On Monday 16 February 2009 23:29:48 N wrote: > Take a simple example like > > from dolfin import * > mesh = UnitSquare(4, 4) > V = FunctionSpace(mesh, "CG", 1) > v = TestFunction(V) > u = TrialFunction(V) > a = dot(grad(v), grad(u))*dx > A = assemble(a) > > The matrix A (with boost as the linear algebra base) gives the following > for attribute data: data(self) -> > std::tr1::tuple<(p.q(const).std::size_t,p.q(const).std::size_t,p.q(const).d >ouble,int)> > > Return pointers to underlying compressed storage data. See > GenericMatrix for documentation. > > > How can this be used to gain access to the column data, row pointer, and > data elements in the matrix? I would like to have access to this data in > Numpy arrays without copying the data. Is this possible?
You now can. Sorry that I did not made it to the release ;) You can now do: >>> rows, cols, values = A.data() Here rows, cols and values are numpy arrays based on pointers to the underlaying data structures in c++. It works for the linear algebra backends that supports the feature in C++, uBLAS and MTL4. Error is raised for the other backends. Also added direct access to Vector data through the same data() function: >>> values = v.data() This is similare to v.array(), but with no copying. This feature is also only supported for the uBLAS and MTL4 backends. Cheers! Johan _______________________________________________ DOLFIN-dev mailing list [email protected] http://www.fenics.org/mailman/listinfo/dolfin-dev
