The full documentation is online at https://mabarnes.github.io/moment_kinetics.
First clone this git repository, e.g. (to clone it into a directory with the
default name moment_kinetics
)
$ git clone git@github.com:mabarnes/moment_kinetics
The command above assumes that you have an account on Github.com, and that account has ssh keys set up. If that is not the case you can clone using https instead
$ git clone https://github.com/mabarnes/moment_kinetics
When using https some things (e.g. pushing to the remote repository) may require you to use 2-factor authentication, see https://docs.github.com/en/get-started/getting-started-with-git/about-remote-repositories#cloning-with-https-urls.
!!! warning Do not download the zip-file from the Github.com page. This gives you the source code files but does not create a git repository. We get some version information from git when running the code, so without the git repository you will not be able to run a simulation.
1) If you have already installed Julia, ensure that the Julia version is >= 1.9.0 by doing
$ julia --version
at command line. The setup script in step 2 can also download a Julia
binary if you have not already installed Julia.
2) If you are running on a desktop/laptop (rather than an HPC cluster) ensure
that you have an MPI implementation installed (using whatever the usual way
of installing software is on your system). It should not matter which MPI
implementation - openmpi
is often a good choice if you have no reason to
prefer a particular one. Check that the MPI compiler wrapper mpicc
is
available, e.g.
$ mpicc --version
should run without an error.
3) Run the setup script
$ machines/machine_setup.sh
This script will prompt you for various options. The default choices should
be sensible in most cases. On a laptop/desktop the 'name of machine to set
up' will be 'generic-pc' and will set up for interactive use. On supported
clusters, 'name of machine' will be the name of the cluster. On other
clusters 'generic-batch' can be used, but requires some manual setup (see
`machines/generic-batch-template/README.md`).
For more information, see
[`machine_setup` notes](https://mabarnes.github.io/moment_kinetics/dev/machine_setup_notes/).
If you want or need to set up 'by hand' without using
`machines/machine_setup.sh`, see
[Manual setup](https://mabarnes.github.io/moment_kinetics/dev/manual_setup/).
Some other notes that might sometimes be useful:
To speed up running scripts or the first call of run_moment_kinetics
in a
REPL session, it is possible to compile a 'system image'
(moment_kinetics.so
). By running
$ julia --project -O3 precompile.jl
and then start Julia by running for example
$ julia --project -O3 -Jmoment_kinetics.so
this significantly decreases the load time but prevents code changes from
taking effect when moment_kinetics.so
is used until you repeat the
compilation of the system image. Note that this also prevents the Revise
package from updating moment_kinetics
when you edit the code during and
interactive session.
System images are created by default on HPC clusters, and are required to
use the provided jobscript-*.template
submission scripts (used by
submit-run.sh
and submit-restart.sh
). This is to try and minimise the
compilation that has to be replicated on all the (possibly thousands of)
processes in a parallel run. After changing source code, you should run
$ precompile-submit.sh
(to re-compile the moment_kinetics.so
system image).
In the course of development, it is sometimes helpful to upgrade the Julia
version. Upgrading the version of Julia or upgrading packages may require a
fresh installation of moment_kinetics
. To make a fresh install with the
latest package versions you should be able to just run
pkg> update
(to enter 'Package mode' enter ']' at the julia>
prompt). It might
sometimes necessary or helpful to instead remove (or rename) the
Manifest.jl
file in the main directory, and re-run the setup from step 2)
above. It can sometimes be necessary to remove or rename the .julia/
directory (located by default in your home directory) to force all the
dependencies to be rebuilt.
When using the Plots
-based post-processing library, one may have to set an
environment variable to avoid error messages from the Qt library. If you
execute the command
$ julia --project run_post_processing.jl runs/your_run_dir/
and see the error message
qt.qpa.xcb: could not connect to display
qt.qpa.plugin: Could not load the Qt platform plugin "xcb" in "" even though it was found.
This application failed to start because no Qt platform plugin could be initialized. Reinstalling the application may fix this problem.
this can be suppressed by setting
export QT_QPA_PLATFORM=offscreen
in your .bashrc
or .bash_profile
files.
To run julia with optimization, type
$ julia -O3 --project run_moment_kinetics.jl input.toml
Note that the middle character in -O3
is a capital letter 'O', not a zero. (On
HPC clusters, or if you selected the "set up separate packages for post
processing" option from machines/machine_setup.sh
, you should use -O3 --check-bounds=no
instead of just -O3
, and the same in the
Restarting
section.)
Options are specified in a TOML file, e.g. input.toml
here. The defaults are
specified in moment_kinetics_input.jl
.
mpirun -np <n>
in front of the call you would
normally use, with <n>
the number of processes to use.moment_kinetics
more than once to
work from the Julia REPL, e.g.
