Conversion with pvpython

This is my favourite starting point for data if I'm interested in whole movies and not only particular snapshots. Yet, it might become inconvenient for large datasets, as it is too slow.

The command line, Python-based conversion of Peano works if and only if you have Paraview plus Python installed. First, ensure Peano's visulisation component is in your Python path:

     export PYTHONPATH=Peano-dir/python

and consult the remarks on third party software for further troubleshooting if there are further issues.

After that, use the conversion script there to translate your peano-patch-file. It is important that you interpret this script through pvpython:

     pvpython Peano-dir/python/peano4/visualisation/render.py --help

You should see a usage message now. The API comes along with all kind of filters and stuff (you can for example filter out coarse grid data, which is something I'd recommend, or you can rescale individual patches).

Peano applications dump their data into peano-patch-file files. These files in turn can include other files. Usually, you have one top-level file, and then this file references multiple files per time step - typically one file per subpartition per time step. The convert tools fuses all snapshots per time step and glues them together into one huge vtu file. On top of this, it generates (unless told otherwise) a pvd file which is really only an index over the vtu files and allows you to load all of them as one video within Paraview.

Please note that this variant to convert data requires you to install Paraview with Python. This is now the default if you install Paraview on your Linux distribution. We have however seen several cases where container images do not ship Paraview with Python. In this case, you might have to install it manually (or leave the container for the conversion).

Command-line conversion with a C++ executable

This variant has not been maintained for a while and might require some debugging. On the other hand, nothing has changed here for months, i.e. it might as well also work.

Installing Peano's VTK command line tools can be quite tricky - depending on your local VTK installation. At the same time, the command line conversion via executables, i.e. not via the Python terminal as discussed above, is by far the fastest route. It can even exploit multiple cores. I recommend to stick to the Python-based postprocessing route as long as possible and to switch to the offline postprocessing only for large-scale production data where Python is just too slow and/or all postprocessing has to happen on a remote supercomputer as you cannot transfer the (raw) Peano data files.

If you configure with VTK support, Peano builds a command-line tool convert which you can use to convert 's patch files into plain (binary) vtk.

Installing VTK support can be challenging. Here's some things you might want to consider/check:

To build the conversion/visualisation tools, you have to translate the code with --with-vtk. If you don't specify a path, then assumes that all VTK is installed within /usr/include.
We next have to know which VTK version you are using. VTK builds a different set of libraries in each generation. has to know which version you want to use to link against the right set of libraries. The installation script will automatically try to detect your version, but the detection is not very robust. If it is the wrong version, use the –with-vtk-version=x switch to set the version manually. We currently support x 7,8,9, i.e. we are only interested in the major version of your VTK installation. If you have version 8.90, it is the 8 we are interested in.
Next, the script will search for the right libraries. By default, our script assumes that the libraries are have a suffix with the major dot minor number. So we assume that the VTK version 8.90 yields a library vtkIOCore-8.90. This is the default. The script will try to find this default library and give you some feedback (be careful: if it doesn't find the library, it will still continue as you might want to change the pathes later on). If your library naming convention is different—we've seen systems dropping the version numbers or Paraview installations which append something alike pv8.90 - then specify your suffix manually through –with-vtk-suffix. If your installation does not have a version suffix, as is the case in Fedora, you should pass an empty string to this: –with-vtk-suffix=”
If your compile passes through but fails in the linking state (object not found), then you have to check if the VTK libraries are in the search path. If they are not (very likely), then add them prior to the compile call:
```
      export LDFLAGS="-L/opt/vtk/lib64"
```
Once you have managed to configure with VTK and the build process has terminated successfully, your directory src/convert holds the actual convert script. If you call convert without arguments, you get a usage message. convert allows you to extract data from a data dump, and store it within the patch files under a new identifier. It also allows you to extract a dataset from patch files into vtu. vtu is one of VTK's binary file formats.

A standard postprocessing workflow on the command line reads as follows:

Use convert to extract certain data from the dumped data files. You might want to extract only the finest grid level, e.g., or to pick a particular unknown. convert is given a new identifier (name) for the new tdata set, and it stores the extracted data either within the existing patch files or a new, separate one.
Use convert to pick one particular dataset via its identifier and to dump it into a vtu file.
Use Paraview or VisIt to display the vtu file.

Unfortunately, our C++ conversion kernels do not offer all the functionality we now find within the Python-based tools. Things alike mesh distortion are missing here.

Table of Contents

Conversion with pvpython

Command-line conversion with a C++ executable