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Description:

fluxgapfill 0.2.1

methane-gapfill-ml
Python codebase for our manuscript "Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands".
This codebase implements the following features:

Data preprocessing
Model training
Model testing
Uncertainty estimation, calibration/sharpness, and post-processing calibration
Gap-filling a dataset with a trained model

Jupyter Notebook Tutorial
https://github.com/stanfordmlgroup/methane-gapfill-ml/blob/pip-package/fluxgapfill_tutorial.ipynb
Installation
pip install gapfluxfill

Usage
Prepare data in a CSV and include the following headers:

TIMESTAMP_END: Format YYYYMMDDHHmm (e.g. 201312060030)
FCH4: Methane flux in nmol m-2 s-1
All other headers will be treated as input predictors.

Create a folder called data/ and make another folder in data/
using the site ID (data/{SiteID}/). This is where all of the processed
data for the site will be written.
Place the CSV in this folder and name it raw.csv, so the full path to the
CSV should be data/{SiteID}/raw.csv, where {SiteID} should be replaced
with the actual ID of the site.
Preprocess the data
python main.py preprocess

Train models
python main.py train

Evaluate a trained model
python main.py test

Gapfill using a trained model
python main.py gapfill

Run all steps, including data preprocessing, model training, model evaluation, and gapfilling
python main.py run_all

Run python main.py {preprocess,train,test,gapfill} --help for descriptions of all of the command-line arguments.
Example commands using the sample data in the repository:
python main.py preprocess --sites NZKop --eval_frac 0.1 --n_train 10
python main.py train --sites NZKop --models [lasso,rf] --predictors_paths predictors/meteorlogical.txt

When specifying multiple values for a parameter, you can either use a comma-separated string or list syntax like in the above command.
Contributions
This tool was developed by Jeremy Irvin, Yulun Zhou, Fred Lu, Vincent Liu, and Sharon Zhou.
License
Apache License 2.0.
Citing
If you're using this codebase, please cite:

The Gapfilling Algorithm:
Irvin, J., Zhou, S., McNicol, G., Lu, F., Liu, V., Fluet-Chouinard, E., ... & Jackson, R. B. (2021). Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands. Agricultural and Forest Meteorology, 308, 108528.

The Python-Toolkit:
In text: "We used the FluxGapfill python toolkit (Version 0.2.0; Irvin, et al, 2021) to complete our work."

References
Irvin, J., Zhou, Y., Lu, F., Liu, V., Zhou, S., McNicol, G., and Liu, J. (2021). FluxGapfill: A Python Interface for Machine-learning Driven Methane Gap-filling. Version 0.2.0. Zenodo. https://doi.org/10.5281/zenodo.5515761. Accessed 2021-09-19.