$ julia -O3 --project
julia> using moment_kinetics
julia> run_moment_kinetics("input.toml")
where input
is the name of a TOML file containing the desired options. It
is also possible to pass a Dict()
containing any non-default options
desired, which might sometimes be useful in tests or scripts
julia> run_moment_kinetics(input)
Especially when developing the code, a lot of compilation time can be saved
by using Revise.jl, and
re-running a test case in the REPL (without restarting julia
) - this is
enabled by default when setting up using machines/machine_setup.sh
for
'generic-pc'.
On an HPC cluster, you can submit a simulation (using the input file
input.toml
) to the batch queue using the convenience script
$ ./submit-run.sh input.toml
See the help text
$ ./submit-run.sh -h
for various command line options to change parameters (e.g. number of nodes, etc.).
If you need to rebuild the system images moment_kinetics.so
and
makie_postproc.so
or plots_postproc.so
because you have updated the code
since they were built, it may be convenient to use
$ ./submit-precompile-and-run.sh input.toml
which will submit jobs for compilation, to run the simulation, and to do post
processing. The simulation job will wait for the compilation job creating
moment_kinetics.so
to finish before starting. The post processing job will
wait for the compilation job creating makie_postproc.so
or
plots_postproc.so
to finish before starting.
When running in the REPL (especially with MPI) interrupting a run using Ctrl-C
can mess things up, and require you to restart Julia. There is also a chance
that you might interrupt while writing the output files and corrupt them. To
avoid these problems, you can stop the run cleanly (including writing the
distribution functions at the last time point, so that it is possible to
restart the run from where you stopped it), by creating an empty file called
stop
in the run directory. For example, if the name of your run is
'my_example'
$ touch runs/my_example/stop
moment_kinetics
checks for this file when it is going to write output, and if
it is present writes all output and then returns cleanly. The 'stop file' is
deleted when a run is (re-)started, if present, so you do not have to manually
delete it before (re-)starting the run again.
To restart a simulation using input.toml
from the last time point in the
existing run directory,
$ julia -O3 --project run_moment_kinetics --restart input.toml
or to restart from a specific output file - either from the same run or (if the
settings are compatible, see below) a different one - here
runs/example/example.dfns.h5
$ julia -O3 --project run_moment_kinetics input.toml runs/example/example.dfns.h5
The output file must include distribution functions. When not using parallel I/O there will be multiple output files from different MPI ranks - any one of these can be passed.
To do the same from the Julia REPL
$ julia -O3 --project
julia> run_moment_kinetics("input.toml", restart=true)
or
julia> run_moment_kinetics("input.toml", restart="runs/example/example.dfns.h5")
When calling the run_moment_kinetics()
function you can also choose a
particular time index to restart from, e.g.
julia> run_moment_kinetics("input.toml", restart="runs/example/example.dfns.h5", restart_time_index=42)
On an HPC cluster, you can submit a restart (using the input file
input.toml
) to the batch queue using the convenience script
$ ./submit-restart.sh input.toml
or to restart from a particular output file
$ ./submit-restart.sh -r runs/example/example.dfns.h5 input.toml
See the help text
$ ./submit-restart.sh -h
for various other command line options to change parameters (e.g. number of nodes, etc.).
If you need to rebuild the system images moment_kinetics.so
and
makie_postproc.so
or plots_postproc.so
because you have updated the code
since they were built, it may be convenient to use
$ ./submit-precompile-and-restart.sh [-r runs/example/example.dfns.h5] input.toml
which will submit jobs for compilation, to restart the simulation, and to do
post processing. The simulation job will wait for the compilation job creating
moment_kinetics.so
to finish before starting. The post processing job will
wait for the compilation job creating makie_postproc.so
or
plots_postproc.so
to finish before starting.
It is possible to restart a run from another output file with different resolution settings or different moment-kinetic options. This is done by interpolating variables from the old run onto the new grid.
When running in parallel, both the old and the new grids must be compatible with the distributed-MPI parallelisation. When not using Parallel I/O, the distributed-MPI domain decomposition must be identical in the old and new runs (as each block only reads from a single file).
makie_post_processing
The default post-processing module, written to be a bit more generic and
flexible than the original Plots-based one, and able to be used interactively,
is provided in makie_post_processing
, see
Post processing.
On an HPC cluster, when you call ./submit-run.sh
or ./submit-restart.sh
, a
job will (by default) be submitted to run
makie_post_processing.makie_post_process
or
plots_post_processing.analyze_and_plot_data
(depending on which you
have set up, or on whether you pass the -o
argument when both are set up) on
the output after the run is finished. You can skip this by passing the -a
argument to ./submit-run.sh
or ./submit-restart.sh
.
This post-processing functionality is now disabled by default, but you can
enable it by entering y
at the "Would you like to set up
plots_post_processing?" prompt in machines/machine_setup.sh
.
