# 5. Using NumPy for processing data¶

In Section 4, we looked at several recipes for writing Python script for data processing that relied heavily on using NumPy for accessing arrays and performing operations on them. In this chapter, we take a closer look at the VTK-NumPy integration layer that makes it possible to use VTK and NumPy together, despite significant differences in the data representations between the two systems.

## 5.1. Teaser¶

Let’s start with a teaser by creating a simple pipeline with Sphere connected to an Elevation filter, followed by the Programmable Filter . Let’s see how we would access the input data object in the Script for the Programmable Filter .

from paraview.vtk.numpy_interface import dataset_adapter as dsa
from paraview.vtk.numpy_interface import algorithms as algs

data = inputs[0]
print(data.PointData.keys())
print(data.PointData['Elevation'])


This example prints out the following in the output window.

['Normals', 'Elevation']
[ 0.67235619  0.32764378  0.72819519  0.7388373   0.70217478  0.62546903
0.52391261  0.41762003  0.75839448  0.79325461  0.77003199  0.69332629
0.57832992  0.44781935  0.72819519  0.7388373   0.70217478  0.62546903
0.52391261  0.41762003  0.65528756  0.60746235  0.53835285  0.46164712
0.39253765  0.34471244  0.58238     0.47608739  0.37453097  0.29782525
0.2611627   0.27180481  0.55218065  0.42167011  0.30667371  0.22996798
0.20674542  0.24160551  0.58238     0.47608739  0.37453097  0.29782525
0.2611627   0.27180481  0.65528756  0.60746235  0.53835285  0.46164712
0.39253765  0.34471244]


The importance lies in the last three lines. In particular, note how we used a different API to access the PointData and the Elevation array in the last two lines. Also note that, when we printed the Elevation array, the output didn’t look like one from a vtkDataArray. In fact:

elevation = data.PointData['Elevation']
print(type(elevation))

import numpy
print(isinstance(elevation, numpy.ndarray))


This will produce the following:

<class 'paraview.vtk.numpy_interface.dataset_adapter.VTKArray'>
True


So, a VTK array is a NumPy array? What kind of trickery is this, you say? What kind of magic makes the following possible?

data.PointData.append(elevation + 1, 'e plus one')
print(algs.max(elevation))
print(algs.max(data.PointData['e plus one']))
print(data.VTKObject)


The output here is:

0.7932546138763428
1.7932546138763428
vtkPolyData (0x7fa20d011c60)
...
Point Data:
...
Number Of Arrays: 3
Array 0 name = Normals
Array 1 name = Elevation
Array 2 name = e plus one


It is all in the numpy_interface module. It ties VTK datasets and data arrays to NumPy arrays and introduces a number of algorithms that can work on these objects. There is quite a bit to this module, and we will introduce it piece by piece in the rest of this chapter.

Let’s wrap up this section with one final teaser.

print(algs.gradient(data.PointData['Elevation']))


Output:

[[ 0.32640398  0.32640398  0.01982867]
[ 0.32640402  0.32640402  0.01982871]
...
[ 0.41252578  0.20134845  0.2212007 ]
[ 0.41105482  0.21514832  0.0782456 ]]


Please note that this example is not very easily replicated by using pure NumPy. The gradient function returns the gradient of an unstructured grid – a concept that does not exist in NumPy. However, the ease-of-use of NumPy is there.

## 5.2. Understanding the dataset_adapter module¶

In this section, let’s take a closer look at the dataset_adapter module. This module was designed to simplify accessing VTK datasets and arrays from Python and to provide a NumPy-style interface.

Let’s continue with the example from the previous section. Remember, this script is being put in the Programmable Filter ‘s Script , connected to the Sphere , followed by the Elevation filter pipeline.

from vtk.numpy_interface import dataset_adapter as dsa
...
print(data)
print(isinstance(data, dsa.VTKObjectWrapper))


This will print:

<paraview.vtk.numpy_interface.dataset_adapter.PolyData object at 0x14b7caa50>
True


We can access the underlying VTK object using the VTKObject member:

print(type(data.VTKObject))


which produces:

<type 'vtkCommonDataModelPython.vtkPolyData'>


What we get as the $inputs$ in the Programmable Filter is actually a Python object that wraps the VTK data object itself. The Programmable Filter does this by manually calling the WrapDataObject function from the vtk.numpy_interface.dataset_adapter module on the VTK data object. Note that the WrapDataObject function will return an appropriate wrapper class for all vtkDataSet subclasses, vtkTable, and all vtkCompositeData subclasses. Other vtkDataObject subclasses are not currently supported.

