Figure 1-1 Module Control Panel
This chapter introduces you to IRIS Explorer using five sample maps, each from a different scientific field. The examples cover:
You can create maps for all occasions, for example, to generate a 3D image of vortices around hydro-electric turbines, or to visualize carbon dioxide concentration as a function of geographic location. These activities involve a number of separate tasks such as reading the data into the map, colormapping a gradient, extracting a 2D slice of a 3D dataset and visualizing the result. The modules in the map carry out tasks like these.
The five maps presented here use authentic data to illustrate the workings of IRIS Explorer. Each map deals with a specific issue, and the modules provide ways of altering or enhancing the data so as to make its significance clear, and perhaps to elicit correlations that might otherwise have gone unnoticed.
The maps are complete. You need only launch, or open, the map in the Map Editor, and then try varying the parameters as suggested in each example. Chapter 2 explains how you set about creating your own maps. Table 1-1 defines some of the terms you will encounter when you start IRIS Explorer.
| Term | Meaning |
|---|---|
| Map Editor | Work area for creating and modifying maps |
| Module Librarian | Lists maps and modules available for use |
| Module | Performs an algorithmic function on the data passing through it |
| Control Panel | Gives the user access to the module functions |
| Widgets | Dials, sliders, buttons, and text slots for changing parameters |
| Map | A collection of modules that carry out a series of related operations on a dataset |
| Image | A 2D array that can be viewed using the DisplayImg module |
| Geometric Object | A collection of points, lines, polygons or other shapes that can be displayed by the Render module |
You can start up IRIS Explorer through its command line
explorerYou can also specify additional keywords, for example to use the IRIS Explorer Scripting Language or to open a map. Three windows will appear; the Module Librarian, Map Editor, and Log window.
Once IRIS Explorer is running, you can use the Module Librarian to open a map. Follow these steps:
You can also hold down the left mouse button, drag the map icon over to the Map Editor and release the button.
When you release the mouse button, the map appears, module by module, on the screen. The menu bar displays a number in angle brackets, for example, <5>, which tells you initially how many modules are in the map; the number decreases as modules appear on screen, indicating how many modules remain to be launched. All the modules are selected (highlighted in white), and you can move the entire map by dragging on the title-bar of a single module.
Click on the background of the Map Editor to deselect the modules in the map before you experiment with individual modules. The highlighting disappears.
If IRIS Explorer is not running, you can use a keyword to open a map at the same time as starting up IRIS Explorer. For example, if /usr/explorer is the directory where IRIS Explorer has been installed:
explorer -map /usr/explorer/maps/mapname.map
or, in general, since the environment variable EXPLORERHOME is defined to point at the installation directory:
explorer -map $EXPLORERHOME/maps/mapname.map
Here, mapname.map is the name of the map you want to open, for example, chemistry.map. IRIS Explorer starts up and the map appears in the Map Editor in the same way as described above.
To quit IRIS Explorer, select Quit from the Admin menu in the Map Editor.
This example illustrates how you can use IRIS Explorer modules to process an image. Please note that the Image Processing modules are not provided with all implementations of IRIS Explorer. The koreaContour map takes an aerial image of a region of the Korean landscape, renders it in 3D, and overlays a contour map showing altitudes. The resulting image is displayed on-screen. The map file is $EXPLORERHOME/maps/koreaContour.map (or koreaContour in the Module Librarian).
To open the koreaContour map, displayed in Figure 1-2, drag the map named koreaContour from the Maps section in the Module Librarian over to the Map Editor and release the mouse button.
The seven modules are connected to one another by blue wires. The two modules on the left read in the image files, and the module on the far right displays the processed image. In between these are four modules that manipulate the image data.
The seven modules in the map have the following functions:
Both the aerial image and the altitude data go through DisplaceLat (see Figure 1-3), which displaces the image data so that topographical features become 3D.
The output of DisplaceLat<2> passes into Contour (see Figure 1-4), which creates a series of lines at varying heights above sea level just as in a topographic map. The scale factor for DisplaceLat<2> should be greater than that for DisplaceLat, to make the contour lines visible.
To set the relationship between scale factors, the Scale parameter output port on DisplaceLat is wired to the Scale parameter input port on DisplaceLat<2>.
The result is shown in Render (see Figure 1-5). You can open the Render window by clicking on the Maximize button and rotate the object in the window.
For more information on using the Render menus to manipulate objects, see Visualizing Data in Chapter 3.
This example illustrates how to use IRIS Explorer modules to read in a short program script describing a mathematical function and visualize the data in the Render window. The mathematical function describes diffusive heat flow. It is a finite difference (footnote) stencil for solving problems in parabolic differential heat conduction.
