Python Calculator

The python calculator is a programmable calculator that provides common mathematical operations and apply them on the point data, cell data or the input datasets directly. It is similar to the python programmable filter and utilizes its functionality to build python scripts for execution. For efficiency, the actual computation is delegated either to corresponding VTK filters or to the numpy library.

Below is a list of mathematical functions currently supported by this python calculator. Notice that these functions are also available in the python programmable filter.

Name	Supported Operand Types	Number of Operands	Data Association
abs	scalar vector tensor	1	point or cell
cross	vector	2	point or cell
curl	vector	1	point or cell
det	tensor	1	point or cell
determinant	tensor	1	point or cell
dot	scalar vector	2	point or cell
eigenvalue	tensor	1	point or cell
eigenvector	tensor	1	point or cell
global_mean	scalar vector tensor	1	point or cell
global_max	scalar vector tensor	1	point or cell
global_min	scalar vector tensor	1	point or cell
gradient	scalar vector	1	point or cell
inverse	tensor	1	point or cell
laplacian	scalar	1	point or cell
ln	scalar vector tensor	1	point or cell
log(==ln)	scalar vector tensor	1	point or cell
log10	scalar vector tensor	1	point or cell
max	scalar vector tensor	1	point or cell
min	scalar vector tensor	1	point or cell
mean	scalar vector tensor	1	point or cell
mag	scalar vector	1	point or cell
norm	scalar vector	1	point or cell
strain	vector	1	point or cell
trace	tensor	1	point or cell
vorticity	vector	1	point or cell

Name	Supported Cell Types	Data Association
area	trig quad	cell
aspect	trig quad tet	cell
aspect_gamma	tet	cell
diagonal	hex	cell
jacobian	quad tet hex	cell
max_angle	trig quad	cell
min_angle	trig quad	cell
shear	quad hex	cell
skew	quad hex	cell
surface_normal	trig	cell
volume	tet hex	cell
vertex_normal	trig	point

Comments

The first list gives all the math functions that work with point data or cell data. The general calling signature is function_name(array) for functions taking one operand or function_name(array1, array2) for functions taking two operands. For example, abs(Normals) computes the absolute value for all normals associated with the dataset component-wise. The array association must be given throuth the GUI interface of the python calculator.

The second list gives all the math functions that directly work with a dataset. Most of them work on the cells of a dataset, with only one exception vertex_normal which computes the normal for each point in a dataset. The calling signature of this category is function_name(inputs[0]) where inputs[0] is defined within the calling context by paraview to be your first input dataset.

In the lists, vector means 3D vectors and tensor stands for 3 by 3 matrix. trig is triangle, quad is quadrilateral, tet is tetrahedron and hex is hexahedron.

Examples

Compute area for each cell

   area(inputs[0]) and array association set to Cell Data

Compute vector length for each point

   sqrt(dot(BrownianVectors,BrownianVectors)) or equivalently mag(BrownianVectors)

where BrownianVectors is an array of 3D vectors that are associated with each point in the dataset. Array association set to Point Data.

A more complex example

   max(abs(trace(inverse(gradient(Normals)))))

Normals is an array of 3D vectors that are associated to each point in the dataset. The expression first computes for each component of the normal vector its gradient, therefore effectively generates a tensor and then it takes the inverse of the generated matrix and then sum everything on the diagonal of the inverse matrix and then computes the absolute value of the sum and then looks for the maximum of all the absolute values.