VTKObjectWrapper forwards VTK methods to its VTKObject so the VTK API can be accessed directy as follows:

print(data.GetNumberOfCells())
96L


However, VTKObjectWrapper s cannot be directly passed to VTK methods as an argument.

from paraview.vtk.vtkFiltersGeneral import vtkShrinkPolyData
s = vtkShrinkPolyData()
s.SetInputData(data)


This attempt to set the data results in an error message.

TypeError: SetInputData argument 1: method requires a VTK object


Instead, we must pass the VTK object to the VTK filter, like so:

s.SetInputData(data.VTKObject)


An important thing to note in the example above is how the Python class vtkShrinkPolyData was imported for use in the script. In VTK, classes are organized into different groups of related functionality, and these groups can be invididually imported as Python modules. To use a class, you first identify the module in which it resides, which can be determined from the Doxygen documentation of the class [KitwareInc]. Go to the Doxygen page for the class, find the path of the file from which the documentation was generated at the bottom of the page, which has the form dox/<first directory>/<second directory>/<class name>. The module is then derived as vtk<first directory><second directory>. As an example, the documentation for vtkShrinkPolyData is generated from dox/Filters/General/vtkShrinkPolyData, hence its module is vtkFiltersGeneral. Then, you can import the class with a statement of the form.

### 5.2.1. Dataset attributes¶

So far, we have a wrapper for VTK data objects that partially behaves like a VTK data object. This gets a little bit more interesting when we start looking at how to access the fields (arrays) contained within this dataset.

For simplicity, we will embed the output generated by the script in the code itself and use the >>> prefix to differentiate the code from the output.

>>> print(data.PointData)

>>> print(data.PointData.keys())
['Normals', 'Elevation']

>>> print(data.CellData.keys())
[]

>>> print(data.PointData['Elevation'])
VTKArray([ 0.5       ,  0.        ,  0.45048442,  0.3117449 ,  0.11126047,
0.        ,  0.        ,  0.        ,  0.45048442,  0.3117449 ,
0.11126047,  0.        ,  0.        ,  0.        ,  0.45048442,
...,
0.11126047,  0.        ,  0.        ,  0.        ,  0.45048442,
0.3117449 ,  0.11126047,  0.        ,  0.        ,  0.        ], dtype=float32)

>>> elevation = data.PointData['Elevation']

>>> print(elevation[:5])
VTKArray([0.5, 0., 0.45048442, 0.3117449, 0.11126047], dtype=float32)
# Note that this works with composite datasets as well:

>>> mb = vtk.vtkMultiBlockDataSet()
>>> mb.SetNumberOfBlocks(2)
>>> mb.SetBlock(0, data.VTKObject)
>>> mb.SetBlock(1, data.VTKObject)
>>> mbw = dsa.WrapDataObject(mb)
>>> print(mbw.PointData)

>>> print(mbw.PointData.keys())
['Normals', 'Elevation']

>>> print(mbw.PointData['Elevation'])


It is possible to access PointData , CellData , FieldData , Points (subclasses of vtkPointSet only), and Polygons (vtkPolyData only) this way. We will continue to add accessors to more types of arrays through this API.

## 5.3. Working with arrays¶

For this section, let’s change our test pipeline to consist of the Wavelet source connected to the Programmable Filter .

In the Script , we access the RTData point data array as follows:

# Code for 'Script'
from paraview.vtk.vtkFiltersGeneral import vtkDataSetTriangleFilter
image = inputs[0]
rtdata = image.PointData['RTData']

# Let's transform this data as well, using another VTK filter.
tets = vtkDataSetTriangleFilter()
tets.SetInputDataObject(image.VTKObject)
tets.Update()

# Here, now we need to explicitly wrap the output dataset to get a
# VTKObjectWrapper instance.
ugrid = dsa.WrapDataObject(tets.GetOutput())
rtdata2 = ugrid.PointData['RTData']


Here, we created two datasets: an image data (vtkImageData) and an unstructured grid (vtkUnstructuredGrid). They essentially represent the same data but the unstructured grid is created by tetrahedralizing the image data. So, we expect the unstructured grid to have the same points but more cells (tetrahedra).

from paraview.vtk.vtkFiltersGeneral import vtkDataSetTriangleFilter


### 5.3.1. The array API¶

numpy_interface array objects behave very similar to NumPy arrays. In fact, arrays from vtkDataSet subclasses are instances of VTKArray, which is a subclass of numpy.ndarray . Arrays from vtkCompositeDataSet and subclasses are not NumPy arrays, but they behave very similarly. We will outline the differences in a separate section. Let’s start with the basics. All of the following work as expected.

