VTK Data Model

Introduction

To use ParaView effectively, you need to understand the ParaView data model. Here, we will introduce the the VTK data model used by ParaView briefly. For more details, you can refer to one of the VTK books.

The most fundamental data structure in VTK is a data object. Data objects can either be scientific datasets such rectilinear grids or finite elements meshes (see below) or more abstract data structures such as graphs or trees. Since ParaView is geared toward scientific visualization, we will focus on the former and touch briefly on the latter when necessary.

Datasets are formed of smaller building blocks: mesh (topology and geometry) and attributes.

Mesh

Even though the actual data structure used to store the mesh in memory depends on the type of the dataset, some abstractions are common to all types. We describe these here and we briefly describe the data structures below for each dataset. In general, a mesh consists of vertices (points) and cells (elements, zones). Cells are used to discretize a region and can have various types such a tetrahedra, hexahedra etc. Each cell contains a set of vertices. The mapping from cells to vertices is called the connectivity. Note that even though it is possible to define data elements such as faces and edges, VTK does not represent these explicitly. Rather, they are implied by a cell's type and its connectivity. One exception to this rule is the arbitrary polyhedron which explicitly stores its faces. Here is an example mesh that consists of 2 cells. The first cell is defined by vertices (0, 1, 3, 4) and the second cell is defined by vertices (1, 2, 4, 5). These cells are neighbors because they share the edge defined by the points (1, 4).

So far, we talked about the topology only. A mesh is fully defined by its topology and the spatial coordinates of its vertices. In VTK, the point coordinates may be implicit or explicitly defined by a data array of dimensions (number_of_points, 3).

Attributes (fields, arrays)

An attribute (aka data array or field) defines the discrete values of a field over the mesh. Examples of attributes include Pressure, Temperature, Velocity and Stress Tensor. Note that VTK does not specifically define different types of attributes. All attributes are stored as data arrays which can have an arbitrary number of components. ParaView makes some assumptions in regards to the number of components. For example, a 3 component array is assumed to be an array of vectors. Attributes can be associated with points or cells. It is also possible to have attributes that are not associated with either. The following example demonstrates the use of a point-centered attribute. Note that the attribute is only defined on the vertices. Interpolation is used to obtain the values everywhere else. The interpolation functions used depends on the cell type. See VTK documentation for details.

The figure below demonstrates the use of a cell-centered attribute. Note that cell-centered attributes are assumed to be constant over each cell. Due to this property, many filters in VTK cannot be directly applied to cell-centered attributes. It is normally required to apply a Cell Data to Point Data filter. In ParaView, this filter is applied automatically when necessary.

Uniform Rectilinear Grid (Image Data)

A uniform rectilinear grid (aka image data) defines its topology and point coordinates implicitly. To fully define the mesh for an image data, VTK uses the following:

- Extents - these define the minimum and maximum indices in each direction. For example, an image data of extents (0, 9), (0, 19), (0, 29) has 10 points in the x-direction, 20 points in the y-direction and 30 points in the x-direction. The total number of points is 10*20*30.

- Origin - this is the position of a point defined with indices (0, 0, 0)

- Spacing - this is the distance between each point. Spacing for each direction can defined independently

The coordinate of each point is defined as follows: coordinate = origin + index*spacing where coordinate, origin, index and spacing are vectors of length 3.

Note that the generic VTK interface for all datasets uses a flat index. The (i,j,k) index can be converted to this flat index as follows: idx_flat = k*(npts_x*npts_y) + j*nptr_x + i.

A uniform rectilinear grid consists of cells of the same type. This type is determined by the dimensionality of the dataset (based on the extents) and can either be vertex (0D), line (1D), pixel (2D) or voxel (3D).

Due to its regular nature, an image data requires less storage than other datasets. Furthermore, many algorithms in VTK have been optimized to take advantage of this property and are more efficient for image data.

Rectilinear Grid

A rectilinear grid defines its topology implicitly and point coordinates semi-implicitly. To fully define the mesh for a rectilinear grid, VTK uses the following:

- Extents - these define the minimum and maximum indices in each direction. For example, a rectilinear grid of extents (0, 9), (0, 19), (0, 29) has 10 points in the x-direction, 20 points in the y-direction and 30 points in the x-direction. The total number of points is 10*20*30.

- 3 arrays defining coordinates in the x-, y- and z-directions. These arrays are of length npts_x, npts_y and npts_z. This is a significant savings in memory as total memory used by these arrays is npts_x+npts_y+npts_z rather than npts_x*npts_y*npts_z.

The coordinate of each point is defined as follows: coordinate = (coordinate_array_x(i), coordinate_array_y(j), coordinate_array_z(k))".

