construct evaluation matrix

Given a domain and a number of points within this domain, I would like to construct the small and fat evaluation matrix E which, given coefficient vector x returns E @ x, the values of the corresponding finite element function at the points. In FEniCS, I could do

  def _build_eval_matrix(V, points):
      """Build the sparse m-by-n matrix that maps a coefficient set for a
      function in V to the values of that function at m given points.
      """
      mesh = V.mesh()

      bbt = BoundingBoxTree()
      bbt.build(mesh)
      dofmap = V.dofmap()
      el = V.element()
      sdim = el.space_dimension()

      rows = []
      cols = []
      data = []
      for i, x in enumerate(points):
          cell_id = bbt.compute_first_entity_collision(Point(*x))
          cell = Cell(mesh, cell_id)
          coordinate_dofs = cell.get_vertex_coordinates()

          rows.append(numpy.full(sdim, i))
          cols.append(dofmap.cell_dofs(cell_id))

          v = el.evaluate_basis_all(x, coordinate_dofs, cell_id)
          data.append(v)

      rows = numpy.concatenate(rows)
      cols = numpy.concatenate(cols)
      data = numpy.concatenate(data)

      m = len(points)
      n = V.dim()
      matrix = sparse.csr_matrix((data, (rows, cols)), shape=(m, n))
      return matrix

The explicit loop doesn’t hurt much since the number of points is usually small.

Any hints?

[kinnala]

Yeah, probes was unreleased so we decided to use this approach.

[kinnala]

Alright, next release will be 3.0.0. I was expecting to further test and document some new features but we can always go for 3.0.x if any issues arise. I need to go through the issues and the changelog one more time and see if we are able to tag 3.0.0 in two weeks or so.