As before, for simplicity, we will embed the output generated by the script in the code itself and use the >>> prefix to differentiate the code from the output.

>>> print(rtdata[0])
60.763466

>>> print(rtdata[-1])
57.113735

>>> print(repr(rtdata[0:10:3]))
VTKArray([  60.76346588,   95.53707886,   94.97672272,  108.49817657], dtype=float32)

>>> print(repr(rtdata + 1))
VTKArray([ 61.76346588,  86.87795258,  73.80931091, ...,  68.51051331,
44.34006882,  58.1137352 ], dtype=float32)

>>> print(repr(rtdata < 70))
VTKArray([ True , False, False, ...,  True,  True,  True])

# We will cover algorithms later. This is to generate a vector field.

# To demonstrate that avector is really a vector
>>> print(algs.shape(rtdata))
(9261,)

>>> print(algs.shape(avector))
(9261, 3)

>>> print(repr(avector[:, 0]))
VTKArray([ 25.69367027,   6.59600449,   5.38400745, ...,  -6.58120966,
-5.77147198,  13.19447994])


A few things to note in this example:

• Single component arrays always have the following shape: (n-tuples,) and not (n-tuples, 1)

• Multiple component arrays have the following shape: (n-tuples, n-components)

• Tensor arrays have the following shape: (n-tuples, 3, 3)

• The above holds even for images and other structured data. All arrays have one dimension (1 component arrays), two dimensions (multi-component arrays), or three dimensions (tensor arrays).

One more cool thing: It is possible to use boolean arrays to index arrays. Thus, the following works very nicely:

>>> print(repr(rtdata[rtdata < 70]))
VTKArray([ 60.76346588,  66.75043488,  69.19681549,  50.62128448,
64.8801651 ,  57.72655106,  49.75050354,  65.05570221,
57.38450241,  69.51113129,  64.24596405,  67.54656982,
...,
61.18143463,  66.61872864,  55.39360428,  67.51051331,
43.34006882,  57.1137352 ], dtype=float32)

>>> print(repr(avector[avector[:,0] > 10]))
VTKArray([[ 25.69367027,   9.01253319,   7.51076698],
[ 13.1944809 ,   9.01253128,   7.51076508],
[ 25.98717642,  -4.49800825,   7.80427408],
...,
[ 12.9009738 , -16.86548471,  -7.80427504],
[ 25.69366837,  -3.48665428,  -7.51076889],
[ 13.19447994,  -3.48665524,  -7.51076794]])


### 5.3.2. Algorithms¶

You can do a lot simply using the array API. However, things get much more interesting when we start using the numpy_interface.algorithms module. We introduced it briefly in the previous examples. We will expand on it a bit more here. For a full list of algorithms, use help(algs) . Here are some self-explanatory examples:

>>> import paraview.vtk.numpy_interface.algorithms as algs
>>> print(repr(algs.sin(rtdata)))
VTKArray([-0.87873501, -0.86987603, -0.52497   , ..., -0.99943125,
-0.59898132,  0.53547275], dtype=float32)

>>> print(repr(algs.min(rtdata)))
VTKArray(37.35310363769531)

>>> print(repr(algs.max(avector)))
VTKArray(34.781060218811035)

>>> print(repr(algs.max(avector, axis=0)))
VTKArray([ 34.78106022,  29.01940918,  18.34743023])

>>> print(repr(algs.max(avector, axis=1)))
VTKArray([ 25.69367027,   9.30603981,   9.88350773, ...,  -4.35762835,
-3.78016186,  13.19447994])


If you haven’t used the axis argument before, it is pretty easy. When you don’t pass an axis value, the function is applied to all values of an array without any consideration for dimensionality. When axis=0 , the function will be applied to each component of the array independently. When axis=1 , the function will be applied to each tuple independently. Experiment if this is not clear to you. Functions that work this way include sum , min , max , std , and var .

Another interesting and useful function is where the indices of an array are returned where a particular condition occurs.

