Welcome to my last blog in the series where we to discover VTK’s numpy_interface module. If you are not familiar with this module, I recommend checking out my previous blogs on it (, , ). In this blog, I will talk about how one can work with composite datasets and arrays using this module.
Let’s start with defining what a composite dataset is. From a class point of view, it is vtkCompositeDataSet or any of its subclasses. From a functionality point of view, it is a way of collecting together a set of vtkDataObjects (usually vtkDataSets). The most generic example is vtkMultiBlockDataSet which allows the creation of an arbitrary tree of vtkDataObjects. Another example is vtkOverlappingAMR which represent a Berger-Colella style AMR meshes. Here is how we can create a multi-block dataset.
>>> import vtk >>> s = vtk.vtkSphereSource() >>> s.Update() >>> c = vtk.vtkConeSource() >>> c.Update() >>> mb = vtk.vtkMultiBlockDataSet() >>> mb.SetBlock(0, s.GetOutput()) >>> mb.SetBlock(1, c.GetOutput())
Many of VTK’s algorithms work with composite datasets without any change. For example:
>>> e = vtk.vtkElevationFilter() >>> e.SetInputData(mb) >>> e.Update() >>> mbe = e.GetOutputDataObject(0) >>> print mbe.GetClassName()
This will output ‘vtkMultiBlockDataSet’. Note that I used GetOutputDataObject() rather than GetOutput(). GetOutput() is simply a GetOutputDataObject() wrapped with a SafeDownCast() to the expected output type of the algorithm – which in this case is a vtkDataSet. So GetOutput() will return 0 even when GetOutputDataObject() returns an actual composite dataset.
Now that we have a composite dataset with a scalar, we can use numpy_interface.
>>> from vtk.numpy_interface import dataset_adapter as dsa >>> mbw = dsa.WrapDataObject(mbe) >>> mbw.PointData.keys() ['Normals', 'Elevation'] >>> elev = mbw.PointData['Elevation'] >>> elev <vtk.numpy_interface.dataset_adapter.VTKCompositeDataArray at 0x1189ee410>
Note that the array type is different than we have seen previously (VTKArray). However, it still works the same way.
>>> from vtk.numpy_interface import algorithms as algs >>> algs.max(elev) 0.5 >>> algs.max(elev + 1) 1.5
You can individually access the arrays of each block as follows.
>>> elev.Arrays VTKArray([ 0.5 , 0. , 0.45048442, 0.3117449 , 0.11126047, 0. , 0. , 0. , 0.45048442, 0.3117449 , 0.11126047, 0. , 0. , 0. , 0.45048442, 0.3117449 , 0.11126047, 0. , 0. , 0. , 0.45048442, 0.3117449 , 0.11126047, 0. , 0. , 0. , 0.45048442, 0.3117449 , 0.11126047, 0. , 0. , 0. , 0.45048442, 0.3117449 , 0.11126047, 0. , 0. , 0. , 0.45048442, 0.3117449 , 0.11126047, 0. , 0. , 0. , 0.45048442, 0.3117449 , 0.11126047, 0. , 0. , 0. ], dtype=float32)
Note that indexing is slightly different.
>>> print elev[0:3] [VTKArray([ 0.5, 0., 0.45048442], dtype=float32), VTKArray([ 0., 0., 0.43301269], dtype=float32)]
The return value is a composite array consisting of 2 VTKArrays. The  operator simply returned the first 4 values of each array. In general, all indexing operations apply to each VTKArray in the composite array collection. Similarly for algorithms such as where.
>>> print algs.where(elev < 0.5) [(array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49]),), (array([0, 1, 2, 3, 4, 5, 6]),)]
Now, let’s look at the other array called Normals.
>>> normals = mbw.PointData['Normals'] >>> normals.Arrays VTKArray([[ 0.00000000e+00, 0.00000000e+00, 1.00000000e+00], [ 0.00000000e+00, 0.00000000e+00, -1.00000000e+00], [ 4.33883727e-01, 0.00000000e+00, 9.00968850e-01], [ 7.81831503e-01, 0.00000000e+00, 6.23489797e-01], [ 9.74927902e-01, 0.00000000e+00, 2.22520933e-01], ... [ 6.89378142e-01, -6.89378142e-01, 2.22520933e-01], [ 6.89378142e-01, -6.89378142e-01, -2.22520933e-01], [ 5.52838326e-01, -5.52838326e-01, -6.23489797e-01], [ 3.06802124e-01, -3.06802124e-01, -9.00968850e-01]], dtype=float32) >>> normals.Arrays <vtk.numpy_interface.dataset_adapter.VTKNoneArray at 0x1189e7790>
Notice how the second arrays is a VTKNoneArray. This is because vtkConeSource does not produce normals. Where an array does not exist, we use a VTKNoneArray as placeholder. This allows us to maintain a one-to-one mapping between datasets of a composite dataset and the arrays in the VTKCompositeDataArray. It also allows us to keep algorithms working in parallel without a lot of specialized code (see my previous blog).
Where many of the algorithms apply independently to each array in a collection, some algorithms are global. For example, min and max as we demonstrated above. It is sometimes useful to get per block answers. For this, you can use _per_block algorithms.
>>> print algs.max_per_block(elev) [VTKArray(0.5, dtype=float32), VTKArray(0.4330126941204071, dtype=float32)]
These work very nicely together with other operations. For example, here is how we can normalize the elevation values in each block.
>>> _min = algs.min_per_block(elev) >>> _max = algs.max_per_block(elev) >>> _norm = (elev - _min) / (_max - _min) >>> print algs.min(_norm) 0.0 >>> print algs.max(_norm) 1.0
Once you grasp these features, you should be able to use composite array very similarly to single arrays as described in previous blogs.
A final note on composite datasets. The composite data wrapper provided by numpy_interface.dataset_adapter offers a few convenience functions to traverse composite datasets. Here is a simple example:
>>> for ds in mbw: >>> print type(ds) <class 'vtk.numpy_interface.dataset_adapter.PolyData'> <class 'vtk.numpy_interface.dataset_adapter.PolyData'>
This wraps up the blog series on numpy_interface. I hope to follow these up with some concrete examples of the module in action and some other useful information on using VTK from Python. Until then, cheers.