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.

Supported Function List

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.

• Functions working on data arrays

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

• Functions working on dataset

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				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.