# Dolfin convert : How to interpolate data at vertices of (3D) cells?

I hope that one of you guys can help me because i have been stuck here for a week. I am trying to read a gmsh file (.msh) using dolfin convert to XML and then download it with dolfin.

The thing is when i assemble the stiffness matrix and the mass matrix and try to generate an exemple (MonteCarlo) using these matrices , i have figured out that they are not the good ones .

I think that dolfin when downloaded the XML file which has already convert it from MSH , he changes the ordering of the numbering of the elements in ascending way. I am sure that he does this, because i have tried my code on a box generated by BoxMesh of fenics and my program worked and if i check mesh.cells() and my XML file i can see that the ordering had been changed, So that’s why i am not having my good matrices

And to be honest, i don’t know how to fix this thing? Does dolfin destroys the ordering of any mesh and build another ordering (in an ascending way) ? how do you guys get the good stiffness matrix and mass one when you download a mesh of your type?

I have read the same problem founded by another person but still cannot understand how to fix that.

Thank you very much in advance

this is my code

from dolfin import *

import os

import numpy as np

from numpy import linalg

import scipy

from scipy.sparse import csr_matrix

from scipy.sparse.linalg import spsolve

import scipy.stats as stats

from scipy.optimize import minimize

alpha=1.22

beta_x=2

beta_y=0.01

beta_z=0.01

mu=-4.25

str_os = ‘dolfin-convert ./mesh/calcul_little_specimen.msh
./calcul_little_specimen.xml’

os.system(str_os)

mesh = Mesh(“calcul_little_specimen.xml”)

V = FunctionSpace(mesh, “Lagrange”, 1)

u = TrialFunction(V)

v = TestFunction(V)

parameters[‘linear_algebra_backend’] = ‘Eigen’

S=as_matrix([[beta_x**2,0,0],[0,0,0],[0,0,0]])

Kx=assemble(kx)

row,col,val = as_backend_type(Kx).data()

Kx_sp=scipy.sparse.csc_matrix((val,col,row))

S=as_matrix([[0,0,0],[0,beta_y**2,0],[0,0,0]])

Ky=assemble(ky)

row,col,val = as_backend_type(Ky).data()

Ky_sp=scipy.sparse.csc_matrix((val,col,row))

S=as_matrix([[0,0,0],[0,0,0],[0,0,beta_z**2]])

Kz=assemble(kz)

row,col,val = as_backend_type(Kz).data()

Kz_sp=scipy.sparse.csc_matrix((val,col,row))

m=uvdx

M = assemble(m)

row,col,val = as_backend_type(M).data()

M_sp=scipy.sparse.csc_matrix((val,col,row))

from sksparse.cholmod import cholesky

factor=cholesky(M_sp,ordering_method=“natural”)

L=factor.L()

W=np.random.randn(M_sp.get_shape()[0],1)

y=L.dot(W)

#generation of an example

e=spsolve(M_sp+Kx_sp+Ky_sp+Kz_sp,alpha*y)

nu=np.ones((M_sp.get_shape()[0],1))*mu

e=e.reshape(-1,1)

e=e+nu

file_mean_kalman = File(‘results/realisation.pvd’)

func = Function(V)

func.vector().set_local(e)

func.rename(‘Kal_mean’,‘Kal_mean’)

file_mean_kalman << func

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• This seems to be a question better suited for FEniCS Q&A. – nicoguaro Oct 16 at 17:21
• Thank you for your response. I have submitted the question in both forums and i hope i can get it solved – nesrine Oct 17 at 13:36