Sea lice (singular sea louse) are a type of parasitic organisms that occur on salmonid species (salmon, trout, char).
The main goal of this project is to find efficient treatment options that maximise a "payoff" function, defined in terms of the number of fish currently alive and well in a farm minus treatment costs. Each agent can decide a range of treatment options and decide to either collaborate with other farmers (ie. by applying treatment on the same day) or not.
This project thus includes the following components:
- a simulator (the core is complete)
- a visualisation tool
- (NEW) fitting on official report
- policy searching strategies
- a game theoretic optimisation framework
Details on required packages and versions can be found in environment.yml which can be used to create a
conda environment for the project.
git clone https://github.com/resistance-modelling/slim slim-master
cd slim-master
# make sure conda is installed and the command is available at this point
conda env update
# This will make the slim module globally available.
pip install -e .NEW: you can launch the simulation with just slim.
To run the model you have to provide a configuration and an artifact output folder.
slim run output_folder/simulation_name simulation_params_directory```
For example:
slim run out/0 config_data/Fyne
Note that slim run is just a short hand for python -m slim.SeaLiceMgmt, which will be kept for compatibility.
We also provide a GUI for debugging and visualisation. Its support is still heavily experimental so please use with caution.
You can launch it via slim gui and provide your run data (generated via the --save-rate option mentioned
above) from the given menu.
Please check our quickstart guide for more information.
To run the original code, enter original directory and run:
python model/slm.py param_module_name output_folder simulation_name
For example:
python model/slm.py dummy_vars /out/ 0
Note that at the moment dummy_vars is a copy of Fyne_vars and code execution takes a while.