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

[Berk Geveci]

Interesting. Is this something relatively recent? I know that this didn't
work in the past.

On Fri, Jun 16, 2017 at 3:12 PM, David Gobbi <david.gobbi at gmail.com> wrote:

> 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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