This tool was renamed to Marvis. See the new repository: https://github.com/diselab/marvis

• Malte Andersch
• Arne Boockmeyer
• Felix Gohla
• Martin Michaelis
• Benedikt Schenkel
• Robert Schmid

Cohydra can be obtained via docker. The easiest solution is using the VSCode Remote - Containers extension. After cloning the repository and opening it in the container, your scenarios will by executing them with python3.

Otherwise, you can build the Dockerfile in the project's root directory yourself by running make. In the container, cohydra will be added to your PYTHONPATH. But you need to make sure, that you run the container with privileges to access the host network in order to have access to the host's network interfaces. You of course need to modify the volume mount to allow cohydra access to your scenarios.

docker run -it --rm --cap-add=ALL -v /var/run/docker.sock:/var/run/docker.sock --net host --pid host --userns host --privileged osmhpi/cohydra:latest

The main image osmhpi/cohydra is based on the images in the docker directory. The osmhpi/cohydra:base image installs all neccessary dependencies for cohydra, osmhpi/cohydra:dev is for development purposes (docker-cli in the container).

Recommended python version: Python 3.7

In the case you do not want to use the prebuilt docker, a normal ns-3 installation with NetAnim Python bindings will work, too. To easily install these have a look at our python wheels repository.

You also need the following packages:

sudo pip3 install pyroute2 nsenter docker paramiko
sudo pip3 install git+https://github.com/active-expressions/active-expressions-static-python

The Python libraries / directory provided by ns-3 and all other packages has to be in your PYTHONPATH, though. To run an example testcase, go to the example folder and run:

python3 basic_example.py

Cohydra so far has only been tested with Debian 10 Buster and Ubuntu 18.04 Bionic Beaver.

We are always happy when somebody contributes to cohydra. Therefore please create a fork and create a pull request to our repository. Make sure, that pylint does not show any additional errors or warnings. Also make sure that your code and your pull request is well documented. The documentation should also contain how to test your feature, if it is more complex. Afterwards we are going to test your new feature and review the code.