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ESP-Lab is an Earth System Predictions Python package that was originally designed to enable users to effectively perform I/O operations and statistics on SMYLE (The Seasonal-to-Multiyear Large Ensemble) data. It provides a foundation for analysis of multiyear prediction of environmental change.
Some of the challenges with multiyear prediction that ESP-Lab addresses include working with lead times ranging from 1 month to 2 years, as well as efficiently analyzing large ensembles.
This package provides utilities which support input/output processes such as methods to return dictionaries of filepaths keyed by initialization year, nested lists of files for particular start years and ensemble members, and dask arrays containing particular hindcast ensembles. ESP-Lab also provides preprocessing which can assist in using intake-esm in conjunction with other data_access functions.
ESP Lab also enables statistics calculations through functions providing tools to perform linear detrending along a particular axis, determine skill metrics based on model and observation DataArrays, and generate a distribution of skill scores using a smaller ensemble member size.
ESP_Lab can be installed from PyPI with pip:
pip install esp-labNote: If you use pip to install esp-lab, you can install esp-lab directly into a pre-existing conda environment (after doing conda activate <environment_name> and any requirements that you do not already have will be added automatically to that environment during installation. Another option is to create a new environment, for instance with conda env create --name esp-lab and then activate that environment with conda activate esp-lab. At that point, you are ready to install esp-lab into the new environment with python -m pip install esp-lab.
One can also install esp-lab as a developer by following these steps:
- git clone https://github.com/CESM-ESPWG/ESP-Lab.git
- cd ESP-Lab
- conda env create --file environment.yml
- conda activate esp-lab
- pip install -e .
Documentation can be found at esp-lab.readthedocs.io