[Paraview] programmable filter, OK in serial, FAILS in mpi

[Berk Geveci]

Hi Andre,

Are you running pvserver explicitly? If you run it explicitly and connect
with the GUI to it, the output of print statements should show up on the
terminal you ran mpiexec/mpirun on. Once you do that and we know what the
error is, I should be able to help more.

PS: What is your data source? (file format?)

Best,
-berk

On Tue, Oct 10, 2017 at 6:59 PM, A <andrealphus at gmail.com> wrote:

> I normally run Paraview on my workstation with mpi support (14 cores).
> It's been working fine like this for a year.
>
> For some reason however, the debug/output messages windows dont work when
> running in mpi (e.g. print "hello", returns nothing). But they do work when
> I turn mpi off.
>
> I recently wrote a few new programmable filters, and while they work
> perfectly with mpi off, the hand and dont do anything with mpi on.
>
> Any idea?
>
> -ashton
>
> p.s. heres one of the filters for example;
>
> from paraview.numpy_support import vtk_to_numpy
>
> import vtkCommonDataModelPython
>
> import numpy as np
>
> from scipy.optimize import curve_fit
>
>
> if type(self.GetInputDataObject(0,0)) is vtkCommonDataModelPython.vtkUnstructuredGrid and type(self.GetInputDataObject(0,1)) is vtkCommonDataModelPython.vtkPolyData:
>
>     g = 0
>
>     p = 1
>
> elif type(self.GetInputDataObject(0,1)) is vtkCommonDataModelPython.vtkUnstructuredGrid and type(self.GetInputDataObject(0,0)) is vtkCommonDataModelPython.vtkPolyData:
>
>     g = 1
>
>     p = 0
>
> else:
>
>     print('ERROR')
>
>     return
>
>
> # import the grid
>
> Vs = inputs[g].PointData['Vs']
>
> depth = inputs[g].PointData['depth']
>
> z = inputs[0].PointData['z']
>
>
> # setup output
>
> output.PointData.append(Vs, 'Vs')
>
> output.PointData.append(depth, 'depth')
>
> output.PointData.append(z, 'z')
>
>
> # import the profile
>
> Vs_profile = inputs[p].PointData['Vs']
>
> depth_profile = inputs[p].PointData['depth']
>
>
> def func(x, a, b, c, d,e):
>
>     return a + b*x + c*x**2 + d*x**3 + e*x**4
>
>
> nanx = np.argwhere(np.isnan(depth_profile))
>
> nany = np.argwhere(np.isnan(Vs_profile))
>
> nani = np.unique(np.append(nanx,nany))
>
> xdata = numpy.delete(depth_profile, nani)
>
> ydata = numpy.delete(Vs_profile, nani)
>
>
> popt, pcov1 = curve_fit(func, xdata, ydata)
>
>
>
> Vs_theory = popt[0] + popt[1]*depth + popt[2]*depth**2 + popt[3]*depth**3 + popt[4]*depth**4
>
>
> diff = Vs - Vs_theory
>
> per_diff=100*diff/Vs_theory
>
> output.PointData.append(per_diff, 'perturbation')
>
>
>
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