When Legolas finishes solving the eigenvalue problem it writes all information to a specially tailored datfile. First a header is written, containing information such as the geometry, chosen equilibrium, parameters, unit normalisations and names of the equilibrium variables. The header is followed by the actual data (eigenvalues, equilibrium arrays etc.), supplemented with eigenfunctions and matrix data if those are requested as well. As all of this data is written out in a binary format for efficient data-storage, it is not so straightforward to actually read its information since you’ll have to keep track of which variables are in there and in which order they are stored.
Since this is far from user-friendly we developed the post-processing Python package,
Pylbo, short for
“Python for Legolas Binary Output”. Pylbo enables you to easily load in Legolas datfiles
and access all information stored within. We even developed special classes to do post-processing, for more information
and examples we refer to this page. Maybe at some point in the future Pylbo will move to its own dedicated
repository and be included as a submodule in the legolas repository, but for now it is included in the
Note that if you do Legolas runs on high resolution and save the eigenfunctions, the files can easily be a few gigabytes in size. It may be useful to know that Pylbo never loads the file into memory. Instead we keep track of the various offsets of the datablocks, which are in turn used to read in data on a query-basis. Doing it like this means a huge boost in performance and decreases memory usage significantly, and implies that you can easily do analysis on larger-than-RAM datasets.
For example, say you did a huge resolution run where you saved the matrices and eigenfunctions, which resulted in a 10 Gb datfile. Loading this into Pylbo will be instantaneous, since the only thing that Pylbo “calculates” are the data offsets. Whenever you query data, Pylbo will seek the corresponding offset in the binary stream (which is a fast operation) and read only that specific datachunk into memory. To put some numbers on it, you can load a series of datfiles each a few Gb in size simultaneously, and Pylbo will use a few Mb of memory, tops.
Note: all interactive API’s (plotting eigenfunctions, continua, etc.) in the Pylbo framework are meant to do a fast visual inspection of the data generated by Legolas, and the resulting figures are not assumed to be publication-worthy. Make use of the easy data access provided by Pylbo instead to create some nice figures.