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=inner(dot(S,grad(u)),grad(v))*dx
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=inner(dot(S,grad(u)),grad(v))*dx
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=inner(dot(S,grad(u)),grad(v))*dx
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