The form of the equation in this example is dimensionless; it serves as a normalized thermal model. A map using a specific form of such an equation with actual data can be used, for example, to test the conductivity of steel.
To open the heat-flux map (see Figure 1-6), drag the map named heat-flux from the Maps section in the Module Librarian over to the Map Editor and release the mouse button.
The map contains six modules, which have these functions:
A lattice is an IRIS Explorer data type, which is used to store an ordered array of data. For more information, see Understanding IRIS Explorer Data Types in Chapter 2.
LatFunction reads in a short program written in the Shape language, which is described in detail in Chapter 10 of the IRIS Explorer Module Writer's Guide. It is a C-like language that has capabilities for operating on whole lattices with a single statement. The program is saved in a file called heat-flux in $EXPLORERHOME/maps and can be run as often as you choose.
The data passes from LatFunction to Displacelat, which displaces the 2D data into a 3D array to show the heat source as a peak and the heat sink as a trough. You can change the degree to which the peaks project from the surface by turning the dial on Displacelat. This causes the data to be displaced out of the plane to a greater or lesser degree.
GenerateColormap colormaps the heat-flux data according to temperature value. You can change the colors associated with each temperature value by using the option menus on the control panel. Refer to Creating Colormaps in Chapter 3 for more information.
LatToGeom uses the heat-flux and colormap data (which are both IRIS Explorer lattices) to create geometry for display in Render (see Figure 1-7). Render can only display geometry, and all lattice data must be converted to this datatype first.
Once the data is displayed in Render, you can change the background color of the window by selecting Edit Background Color... from the Viewing menu. When the Background Color Picker appears:
This example illustrates how you can use IRIS Explorer modules to construct a 3D model of a complex organic molecule and then calculate the optimal radius and likely path for a probe atom investigating the target molecule.
To open the chemistry map (see Figure 1-8) drag the map named chemistry from the Maps section in the Module Librarian over to the Map Editor and release the mouse button.
The map takes configuration data for a buckminsterfullerene molecule (C60) and creates a ball-and stick representation of it and a dot surface that shows the region which is accessible by a probe atom.
The map contains seven modules:
The molecular configuration data is read in by ReadPDB. The input file is in the Brookhaven Protein DataBase (PDB) format, a commonly used format for saving descriptions of proteins, including atom positions and properties, and bond locations.
BallStick (see Figure 1-9) receives the data in pyramid form and generates sphere-and-cylinder geometry. (The pyramid is an IRIS Explorer data type.) The radius of each sphere is calculated according to the van der Waals' radius of the corresponding atom.
The molecule data also passes into AtomicSurf (see Figure 1-10). It uses the van der Waals' radius of each atom and adds a solvent probe radius to it. The surface points in this solvation layer represent the positions in which a solvent probe would be in contact with the target molecule.
It is best to use lower densities for large molecules, otherwise the visualization becomes very cluttered. A density of about 10 dots/Angstrom2 works well for molecules which have sizes comparable to that of buckminsterfullerene.
GenerateColormap creates a default colormap for the molecule based on atomic number. The domain is set at a minimum of 0 and a maximum of 16 to set commonly accepted colors for the elements (here, all the atoms in buckminsterfullerene are carbon, which are usually colored green).
It also colormaps the dots in the solvent-accessible surface according to the parent atom of each dot. This provides you with information about which atoms a given probe will touch as it traverse the surface of the molecule.
You can use the capacities of Render to look at the buckminsterfullerene molecule in great detail (see Figure 1-11). You can:
For more information on using Render, read Visualizing Data in Chapter 3.
You can use FileList to select another molecule to examine.
This example illustrates how you can use IRIS Explorer modules to visualize atmospheric data. Intense storms can produce tornadoes, high winds, and hail, and it is possible to simulate these storms by integrating a set of mathematical flow equations. These equations can, for example, predict values for the wind speed and direction, air temperature, humidity, pressure, and water content every 5 to 10 seconds on a lattice of grid points 500 to 1000 meters apart.
The data in the example map is taken from a simulation of a single severe storm made by the storm group at the University of Illinois (footnote). The map shows an isosurface of rain density and a volume rendering of air buoyancy. An isosurface is a surface which passes through all points in a 3-D dataset where the data has a particular value. The data is in the form of a uniform lattice.
To open the volume map (see Figure 1-12), drag the map named volume from the Maps section in the Module Librarian over to the Map Editor and release the mouse button.
The map contains eight modules:
For volume rendering techniques to display the volume as shown in this map, you require alpha blending hardware. Some computer displays do not have this hardware, but they can display the volume adequately if the Splat type on VolumeToGeom is set to Point (see Experimenting with the Volume Map below).
