Introduction#

What is it#

Python framework for Nek5000

Documentation: https://snek5000.readthedocs.io/

Snek5000 is a Python package which allows one to write Fluidsim solvers based for the simulations on the Fortran CFD code Nek5000. There are open-source solvers (in particular snek5000-phill, snek5000-cbox and snek5000-tgv) and it’s not difficult to write your own solver based on your Nek5000 cases (as shown in this tutorial).

With a Snek5000-Fluidsim solver, it becomes very easy to

launch/restart simulations with Python scripts and terminal commands,
load simulations, read the associated parameters/data and produce nice figures/movies.

Snek5000 can be seen as a workflow manager for Nek5000 or a Python wrapper around Nek5000. It uses Nek5000 on the background and is thus NOT a rewrite of Nek5000!

Snek5000 is powered by nice Python packages such as Snakemake, Fluidsim, Pymech, Matplotlib, Jinja, Pytest, Xarray, etc.

Quick start#

Install it as follows:

export NEK_SOURCE_ROOT="/path/to/Nek5000"

pip install snek5000

See here for detailed installation instructions and notes on incompatible Nek5000 versions.

Why Snek5000?#

The snek5000 Python API allows you to launch/restart/load simulations. For example, the periodic hill Nek5000 example can be launched with our snek5000-phill solver (installable with pip install snek5000-phill) as follows:

from phill.solver import Simul

params = Simul.create_default_params()

# modify parameters as needed, for example
params.output.sub_directory = "examples_snek_phill"
params.short_name_type_run = "readme"

params.oper.nx = 12
params.oper.ny = 10
params.oper.nz = 8

params.nek.general.write_interval = 4
params.nek.general.num_steps = 12

...

# instantiate the object representing the simulation
sim = Simul(params)

# compile and launch the simulation (blocking)
sim.make.exec("run_fg")

A simulation directory is created automatically (with this example, something like ~/Sim_data/examples_snek_phill/phill_readme_12x10x8_V1.x1.x1._2022-10-27_15-21-58). Then, the simulation object can be recreated from this directory. An easy way would be to go into this directory, start IPython with the snek-ipy-load command. Then, to post-process the results run:

# get/print the simulation parameters from the object
sim.params

# few examples of various read and plots
sim.output.print_stdout.plot_dt_cfl()
sim.output.print_stdout.plot_nb_iterations()

sim.output.phys_fields.plot_hexa()
sim.output.phys_fields.animate("pressure", interactive=True)
sim.output.phys_fields.animate(
    "pressure", dt_frame_in_sec=0.1, equation="y=0.5", save_file="my_great_movie.gif"
)

As another example, this movie has been produced by a sim.output.phys_fields.animate call from a snek5000-cbox simulation:

https://user-images.githubusercontent.com/8842662/202872147-4ea3c749-dc63-4a73-98a0-6c787edb9cd3.mp4

Solvers can also have extra capabilities such as processing data from history points. To see how this works, run a short snek5000-cbox simulation script where history points are defined, change to the simulation directory, execute snek-ipy-load command and then

# Plot all history points
sim.output.history_points.plot()
# Print the coordinates of the history points
sim.output.history_points.coords
# Load one history point as a dataframe for further post-processing
coords, data = sim.output.history_points.load_1point(2)

Check out the tutorials to learn how to use Snek5000.

Need more reasons to use snek5000?

Advantages#

Parameters, get started without studying the whole documentation#

Saves you from the trouble in setting up multiple source files (.box, .par, SIZE)
Uses sensible names and defaults for the parameters
Avoids typos and human errors thanks to a nice parameter container object
Records metadata related to the simulation into human and machine readable files (params_simul.xml, config_simul.yml)
Checks for consistency of parameters
Automatically sets some parameters as Python properties

Workflow#

Out of source build (per run), which can be inspected or executed using the conventional makenek for debugging
Reproducible workflows, not susceptible to changes in environment variables by default
Scriptable simulation execution allowing parametric studies
Easy to load simulation for performing offline post-processing and restarting the simulation

Better than Bash scripting like:

# Build case
cd src/phill/
CASE="phill"
echo "$CASE.box" | genbox
mv -f box.re2 phill.re2
echo "$CASE\n0.01" | genmap
FFLAGS="-mcmodel=medium -march=native" CFLAGS="-mcmodel=medium -march=native" makenek
cd -

# Run case
cd src/phill/
nekmpi $CASE <nb_procs> # foreground
nekbmpi $CASE <nb_procs> # background
cd -

# Clean
makenek clean

Use of Snakemake which is similar to GNU Make, but allows one to blend Bash and Python scripting and uses simple YAML files for managing custom configurations of compilers and flags for different computers.

Coding#

User friendly, modular, object oriented API
Reuse of code (inheritance)
Tested with a good code coverage (>90%)

Disadvantages#

Yet another layer… with the possible associated bugs :-)
Requires some basic knowledge of Python to use (not really a big issue, to be honest).
Deep modification of solvers requires learning how Snakemake functions and how to write Jinja templates (which are not so hard, btw)

Contributing#

Contributions are welcome! You can help by testing out the code, filing issues and submitting patches. See contributing guidelines.