Interfacing with Legolas

Tip: when specifying paths using Python, the standard library package pathlib is really recommended. This will resolve filepaths on any file system and lets you easily work with relative paths as well. That way you can specify the relative path to a file, copy the script over to another machine, run it, and the path strings get automatically updated.

from pathlib import Path
output_dir = Path("output").resolve() # <-- this contains the full path, starting from root

Generating parfiles

Pylbo comes with a method to generate parfiles for you, something that is extremely handy when doing multiruns and you want to vary one or more parameters. All that is needed is to specify the namelist items in a dictionary, and pass them to the generate_parfiles method. You don’t have to specify which variable goes where, Pylbo will automatically take care of that and places them in their corresponding namelist. Additionally, Pylbo will do typechecking on all parameters that you supply to the generator, such that you’re sure that the correct datatypes are passed on to Legolas. Furthermore, you can ask that filepaths and output folders are automatically resolved and placed in the parfile as well, see below for an example.

Generating a single parfile

To generate a single parfile, specify the setup as a dictionary. An example for Suydam cluster modes is given below:

import pylbo

config = {
    "geometry": "cylindrical",
    "x_start": 0,
    "x_end": 1,
    "gridpoints": 101,
    "parameters": {
        "k2": 1.0,
        "k3": -1.2,
        "cte_rho0": 1,
        "cte_v02": 0,
        "cte_v03": 0.14,
        "cte_p0": 0.05,
        "p1": 0.1,
        "alpha": 2.0
    "equilibrium_type": "suydam_cluster",
    "write_eigenfunctions": True,
    "basename_datfile": "suydam_modes",
    "output_folder": "output"
parfile = pylbo.generate_parfiles(parfile_dict=config)

When the key parameters (this should be a dictionary itself) is found, Pylbo automatically sets use_defaults to False. No additional arguments are given to the generator, so the above code sample will create a parfile named parfile.par by default in a folder parfiles in the current working directory. The actual parfile looks like this:

    equilibrium_type = 'suydam_cluster'
    use_defaults = .false.

    geometry = 'cylindrical'
    gridpoints = 101
    x_end = 1
    x_start = 0

    alpha = 2.0
    cte_p0 = 0.05
    cte_rho0 = 1
    cte_v02 = 0
    cte_v03 = 0.14
    k2 = 1.0
    k3 = -1.2
    p1 = 0.1

    basename_datfile = 'suydam_modes'
    output_folder = 'output'
    write_eigenfunctions = .true.

which is a perfectly formatted Legolas namelist.

Generating multiple parfiles

You’ll probably be able to generate a single parfile faster by hand instead of using Pylbo. However, the method described above excels in varying parameters, and modifying the parfiles accordingly. Say, you want to vary the parameter alpha in the above example between 1 and 4, and do 50 runs. This means you have to create and edit 50 parfiles by hand, which is a long and painful chore. Instead, simply edit the above config dictionary with the following:

import numpy as np
"number_of_runs": 50,
"parameters": {
    "alpha": np.linspace(1, 4, 50),

where you have to add the number_of_runs entry. This is purely for a sanity check, and to make sure that everything is nice and consistent. Pylbo will throw an appropriate error if there is an inconsistency between this number and the number of runs you want.

Now you call generate_parfiles in exactly the same way, which will create 50 parfiles where a number xxxx is prepended in front of the filename, the same holds true for the datfiles. You can even combine multiple variations, see the example below:

import numpy as np
import pylbo

config = {
    "config": {
        "geometry": "Cartesian",
        "x_start": 0,
        "x_end": 1,
        "number_of_runs": 50,
        "gridpoints": 100,
        "parameters": {
            "k2": 0.0,
            "k3": np.linspace(1, 5, 50),
            "beta": 0.25,
            "cte_rho0": 1.0,
            "cte_B02": 0.0,
            "cte_B03": 1.0,
        "equilibrium_type": "resistive_homo",
        "resistivity": True,
        "use_fixed_resistivity": True,
        "fixed_eta_value": np.linspace(0.001, 0.01, 50),
        "logging_level": 0,
        "show_results": False,
        "write_eigenfunctions": True,
        "write_matrices": False,
        "solver": "arnoldi",
        "arpack_mode": "shift-invert",
        "sigma": 0.5 - 3j,
        "number_of_eigenvalues": 100,
parfiles = pylbo.generate_parfiles(
    parfile_dict=config, basename="parfile_resistive", output_dir="parfile_output"

The above example will generate 50 parfiles, in which the parameter k3 is varied between 1 and 5, together with a variation of the resistivity fixed_eta_value. This means that the first parfile will have k3 = 1 and eta = 0.001, and the last one will have k3 = 5 and eta = 0.01. The eigenvalue problem will be solved using the Arnoldi shift-invert method, for 100 eigenvalues around a sigma-value of 0.5 - 3i.

Note that you can change as many parameters as you want, simply add any namelist item as a dictionary key. The parfiles will be called xxxxparfile_resistive.pardue to the fact that we added the basename argument. All parfiles will be placed in a directory called parfile_output relative to the current working directory. If this is not desired you can supply a full path instead, either as a string (e.g.output_dir="/users/Documents/parfiles") or a PathLike object (e.g. Path("../parfiles").resolve()). More information on the parfile generator can be found here.

Running Legolas with Pylbo

The parfiles generated in the above examples can be passed on to Pylbo, which in turn will pass those on to Legolas.

Remark: Note that Pylbo will always link to the executable in the Legolas home directory by default. It is therefore good practice to explicitly specify the Legolas executable when calling the runner.

Single run

To do a single Legolas run you specify the parfile and call the runner, like so:

import pylbo 
pylbo.run_legolas("parfiles/my_parfile.par", executable="legolas")

This will use my_parfile.par from the parfiles directory, and the executable legolas, both relative to the current working directory. Parfiles can be supplied either as (a list or array of) PathLike objects or strings.

Multiple runs

Pylbo can run multiple parfiles at the same time. Say you have a local directory called parfiles containing a bunch of generated parfiles, and would like to run Legolas on all of them. You can either pass the result from the parfile generator directly to the runner, or you can do it manually. In case of the latter then pathlib comes in handy: PathLike objects support glob. This means that you can automatically search for parfiles based on a given string, and don’t have to go through the trouble of adding all paths manually.

In the example below, we first search for all files with a .par extension in the parfiles directory and sort them name-wise. When we have the list of parfiles we simply pass them to the runner, which can either be done single-threaded or multi-threaded depending on whether or not the optional keyword nb_cpus is given.

from pathlib import Path
import pylbo 

parfiles = sorted(Path("parfiles").glob("*.par"))
# this runs single-threaded
pylbo.run_legolas(parfiles, executable="legolas")
# this runs multi-threaded
pylbo.run_legolas(parfiles, nb_cpus=4, executable="legolas")

The second case will use Python’s multiprocessing module to parallelise the number of runs across the amount of CPUs requested (4 in this case). Every CPU will have 1 instance of Legolas running, and a progressbar will be printed to keep track of the progress.

The optional keyword argument remove_parfiles can be supplied as well, which is False by default. If this is set to True, the parfiles will be removed after the runs are completed. Only if the folder containing the parfiles is empty after all parfiles are removed, the folder is removed as well. This may be handy in case you want to automatically clean up afterwards. More information on the Legolas runner can be found here.