To make plots and calculate frequencies/growth rates, run
$ julia --project -O3 run_post_processing.jl runs/<directory to process>
passing the directory to process as a command line argument. Input options
for post-processing can be specified in post_processing_input.jl
. Note that
even when running interactively, it is necessary to restart Julia after
modifying post_processing_input.jl
.
Post processing can be done for several directories at once using
$ julia --project -O3 post_processing_driver.jl runs/<directory1> runs/<directory2> ...
passing the directories to process as command line arguments. Optionally pass a number as the first argument to parallelise post processing of different directories.
To enable parallel I/O, HDF5.jl needs to be configured to use an HDF5 library
which has MPI enabled and is compiled using the same MPI as you run Julia with.
To ensure this happens, machines/machine_setup.sh
will download the HDF5
source code and compile a local copy of the library under machines/artifacts
,
unless you enter n
at the "Do you want to download, and compile a local
version of HDF5" prompt (except on known HPC clusters where an MPI-enabled HDF5
is provided by a module - this is currently true on ARCHER2 - where the
module-provided HDF5 is used).
Parameter scans (see Parameter scans) can be performed by running
$ julia -O3 --project run_parameter_scan.jl path/to/scan/input.toml
If running a scan, it can be parallelised by passing the -p
argument to julia, e.g. to run on 8 processes
$ julia -p 8 -O3 --project run_parameter_scan.jl path/to/scan/input.toml
There is a test suite in the test/
subdirectory. It can be run in a few ways:
$ julia -O3 --project moment_kinetics/test/runtests.jl
or in the REPL run
julia> include("moment_kinetics/test/runtests.jl")
Individual test files can also be used instead of runtests.jl
, which runs all the tests.
Pkg
. Either using pkg>
mode
$ julia -O3 --project
julia> <press ']' to enter pkg mode>
(moment_kinetics) pkg> test moment_kinetics
using Pkg
in the REPL
$ julia -O3 --project
julia> import Pkg
julia> Pkg.test("moment_kinetics")
or run on the command line
julia -O3 --project -e "import Pkg; Pkg.test("moment_kinetics")`
The downside of this method is that it will cause NCDatasets
to be
installed if you did not install it already, which might sometimes cause
linking errors (related to the HDF5 library, see Optional
dependencies).
By default the test suite should run fairly quickly (in a few minutes). To do
so, it skips many cases. To run more comprehensive tests, you can activate the
--long
option:
julia> push!(ARGS, "--long")
before running the tests.
$ julia -O3 --project --long moment_kinetics/test/runtests.jl
test_args
argument
julia> Pkg.test("moment_kinetics"; test_args=["--long"])
Note the semicolon is necessary.
To get more output on what tests were successful, an option --verbose
(or
-v
) can be passed in a similar way to --long
(if any tests fail, the output
is printed by default).
In addition to the test suite in the test/
subdirectory, the moment_kinetics
project
utilises the method of manufactured solutions to test more complicated models in 1D1V,
and 2D2V or 2D3V (for neutral particles). To run these tests we run a normal moment_kinetics
simulation, making use of the manufacted solutions test TOML options. We describe how to use
the existing tests below. To set up moment_kinetics
to use the manufactured solutions features,
take the following steps:
Install moment_kinetics
using the setup instructions above (Setup),
using the plots_post_processing
project and make sure that the Symbolics
package is installed, e.g., if following
the manual setup instructions (Manual setup), these commands would be
$ julia -O3 --project
julia> ]
develop ./moment_kinetics
develop ./plots_post_processing/plots_post_processing
add Symbolics
if you will run the tests with MPI, make sure that MPI is also installed at this step.
Select an input file representing the desired test. For example, we can pick from the list MMS input TOML list.
Run the input file using the usual command.
julia> using moment_kinetics
julia> run_moment_kinetics("runs/your_MMS_test_input.toml")
Use the post processing module to test the error norms for the simulation of interest.
julia> using plots_post_processing
julia> analyze_and_plot_data("runs/your_MMS_test_input")
This will print out a series of numbers to the terminal which represent the error norms for each field and distribution function compared to the exact analytical solution, at each time step in the simulation. This error data can be computed for different resolutions.
Finally, to partially automate this last step when a resolution scan is performed, we provide
functions for generating plots of the error data versus resolutions in the file plot_MMS_sequence.jl
in the plots_post_processing
project. This can be accessed by using the run_MMS_test.jl
script from the command line
$ julia -O3 --project run_MMS_test.kl
or by using the underlying functions in the REPL
import plots_post_processing
using plots_post_processing.plot_MMS_sequence
run_mms_test()
Note that currently the lists of files used as input for the plotting functions are hardcoded for the purposes of self-documenting the tests -- these lists could be made input parameters to improve these scripts.