[vtk-developers] More zero-copy array support for Python

[David Gobbi]

Hi Berk,

That shouldn't be the case.  When a vtk-python object destructs, its
attributes aren't deleted until a future GC cycle determines that the
vtk-c++ object has destructed.  E.g.:

c = numpy.array([1,2,3], 'f')
a = vtk.vtkFloatArray()
a.SetArray(c, c.size, True)
a.array = c

pd = vtk.vtkPointData()
pd.AddArray(a)
del a
del c

a = pd.GetArray(0)
a.array
-> array([ 1.,  2.,  3.], dtype=float32)

This behavior is tested in Common/Core/Testing/Python/TestGhost.py.  But I
notice that numpy_to_vtk() doesn't use this trick...

There could also be other isues that I'm not aware of, so I can look at
this in more depth.

 - David



On Fri, Jun 16, 2017 at 11:18 AM, Berk Geveci <berk.geveci at kitware.com>
wrote:

> This is fantastic David!!!
>
> One word of caution to people using numpy array -> VTK array route:
>
> c = numpy.array([1,2,3], 'f')
> a = vtk.vtkFloatArray()
> a.SetArray(c, c.size, True)
> a.array = c  # keep reference to "c"
>
> This is safe only as long as you keep the a object around. If you do
> something like this, you are in trouble:
>
> pd = vtk.vtkPointData()
> pd.AddArray(a)
> del a
>
> In this case, the Python object goes away and therefore releases the
> reference to c. In the dataset_adapter module, I implemented a way around
> this issue. I suggest taking a look at that which is in
> the numpyTovtkDataArray() method.
>
> Best,
> -berk
>
>
>
> On Fri, Jun 16, 2017 at 8:37 AM, David Gobbi <david.gobbi at gmail.com>
> wrote:
>
>> Hi All,
>>
>> Yesterday I merged the wrapping of the vtkSOADataArrays, and also added a
>> new wrapper hint to assist with VTK's zero-copy methods.
>>
>> Zero-copy is when an array uses an existing block of memory.  In the
>> Python wrappers, this generally means making a VTK array from a numpy array
>> or vice-versa.  Or making a VTK image from a PIL image.  Or using memmap to
>> share a VTK data set among multiple processes.
>>
>> The new wrapper hint VTK_ZEROCOPY can be applied to method parameters
>> that are used for zero-copy operations:
>>
>>   void vtkFloatArray::SetArray(VTK_ZEROCOPY float *ptr, vtkIdType n, int
>> save);
>>
>> This hint allows the Python wrappers to do the zero-copy magic with
>> SetArray:
>>
>>   import numpy
>>   import vtk
>>   c = numpy.array([1,2,3], 'f')
>>   a = vtk.vtkFloatArray()
>>   a.SetArray(c, c.size, True)
>>   a.array = c  # keep reference to "c"
>>
>> Astute VTKers will note that this was already possible with
>> SetVoidArray(), as used by the vtk.numpy_support module, but SetArray()
>> adds type checking.
>>
>> Also, SetArray() works with the newly-wrapped vtkSOADataArrays, so
>> perhaps in the future numpy_support.numpy_to_vtk() can be expanded to
>> support SOA arrays:
>>
>>   c0 = numpy.array([1,2,3], 'f')
>>   c1 = numpy.array([4,5,6], 'f')
>>   a = vtk.vtkSOADataArrayTemplate[c0.dtype]()
>>   a.SetNumberOfComponents(2)
>>   a.SetArray(0, c0, c0.size, True, True)
>>   a.SetArray(1, c1, c1.size, True, True)
>>
>> In the future this could be done with  a = vtk.numpy_support.numpy_to_vtk(
>> (c0,c1) ).
>>
>> I haven't yet implemented the mechanism to go in the other direction,
>> i.e. to create a numpy array from a VTK SOA array.
>>
>> Also: vtkSOADataArrayTemplate is now wrapped, but vtkAOSDataArrayTemplate
>> is not.  Currently the wrappers think that vtkDataArray is the immediate
>> superclass of vtkFloatArray etc.  This could be changed, if there are use
>> cases for directly instantiating vtkAOSDataArrayTemplate.
>>
>> Cheers,
>>  - David
>>
>
>
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