This map uses both surface rendering (the isosurface) and volume rendering (the haze) in one visualization to show the relationship between a number of variables which have been calculated in 3-D space during the modelling of the evolution of the storm.
IsosurfaceLat (see Figure 1-13) calculates an isosurface from the density data and outputs it as geometry. The threshold value is the density value for which an isosurface is created.
VolumeToGeom volume-renders the buoyancy data as a hazy cloud surrounding the isosurface. The entities that make up the cloud are called splats, small planar shapes that simulate fuzzy blobs on screen.
Rendering splats can may take a long time. You can set and adjust an error tolerance to get a balance between rendering time and splat quality.
ReadLat<3> reads the colormap settings for the volume rendering of the water buoyancy data into GenerateColormap.
Once the data is in Render, you can analyze it in detail. Figure 1-14 displays the reflectivity from the storm (footnote). The pale pink isosurface encloses the area containing most of the large drops and hail. Several light sources illuminate the surface. You can see the overhang, observed by weather radar in many severe storms, at mid-level where the reflectivity surface comes out toward the viewer.
Above the storm, there is a region of many colors volumetrically rendered from the buoyancy, a quantity used in the equations for vertical velocity. It represents the instantaneous effects of temperature, moisture, and water mixed with ice on the velocity acceleration, and is large at and above the top of the storm.
A wavy appearance is evident, indicating the presence of strong gravity
waves. From the colormap visible in You can change the opacity values on the colormap to affect the look of the
volume rendering (see Creating Colormaps in
Chapter 3).
This example illustrates how you can use IRIS Explorer modules to study the
dynamics of air flow over the nose of an aeroplane. The cfd map shows
variations in air density surrounding the surface of the aeroplane.
The map generates an air flow field around the plane, extracts 2D slices of
the volume, and colormaps them by density value, thus providing a means of
examining different areas of the field.
The CFD group at Silicon Graphics created the plane from a model aeroplane by
a digitized 3D scanning process, and the air flow data was generated from NASA's
fluid dynamics program, ARC3D (footnote).
To open the cfd map (see Figure 1-17),
drag the map
named cfd from the Maps section in the Module Librarian over to the Map
Editor and release the mouse button.
The map contains nine modules:
OrthoSlice (see Figure 1-15) operates on
the air density data to produce a curvilinear grid depicting the density field
around the plane. The slice number is 3 and this index increases in a direction
normal to the plane's surface. For J = 1, the grid actually lies along
the surface of the plane.
As you vary the slice numbers, you get information about air densities at
the plane surface and in its vicinity. High air densities occur at the nose
when there is a massive deceleration of fluid. As the air flows away from
the leading edge and accelerates over the wing, the density drops.
ProbeLat and IsosurfaceLat complement each other.
IsosurfaceLat generates an isosurface from the air density data. This
tells you how all the air of a given density is distributed around the plane.
You can change the threshold (the value at which the isosurface is calculated)
by turning the dial on the control panel.
ProbeLat (see Figure 1-16) probes the
curvilinear flow field along an arbitrary axis. The slice shows all density
values in that region, colormapped by value.
The axis along which the plane is cut is controlled by
TransformGen. You can slice in any direction you choose by using the
widgets on the TransformGen control panel to set a different axis. You
can alter the orientation of the probe by using the mouse to move the object in
TransformGen's window.
GenerateColormap colormaps the grid produced by OrthoSlice
according to its air density values. It is a scalar field, with one value at
every point. The domain in GenerateColormap is set to a density minimum
of about 0.7 and maximum of 1.1. This colormap is also used to color the probe
surface according to air density values.
PyrToGeom and LatToGeom convert pyramid and lattice data
respectively to geometry which can be displayed by Render.
Once the data is displayed in Render (see Figure 1-18), you can enhance the visualized data by
using the Render menus. For example, click on the background of the
Render window using the right mouse button to bring up the Render
pop-up menu. Select the Draw Style option, and then select
wireframe from the cascading menu. To return all the surfaces to their
original state, bring up the menu again and select the as is option.
The grid, isosurface, and probe surface are separate objects, which can be
separately treated in Render. For example, you could make the probe
surface transparent, which would reveal the features behind it.
Computational Fluid Dynamics
Opening the Cfd Map
Experimenting with the Cfd Map
Figure 1-15 OrthoSlice Control Panel
Figure 1-16 ProbeLat Control Panel
Figure 1-17 Different Levels of Air Density
Figure 1-18 The Rendered Flow Field
Last modified: Jan 20 15:00 1999
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© NAG Ltd. Oxford, UK, 1999