1. Slide 1 Visualization of scientific data under Linux - Techniques and data wrangling Mike Walterman, Manager of Graphics Programming, Scientific Computing and Visualization Group, Boston University

2. Slide 2 Introduction Present techniques for importing and visualizing data Will use VTK and IDL running under LINUX as examples Main Topics: - Planning and Implementing a visualization application - What are data wrangling, filtering, and mapping - Models and Mechanisms in VTK - Models and Mechanisms in IDL - Conclusions

3. Slide 3 Planning and Implementing a Visualization Main Objective: Creating pictures from source data Step 1: Plan your visualization Step 2: Implement the visualization

4. Slide 4 Planning a Visualization Decide on the scene and contents - 3D dynamic - 3D static - 2D images - Plots Determine how source data will effect elements that create, or are in the scene - Material Properties (color, shading) - Geometric Properties (shape, position) - Rendering Properties (cameras, lights) Systems performance issues - Data set reduction - Scene overload management (culling and clipping)

5. Slide 5 Implementing a Visualization Construction - From scratch (OpenGL, VTK) - Data flow model (AVS, OpenDX) - Functional model (MatLab, IDL) - Scene graph (OpenInventor, Performer) Data Import - Creating custom I/O for you data set - Create custom reader - Write data to file format of an existing reader

6. Slide 6 What are data wrangling, filtering and mapping Data Wrangling: Getting your data known to the sytem. Filtering: The process of mapping between data sets. Mapping: The process of mapping data to scene attributes. Types of sources: - Static data ( fields, cells, scalars, vectors, tensors ) - Procedural Data (functional specification) - Mixed ( functions with constraining static data ) Types of scene attributes - Material Properties (color, shading) - Geometric Properties (shape, position) - Rendering Properties (cameras, lights)

7. Slide 7 Filters Source data to internal SciVis tool representation (Readers) - JPEG readers - Inventor readers - Volume readers Conversion between internal representations (Filters) - Convolution operators (Frequency filtering) - Geometric transformations - Field transformations (e.g. color mapping) Conversion from data set to graphics data/attributes (Mappers) - Determines attributes of visible objects - Determines attributes of cameras and lights

8. Slide 8 Data Set Formats Structured Points - 1D, 2D, or 3D - Define dimensions - Define spacing - Define origin Structured Grid - Define dimensions - Define number of points - Define list of points – coordinates

9. Slide 9 Data Set Formats (continued) Rectilinear Grid - Define dimensions - Define X coordinate list - Define Y coordinate list - Define Z coordinate list Polygonal Date - Arbitrary combinations of: * Surface graphics primitives * Lines * Polygons * Triangle Strips

10. Slide 10 Data Set Formats (continued) Unstructured Grid - Define list of points - coordinates - Define list of cells (groups of points) - Define list of cell types

11. Slide 11 What is VTK? Set of C++ classes Interfaces to tcl, Java, and Python Extensible through Object oriented means Data flow model Programmer's Environment Kitware website – www.kitware.comwww.kitware.com

12. Slide 12 Models and Mechanisms in VTK Graphics Pipeline - Transforms graphical data into pictures Visualization Pipeline - Transforms information into graphical data

13. Slide 13 VTK Visualization Pipeline Elements Reader - Reads in data from a source file Source - Initializes the Visualization Pipeline by invoking a reader Data Objects - Internal data representations that VTK understands Filters – Processes the convert between Data Objects Mappers - transforms Data Objects into Graphical Data

14. Slide 14 The Mapping Chain Convert sources into internal data objects - Write data to a VTK understood data format - Create a VTK reader that understands source format and creates a VTK Data Object Map Data Objects to Data Objects using Filters Map Data Objects to Graphical Data using Mappers

15. Slide 15 VTK example # Image pipeline vtkImageBlockReader reader reader SetFilePattern "tmp/blocks_%d_%d_%d.vtk" reader SetDivisions 4 4 4 reader SetOverlap 3 reader SetWholeExtent 0 255 0 255 1 33 reader SetNumberOfScalarComponents 1 reader SetScalarType $VTK_UNSIGNED_SHORT vtkImageViewer viewer viewer SetInput [reader GetOutput] viewer SetZSlice 14 viewer SetColorWindow 2000 viewer SetColorLevel 1000 viewer SetPosition 50 50 viewer Render

16. Slide 16 IDL Mature Robust System Handles large data sets gracefully Interpretative, allows interactive use Command-line driven, language with Fortran-like feel Can be driven via scripts and in batch mode Extensible via user defined functions Many built-in numeric and statistical functions Many built-in image-processing functions Devoted user communitites in remote sensing, atmospheric sciences, Astronomy Scientists Tool Research Systems/Kodak - www.rsinc.comwww.rsinc.com

17. Slide 17 IDL Example Creates a 3D Plot of a surface stored in a data file ; File: xsurface.pro ; Author: Erik Brisson c = fltarr(60,60) openr, 3, 'dat/ex_surf_60x60.dat' readf, 3, c xsurface, c end

18. Slide 18 Conclusions Import your data - Create a custom reader - Convert data to file format that the tool can read Filter your data (convert between internal formats) Map your data (map internal format to graphical data)