>>> print(repr(algs.where(rtdata < 40)))
(array([ 420, 9240]),)
# For vectors, this will also return the component index if an axis is not
# defined.

>>> print(repr(algs.where(avector < -29.7)))
(VTKArray([4357, 4797, 4798, 4799, 5239]), VTKArray([1, 1, 1, 1, 1]))


So far, all of the functions that we discussed are directly provided by NumPy. Many of the NumPy ufuncs are included in the algorithms module. They all work with single arrays and composite data arrays. Algorithms also provide some functions that behave somewhat differently than their NumPy counterparts. These include cross, dot, inverse, determinant, eigenvalue, eigenvector, etc. All of these functions are applied to each tuple rather than to a whole array/matrix. For example:

>>> amatrix = algs.gradient(avector)
>>> print(repr(algs.determinant(amatrix)))
VTKArray([-1221.2732624 ,  -648.48272183,    -3.55133937, ...,    28.2577152 ,
-629.28507693, -1205.81370163])


Note that everything above only leveraged per-tuple information and did not rely on the mesh. One of VTK’s biggest strengths is that its data model supports a large variety of meshes, while its algorithms work generically on all of these mesh types. The algorithms module exposes some of this functionality. Other functions can be easily implemented by leveraging existing VTK filters. We used gradient before to generate a vector and a matrix. Here it is again:

>>> avector = algs.gradient(rtdata)


Functions like this require access to the dataset containing the array and the associated mesh. This is one of the reasons why we use a subclass of ndarray in dataset_adapter:

>>> print(repr(rtdata.DataSet))


Each array points to the dataset containing it. Functions such as gradient use the mesh and the array together. NumPy provides a gradient function too, you say. What is so exciting about yours? Well, this:

>>> print(repr(algs.gradient(rtdata2)))
VTKArray([[ 25.46767712,   8.78654003,   7.28477383],
[  6.02292252,   8.99845123,   7.49668884],
[  5.23528767,   9.80230141,   8.3005352 ],
...,
[ -6.43249083,  -4.27642155,  -8.30053616],
[ -5.19838905,  -3.47257614,  -7.49668884],
[ 13.42047501,  -3.26066017,  -7.28477287]])
>>> print(rtdata2.DataSet.GetClassName())
vtkUnstructuredGrid


Gradient and algorithms that require access to a mesh work whether that mesh is a uniform grid, a curvilinear grid, or an unstructured grid thanks to VTK’s data model. Take a look at various functions in the algorithms module to see all the cool things that can be accomplished using it. In the remaining sections, we demonstrate how specific problems can be solved using these modules.

## 5.4. Handling composite datasets¶

In this section, we take a closer look at composite datasets. For this example, our pipeline is Sphere source, and Cone source is set as two inputs to the Programmable Filter .

We can create a multiblock dataset in the Programmable Filter ‘s Script as follows:

# Let's assume inputs[0] is the output from Sphere and
# inputs[1] is the output from Cone.
mb = vtk.vtkMultiBlockDataSet()
mb.SetBlock(0, inputs[0].VTKObject)
mb.SetBlock(1, inputs[1].VTKObject)


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.

Now that we have a composite dataset with a scalar, we can use numpy_interface . As before, for simplicity, we will embed the output generated by the script in the code itself and use the >>> prefix to differentiate the code from the output.

>>> from paraview.vtk.numpy_interface import dataset_adapter as dsa
>>> mbw = dsa.WrapDataObject(mbe)
>>> print(repr(mbw.PointData.keys()))
['Normals', 'Elevation']
>>> elev = mbw.PointData['Elevation']
>>> print(repr(elev))


Note that the array type is different than we have previously seen (VTKArray). However, it still works the same way.

>>> from paraview.vtk.numpy_interface import algorithms as algs
>>> print(algs.max(elev))
0.5
>>> print(algs.max(elev + 1))
1.5


You can individually access the arrays of each block as follows.

>>> print(repr(elev.Arrays[0]))
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 two VTKArrays. The [] operator simply returned the first four values of each array. In general, all indexing operations apply to each VTKArray in the composite array collection. It is similar for algorithms, 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']
>>> print(repr(normals.Arrays[0]))
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)
>>> print(repr(normals.Arrays[1]))


Notice how the second array 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.

Where many of the algorithms apply independently to each array in a collection, some algorithms are global. Take min and max , for example, 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))
[0.5, 0.4330127]


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 arrays very similarly to single arrays.

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))