Running

Pyro can be run in two ways: either from the commandline, using the pyro.py script and passing in the solver, problem and inputs as arguments, or by using the Pyro class.

Commandline

The pyro.py script takes 3 arguments: the solver name, the problem setup to run with that solver (this is defined in the solver’s problems/ sub-directory), and the inputs file (again, usually from the solver’s problems/ directory).

For example, to run the Sedov problem with the compressible solver we would do:

./pyro.py compressible sedov inputs.sedov

This knows to look for inputs.sedov in compressible/problems/ (alternately, you can specify the full path for the inputs file).

To run the smooth Gaussian advection problem with the advection solver, we would do:

./pyro.py advection smooth inputs.smooth

Any runtime parameter can also be specified on the command line, after the inputs file. For example, to disable runtime visualization for the above run, we could do:

./pyro.py advection smooth inputs.smooth vis.dovis=0

Note

Quite often, the slowest part of the runtime is the visualization, so disabling vis as shown above can dramatically speed up the execution. You can always plot the results after the fact using the plot.py script, as discussed in Analysis routines.

Pyro class

Alternatively, pyro can be run using the Pyro class. This provides an interface that enables simulations to be set up and run in a Jupyter notebook – see examples/examples.ipynb for an example notebook. A simulation can be set up and run by carrying out the following steps:

  • create a Pyro object, initializing it with a specific solver
  • initialize the problem, passing in runtime parameters and inputs
  • run the simulation

For example, if we wished to use the compressible solver to run the Kelvin-Helmholtz problem kh, we would do the following:

from pyro import Pyro
pyro = Pyro("compressible")
pyro.initialize_problem(problem_name="kh",
                        inputs_file="inputs.kh")
pyro.run_sim()

Instead of using an inputs file to define the problem parameters, we can define a dictionary of parameters and pass them into the initialize_problem function using the keyword argument inputs_dict. If an inputs file is also passed into the function, the parameters in the dictionary will override any parameters in the file. For example, if we wished to turn off visualization for the previous example, we would do:

parameters = {"vis.dovis":0}
pyro.initialize_problem(problem_name="kh",
                        inputs_file="inputs.kh",
                        inputs_dict=parameters)

It’s possible to evolve the simulation forward timestep by timestep manually using the single_step function (rather than allowing run_sim to do this for us). To evolve our example simulation forward by a single step, we’d run

pyro.single_step()

This will fill the boundary conditions, compute the timestep dt, evolve a single timestep and do output/visualization (if required).

Runtime options

The behavior of the main driver, the solver, and the problem setup can be controlled by runtime parameters specified in the inputs file (or via the command line or passed into the initialize_problem function). Runtime parameters are grouped into sections, with the heading of that section enclosed in [ .. ]. The list of parameters are stored in three places:

  • the pyro/_defaults file
  • the solver’s _defaults file
  • problem’s _defaults file (named _problem-name.defaults in the solver’s problem/ sub-directory).

These three files are parsed at runtime to define the list of valid parameters. The inputs file is read next and used to override the default value of any of these previously defined parameters. Additionally, any parameter can be specified at the end of the commandline, and these will be used to override the defaults. The collection of runtime parameters is stored in a RuntimeParameters object.

The runparams.py module in util/ controls access to the runtime parameters. You can setup the runtime parameters, parse an inputs file, and access the value of a parameter (hydro.cfl in this example) as:

rp = RuntimeParameters()
rp.load_params("inputs.test")
...
cfl = rp.get_param("hydro.cfl")

When pyro is run, the file inputs.auto is output containing the full list of runtime parameters, their value for the simulation, and the comment that was associated with them from the _defaults files. This is a useful way to see what parameters are in play for a given simulation.

All solvers use the following parameters:

  • section: [driver]

    option value description
    tmax 1.0 maximum simulation time to evolve
    max_steps 10000 maximum number of steps to take
    fix_dt -1.0  
    init_tstep_factor 0.01 first timestep = init_tstep_factor * CFL timestep
    max_dt_change 2.0 max amount the timestep can change between steps
    verbose 1.0 verbosity

  • section: [io]

    option value description
    basename pyro_ basename for output files
    dt_out 0.1 simulation time between writing output files
    n_out 10000 number of timesteps between writing output files
    do_io 1 do we output at all?

  • section: [mesh]

    option value description
    xmin 0.0 domain minumum x-coordinate
    xmax 1.0 domain maximum x-coordinate
    ymin 0.0 domain minimum y-coordinate
    ymax 1.0 domain maximum y-coordinate
    xlboundary reflect minimum x BC (‘reflect’, ‘outflow’, or ‘periodic’)
    xrboundary reflect maximum x BC (‘reflect’, ‘outflow’, or ‘periodic’)
    ylboundary reflect minimum y BC (‘reflect’, ‘outflow’, or ‘periodic’)
    yrboundary reflect maximum y BC (‘reflect’, ‘outflow’, or ‘periodic’)
    nx 25 number of zones in the x-direction
    ny 25 number of zones in the y-direction

  • section: [particles]

    option value description
    do_particles 0 include particles? (1=yes, 0=no)
    n_particles 100 number of particles
    particle_generator random how do we generate particles? (random, grid)

  • section: [vis]

    option value description
    dovis 1 runtime visualization? (1=yes, 0=no)
    store_images 0 store vis images to files (1=yes, 0=no)