I am solving a stationary navier stokes problem that have a problem on the pressure field on one point of the inlet section. I was told that changing the ordering of the cells around that region might resolve the issue.

I was wondering if there is any way of permuting the ordering of a given mesh without having to reconstructing it.

Here is a MWE that might clarify what I’m trying to achieve:

```
import dolfinx.fem.petsc
import numpy as np
import pyvista
from dolfinx import fem
import dolfinx.plot as dplt
from dolfinx.fem import (Constant, VectorFunctionSpace, Function, FunctionSpace, assemble_scalar,
dirichletbc, form, locate_dofs_topological,extract_function_spaces)
from dolfinx.fem.petsc import LinearProblem, NonlinearProblem
from dolfinx.mesh import create_unit_square, locate_entities, meshtags, create_rectangle, CellType, Mesh
from mpi4py import MPI
from petsc4py.PETSc import ScalarType
from petsc4py import PETSc
from ufl import (VectorElement,FiniteElement,FacetNormal, Measure, SpatialCoordinate, TestFunction, TrialFunction, TrialFunctions, TestFunctions,
div, dot, dx, grad, inner, lhs, rhs, MixedElement, nabla_grad, split)
from dolfinx.io import XDMFFile
from dolfinx import cpp as _cpp
msh = create_rectangle(comm=MPI.COMM_WORLD,
points=((0.0, 0.0), (1.0, 3.0)), n=(25, 75),
cell_type=CellType.triangle)
fdim = msh.topology.dim
msh.topology.create_connectivity(msh.topology.dim, msh.topology.dim-1)
num_facets_owned_by_proc = msh.topology.index_map(fdim).size_local
facets_entitites = _cpp.mesh.entities_to_geometry(msh, fdim, np.arange(num_facets_owned_by_proc, dtype=np.int32), False)
num_facets_owned_by_proc_1 = msh.topology.index_map(fdim-1).size_local
edges_entitites = _cpp.mesh.entities_to_geometry(msh, fdim-1, np.arange(num_facets_owned_by_proc_1, dtype=np.int32), False)
points = msh.geometry.x
for e, entity in enumerate(facets_entitites):
print(e,entity)
for e, entity in enumerate(edges_entitites):
print(e,entity)
# I want to shuffle the cells and edges within the msh object as I can do with the retrived data
np.random.seed(1)
np.random.shuffle(facets_entitites)
np.random.shuffle(edges_entitites)
```

Thanks