# Sampling the solution over a line¶

:

from matplotlib import pyplot
import numpy
try:
import pygmsh
geom = pygmsh.built_in.Geometry()
[ 0.0,  0.5, 0.0], [-0.1,  0.1, 0.0], [-0.5,  0.0, 0.0],
[-0.1, -0.1, 0.0], [ 0.0, -0.5, 0.0], [ 0.1, -0.1, 0.0],
[ 0.5,  0.0, 0.0], [ 0.1,  0.1, 0.0] ], lcar=0.05)
axis = [0, 0, 1]
geom.extrude( poly, translation_axis=axis, rotation_axis=axis,
point_on_axis=[0, 0, 0], angle=2.0 / 6.0 * numpy.pi)
mesh = pygmsh.generate_mesh(geom, verbose=False)
points, cells = mesh.points, mesh.cells
domain3d = {"vertices":points, "simplices":cells["tetra"]}
except ImportError: # pygmsh not installed - use a simple cartesian domain
from dune.grid import cartesianDomain
domain3d = cartesianDomain([-0.25,-0.25,0],[0.25,0.25,1],[30,30,60])

from dune.alugrid import aluSimplexGrid as leafGridView3d
gridView3d  = leafGridView3d(domain3d)


As before we solve a simple Laplace problem

:

from dune.fem.space import lagrange as solutionSpace
from dune.fem.scheme import galerkin as solutionScheme
from ufl import TrialFunction, TestFunction, SpatialCoordinate, dot, grad, dx, conditional, sqrt

space3d = solutionSpace(gridView3d, order=1)
u = TrialFunction(space3d)
v = TestFunction(space3d)
x = SpatialCoordinate(space3d)
conditional(dot(x,x)<.01,100,0)*v*dx,
solver='cg')
uh3d = space3d.interpolate(,name="solution")
info = scheme3d.solve(target=uh3d)


Instead of plotting this using paraview we want to only study the solution along a single line. This requires findings points $$x_i = x_0+\frac{i}{N}(x1-x0)$$ for $$i=0,\dots,N$$ within the unstructured grid. This would be expensive to compute on the Python so we implement this algorithm in C++ using the LineSegmentSampler class available in Dune-Fem. The resulting algorithm returns a pair of two lists with coordinates $$x_i$$ and the values of the grid function at these points:

#include <vector>
#include <utility>
#include <dune/fem/misc/linesegmentsampler.hh>

template <class GF, class DT>
std::pair<std::vector<DT>, std::vector<typename GF::RangeType>>
sample(const GF &gf, DT &start, DT &end, int n)
{
Dune::Fem::LineSegmentSampler<typename GF::GridPartType> sampler(gf.gridPart(),start,end);
std::vector<DT> coords(n);
std::vector<typename GF::RangeType> values(n);
sampler(gf,values);
sampler.samplePoints(coords);
return std::make_pair(coords,values);
}

:

import dune.generator.algorithm as algorithm
from dune.common import FieldVector
x0, x1 = FieldVector([0,0,0]), FieldVector([0,0,1])
p,v = algorithm.run('sample', 'utility.hh', uh3d, x0, x1, 100)
x,y = numpy.zeros(len(p)), numpy.zeros(len(p))
length = (x1-x0).two_norm
for i in range(len(x)):
x[i] = (p[i]-x0).two_norm / length
y[i] = v[i]
pyplot.plot(x,y)
pyplot.show() Note: the coordinates returned are always in the interval $$[0,1]$$ so if physical coordinates are required, they need to be rescaled. Also, function values returned by the sample function are always of a FieldVector type, so that even for a scalar example a v[i] is a vector of dimension one, so that y[i]=v[i] has to be used.

A mentioned above any grid function can be passed in as argument to the sample function. So for example plotting $$|\nabla u_h|$$ is straight forward using the corresponding ufl expression. Since in this case automatic conversion from the ufl expression (available for example in the plotting function) to a grid function, we need to do this explicitly:

:

from dune.ufl import expression2GF
p,v = algorithm.run('sample', 'utility.hh', absGrad, x0, x1, 100)
for i in range(len(x)):
y[i] = v[i]
pyplot.plot(x,y)
pyplot.show() Similar we can plot both partial derivatives of the solution over the given line:

:

from dune.ufl import expression2GF
p,v = algorithm.run('sample', 'utility.hh', absGrad, x0, x1, 100)
dx,dy = numpy.zeros(len(p)), numpy.zeros(len(p))
for i in range(len(x)):
dx[i] = v[i]
dy[i] = v[i]
pyplot.plot(x,dx)
pyplot.plot(x,dy)
pyplot.show() Remark: plotting over a line has been included as a utility function so can easily be called as shown below

:

from dune.fem.utility import lineSample
x,y = lineSample(uh3d,[0,0,0],[0,0,1],100)
pyplot.plot(x,y)
pyplot.show() 