Skip to content
A framework to manage ns-3 simulation campaigns: let SEM perform multiple parallelized executions of your ns-3 scenario, permanently save the results and output them in plotting-friendly data structures. All from the comfort of the command line or in a few, clean lines of Python code.
Python Other
  1. Python 99.9%
  2. Other 0.1%
Branch: master
Clone or download
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
docs Add details on CLI usage Jun 12, 2019
examples Add smarter simulation scheduler Jun 3, 2019
res Add logo to readme Jun 12, 2019
sem Allow users to select the runner type from the CLI Jun 24, 2019
tests
.coveragerc Setup coverage and documentation testing Jul 9, 2018
.gitignore Add runner and cli tests Jun 5, 2019
.gitmodules Update requirements Jul 9, 2018
.travis.yml Fix build on Travis Apr 5, 2019
LICENSE Initial commit May 21, 2018
Makefile Update webpage May 23, 2018
Pipfile Apply corrections to documentation Aug 6, 2018
README.md Add logo to readme Jun 12, 2019
pytest.ini Setup coverage and documentation testing Jul 9, 2018
requirements.txt
setup.cfg Add requirements for Python version Aug 10, 2018
setup.py Correct errors in setup to upload to PyPi May 31, 2019

README.md

A Simulation Execution Manager for ns-3

Build Status codecov Join the chat at https://gitter.im/ns-3-sem/Lobby

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.

Contributing

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 during installation.

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 sphinx, pygments and pytest that handle documentation and tests.

Finally, you can spawn a sub-shell using the new virtual environment by calling:

pipenv shell

Now, 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.

Running tests

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/ ./tests

These 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.

Building the documentation

Documentation can be built locally using the makefile's docs target:

make docs

The documentation of the current version of the package is also available on readthedocs.

Running examples

The scripts in examples/ can be directly run:

python examples/wifi_plotting_xarray.py
python examples/lorawan_parsing_xarray.py

Troubleshooting

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

Authors

Davide Magrin

You can’t perform that action at this time.