Note that the generic VTK interface for all datasets uses a flat index. The (i,j,k) index can be converted to this flat index as follows: idx_flat = k*(npts_x*npts_y) + j*nptr_x + i.

A rectilinear grid consists of cells of the same type. This type is determined by the dimensionality of the dataset (based on the extents) and can either be vertex (0D), line (1D), pixel (2D) or voxel (3D).

Curvilinear Grid (Structured Grid)

A curvilinear grid defines its topology implicitly and point coordinates explicitly. To fully define the mesh for a curvilinear grid, VTK uses the following:

- Extents - these define the minimum and maximum indices in each direction. For example, a curvilinear grid of extents (0, 9), (0, 19), (0, 29) has 10*20*30 points regularly defined over a curvilinear mesh.

- An array of point coordinates. This arrays stores the position of each vertex explicitly.

The coordinate of each point is defined as follows: coordinate = coordinate_array(idx_flat)". The (i,j,k) index can be converted to this flat index as follows: idx_flat = k*(npts_x*npts_y) + j*nptr_x + i.

A curvilinear grid consists of cells of the same type. This type is determined by the dimensionality of the dataset (based on the extents) and can either be vertex (0D), line (1D), quad (2D) or hexahedron (3D).

AMR Dataset

VTK natively support Berger-Oliger type AMR (Adaptive Mesh Refinement) datasets. An AMR dataset is essentially a collection of uniform rectilinear grids grouped under increasing refinement ratios (decreasing spacing). VTK's AMR dataset does not force any constraint on if and how these grids should overlap. However, it provides support for masking (blanking) sub-regions of the rectilinear grids using an array of bytes. This allows VTK to process overlapping grids with minimal artifacts. VTK can automatically generate the masking arrays for Berger-Oliger compliant meshes.

Unstructured Grid

An unstructured grid is most general primitive dataset type. It stores topology and point coordinates explicitly. Even though VTK uses a memory-efficient data structure to store the topology, unstructured grid uses significantly more memory to represent its mesh. Therefore, use an unstructured grid only when you cannot represent your dataset as one of the above datasets. VTK supports a large number of cell types all of which can exist (heterogeneously) within one unstructured grid. The full list of all cell types supported by VTK can be found in the file vtkCellType.h in the VTK source code. Here is the list as of when this document was written:

- VTK_EMPTY_CELL

- VTK_VERTEX

- VTK_POLY_VERTEX

- VTK_LINE

- VTK_POLY_LINE

- VTK_TRIANGLE

- VTK_TRIANGLE_STRIP

- VTK_POLYGON

- VTK_PIXEL

- VTK_QUAD

- VTK_TETRA

- VTK_VOXEL

- VTK_HEXAHEDRON

- VTK_WEDGE

- VTK_PYRAMID

- VTK_PENTAGONAL_PRISM

- VTK_HEXAGONAL_PRISM

- VTK_QUADRATIC_EDGE

- VTK_QUADRATIC_TRIANGLE

- VTK_QUADRATIC_QUAD

- VTK_QUADRATIC_TETRA

- VTK_QUADRATIC_HEXAHEDRON

- VTK_QUADRATIC_WEDGE

- VTK_QUADRATIC_PYRAMID

- VTK_BIQUADRATIC_QUAD

- VTK_TRIQUADRATIC_HEXAHEDRON

- VTK_QUADRATIC_LINEAR_QUAD

- VTK_QUADRATIC_LINEAR_WEDGE

- VTK_BIQUADRATIC_QUADRATIC_WEDGE

- VTK_BIQUADRATIC_QUADRATIC_HEXAHEDRON

- VTK_BIQUADRATIC_TRIANGLE

- VTK_CUBIC_LINE

- VTK_CONVEX_POINT_SET

- VTK_POLYHEDRON

- VTK_PARAMETRIC_CURVE

- VTK_PARAMETRIC_SURFACE

- VTK_PARAMETRIC_TRI_SURFACE

- VTK_PARAMETRIC_QUAD_SURFACE

- VTK_PARAMETRIC_TETRA_REGION

- VTK_PARAMETRIC_HEX_REGION

Many of these cell types are straightforward. For details, see VTK documentation.

Polygonal Grid (Polydata)

A polydata is a specialized version of unstructured grid designed for efficient rendering. It consists of 0D cells (vertices and polyvertices), 1D cells (lines and polylines) and 2D cells (polygons and triangle strips). Certain filters that generate only these cell types will generate a polydata. Examples include the Contour and Slice filters. An unstructured grid, as long as it has only 2D cells supported by polydata, can be converted to a polydata using the Extract Surface filter. A polydata can be converted to an unstructured grid using Clean to Grid.

