Installation

Pip

First, you need to install the lcode package along with its dependencies.

If you want to work with a virtual environment, then you need to create and activate it beforehand.

# linux
python -m venv lcode-env
source lcode-env/bin/activate # linux

# Windows
python -m venv lcode-env
.\lcode-env\scripts\Activate

Then install lcode:

pip install lcode

From source

Create and activate the virtual environment as at the beginning of the Pip section.

Then clone the lcode source code to yourself:

git clone https://github.com/lcodePy-team/lcodePy.git
cd lcodepy

Now install all dependencies and lcode itself

pip install -r requirements.txt
pip install -e .

Note

If you want to use GPU, you need to install the cupy library yourself [Install Cupy].

Conda

We recommend installing lcode in a separate environment. Any other python installation should work fine, but has not been tested.

  • Create a new environment and install the dependencies:

conda create -n lcode-env -c conda-forge numba numpy scipy matplotlib mpi4py

or

conda env create -f conda-env.yml

where conda-env.yml is avalible in sources.

  • Acivate the new environment:

conda activate lcode-env

  • Optional: in order to run simulations on GPU, add cupy and other necessary packages to the line of dependencies when creating a new eviroment. Check in advance if you have the drivers for your GPU installed. It is not necessary to install CUDA Toolkit in advance. For any other questions about cupy, please check https://docs.cupy.dev/en/stable/install.html

conda create -n lcode-env -c conda-forge numba numpy scipy matplotlib cupy