_ = visualize_function(h_h_numpy)
```
## performance considerations
The way a function is constructed may have a large impact on its performance. Let us consider the function $h(x) = exp(x_0\,x_1)$ from above.
We should use a finer grid to obtain some representative timings.
**Note**: by choosing a `Simplex` grid, we obtain an instance of an unstructured `dune-alugrid` grid, which may be runtime efficient, but at the cost of a higher memory footprint.
Choosing a `Cube` grid, on the other hand, would give us an instance of `dune-grid`s structured `YaspGrid` with nearly no memory footprint, at the price of lower runtime performance.
```{code-cell}
from timeit import default_timer as timer
tic = timer()
grid = make_cube_grid(Dim(d), Simplex(),
lower_left=omega[0], upper_right=omega[1], num_elements=[512, 512])
grid.global_refine(1)
toc = timer() - tic
print(f'grid has {grid.size(0)} elements, '
f'{grid.size(d - 1)} edges and {grid.size(d)} vertices (took {toc}s)')
```
```{code-cell}
tic = timer()
V_h = DiscontinuousLagrangeSpace(grid, order=3)
toc = timer() - tic
print(f'space has {V_h.num_DoFs} DoFs (took {toc}s)')
```
### interpolation test
One measure of performance is the time it takes to interpolate a data function. Note that the comparison greatly depends on the grid and the polynomial order of `V_h`.
```{code-cell}
tic = timer()
h_dune_expression = GridFunction(
grid,
ExpressionFunction(dim_domain=Dim(d), variable='x', order=10,
expression='exp(x[0]*x[1])'))
h_dune_expression_h = default_interpolation(h_dune_expression, V_h)
t_dune = timer() - tic
print(f'interpolating h_dune_expression took {t_dune}s')
```
```{code-cell}
tic = timer()
h_python_lambda = GF(grid,
order=10, evaluate_lambda=lambda x, _: [np.exp(x[0]*x[1])],
dim_range=Dim(1), name='h')
h_python_lambda_h = default_interpolation(h_python_lambda, V_h)
t_python = timer() - tic
print(f'interpolating h_python_lambda took {t_python}s')
```
```{code-cell}
print(f'using a lambda expression in an interpolation test is {t_python/t_dune} times slower')
```
### discretization test
For a more realistic comparison, we use the `discretize_elliptic_cg_dirichlet_zero` function as explained in the [tutorial on continuous Finite Elements for the stationary heat equation](dune_gdt_tutorial_on_cg_fem_for_the_stationary_heat_equation.md).
```{code-cell}
from discretize_elliptic_cg import discretize_elliptic_cg_dirichlet_zero
tic = timer()
u_h_dune = discretize_elliptic_cg_dirichlet_zero(grid, h_dune_expression, 1)
t_dune = timer() - tic
print(f'discretizing using h_dune_expression took {t_dune}s')
```
```{code-cell}
tic = timer()
u_h_dune = discretize_elliptic_cg_dirichlet_zero(grid, h_python_lambda, 1)
t_python = timer() - tic
print(f'discretizing using h_python_lambda took {t_python}s')
```
```{code-cell}
print(f'using a lambda expression in a discretization test is {t_python/t_dune} times slower')
```
This test is more realistic than the pure interpolation one. Since evaluating the diffusion is only a small part of the overall computation, the performance loss using the Python lambda is much smaller.
Overall, the gain in flexibility outweighs the loss in performance (at least for quick tests).
Download the code:
{download}`dune_gdt_tutorial_on_data_functions_and_interpolation.md`
{nb-download}`dune_gdt_tutorial_on_data_functions_and_interpolation.ipynb`