Jupyter notebooks using ESSM

Author: Stan Schymanski, Luxembourg Institute of Science and Technology

ESSM stands for Environmental Science using Symbolic Math (see http://essm.rtfd.io), which is a Python package facilitating the use of sympy for mathematical derivations and computations in environmental sciences.

The repository contains another repo as a submodule (folder ESSM_plotting, from https://github.com/schymans/ESSM_plotting). After cloning, you therefore need to execute in the base folder:

git submodule init
git submodule update

At any stage, you can update all submodules in your repo with a single command:

git submodule update --remote --merge

To see all submodules, look into the file .gitmodules, e.g. by executing:

cat .gitmodules

Contents

For a notebook showcasing general features of ESSM, look at api_documentation_master.ipynb.

The notebook E_PM_eqs_essm.ipynb provides equations and examples related to modelling the energy balance of a plant leaf and its exchange of water and energy with the atmosphere. The definitions and equations can be imported into other notebooks by executing: from E_PM_eqs_defs.py import *

The notebooks Priestley-Taylor_short.ipynb and Priestley-Taylor_equation1.ipynb illustrate the benefit of ESSM's automatic dimensional consistency testing, and how to analyse and repair equations with inconsistent units.

Running the notebooks locally

The best approach is to create a dedicated conda/mamba environment with required dependencies. The repo contains a file (environment.yml) with all required information. To create a conda/mamba environment based on this file, use a terminal, and in the cloned folder, execute:

mamba env create -n essm -f environment.yml
mamba activate essm
jupyter lab

This will create a mamba environment called essm with the dependencies listed in environment.yml, activate the environment and then start jupyter.