This repository contains code/data for work on a machine learning model of Ecohydrology.
Creating a conda environment with all the packages we use requires a lot of time to solve. This can be avoided by simply installing the packages one at a time, using pip when conda is taking a long time needlessly.
conda create --name ml
conda activate ml
conda install pandas
conda install intake-parquet
conda install matplotlib seaborn
conda install scikit-learn
pip install tensorflow
pip install aiohttp
As we find new sources of leaf-level fluxes, we are adding them to the intake catalog which you may access in your own python scripts by the following code snippet.
import intake
cat = intake.open_catalog("https://raw.githubusercontent.com/rubisco-sfa/MLEcohydrology/main/leaf-level.yaml")