A simulation execution manager for ns-3
A Simulation Execution Manager for ns-3
This is a Python library to perform multiple ns-3 script executions, manage the results and collect them in processing-friendly data structures. For complete step-by-step usage and installation instructions, check out readthedocs.
If you want to contribute to sem development, first of all you'll need an installation that allows you to modify the code, immediately see the results and run tests.
Building the module from scratch
This module is developed using
pipenv: in order to correctly
manage virtual environments and install dependencies, make sure it is installed.
Typically, the following is enough:
pip install -U pipenv
Note that, depending on the specifics of your python installation, you may need to add
~/.local/bin to your path. In case this is needed,
pip should warn you
Then, clone the repo (or your fork, by changing the url in the following
command), also getting the
ns-3 installations that are used for running
examples and tests:
git clone https://github.com/DvdMgr/sem cd sem git submodule update --init --recursive
From the project root, you can then install the package and the requirements with the following:
pipenv install --dev
This will also get you a set of tools such as
that handle documentation and tests.
Finally, you can spawn a sub-shell using the new virtual environment by calling:
Now, you can start a python REPL to use the library interactively, issue the
sem program, run tests and compile the documentation of your local copy
This project uses the
for running tests. Tests can be run, from the project root, using:
python -m pytest --doctest-glob='*.rst' docs/ python -m pytest -x -n 3 --doctest-modules --cov-report term --cov=sem/ ./tests
These two commands will run, respectively, all code contained in the
folder and all tests, also measuring coverage and outputting it to the terminal.
Since we are mainly testing integration with ns-3, tests require frequent
copying and pasting of folders, ns-3 compilations and simulation running.
Furthermore, documentation tests run all the examples in the documentation to
make sure the output is as expected. Because of this, full tests are far from
instantaneous. Single test files can be targeted, to achieve faster execution
times, by substituting
./tests in the second command with the path to the test
file that needs to be run.
Building the documentation
Documentation can be built locally using the makefile's
The documentation of the current version of the package is also available on readthedocs.
The scripts in
examples/ can be directly run:
python examples/wifi_plotting_xarray.py python examples/lorawan_parsing_xarray.py
In case there are problems with the
pandas installation (this will happen in
macOS, for which no binaries are provided), use the following command for
installation (and see this pandas
issue as a reference):
PIP_NO_BUILD_ISOLATION=false pipenv install
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