Getting Started
RASCAL is available to download in PyPi and GitHub. To install RASCAL, it is recommended to create a new environment to avoid possible conflicts wioth its required dependencies.
(base) $ conda create --name rascal_env
(base) $ conda activate rascal_env
Required dependencies
RASCAL runs with Python 3.10.
These are the dependencies of RASCAL:
numpy 1.26.4
pandas 2.2.1
dask 2024.4.1
xarray 2024.3.0
scipy 1.13.0
tqdm 4.65.0
scikit-learn 1.4.1.post1
seaborn 0.13.2
eofs 1.4.1
cfgrib 0.9.12.0
netCDF4 1.7.0
Installation via PyPi
RASCAL can be installed via PyPi:
(rascal_env) $ pip install rascal-ties
Installation via GitHub
RASCAL can be used via GitHub:
(rascal_env) $ git clone https://github.com/alvaro-gc95/RASCAL
The GitHub repository also contains the following scripts:
multiple_runs_example.py
to automatize running several configurations of similarity methods and pool sizes for various stations and variables. This can be configured through theconfig.yaml
file
projection_example.py
Mostly the same asmultiple_runs_example.py
, but including a split in training and testing periods for the PCA, and an added year as a projection onto the training period PCs
RASCAL_evaluation.ipynb
a Jupyter Notebook to plot and validate the reconstructions