def write_vtu(self, fname=None): if fname is None: fname = self.meshname.split('.')[0] + '.vtu' from vtk_writer import write_vtu vtk_id = {1: 3, 2: 5, 4: 10, 15: 1} Cells = {} cdata = {} k = 0.0 for g_id, E in self.Elmts.iteritems(): k += 1.0 if g_id not in vtk_id: raise NotImplementedError('vtk ids not yet implemented') Cells[vtk_id[g_id]] = E[1] cdata[vtk_id[g_id]] = k * numpy.ones((E[1].shape[0], )) write_vtu(Verts=self.Verts, Cells=Cells, cdata=cdata, fname=fname)
def write_vtu(self, fname=None): if fname is None: fname = self.meshname.split(".")[0] + ".vtu" from vtk_writer import write_vtu vtk_id = {1: 3, 2: 5, 4: 10, 15: 1} Cells = {} cdata = {} k = 0.0 for g_id, E in self.Elmts.items(): k += 1.0 if g_id not in vtk_id: raise NotImplementedError("vtk ids not yet implemented") Cells[vtk_id[g_id]] = E[1] cdata[vtk_id[g_id]] = k * np.ones((E[1].shape[0],)) write_vtu(Verts=self.Verts, Cells=Cells, cdata=cdata, fname=fname)
def vis_aggregate_groups(Verts, E2V, Agg, mesh_type, output='vtk', fname='output.vtu'): """ Coarse grid visualization of aggregate groups. Create .vtu files for use in Paraview or display with Matplotlib Parameters ---------- Verts : {array} coordinate array (N x D) E2V : {array} element index array (Nel x Nelnodes) Agg : {csr_matrix} sparse matrix for the aggregate-vertex relationship (N x Nagg) mesh_type : {string} type of elements: vertex, tri, quad, tet, hex (all 3d) fname : {string, file object} file to be written, e.g. 'output.vtu' output : {string} 'vtk' or 'matplotlib' Returns ------- - Writes data to .vtu file for use in paraview (xml 0.1 format) or displays to screen using matplotlib Notes ----- - Works for both 2d and 3d elements. Element groupings are colored with data equal to 2.0 and stringy edges in the aggregate are colored with 3.0 Examples -------- >>> from pyamg.aggregation import standard_aggregation >>> from pyamg.vis.vis_coarse import vis_aggregate_groups >>> from pyamg.gallery import load_example >>> data = load_example('unit_square') >>> A = data['A'].tocsr() >>> V = data['vertices'] >>> E2V = data['elements'] >>> Agg = standard_aggregation(A)[0] >>> vis_aggregate_groups(Verts=V, E2V=E2V, Agg=Agg, mesh_type='tri', output='vtk', fname='output.vtu') >>> from pyamg.aggregation import standard_aggregation >>> from pyamg.vis.vis_coarse import vis_aggregate_groups >>> from pyamg.gallery import load_example >>> data = load_example('unit_cube') >>> A = data['A'].tocsr() >>> V = data['vertices'] >>> E2V = data['elements'] >>> Agg = standard_aggregation(A)[0] >>> vis_aggregate_groups(Verts=V, E2V=E2V, Agg=Agg, mesh_type='tet', output='vtk', fname='output.vtu') """ check_input(Verts=Verts,E2V=E2V,Agg=Agg,mesh_type=mesh_type) map_type_to_key = {'tri':5, 'quad':9, 'tet':10, 'hex':12} if mesh_type not in map_type_to_key: raise ValueError('unknown mesh_type=%s' % mesh_type) key = map_type_to_key[mesh_type] Agg = csr_matrix(Agg) # remove elements with dirichlet BCs if E2V.max() >= Agg.shape[0]: E2V = E2V[E2V.max(axis=1) < Agg.shape[0]] ##### # 1 # # Find elements with all vertices in same aggregate # account for 0 rows. Mark them as solitary aggregates # TODO: (Luke) full_aggs is not defined, I think its just a mask # indicated with rows are not 0. if len(Agg.indices) != Agg.shape[0]: full_aggs = ((Agg.indptr[1:] - Agg.indptr[:-1]) == 0).nonzero()[0] new_aggs = numpy.array(Agg.sum(axis=1),dtype=int).ravel() new_aggs[full_aggs==1] = Agg.indices # keep existing aggregate IDs new_aggs[full_aggs==0] = Agg.shape[1] # fill in singletons maxID+1 ElementAggs = new_aggs[E2V] else: ElementAggs = Agg.indices[E2V] ##### # 2 # # find all aggregates encompassing full elements # mask[i] == True if all vertices in element i belong to the same aggregate mask = numpy.where( abs(numpy.diff(ElementAggs)).max(axis=1) == 0 )[0] #mask = (ElementAggs[:,:] == ElementAggs[:,0]).all(axis=1) E2V_a = E2V[mask,:] # elements where element is full Nel_a = E2V_a.shape[0] ##### # 3 # # find edges of elements in the same aggregate (brute force) # construct vertex to vertex graph col = E2V.ravel() row = numpy.kron(numpy.arange(0,E2V.shape[0]),numpy.ones((E2V.shape[1],),dtype=int)) data = numpy.ones((len(col),)) if len(row)!=len(col): raise ValueError('Problem constructing vertex-to-vertex map') V2V = coo_matrix((data,(row,col)),shape=(E2V.shape[0],E2V.max()+1)) V2V = V2V.T * V2V V2V = triu(V2V,1).tocoo() # get all the edges edges = numpy.vstack((V2V.row,V2V.col)).T # all the edges in the same aggregate E2V_b = edges[Agg.indices[V2V.row] == Agg.indices[V2V.col]] Nel_b = E2V_b.shape[0] ####### # 3.5 # # single node aggregates sums = numpy.array(Agg.sum(axis=0)).ravel() E2V_c = numpy.where(sums==1)[0] Nel_c = len(E2V_c) ##### # 4 # # now write out the elements and edges colors_a = 3*numpy.ones((Nel_a,)) # color triangles with threes colors_b = 2*numpy.ones((Nel_b,)) # color edges with twos colors_c = 1*numpy.ones((Nel_c,)) # color the vertices with ones Cells = {1:E2V_c, 3:E2V_b, key:E2V_a} cdata = {1:colors_c, 3:colors_b, key:colors_a} # make sure it's a tuple write_vtu(Verts=Verts, Cells=Cells, fname=fname, cdata=cdata)