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 our documentation.
If you used SEM for your ns-3 analysis, please cite the following paper, both to provide a reference and help others find out about this tool:
Davide Magrin, Dizhi Zhou, and Michele Zorzi. 2019. A Simulation Execution Manager for ns-3: Encouraging reproducibility and simplifying statistical analysis of ns-3 simulations. In Proceedings of the 22nd International ACM Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWIM '19). ACM, New York, NY, USA, 121-125. DOI: https://doi.org/10.1145/3345768.3355942
This section contains information on how to contribute to the project. If you are only interested in using SEM, check out the documentation.
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.
This module is developed using
poetry: in order to correctly
manage virtual environments and install dependencies, make sure it is installed.
Typically, the following is enough:
curl -sSL https://install.python-poetry.org | python3 -Note that, if poetry's installer does not add poetry's path to your shell's startup file properly, you may need to add
source $HOME/.poetry/env to your startup file. You can tell that you need to add it if your shell cannot find the poetry command the next time you open a terminal window.
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/signetlabdei/sem
cd sem
git submodule update --init --recursiveFrom the project root, you can then install the package and the requirements with the following:
poetry installThis will also get you a set of tools such as sphinx, pygments and pytest
that handle documentation and tests.
Finally, you can spawn a sub-shell using the new virtual environment by calling:
poetry shellNow, you can start a python REPL to use the library interactively, issue the
bash sem program, run tests and compile the documentation of your local copy
of sem.
This project uses the pytest framework
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/ ./testsThese two commands will run, respectively, all code contained in the docs/
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.
Documentation can be built locally using the makefile's docs target:
make docsThe scripts in examples/ can be directly run:
python examples/wifi_example.pypip currently requires a setup.py file to install projects in editable mode.
As explained here, poetry
actually already generates a setup.py. After building the project, you can
extract the file from the archive using the following command:
tar -xvf dist/*.tar.gz --wildcards --no-anchored '*/setup.py' --strip=1After this step, it becomes possible to install SEM in editable mode.
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