Visualizing rich datasets: the fire_ug.vtu exampleΒΆ

Like heart.vtk, the fire_ug.vtu example dataset is available in the examples/data directory. This dataset is an unstructured grid stored in a VTK XML file. It represents a room with a fire in one corner. A simulation of the fluid flow generated by this fire was performed and the resulting data at a particular instant of time is stored in the file. The dataset was provided by Dr. Philip Rubini, who at the time was at Cranfield University. A VRML file (room_vis.wrl) is also provided to show the context of the room in which the fire is taking place.

  1. With mayavi2 started, select File->Load data->Open file to load the data. Again, you will see a node on the tree view on the left but nothing on the TVTK scene. This dataset contains different scalars and vectors in the same data file. If you select the VTK XML file ... node on the left the reader may be configured in the object editor pane of the UI. On this, you will see a drop list of all the scalars, vectors etc. in this data file. Select any that you wish to view.

  2. Create an outline of the data as described earlier using an Outline module. View an iso-surface of the data by creating an IsoSurface module. Also experiment with the ScalarCutPlane module.

  3. Show the scalar bar that represents the color mapping (via a Look up table that maps scalar values to colors) by clicking on the Colors and legends node and enabling the Show scalar bar. Experiment with the different color maps provided by default.

  4. Now click on the VTK XML file ... and select different scalar values to see how the data has changed. Your legend should automatically update when the scalar value is changed.

  5. This data also features vectors. The scalar data has u, v and w but not the magnitude of the velocity. Let’s say we’d like to be able to view iso-contours of the magnitude of the velocity. To do this let’s use the ExtractVectorNorm filter. This is created by choosing the Visualize->Filters->Extract Vector Norm menu.

  6. If you now create a ScalarCutPlane, you will see a new Colors and legends node under the ExtractVectorNorm node. This scalar cut plane is displaying colors for the velocity magnitude that the filter has created. You can drag the iso-surface module from the other Colors and legends node and drop it on this Colors and legends node so that the IsoSurface generated is for the velocity magnitude and not for the scalars chosen in the data.

    Note that the view on the left represents a pipeline of the flow of the data from source -> filter -> modules. Essentially the data flows from the parent node down to the children nodes below it.

    Now if you want to visualize something on a different “branch” of the pipeline, let’s say you want to view iso-surfaces of the temperature data you must first click on the modules or the source object (the VTK XML File ... node) itself and then select the menu item. When you select an item on the tree, it makes that item the current object and menu selections made after that will in general create new modules/filters below the current object.

  7. You can filter “filtered data”. So select the ExtractVectorNorm node to make it the active object. Now create a Threshold filter by selecting Visualize->Filters->Threshold. Now set the upper and lower thresholds on the object editor for the Threshold to something like 0.5 and 3.0. If you create a VectorCutPlane module at this point and move the cut plane you should see arrows but only arrows that are between the threshold values you have selected. Thus, you can create pretty complicated visualization pipelines using this approach.

  8. There are several vector modules. VectorCutPlane, Vectors, WarpVectorCutPlane and Streamlines. If you view streamlines then mayavi will generate streamlines of vector data in your dataset. To view streamlines of the original dataset you can click on the original Outline module (or the source) and then choose the Streamline menu item. The streamline lets you move different type of seeds on screen using 3D widgets. Seed points originating from these positions are used to trace out the streamlines. Sphere, line and plane sources may be used here to initialize the streamline seeds.

  9. You can view the room in which the fire is taking place by opening the VRML file by the File->Open->VRML2 file menu item and selecting the room_vis.wrl file included with the data.

  10. Once you setup a complex visualization pipeline and want to save it for later experimentation you may save the entire visualization via the File->Save Visualization menu. A saved file can be loaded later using the File->Load Visualization menu item. This option is not 100% robust and is still experimental. Future versions will improve this feature. However, it does work and can be used for the time being.

Once again, the visualization in this case was created by using the user interface. It is possible to script this entirely using Python scripts. A simple script demonstrating several of the above modules is available in examples/streamline.py. This file may be studied. It can be run either likes so:

$ cd examples
$ python streamline.py

or so:

$ mayavi2 -x streamline.py

As can be seen from the example, it is quite easy to script mayavi to visualize data. An image of a resulting visualization generated from this script is shown below.

Sample visualization of the ``fire_ug.vtu`` dataset.