ParaView project is an application designed with the need to visualize large data sets in mind. The goals of the ParaView project include the following:
- Develop an open-source, multi-platform visualization application.
- Support distributed computation models to process large data sets.
- Create an open, flexible, and intuitive user interface.
- Develop an extensible architecture based on open standards.
ParaView runs on distributed and shared memory parallel as well as single processor systems and has been succesfully tested on Windows, Linux and various Unix workstations and clusters. Under the hood, ParaView uses the Visualization Toolkit as the data processing and rendering engine and has a user interface written using a unique blend of Tcl/Tk and C++. Please go here for a detailed list of features.
ParaView was created by Kitware in conjunction with Jim Ahrens of the Advanced Computing Laboratory at Los Alamos National Laboratory (LANL). Contributors and developers of ParaView currently include: Kitware, LANL, Sandia National Laboratories, and Army Research Laboratory. ParaView is funded by the US Department of Energy ASCI Views program as part of a three-year contract awarded to Kitware, Inc. by a consortium of three National Labs - Los Alamos, Sandia, and Livermore. The goal of the project is to develop scalable parallel processing tools with an emphasis on distributed memory implementations. The project includes parallel algorithms, infrastructure, I/O, support, and display devices. One significant feature of the contract is that all software developed is to be delivered open source. Hence ParaView is available as an open-source system.
Here are some key features of "ParaView":
· Handles structured (uniform rectilinear, non-uniform rectilinear, and curvilinear grids), unstructured, polygonal and image data.
· All processing operations (filters) produce datasets. This allows the user to either further process or save as a data file the result of every operation. For example, the user can extract a cut surface, reduce the number of points on this surface by masking, and apply glyphs (for example, vector arrows) to the result.
· Contours and isosurfaces can be extracted from all data types using scalars or vector components. The results can be colored by any other variable or processed further. When possible, structured data contours/isosurfaces are extracted with fast and efficient algorithms which make use of the special data layout.
· Vectors fields can be inspected by applying glyphs (arrows, cones, lines, spheres, and various 2D glyphs) to the points in a dataset. The glyphs can be scaled by scalars, vector component or vector magnitude and can be oriented using a vector field.
· A sub-region of a dataset can be extracted by cutting or clipping with an arbitrary plane (all data types), specifying a threshold criteria to exclude cells (all data types) and/or specifying a VOI (volume of interest - structured data types only)
· Streamlines can be generated using constant step or adaptive integrators. The results can be displayed as points, lines, tubes, ribbons, etc., and can be processed by a multitude of filters.
· The points in a dataset can be warped (displaced) with scalars (given a user defined displacement vector) or with vectors (unavailable for non-linear rectilinear grids).
· With the array calculator, new variables can be computed using existing point or cell field arrays. A multitude of scalar and vector operations are supported.
· Data can be probed at a point or along a line. The results are displayed either graphically or as text and can be exported for further analysis.
· ParaView provides many other data sources and filters by default (edge extraction, surface extraction, reflection, decimation, extrusion, smoothing...) and any VTK filter can be added by providing a simple XML description (VTK provides hundreds of sources and filters, see VTK documentation for a complete list).