Table

A table is a tabular dataset that consists of rows and columns. All chart views have been designed to work with tables. Therefore, all filters that can be shown within the chart views generate tables. Also, tables can be directly loaded using various file formats such as the comma separated values format. Tables can be converted to other datasets as long as they are of the right format. Filters that convert tables include Table to Points and Table to Structured Grid.

Multiblock Dataset

You can think of a multi-block dataset as a tree of datasets where the leaf nodes are "simple" datasets. All of the data types describe above, except AMR, are "simple" datasets. Multi-block datasets are used to group together datasets that are related. The relation between these datasets is not necessarily defined by ParaView. A multi-block dataset can represent an assembly of parts or a collection of meshes of different types from a coupled simulation. Multi-block datasets can be loaded or created within ParaView using the Group filter. Note that the leaf nodes of a multi-block dataset don't all have to have the same attributes. If you apply a filter that requires an attribute, it will be applied only to blocks that have it.

Multipiece Dataset

Multi-piece datasets are similar to multi-block datasets in that they group together simple datasets. However, there is one big difference. Multi-piece datasets group together datasets that are part of a whole mesh - datasets of the same type and with same attributes. This data structure is used collect datasets produced by a parallel simulation without having to append the meshes together. Note that there is not way to create a multi-piece dataset within ParaView. They are created by certain readers. Furthermore, multi-piece datasets act, for the most part, as simple datasets. For example, it is not possible to extract individual pieces or obtain information about them.

Information Panel

Introduction

Clicking on the Information button on the Object Inspector will take you to the Information Panel. The purpose of this panel is to provide you with information about the output of the currently selected source, reader or filter. The information on this panel is presented in several sections. We start by describing the sections that are applicable to all dataset types then we describe data specific sections.

File Properties

If the current pipeline object is a reader, the top section will display the name of the file and its full path.

Data Statistics

The Statistics section displays high-level information about the dataset including the type, number of points and cells and the total memory used. Note that the memory is for the dataset only and does not include memory used by the representation (for example, the polygonal mesh that may represent the surface). All of this information is for the current time step.

Array Information

This section shows the association (point, cell or global), name, type and range of each array in the dataset. In the example, the top 3 attributes are point arrays, the middle 3 cell arrays and the bottom three global (field) arrays. Note that for vectors, the range of each component is shown separately. In case, the range information does not fit the frame, the tooltip will display all of the values.

Bounds

The Bounds section will display the spatial bounds of the dataset. These are the coordinates of the smallest axis-aligned hexahedron that contains the dataset as well as its dimensions.

Timesteps

The Time section shows the index and value of all time steps available in a file or produceable by a source. Note that this section display values only when a reader or source is selected even though filters downstream of such sources also have time varying outputs. Also note that usually only one time step is loaded at a time.

Extents

The Extents section is available only for structured datasets (uniform rectilinear grid, rectilinear grid and curvilinear grid). It displays the extent of all three indices that define a structured datasets. It also displays the dimensions (the number of points) in each direction. Note that these refer to logical extents and the labels X Extent, Y Extent and Z Extent can be somehow misleading for curvilinear grids.

Data Hierarchy (AMR)

For AMR datasets, the Data Hierarchy section shows the various refinement levels available in the dataset. Note that you can drill down to each level by clicking on it. All of the other sections will immediately update for the selected level. For information on the whole dataset, select the top parent called "AMR Dataset".

Data Hierarchy (Multi-Block Dataset)

For multi-block datasets, the Data Hierarchy section shows the tree that forms the multi-block dataset. By default, only the first level children are shown. You can drill down further by clicking on the small triangle to the left of each node. Note that you can drill down to each block by clicking on it. All of the other sections will immediately update for the selected block. For information on the whole dataset, select the top parent called "Multi-Block Dataset".

Statistics Inspector

The Statistics Inspector (View -> Statistics Inspector) can be used to obtain high-level information about the data produced by all sources, readers and filters in the ParaView pipeline. Some of this information (and more) is also available through Object Inspector's Information panel. The information presented in the Statistics Inspector include the name of the pipeline object that produced the data, the data type, the number of cells and points, memory used by the dataset, memory used by the visual representation of the dataset (usually polygonal data), the spatial bounds of the dataset and the spatial bounds of the dataset (the minimum and maximum time values for all available time steps).

Note that the selection in the Statistics Inspector is linked with the Pipeline Browser. Selecting an entry in the Selection Inspector will update the Pipeline Browser and vice versa.