Transcript Visualization of scientific data under Linux - Techniques and data wrangling Mike Walterman,

Visualization of scientific data
under Linux - Techniques and
data wrangling
Mike Walterman,
Manager of Graphics Programming,
Scientific Computing and Visualization Group,
Boston University
Slide 1
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
Slide 2
Planning and Implementing a
Visualization
• Main Objective: Creating pictures from source data
• Step 1: Plan your visualization
• Step 2: Implement the visualization
Slide 3
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)
Slide 4
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
Slide 5
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)
Slide 6
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
Slide 7
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
Slide 8
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
Slide 9
Data Set Formats (continued)
• Unstructured Grid
- Define list of points - coordinates
- Define list of cells (groups of points)
- Define list of cell types
Slide 10
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.com
Slide 11
Models and Mechanisms in VTK
• Graphics Pipeline - Transforms graphical data into pictures
• Visualization Pipeline - Transforms information into graphical
data
Slide 12
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
Slide 13
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
Slide 14
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
Slide 15
IDL
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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.com
Slide 16
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
Slide 17
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)
Slide 18