Running the model

To run the biosnicar model, you need to have installed on your machine. Then, you can download the model code from the Github repository (opens in a new tab). Finally, you can install the project dependencies and start using biosnicar!

Installing Environment/Dependencies

If you do not have Python installed, download Python >3.7. It is recommended to use a fresh environment using conda or venv. Once activated, install the project dependencies with:

pip install -r requirements.txt

The previous command may take an unreasonable amount of time to complete with anaconda. If that is the case, you can simply open the requirements.txt file and pip install each dependency in the fresh environment. Then, install biosnicar:

pip install -e .

Finally, if you do not wish to install anything on your computer, but you use VSCode and Docker, then you can use the devcontainer config provided to run this code in a remote container. This requires the "remote containers" extension to be added to VSCode. Further instructions are available here: https://code.visualstudio.com/docs/remote/containers (opens in a new tab)

Running the code

The model driver (main.py) is in the root repository and all the core source code can be found in /biosnicar. From the top level directory, run:

python main.py

This will run the model with all the default settings. The user will see a list of output values printed to the console and a spectral albedo plot appear in a separate window. The code can also be run in an interactive session (Jupyter/iPython) in which case the relevant data and figure will appear in the interactive console, as well as in the command line outside of the top level directory, as long as the path to main.py is adjusted.

Most users will want to experiment with changing input parameters. This is achieved by adjusting the values in the config file inputs.yaml. The nature of each parameter is described in in-line annotations to guide the user. Invalid combinations of values will be rejected by our error-checking code. Most users should have no reason to modify any other file in this repository except for the those in inputs.yaml.

More complex applications of the model code, for example model inversions, field/model comparisons etc are all possible, with details provided in these docs under Usage Guides.

Choosing Inputs

It is straightforward to adjust the model configuration by updating the values in inputs.yaml.

However there is a lot of nuance to setting up the model to provide realistic simulations, and the meaning of the various parameters is not always obvious. However, we are developing a guide for choosing inputs that will be shipped soon!