Issues running example code navier_stokes_channel.py

[gakabani]

I am running python 3.8 and I am having an issue with the line
error = np.abs(u_e.vector().array() - u_.vector().array()).max()

The runtime error is below.

(fenicsproject) MacBook-Pro-2017-i7 Python_Codes % python navier_stokes_channel.py
Calling FFC just-in-time (JIT) compiler, this may take some time.
Calling FFC just-in-time (JIT) compiler, this may take some time.
Calling FFC just-in-time (JIT) compiler, this may take some time.
Calling FFC just-in-time (JIT) compiler, this may take some time.
Traceback (most recent call last):
File "navier_stokes_channel.py", line 117, in
error = np.abs(u_e.vector().array() - u_.vector().array()).max()
AttributeError: 'dolfin.cpp.la.PETScVector' object has no attribute 'array'

[gdmcbain]

I hit this too, a little while ago. I modified the code to

    u_e = Expression(('4*x[1]*(1.0 - x[1])', '0'), degree=2)
    u_e = interpolate(u_e, V)
    error = np.abs(u_e.compute_vertex_values() -
                   u_.compute_vertex_values()).max()
    print('t = %.2f: error = %.3g' % (t, error))
    print('max u:', u_.compute_vertex_values().max())

[gakabani]

It worked and it was faster than a previous revision using error = np.abs( np.array(u_e.vector()) - np.array(u_.vector()) ).max() Thank you! Gamal Akabani

…

On Fri, May 1, 2020 at 1:26 AM Geordie McBain ***@***.***> wrote: I hit this too, a little while ago. I modified the code to u_e = Expression(('4*x[1]*(1.0 - x[1])', '0'), degree=2) u_e = interpolate(u_e, V) error = np.abs(u_e.compute_vertex_values() - u_.compute_vertex_values()).max() print('t = %.2f: error = %.3g' % (t, error)) print('max u:', u_.compute_vertex_values().max()) — You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub <#72 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AIBK772FMFXWD4P73HVRTVTRPJTRVANCNFSM4MW4UEJA> .

[azerad]

just replace array() by get_local()

correction array obsolete replaced by get_local

error = np.abs(u_e.vector().get_local() - u_.vector().get_local()).max()
print('t = %.2f: error = %.3g' % (t, error))
print('max u:', u_.vector().get_local().max())